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A WORLD BANK COUNTRY STUDY

MalaysiaEnterprise Training, Technology,and Productivity

The World BankUnited Nations Development ProgrammeGovernment of Malaysia

Washington, D.C.

Copyright (C 1997The International Bank for Reconstructionand Development/THE WORLD BANK1818 H Street, N.W.Washington, D.C. 20433, U.S.A.

All rights reservedManufactured in the United States of AmericaFirst printing September 1997

World Bank Country Studies are among the many reports originally prepared for internal use as partof the continuing analysis by the Bank of the economic and related conditions of its developing membercountries and of its dialogues with the governments. Some of the reports are published in this series withthe least possible delay for the use of governments and the academic, business and financial,and development communities. The typescript of this paper therefore has not been preparedin accordance with the procedures appropriate to formal printed texts, and the World Bank accepts noresponsibility for errors. Some sources cited in this paper may be informal documents that are not readilyavailable.

The World Bank does not guarantee the accuracy of the data included in this publication and acceptsno responsibility whatsoever for any consequence of their use. The boundaries, colors, denominations,and other information shown on any map in this volume do not imply on the part of the World BankGroup any judgment on the legal status of any territory or the endorsement or acceptance of suchboundaries.

The material in this publication is copyrighted. Requests for permission to reproduce portions of itshould be sent to the Office of the Publisher at the address shown in the copyright notice above. TheWorld Bank encourages dissemination of its work and will normally give permission promptly and, whenthe reproduction is for noncommercial purposes, without asking a fee. Permission to copy portions forclassroom use is granted through the Copyright Clearance Center, Inc., Suite 910, 222 Rosewood Drive,Danvers, Massachusetts 01923, U.S.A.

Cover photos: Photos used by permission of the Malaysian Government.

ISBN: 0-8213-4059-XISSN: 0253-2123

TABLE OF CONTENTS

FoREwoRD FROM EcONOMIC PLANNING UNIT, GOVERNMENT OF MALAYSIA Vii

ABSTRACT Viii

AcKNowLEDGmENTs ACRONYMS/ABBREVIATIONS x

CHAF,ER ONE: INTRODUCTION 1The MITP Survey 1Analytic Approach 4Overview of Report 6

CHAFFER Two: OVERVIEW OF TRAINING 10

Incidence of Training 10Sources of Enterprise Training 12Workers Getting Training by Source 14Factors Shaping Training Decisions of Firms 17Findings and Policy Implications 21

CHAPTER THREE: PRODUCTIVITY AND WAGE OuTCOMEs 24Estimating the Productivity Impact of Training 24Productivity Effects of Training for Different Firms 25Productivity Outcomes by Skill Group and Training Source 30Firm-Level Wage Outcomes of Training 35Compensation Policy and Labor Turnover 38Findings and Policy Implications 43

CHAPFER FouR: TRAINING POLICIES 46Constraints on Training: An Employer Perspective 46The Double Deduction Incentive for Training Scheme 48Human Resource Development Fund 52Findings and Policy Implications 61

CHAFFER FIVE: TECHNOLOGY, QUALITY AND SKILLS 63Technological Characteristics of Firms 63ISO-9000 and Quality Assurance 70ISO-9000 and Export Orientation 73New Technology and Changing Skill Needs 77Findings and Policy Implications 81

iii

CHAPTER Six: FIRM EFFICIENCY AND ITS DISTRIBUTION 86Measuring Technical Efficiency 87Distribution of Efficiency by Firm Size 90A Profile of Efficient Firms by Size 92Ownership, Efficiency Difference and FDI Spillovers 99Findings and Policy Implications 105

CHAPrER SEVEN: CONCLUSIONS AND RECOMMENDATIONS 108Summary of Main Findings 108Policy Recommendations 112

ANEXE

2.1 Probit Estimates of the Likelihood of Formal Training 235.1 Introduction of New Technology and Training 835.2 Introduction of New Technology and Firm-Level Productivity 856.1 Stochastic Frontier Production Functions 107

NoarS 121

REFERENCES 125

TABLES1.1 Key Variables in the MITP Survey 41.2 The MITP Sample and Response rates 52.1 Incidence of Training in Manufacturing and by Firm Size 112.2 Incidence of Training by Industry 112.3 Internal and External Sources of Training 122.4 Sources of Training by Firm Size 132.5 Workers Trained: Overall and by Firm Size 142.6 Number of Workers Trained by Industrial Sector 152.7 Workers Getting Formal In-House Training by Skill Group 162.8 Workers Trained from External Sources by Occupation 172.9 Marginal Effects of the Likelihood of Formal Training 183.1 Production Function Estimates by Firm Size 253.2 Production Function Estimates by Technology Level 283.3 Production Function Estimates by Export Orientation

and Ownership 303.4 Production Function Estimates with Predicted Training

by Worker Groups 323.5 Production Function Estimates: In-house vs. External Training 333.6 Production Function Estimates: Training from External Sources 343.7 Productivity Effects of Increased Training Intensity 35

iv

3.8 Wage Model Estimates with Training Indicator and Predicted Values 373.9 Wage Effects of Training by Technology, Exports

and Ownership 373.10 Occupation-Specific Wage Effects on Training 383.11 Summary Statistics on Quits and Compensation Policies 413.12 Compensation and Overall Quit Rates by Training Status 423.13 Compensation and Quit Rates by Occupation and

Training Status 434.1 Reasons for Little or No Training: Overall and by Firm Size 484.2 Participation in DDIT by Industrial Sector 504.3 Reason Given by Firms for Not Using DDIT 514.4 Reason for Not Using DDIT by Firm Size 524.5 Use of HRDF by MITP Firms, 1994 534.6 Eligible Firms Not Registered with HRDF by Size and Industry 544.7 Probit Estimates of Non-Compliance with HRDF 554.8 Registerd Firms Not Claiming from HRDF by Training Status 564.9 Probit Estimates of Not Claiming from HRDF 574.10 Training Centers and Training Plans in MITP by Firm Size 584.11 Joint Training Programs in MITP by Firm Size 584.12 Probit Estimates of Increased Training Under HRDF 604.13 Changes in Training Levels Over Past Three Years:

Frims Registered with HRDF and Unregisterd Firms 605.1 Technology Characteristics by Firm Size and Ownership 645.2 Technology Characteristics by Industry 665.3 Quality Control and Precision in Production 675.4 ISO-9000 Status and Quality Control Systems 715.5 ISO-9000 by Firm Size and Ownership 725.6 ISO-9000 and Export Orientation 735.7 ISO-9000 and Export Propensity by Principal Markets 755.8 Introduction of New Technology since 1992 765.9 Effects of New Technology on Skill Needs and Employment 765.10 New Technology and Changes in Training since 1992 775.11 Impact of New Technology on Training 785.12 Impact of New Technology on Productivity by Firm Size 806.1 Stochastic Frontier Production Function Estimates 896.2 Distribution of Efficiency by Firm Size and Economy 906.3 Stachastic Frontier Production Function Estimates

by Ownership 1016.4 Stochastic Frontier Production Function Estimates with

FDI Spillovers 104

v

FIGURES

3.1 Quit rates and Wage Policies: Training and Non-Training Firms 395.1 Quality Control Systems by Firm Size and Ownership 685.2 ISO-9000 and Exports 746.1 Distribution of Efficiency by Economy 916.2 Malaysia- Distribution of Efficiency by Firm Size 926.3 Technology Attributes of Efficient and Inefficient Firms 936.4 Training Attributes of Efficient and Inefficient Firms 946.5 Quality Control in Efficient and Inefficient Firms 956.6 Quits and Compensation in Efficient and Inefficient Firms 966.7 Technology and Training in Past Three Years 97

BOXES

1.1 Cross-National Enterprise Training Study 23.1 Enterprise Training and Productivity in Developing Countries 253.2 Technology Raises the Productivity of Training in

Taiwan, China 275.1 Use of External Sources of Technical Support by Firms 695.2 Diffusion and Impact of ISO-9000 in Brazil 706.1 Mexico's Proactive Approach to SMI Support 1006.2 Promoting SMI Networks in Chile 103

vi

FoREwoRD FROM THE ECONOMIC PLANNING UNIT,

GOVERNMENT OF MALAYSIA

The quality of a nation's workforce is the key ingredient to economic growth. Increasinglabor productivity and upgrading the skills and flexibility of workers through training andretraining are essential strategies for developing a quality labor force to support sustainedgrowth and economic development of the country.

To achieve the status of a fully developed industrialized country by the year 2020, Malaysiahas made human resource development one of its major development strategies. The govern-ment has, and will continue to, play a strong role in strengthening the educational and workforceskills of the population. But the government cannot do it on its own. Most technologicalinnovations now enter Malaysia through industries; furthermore, learning is a lifelong pro-cess, and relevant skills are best acquired in the workplace. This means that employers-who have the expertise and technical know-how to train-will have to assume greater re-sponsibility for training and upgrading the existing skill levels of their employees to meet theskill requirements of new technology. For its part, the government has introduced the HumanResource Development Fund, to encourage and promote enterprise training in industry, aswell as complementary research and development (R&D) incentives and policies to assistsmall and medium industries (SMIs).

This report, which is based on a large survey of enterprise training, technology and produc-tivity in the manufacturing sector, is written for policy makers and company executives whohave to make critical decisions and design training policies. It provides the first broad-based look at the existing level and incidence of private sector-led training in Malaysia, andit relates training efforts to corporate strategies on R&D, technology licensing, and qualitycontrol, as well as the effects of training on productivity and wages in companies. Theanalyses reported here can be used to support formulation of more effective public policiesand corporate strategies for strengthening industrial training to meet the challenges of sus-tained economic growth and globalization. It is hoped that this report will encourage theprivate sector to play a greater role in developing the country's skill abilities to supportMalaysia's strategic vision of attaining our Vision 2020.

Tan Sri Dato' Seri Ali Abul Hassan b. SulaimanDirector GeneralEconomic Planning UnitPrime Minister's DepartmentGovernment of Malaysia

vii

ABSTRACT

This report presents the findings of a study of enterprise-based training in Malaysia'smanufacturing sector which was jointly sponsored by the World Bank, the UnitedNations Development Programme, and the Economic Planning Unit, Prime Minister'sDepartment. Using data from a survey of 2,200 companies, the study investigates theincidence and productivity outcomes of employer-sponsored training in in-house com-pany programs and from external training providers, and the role of government poli-cies and incentives in encouraging private sector training. The study also looks morebroadly at technology in firms, their use of quality control systems, and the skill re-quirements associated with the use of new technology and organizational change.

The report concludes that while some firms, especially the larger, more technologicallyprogressive ones and the multi-national companies do provide training, in general,most Malaysian firms underinvest in employee training. It documents the primacy ofthe private sector as the most important source of in-service training, and suggests thatexisting public sector training institutions need to become more demand responsive. Itdemonstrates that training firms are also making complementary investments in newtechnology, and that the productivity of local firms lags behind that of foreign-ownedfirms, in large part because local firms invest relatively less in training and new tech-nology. The report also offers recommendations on improving collection and dissemi-nation of training information, making training and technology policies more effective,and developing better coordinated, proactive policies to support small and mediumindustries.

viii

ACKNOWLEDGMENTS

This report was prepared as part of the Malaysian Industrial Training and Productiv-ity (MITP) Study, a joint project of the World Bank, the United Nations Develop-ment Programme (LTNDP), and the Economic Planning Unit (EPU), Prime Minister'sDepartment. The project, directed by Hong Tan, was conducted by several teams-- a World Bank team, including Hong Tan and Geeta Batra; a local team includingProfessors Rajah Rasiah, Osman Rani, and Anwar Ali from Universiti KebangsaanMalaysia; and staff from the Human Resource Section of EPU, especially PuanFaizah Mohd. Tahir (Director), Dato Zainol Abidin Rashid (former Director), YapKim Lian, Asri Hamidon, Mohd. Hanafi Sakri and Muhd. Fikri Nawawi. The MITPsurvey was fielded by Survey Research Malaysia (SRM) under the able direction ofEugene Wong, Cheah Swee Kit and Christine Kwan. The MITP survey relied on asampling frame provided by the Department of Statistics (DOS), and used a surveyinstrument developed by the World Bank and adapted for the MITP Study by theproject team and SRM. This report was written by Hong Tan and Geeta Batra ofthe Private Sector Development Department.

This MITP Study would not have been possible without the financial support ofUNDP, the World Bank Research Committee (RPO 678-39), and EPU. We thankAmeerah Haq, Neil Buhne, and Selva Ramachandran of UNDP for their support.We gratefully acknowledge the active support of Dato Annuar Ma'aruf, DeputyDirector General of EPU, and the many insightful comments provided by membersof the project's Steering Committee, including representatives from EPU (HumanResources, Industry and Social Sections), the Ministry of Human Resources, Minis-try of International Trade and Industry, Malaysian Industrial Development Author-ity, Human Resources Development Council, Ministry of Science, Technology andthe Environment, and the Federation of Malaysian Manufacturers.

We benefited from interactions with numerous individuals and both public and pri-vate sector groups. In particular, we acknowledge the staff of DOS, especiallyDorothy Robert, Mat Noh b. Hussin, Lok Chung Lee and Tan Hoe Seng for theirinvaluable assistance with surveys and data; and Mr. Yau De Piyau and his staff atHRDC for data and insights into the operation of the Human Resource Develop-ment Fund. We gained many insights from interviews with the Penang Develop-ment Corporation, the Penang Skills Development Center, Standards and IndustrialResearch Institute of Malaysia, National Productivity Center, and the National Vo-cational Training Council. Finally, we acknowledge the many companies that con-tributed their time generously to participate in the MITP Survey; we trust that youwill find the research and policy recommendations in the Report useful in formulat-ing your skills and technology development strategies.

ix

AcRoNYms/ABBREVIATIONS

APrTD Action Plan for Industrial Technology DevelopmentCIAST Center for Industrial and Advanced Skills TrainingDDIT Double Deduction Incentive for TrainingDOS Department of StatisticsEPU Economic Planiing UnitFDI Foreign Direct InvestmentGMI German-Malaysia InstituteGTS Group Training SchemeHRDC Human Resource Development CouncilHRDF Human Resource Development FundIKM Institute Kemahiran MaraIMP Industrial Master PlanITI Industrial Training InstituteiMI Japan-Malaysia InstituteJTS Joint Training SchemeMASTIC Malaysian Science and Technology Information CenterMFI Malaysia-France InstituteMIDA Malaysia Industrial Development AuthorityMm Ministry of International Trade and IndustryMITP Malaysia Industrial Training and Productivity SurveyMLFS Malaysia Labor Flexibility SurveyMNIN Multi-national CorporationNPC National Productivity CorporationNVTC National Vocational Training CouncilOJT On the Job TrainingQCC Quality Control CirclesQIP Quality Improvement PracticesSDC Skill Development CenterSIRIM Standards and Industrial Research Institute of MalaysiaSMI Small and Medium Scale IndustrySM1DE1C Small and Medium Industrial Development CorporationSPC Statistical Process ControlSRM Survey Research MalaysiaTNA Training Needs AnalysesUNDP United Nations Development ProgrammeVET Vocational Education and TrainingVTE Vocational and Technical EducationYTC Youth Training Center

x

CHAPTER ONE: INTRODUCTION

This report seeks to inform policy discussions on employers' technology - whether they invest in re-private sector-led training through a survey of firms search and development (R&D) or purchase theirand rigorous analyses of their responses. technology through licensing agreements, whether

they have quality control systems - and how theseThe Malaysia Industrial Training and Productivity relate to training strategies. It characterizes the dis-(hereafter MITP) survey, was fielded to 2,200 manu- tribution of employers' technical efficiency levelsfacturing firms in 1994 and 1995. The MITP sur- relative to the best-practice frontier, and identifiesvey elicited information on firm-sponsored training, the key training and technological factors associatedand on a wide range of firms' attributes including with high efficiency levels.size, industry, local or foreign ownership, equip-ment, technology, quality control systems, marketsand exports, work force characteristics, wages and The MITP Surveyother compensation and production.

The firm-level data needed to study private sectorThese data allow us to document, for the first time, training do notcurrendy exist in Malaysia. A primarythe incidence and characteristics of training in Ma- data collection effort was deemed necessary tolaysian industry, throughout firms of different sizes, develop the requisite data from a statisticallyownership, and industrial sector. Thedataalsopro- representative sample of manufacturingvide unique insights into where firms get their train- enterprises. The MITP project team adapted, toing - from in-house trainingprograms, fromprivate Malaysian conditions, a survey instrumentsector providers, and from different government developed by the World Bank as part of its cross-training institutions; which groups of workers get national study of Enterprise Training andtraining and how much; and what are the outcomes Productivity (see Box 1. 1).of training on firm-level productivity and wages.

Survey QuestionsThis report addresses the issue of whether firms in Table 1.1 lists the main types of questions asked inMalaysia under-invest in training. It asks employ- the MITP survey. It elicits a variety of informationers about why they do little or no training, and in- about the attributes of the enterprise; its market andvestigates the factors which shape employers' technology, including research and development,training decisions. It evaluates the efficacy of dif- technology licensing, equipment, and quality con-ferent training incentives in promoting in-service trol systems; its work force structure, skills andtraining, and suggests ways of overcoming their limi- compensation system; its training facilities andtations. worker training by source and type; and produc-

tion inputs and outputs.It investigates the links between training and firm-level productivity, a critical issue not only for firms The MITP survey asked detailed questions aboutbut also for policymakers. It addresses this issue by employer-sponsored training. The multifacetedestimating the productivity and wage outcomes of nature of training makes it notoriously difficultdifferent kinds of training provided to different to quantify. It can either be provided informallygroups of workers. on-the-job through instruction from co-workers

and supervisors, or formally through structuredFinally, the report studies the role of new tech- courses of classroom instruction combined withnology in raising skill requirements. It looks at on-the-job training.

2 ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

Training can take place in company training centers Employer responses can be used to characterize,or be provided by a variety of external sources infor the first time, the incidence and intensity ofcluding public and private training institutes, inin-service training in Malaysia: how much in-dustry associations, foreignjoint-venture partners, service training goes on in Malaysia; where areand buyers and suppliers. employers training their workers, in company

training programs or through external trainingThe content of training can vary, from machinery providers; which external training sources areoperation to statistical process control to production most in demand - public training institutions suchmanagement. Training provided to differentoccupa- as ITIs or IKMs, skill development centerstional groups can differ, both in the numbers trained (SDCs) or advanced training institutes, or otherand in the types and sources of training provided. private sector providers? They will also allow usOther dimensions of training - duration, intensity, to identify which of the firms train and whichcost, and the quality of instruction - are also impor- do not, and which groups of workers are beingtant, but are poorly measured in the MITP survey. trained.

Box 1.1 Cross-National Enterprise Training Study

This study was based on five developing economies. Three countries -- Columbia, Indonesia, andMalaysia -- fielded surveys of manufacturing firms based on a World Bank survey instrument. Afourth country, Mexico, used a survey instrument developed jointly by the Secretariat of Labor andSocial Welfare and the International Labor Organization (ILO), with input from the World Bank toensure its comparability with the other surveys. Tawain, China was included in this sample becausekey training, technology and production information was elicted in its 1986 Census of Manufacturing.It was also attractive both for its large sample size and as a benchmark for the other developingeconomies.

Table 1.2 presents some summary statistics on these economies. The five economies in the samplerepresent considerable diversity in the level of per capita income, stage of industrialization, and exportperformance. The World Development Report (1995) classifies Indonesia and Columbia as lower-middle income economies. Inl 986, the year for which we have data, Taiwan, China would have beenranked as being higher-middle income by this classification system. These economies experiencedstrikingly different growth patterns over the 1980s and early 1990s, with stagnant or low groth of percapita GNP and manufacturing output in Mexico and Columbia, and rapid growth in Indonesia, Malaysiaand Taiwan, China.

Characteristics of Economies in the Enterprise Training Study

Developing GNP per Capita GNP Growth Manufactures ExportEconomy US$ 1993 1980-93 1980-93 1980-93

Indonesia $740 4.2 11.8 6.7Columbia $1,400 1.5 3.5 11.0Malaysia $3,140 3.5 10.3 12.6Mexico $3,610 -0.5 2.1 5.4Taiwain, China $3,687a 6.2b 7.6b 12.7b

Notes: For Taiwan, China, a refers to 1986 and b refers to the 1980-86 period.

Sources: World Development Report, 1995; Taiwan Statistical Yearbook, 1988.See Tan and Batra, Enterprise Training in Developing Countries, World Bank (1995)

INTRODUCTION 3

The survey included a comprehensive set of ques- vey, this information provides an unprecedentedtions about the attributes of the enterprise. These opportunity to explore the critical inter-dependen-variables- total employment size, research and de- cies that exist between key strategic variables, andvelopment spending, licensing of technology, for- that ultimately determine the productivity levelseign capital participation, exports, use of automatic and competitiveness of firms in the economy.equipment, quality control system, education andsex composition of the work force, and labor turn- The Sampling Frameover - are critical for understanding why firms train. The design of the MITP sampling frame reflected

several considerations. First, we wanted a large, na-They allow us to address questions of how skill and tionally representative sample of manufacturing en-training requirements are influenced by firm size, terprises. While the overall sample would beby the technology and quality control system used, representative of the composition of the manufac-by foreign capital participation either as joint ven- turing sector, it would be stratified by three firmtures or as wholly foreign-owned firms, and by the sizes with larger firms being over-sampled relativecharacteristics of its workforce. to their true weight in the population. A sample

size of approximately 2,200 was thought to be ad-The survey elicited information on production and equate for ensuring adequate representation in eachcompensation, data critical to understanding the eco- industry-finn size cell.nomic motive for why firms train and how theseinvestments in training affect firm-level productiv- Second, we wanted to build in the potential for link-ity and the wages paid to employees. Information ing the MITP survey to the 1988 Malaysiaon production inputs and outputs allow us to esti- LaborFlexibility Survey (MLFS). While its fo-mate production functions and, after accounting for cus was on labor market adjustment, the MLFS alsodifferences in capital, labor and other firm attributes, elicited relevant information, such as skill com-to relate investments in training to improvements position of employees, and adoption of new tech-in firm-level productivity. nology. To this end, two samples of firms were

created - respondents of the 1988 MLFS still pre-This ability to relate training to productivity out- sumed to be in existence in 1994, the "survivors"comes is important since different types and sources sample; and firms not in the MLFS that began op-of training may have different effects on productiv- eration between 1989 and 1994, the "new entrants"ity, with implications for where and how samnple.policymakers and enterprises should allocate theirtraining resources. Similarly, the ability to relate The MITP survey was carried out by Survey Re-training to wages will allow us to address the issues search Malaysia (SRM), using a sampling frame pro-of how the productivity benefits of training are vided by the Department of Statistics (DOS), andshared with workers, and if the factors that shape with participation of the local research team, the Eco-training changes, (such as adoption of new technol- nomic Planning Unit (EPU), and the World Bank.ogy) what are the consequences for income distri- The fieldwork involved several activities: track-bution and inequality? ing down firms in the DOS list, verifying the de-

mise or continued existence of firms and conductingFinally, many variables elicited in the survey are pilot interviews to field-test and refine the MITPalso important in their own right. They represent survey instrument.key elements of private sector firms' innovation,human resource, organization, and marketing strat- The survey enumeration was carried out over a pe-egies as well as important areas of government riod of four and a half months between Decemberpolicymaking. When brought together in one sur- 1994 and May 1995. Questionnaires were mailed to


Table 1.1 Key Variables in the MITP Survey

Firm attributes Types of questions asked

Firm characteristics Single or multi-plant firm, age of enterpriseForeign capital by country of originPrincipal product and exports by destination market

Markets and Technology Prior growth and future growth expectationsCapital stock-automation, vintage, investment plansR&D as % of sales, any technology licensesQuality control system, ISO-9000 certification

Workforce and Compensation Education and worker attributes by broad occupationWages, fringe and statutory benefits by occupationRecruitment and labor turnover by occupation

Training system Training facilities and training specialistsInformal OJT vs formal, structured trainingNumbers trained in-house and mode by occupationNumbers trained by detailed external sourceReasons for low investments in worker training

Production and Inputs Value of output, capacity utilization rateCost of intermediate inputs and energy

each firm that could be located, accompanied by a sponse rates being in the Wilayah Persekutuan area.letter from the EPU explaining the purpose of the All the analyses in this report are based on a samplesurvey, assuring them of confidentiality, and arrang- of the first 2,200 firms that returned completed ques-ing for a face-to-face interview after respondents had tionnaires. In the analyses, no distinction is madean opportunity to assemble all relevant data. A sec- between the survivors and new entrants samples.2

ond letter from the Human Resource DevelopmentCouncil was also sent out to emphasize the impor- Analytic Approachtance of responding to the MITP survey.

Our analytic approach is motivated by an economicTable 1.2 shows the final composition of the MlTP model in which firms develop technological capa-sample and survey response rates by state. Out of bilities through conscious investments in knowledge-the 4,583 names provided by DOS, SRM verified generating activities.and mailed out or delivered questionnaires to a to-tal of 3,373 firms; of these, a total of 2,318 firms Our definition of technological capability followsreturned completed and usable questionnaires. Bell and Pavitt (1992), who distinguish between

"production capacity" and "technological capability."The overall response rate-68 percent-is extremely The former concept measures the capacity of firmshigh, especially given the length and complexity of to produce output at given levels of efficiency, withthe MTP questionnaire. Response rates were some- existing inputs of capital, labor, and technology; thewhat lower for the new entrant sample (66 percent) latter incorporates the additional and distinct re-as compared to the survivor sample (71 percent), and sources needed to generate and manage technologi-varied considerably across states, with the lowest re- cal change, including specialized managerial and

INTRODUCTION 5

technical skills, knowledge and experience, and infirms operating in the local markets (Westphalter-firm linkages. Employers with these technologi- etal, 1984; Pack, 1992).cal capabilities have a productivity advantage over Firms can invest in the skills of their employ-

less capable firms. ees. Whether importing foreign technology,

Technological capabilities can be developed in or using, adapting and redesigning technol-several ways. ogy through deliberate investments in R&D,

firms can build technological capacity by in-* Firms can invest in their own R&D or pur- vesting in the skills and training of the

chase technology and know-how through fi- workforce.censing agreements with foreign firms. The Several factors are at the heart of why educationevidence from developing countries suggests and training are so critical to developing a firm'sthat reverse engineering, imitation, and modi- technological capabilities. First, we know that thefication of foreign technology are often more productivity advantage of new technology is onlycritical to developing technological capabili- attained through an intensive learing process. Thereties than investments in basic research and is evidence from technology literature that much ofinnovation (Pack, 1992). the productivity gains from introducing a new in-

• Firms can acquire relevant and best-practice novation comes from making cumulative smalltechnology through their links with foreign buy- modifications in it, essentially through an inten-ers of exported products as well as from foreign sive learning-by-doing process (Bell and Pavitt,

Table 1.2 The MITP Sample and Response Rates

DOS Sample Number Surveyed Response Rate %State NE S NE S NE S

Johor 340 331 247 276 84 79Kedah 90 93 83 90 88 91Kelantan 34 45 26 45 88 78Malacca 69 70 51 50 76 80Negri Sembilan 38 57 37 53 95 91Pahang 38 65 29 60 86 100Penang 218 284 186 265 70 76Perak 150 272 91 224 91 94Perlis 5 4 4 3 100 100Selangor 346 517 263 418 63 56Wilayah Per. 601 358 327 249 14 37Trengganu 23 37 19 34 100 94Sabah/Sarawak 251 249 143 110 71 91

TOTAL 2,450 2,133 1,615 1,757 66 71

Note: NE = new entrant sample, S = survivor sample.Source: 1995 MITP Survey


1992; Enos, 1962). The challenge for employers which its graduates bring to the employer - willis to motivate and provide workers with incen- determine how cost effective it is for enterprises totives to learn about the new technology. rely on outside training institutions rather than pro-

viding these skills in-house.Second, innovative firms are more likely to usehighly educated and skilled workers because they The technology discussion suggests another set ofare more adept at critically evaluating new infor- determining factors. If the productivity advantagemation, and thus learn more. Being more efficient of technology is revealed only through learning bylearners, they are more productive when exposed to doing, then innovative firms have an incentive tonew and unfamiliar information. train in-house to internalize the new technology in

the skills of its workforce.Microeconomic case studies have identified the criti-cal role of educated workers in the innovation pro- In contrast, outside training providers are typicallycess (Setzer, 1974; Pack, 1992). There is a large body not well-prepared to impart skills associated withof substantiating evidence for these views. the most recent, and still evolving, technologies.

T hey play an increasingly important role (and theirHuman capital studies, have shown that educated training services are utilized more intensively byfarmers and workers are more productive in a rap- firms), when technologies become standardized andidly changing environment, and thus earn higher their productive characteristics become well-under-incomes (Welch, 1970; Tan, 1980; Mincer, 1989). stood.There is evidence from industrialized and devel-oping countries that industries experiencing rapid These perspectives-on the relative importance oftechnological change are more likely to train their in-house company training when firms are en-workers, and that these training investments give gaged in innovation-are supported by the researchrise to higher wages (Carnoy, 1990; Lillard and of Lillard and Tan (1992) and Tan et al (1992). InTan, 1992; Tan et al, 1992). Finally, using firm- their study of the sources of worker training inlevel data from Taiwan, Aw and Tan (1994) show high- and low-technology industries in the U.S., theythat worker training has a large positive impact find that in-house training programs are empha-on firm-level productivity, and that this effect is sized when employers are engaged in developinglarger when worker training is accompanied by new technology.complementary investments in both R&D and for-eign technology licenses. These trends may be less pronounced in developing

countries, such as Malaysia, where older, and moreTo date, however, the literature has been relatively standardized, technologies are in common use andsilent about the types of training that are most perti- firms have limited in-house training capabilities.nent to technological change. Employers must makedecisions not only about whether to train, but alsowhat kinds of training to provide. They may pro- Overview of the Reportvide training in-house, or rely on outside trainingproviders, depending upon their in-house training The report is divided into two broad sections. Thecapabilities, and the vocational education and train- first section, which comprises Chapters Two throughing (VET) system in the country. Four, focuses on the incidence and productivity out-

comes of employer-sponsored training and on gov-

The VET system- its ability to meet the skill re- ernment policies and incentives designed toquirements of enterprises, the quality of technical encourage training by employers. The second sec-training provided, and the job relevance of skills tion, Chapters Five and Six, looks more broadly at

INTRODUCTION 7

technology in firms, the use of quality control sys- training are larger for small and medium size firms,tems and ISO-9000 certification, and the skill re- who do relatively little training; for firms investingquirements associated with the use of new in new technology, especially through technologytechnologies and organizational change. The report licensing; and for export-oriented firms and firmsconcludes in Chapter Seven with a summary of with some foreign capital participation.findings and policy recommendations.

The production function analyses also revealedChapter Two uses the MITP survey to paint a marked differences in the productivity effects ofbroad brush picture of enterprise training in the training provided to different groups of workers andmanufacturing sector of Malaysia. It reports sum- training from different sources. The results showmary statistics on the incidence of training across that while skilled worker training leads to gains infirms of different sizes and industries, and from inproductivity, training provided to unskilled work-ternal and external sources. The latter include ers has no measurable productivity effects.polytechnics, vocational schools, skill develop-ment centers (SDCs), advanced training institutes Among training sources, in-house company training(CIAST), training institutions sponsored by dif- is most strongly associated with productivity gainsferent government ministries (ITIs, IKMs, and except in local firms where training capabilities areYTCs), and various private training institutes, buy- weak. The productivity effects of external trainingers and suppliers, joint venture partners, and train- varies by source for different firms, with SDCs anding overseas. CIAST being most important for local firms and

other private training providers for foreign firms.The key finding is that most firms either meet theirskill needs in-house or through largely private sec- This chapter also analyzes the effects of training ontor providers. With the exception of SDCs and the average monthly wages of employees. The re-CIAST, other public training institutions play a rela- sults show that training leads to higher monthlytively minor role in meeting the in-service training wages. However, wage effects are smaller than pro-needs of private sector firms. Though they currently ductivity effects, suggesting that employers shareplay a greater role in providing pre-employment part of the productivity gains from training withtraining, in future they will need to become more their employees. The pattern of wage effects fromdemand driven and work closely with the private training parallels the productivity results, namely,sector. that the wage effects of training are larger in firms

that make complementary investments in new tech-Analyses of the determinants of firm training also nology, in foreign-owned firms, and to a lesser ex-yielded other findings. They show that firms' train- tent in firms that export. Like the productivitying decisions are shaped primarily by firm size, by results, training provided to skilled workers resultsthe educational, skill and sex mix of employees, by in wage gains but not training to unskilled workers.its technology level, whether it exports, foreign own- Finally, it provides some evidence that firms canership, the type of equipment used and whether or lower job turnover by the employees through high-not employers emphasize quality control. wage policies. Productivity gains from increased

training that comes from greater job retention ofChapter Three analyzes the productivity impacts of trained workers can offset higher wage costs.formal, structured training provided by employers.Using a production function framework, it shows Chapter Four motivates the discussion of trainingthat training has a positive impact on raising the pro- policies by reporting employer perspectives on whyductivity levels of firms. Furthermore, it demon- they do little or no training. This reveals that whilestrates that the beneficial productivity impacts of most firms do not train because of the low skill re-


quirements of relatively simple, standardized tech- to invest in R&D and technology licenses thannologies used, a large number of firms, small and wholly foreign-owned firms of comparable size,medium size employers in particular, also cited high which may reflect the greater reliance of wholly-labor turnover, lack of knowledge about how to train, owned subsidiaries on the technology stock andand limited resources, as reasons for not training. R&D of the parent MNC.

These latter responses, coupled with evidence from This chapter also touches on ISO-9000, a voluntaryprevious chapters about the low incidence of train- standard of the International Standards Organiza-ing and high potential returns, suggest that many tion that Malaysia has adopted. Over ten percentMalaysian firms under-invest in training, and that of firms in the MITP survey had some level ofseveral market failures pose important constraints ISO-9000 certification, and 33 percent expectedon training. The chapter then presents the results of to be certified within the next three years. How-detailed analyses of two training policies designed ever, ISO-9000 adoption will still be relativelyto encourage employers to train-the Double Deduc- low in micro, small and medium firms, and shouldtion Incentive for Training (DDIT) and the Human be an important area of focus-both in terms of dis-Resource Development Fund (HRDF). semination and technical assistance-for

policymakers. The analyses indicate that firmsIt describes the limited use of the DDIT by firms with ISO-9000 certification, or those activelyand the reasons why many firms did not use this seeking it, are more successful in exporting to in-training incentive. It reports some teething problems dustrialized country markets.with the HRDF, including what appears to be seri-ous noncompliance to register and contribute to the Chapter Six draws together the analyses of trainingHRDF, and failure to take advantage of training re- and technology by investigating firm-level tech-imbursements. It is too early tojudge the efficacy of nical efficiency and its distribution. Using a fron-HRDF, but there is some evidence that it has indeed tier production function framework, it estimatespromoted training and skill upgrading among the of how far each firm is from "best practice" tech-sample of firms that have registered with the Hu- nology, and what factors determine its level ofman Resource Development Council. efficiency. The overall results echo many of the

main findings reported in previous chapters-Chapter Five shifts the focus to use of new technol- younger, export-oriented firms, firms that employogy, quality control systems, and ISO-9000 certifi- a more educated workforce, and those that pro-cation in Malaysian firms, and their implications vide training, skilled worker training in particu-for changing skill requirements. It provides a broad lar, are more efficient.overview of research and development, technologylicensing, use of testing equipment, automation, and The efficiency estimates are used to characterizeequipment age among firms by size, local and foreign the size distribution of efficiency in the MITPownership, and industry. sample. The results show that SMIs are not nec-

essarily inefficient-some SMIs actually are moreWhile the MITP survey reveals more private sector efficient than many larger firms. Their low aver-R&D than the 1992 MASTIC survey, its R&D esti- age efficiency level, compared to larger firms, ismates are still relatively low compared to other coun- due to the fact that a high proportion of SMIs havetries. It shows marked differences in these low efficiency and a high proportion of largertechnology indicators across firms, with joint ven- firms have high efficiency. If SMIs are not in-tures and wholly foreign-owned firms being more herently inefficient, then it follows that their ef-technologically advanced as compared to local firms. ficiency levels can be improved through policyIt finds, however, thatjoint ventures are more likely interventions.

INTRODUCTION 9

Potentially important areas for policy are sug- Finally, Chapter Six reports some preliminary analy-gested by the profile of efficient firms by size. ses of efficiency spillovers to local firms from linkagesHighly efficient firms tend to have technology li- withjoint ventures and foreign firms. The results in-censes but not necessarily R&D; they export and/ dicate that a higher foreign presence is associated withor; have some foreign capital equity; they pro- efficiency improvements for local firms, and thatpartvide formal structured training to both skilled ofthesegainscomefromtheR&Ddonebyjointven-and unskilled workers, and do not rely only on tures, andpartcomes fromthe training thatwholly for-informal OJT. Efficient firms emphasize qual- eign-owned firms give their employees.ity, especially statistical process control; they useprecision measuring instruments and do not rely The report concludes with Chapter Seven. It sum-on visual inspection and are more likely to be marizes the main findings and draws out their policyseeking ISO-9000 certification. Highly efficient implications in five areas: (i) collection and disserni-firms have formed work organizations that seek nation of training information; (ii) expanded roleto reduce job turnover, using high-wage policies of education and training institutions; (iii) more ef-and compensation that includes severance pay, fective training policies; (iv) technology diffusionprofit-sharing and bonuses to attract and retain and promotion; and (v) better coordinated and pro-workers. active SMI policies.

CHAPTER Two: OVERVIEW OF TRANING

In this chapter, the MITP Survey is used to paint a training nor formal training; those that rely exclu-broad picture of enterprise training in the manufac- sively on informal on-the-job trainring from co-work-turing sector. We describe the incidence of training ers and supervisors; and those that provide formalby firm size and industry. We present estimates on training, either in-house or from external sources.training provided by employers and by a variety ofexternal training institutions, both in terms of the The figures on training are adjusted using samplingproportions of employers using each training source weights constructed from the 1988 industrial surveyand in terms of the number of workers trained. We whenever aggregate figures are reported for theuse employer responses to gain insights into why a manufacturing sector as a whole or by industry. I Thesubstantial proportion of firms provide little or no data are not weighted when figures are reported byformal training to their employees. Finally, we esti- size since the MITP survey is already stratified bymate regression models to identify the important fac- size. For the purposes of this report, we define fourtors which shape company decisions to train firm size categories-micro firms (with 15 or fewerdifferent groups of workers and to rely on in-house workers), small firms (with 16-100 workers), mediumversus external training providers. firms (101-250 workers) and large firms (with more

ihan 250 workers).

Incidence of Training Table 2.1 shows the incidence of enterprise-spon-sored formal training for the manufacturing sector as

Tlhe MITP Survey elicited a wealth of information a whole and by firm size. Two points stand out. First,on training. It asked a limited number of questions a very high fraction of firms either provide theirabout informal on-the-job training provided by co- workers with no training (32 percent), or they relyworkers and supervisors, and detailed questions exclusively on informal, on-the-job training (48 per-about formal, structuredtraining-the number of work- cent). Only 21 percent of all employers provideers getting formal training over the past year, by their workers with any formal, structured training.broad occupational group and by source of training.

Secondly, there are very marked differences in theIt distinguished between formal training provided incidence of training by firm size. The proportionin-house by the employer, and formal training ob- of firms that do not provide any training is highesttained from a variety of extemnal training institutions, among the micro firms (34 percent) and lowest amongboth public and private. The public training institu- the largest size firms (four percent). Conversely,tions included polytechnics, vocational and techni- formal training is most common among the large firmscal schools, advanced skills training institutes (71 percent) and lowest among the smallest firms (10(CIAST), Industrial Training Institutes (rI), Insti- percent). Most firms which provide formal trainingtute Kemahiran Mara (IKM), Youth Training Cen- also have informal on-the-job training, a point that isters (YTC), Skill Development Centers (SDC), and apparent from a comparison of the last two rows ofother government institutes. The private training Table 2.1.sources include buyers and material suppliers, joint-venture partners, and private sector training institutes. Table 2.2 presents the corresponding estimates of

training incidence by 16 industrial sectors. TheyWe can broadly characterize training incidence by reveal considerable cross-industry variation in theclassifying firms into three groups: those thatprovide proportion of firms that do no training and those thatno training of any kind, neither informal on-the-job provide formal training.

OVERVIEW OF TRAINING 11

First, consider the industries where large numbers electrical machinery, iron and basic metals, trans-of firms do no training. These include such tradi- port equipment, textiles, apparel, and rubber indus-tional domestic-oriented industries as wood and fur- tries are relatively training-intensive, with over 35niture, paper and printing, glass and pottery, percent reporting formal training.fabricated metals, machinery, and food productswhere only 10-25 percent of firms provide formal The high proportion of firms providing formaltraining to their employees. On the other hand, the training in electrical machinery, transport equip-

Table 2.1 Incidence of Training in Manufacturing and by Firm Size

Mean Characteristics Overall Micro Small Medium Large

Number of firms with training data 2,200 247 959 535 454

% Firms not training 31.8 33.6 14.8 5.2 3.7% Firms with only informal training 47.6 56.3 58.7 43.6 25.6% Firms doing formal training 20.7 10.1 26.5 51.2 70.7% Firms formal & informal training 17.0 6.9 24.5 48.4 66.5

Notes: Overall estimates are weighted; estimates by firm size are not weightedmicro = 15 or fewer workers; small = 16-100 workers; medium = 101-250 workers;large = over 250 workers.

Source: 1995 MITP Survey

Table 2.2 Incidence of Training by Industry

Industry # Firms % Firms % Firms % Firmswith Training not only Informal with Formal

Data Training Training Training

All Industries 2,195 31.8 47.6 20.7Food 265 34.2 40.4 25.4Beverages & tobacco 152 30.0 68.5 1.5Textiles 107 23.6 17.7 58.7Apparel 116 13.8 49.2 37.0Wood & Furniture 306 58.1 31.1 10.7Paper& Printing 126 55.5 26.8 17.6Chemicals 90 16.9 57.5 25.6Rubber 131 32.1 32.8 35.1Plastics 133 10.4 77.5 12.1Glass & Pottery 143 36.4 42.2 21.4Basic Metals 71 6.1 30.9 63.0Fabricated Metals 110 43.3 38.8 17.9Machinery 86 38.8 45.9 15.3Electric Machinery 213 1.8 50.2 48.0Transport equipment 78 9.8 41.1 49.1Other Industries 73 23.9 68.1 7.9

Note: Estimates by industry are weighted using 1988 Industrial Survey weights.Source: 1995 MITP Survey


ment, ironandbasic metals isnot surprising since Sources of Enterprise Trainingthese capital-intensive industries tend to be quitetechnology-intensive.2 Electrical machinery, along Table 2.3 shows the different ways in which firmswith rubber and apparel are also major export- provide formal in-service training. It distinguishesoriented industries, and we speculate that export- between formal in-house company training anders have greater incentives to train so as to external sources of training, both public and pri-produce high-quality products for international vate. Of the 21 percent of employers that trainmarkets.3 formally, about an equal proportion of them (13

percent) use in-house resources as external train-In summary, these data appear to substantiate con- ing providers.ventional beliefs about training in Malaysia, namely,that the larger firms are more likely to train their The bottom panel of Table 2.3 shows the relativeemployees than smaller employers, and that enter- importance of each external training source as re-prise training is related to capital intensity, technol- ported by enterprises. Conditional on the employerogy and export-orientation of industries. However, providing external training, the most commnonlywhat is especially striking is the presence of large cited external sources are private training insti-numbers of finms withoutany system of worker train- tutes (34.9 percent), followed by Skills Develop-ing at all, formal or informal. This should be of con- ment Centers (25. 8 percent), Advanced Skillscern to Malaysian policymakers, given the critical Training Institutes (21.3 percent), and their buy-role that skills play in technology acquisition and de- ers and material suppliers (1 1 percent).velopment, and their presumed beneficial effects onproductivity and wages. (These links are quantifiedin Chapters Three and Five.) Table 2.3 Internal and External Sources

of Training

Also worrisome is the high proportion of employers Source of Training, Percentage of Firms(48 percent) that rely exclusively on informal on- % Any Formal Training b 20.7the-job training (OJT). Informal OJT, while an inte- % Internal Formal Training 12.6gral part of the skill acquisition process, typically % External Formal Training 13.0involves fairly basic skills such as familiarizing newhires with the fan's equipment and operating pro- Polytechnics 4T0cedures-the "how to"-rather than the "why. " It Vocational/Technical Schools 3.2excludes the higher-level problem-solving skills Advanced Skills Training Institutes 21.3that can come from structured training courses Skills Development Centers (SDC) 25.8grounded in theory. Institute Kemahiran Mara (IKM) 1.2

Industrial Training Institute (ITI) 5.3Youth Training Centers (YTC) 0.5

Both kinds of skills are needed; indeed, as noted Other Government Institutes 8.2earlier, most firms that provide formal training also Joint Venture Partners 3.6train infonUally. What is ofconcem is that firms which Buyers/Material Suppliers 11.0rely only on informal training develop few of the Private Training Institutes 34.9critical problem-solving skills needed to acquire and Overseas Training 4.6

master new technologies and improve productiv- a The numbers are weighted using 1988 Industriality. This fact, coupled with evidence indicating Survey weights.that informal OJT has no measurable impact on b Includes firms that train formally either inside thewages or firm-level productivity,4 leads us to focus firm or from external sources.

Conditional on doing external training.on formal structured training in the remainder of this Source: 1995 M ITP Surveyreport.


It is plausible that these are external providers with skills, not for the internediate or advanced-level sldllscapabilities to flexibly provide higher-level skills that are needed after entering employment.training to firms. The high proportion of firms thatreportusing skill development centers (SDCs) is strik- For policymakers, the issue is whether these publicing, especially since most of them (other than the institutions should continue to limit their training ac-Penang SDC) were only established in the past three tivities to pre-employment training, or whether theyyears. The least commonly cited external sources also have a role to play in post-employment skillsof training are government-run training institu- upgrading. One aspect of this issue-the limited in-tions-the Youth Training Centers (0.5 percent), IKM service training provided by these institutions-caninstitutes (1.2 percent), vocational and technical be studied (see Chapter Three); however, theschools (3.2 percent), and other government insti- broader issue can only be addressed by a differenttutes (8 .2 percent). study and is beyond the scope of this report.

The relatively small role of government training in- Table 2.4 disaggregates the different sources of train-stitutes reflects their focus on pre-employment training by firm size. The top panel shows the propor-ing, not in-service training that is the subject of the tions of firms that provide formal training in-housesurvey. The exceptions are the public agencies in and externally. In general, the use of both trainingthe "other" category, such as SIRIM and NPC which sources rises with firm size, with a higher proportionprovide a variety of training and other services di- of small and medium firms training in-house than us-rectly to the private sector.5 This orientation towards ing external training providers.pre-employment training is borne out by data onNational Vocational Training Council (NVTC) ad- The bottom panel shows, for the firms that train ex-ministered trade tests taken by graduates from dif- ternally, the proportion of employers citing eachferent public training institutes. Most YTC, M, and external source of training. (Note that figures for theIKM graduates are tested for competencies in basic micro firm size group are not reliable since less than

Table 2.4 Sources of Training by Firm Size

Source of Training Micro Small Medium Large% Firms training formally 9.1 18.2 44.7 70.6% Firms training in-house 5.2 13.5 31.7 53.6% Firms training externally 5.2 7.6 27.0 51.4

External Sources of Training aPolytechnics 12.5* 2.0* 5.1 9.3Vocational/Technical Schools 12.5* 0.0 3.1 4.2Advanced Skills Training Institutes 12.5* 8.2* 6.3 19.9Skills Development Centers 25.0* 10.2 14.9 28.8Institute Kemahiran Mara (IKM) 0.0 4.1* 2.3 5.1Industrial Training Institute (ITI) 12.5* 0.0 11.0 18.2Youth Training Centers 0.0 2.0* 1.2* 2.1Other Government Institutes 0.0 20.4 22.7 27.1Joint Venture Partners 0.0 10.2 9.8 11.9Buyers/Material Suppliers 25.0* 24.5 25.1 25.0Private Training Institutes 25.0* 28.6 44.3 53.0Overseas Training 0.0 8.2 12.9 21.2

a Conditional on doing external training.* very small sample sizes (3 or less observations).



Table 2.5 Workers Trained, Overall and by Firm Size

Source of Training Number of Workers TrainedOverall Microa Small Medium Large

Anyformaltraining 195,894 35,084 13,917 34,549 112,343Internal formal training 167,614 28,716 12,396 27,286 99,214External formal training 28,279 6,367 1,520 7,262 13,128

% workers with formal training 21.7 8.9 10.4 13.2 29.5% workers with internal training 18.6 7.2 9.2 10.5 26.1% workers with external training 3.1 1.7 1.1 2.8 3.4

Extemal Training SourcesPolytechnics 647 154 24 121 347Vocational schools 477 154 0 92 230Advanced Skills Training Institutes 2,197 1,255 39 198 703Skill Development Centers 7,611 3,844 278 488 3,000ITIs 833 154 0 232 446IKMs 275 0 84 113 77YTCs 96 0 18 34 43Other government institutes 1,605 0 160 697 747Buyers & suppliers 1,792 22 312 548 909Jointventurepartnerfirms 1,508 0 213 355 938Private training institutes 10,359 782 321 3,972 5,283Overseas training 872 0 67 405 399

Estimates not reliable because of small sample size.Note: Estimates of numbers trained are weighted using 1988 Industrial Survey weights.Source: 1995 MITP Survey

five percent of them rely on external training pro- We estirnate these figures by using the firm's responsesviders.) The table clearly shows variation in the use about the numbers of workers trained from eachof different external sources by firms of different source, and inflating them using size-based weightssize. Training provided by private institutes contin- constructed from the 1988 Industrial Survey.6

ues to be the single most commonly cited external train-ing source. We caution that these are rough estimates, given

changes since 1988 not only in the number of firmsAmong the other sources, both small and medium but also their composition. The estimates for microfirms are most likely to cite training from buyers- enterprises are likely to be quite imprecise, givenmaterials suppliers and from other government insti- their small numbers in our sample (153 firms) andtutes. Large firms are most likely to cite SDCs, other correspondingly large weights assigned to them.government institutes, buyers and suppliers, ad- We are much more confident of the estimates forvanced skills training institutes, and to a growing ex- the small, medium, and large firms where ourtent, [Ms as well. sample sizes are larger. We note that this proce-

dure yields an estimate of the manufacturingworkforce of just under one million (900,493),

Workers Getting Training by Source whichistobeexpectedsince 1988 sampleweightsare used.

The number of workers trained provides anotherperspective on the relative importance of the differ- Table 2.5 presents estimates of the number of work-ent in-house and external sources of formal training. ers receiving formal training by source in the manu-


facturing sector, and separately by four firm size firms or on numbers of workers trained. Both mea-categories. sures point to the dominant role of private training

institutes, SDCs, and advanced skills training insti-First, consider the overall estimates. They suggest tutes which provided training for 10,359 workers,that 196,000workersreceivedfomialtraining in 1993, 7,611 workers, and 2,197 workers, respectively.of which 168,000 were trained in-house and just The numbers of workers trained by buyers and28,000 were trained by external providers. As a share materials suppliers and partner firms are as large asof the total workforce, these represent 21.7 percent the numbers trained by "other government trainingfor any formal training, 18.6 percent for in-house institutes," and considerably larger than the indi-training, and 3.1 percent for external training. vidual contributions of lTI, IKMs, YTCs, polytech-

nics and public vocational and technical institutes.The overall results are comparable to those based '

on the proportion of firms that train, but the mix of in-hous andextenal rainng dffer widly. hile Table 2.5 also presents separate estimates of the num-

anuseand equalprop raioniof firmsfeprt usingy Whin e ber of workers trained by firm size. The estimatesan equal proportion of firms report using in-house fomir maelklytbeueibe,ndwl

and xtenaltranin sorces(13perent, te eti- for micro firms are likely to be unreliable, and willa x t i u (.- not be emphasized in the following discussion. For

mates based on workers trained suggest that fim, s the other firm sizes, these worker-based estimatesare giving in-house training to a significantly larger . . . .number of emp)loyees than they are sending outside refocthpotsmdealruinuiiziniFor training, rates of firms. For small firms, training provided by

private training institutes, buyers and materials sup-The relative importance of each external training pliers, and SDCs are of roughly equal importance.source is broadly comparable irrespective of For medium and large firms, private training insti-whether estimates are based on utilization rates of tutes have by far the most significant role in external

Table 2.6 Number of Workers Trained by Industrial Sector

Number of Workers Trained Percent of WorkforceIndustry Any Intemal Extemal Any Intemal External

Formal Formal Formal Formal Formal FormalTraining Training Training Training Training Training

Food 6,331 4,348 1,982 2.9 2.0 0.9Beverages & tobacco 1,661 1,307 353 1.0 0.7 0.2Textiles 11,807 11,180 626 10.4 9.9 0.5Apparel 8,549 8,395 153 3.8 3.8 0.1Wood & Furniture 9,773 8,809 964 22.8 20.6 2.2Paper & Printing 4,139 3,259 880 2.4 1.9 0.5Chemicals 4,157 2,705 1,451 2.3 1.5 0.8Rubber 10,055 7,251 2,803 4.2 3.0 1.2Plastics 7,779 5,871 1,907 8.0 6.0 2.0Glass & Pottery 10,653 9,358 1,294 33.2 29.2 4.0Iron & Basic Metals 32,082 25,683 6,399 73.4 58.8 14.6Fabricated Metals 6,694 6,020 673 3.2 2.9 0.3Machinery 11,129 10,193 936 6.8 6.2 0.6Electric Machinery 58,730 52,701 6028 38.8 34.8 4.0Transportation 5,301 4,319 982 4.7 3.8 0.9Other industries 7,046 6,207 839 3.5 3.1 0.4

Note: Estimates are weighted using 1988 Industrial Survey weights.Source: 1995 MITP Survey


Table 2.7 Workers Getting Formal In-House Training by Skill Group

Occupational Group Number Total Number PercentageTrained of employees Trained

Supervisors 17,109 67,713 25.3Technicians 15,105 47,396 31.9Skilled Production Workers 76,074 462,855 16.4Unskilled Production Workers 59,327 443,051 13.4

Note: Estimatesweighted using 1988 Industrial SurveyweightsSource: 1995 MITP Survey

training, though SDCs, buyers and suppliers, part- They suggest that, on average, a higher proportion ofner firms, and other government institutes are also technicians (32 percent) and supervisors (25 percent)responsible for training a sizeable number of work- are trained as compared to production workers (13-16ers. Particularly striking is the heavy use of SDCs percent); however, skilled production workers areby the largest finms which sent about 3,000 workers more likely to be trained (16 percent) than unskilledfor training in SDCs and 5,283 workers for training production workers (13 percent).in private training institutes.

Though not reported in Table 2.7, the data indicate thatIn Table 2.6, we report more aggregated statistics on production workers are also less likely to get externalthe number of workers trained by industrial sector, training (14 percent) as compared to non-productionas well as their share of the workforce in each indus- workers (28 percent).try. The latter measure is particularly significant giventhe recommendation of the Industrial Master Plan In Table 2.8, we report the numbers trained by ex-(IMP) that employersprovide training to 10 percent of ternal source of training for production and non-their work force (MM, Review of the IMP, 1994).7 production workers, as well as the proportion getting

training in each occupation. The figures show thatBy this yardstick, it appears that the target of 10 per- private training institutes and SDCs are the most im-cent training has only been achieved in five out of portant external sources of training for both groups.the 16 industrial sectors under consideration-iron and However a higher proportion of non-productionbasic metals (73 percent), electric machinery (39 per- workers get training from private training institutescent), glass and pottery (33 percent), wood and fur- (52 percent) than from SDCs (18 percent), while pro-niture (23 percent), and textiles (10 percent). In the duction workers are more likely to get training atother industrial sectors, the proportion ofthe work force SDCs (31 percent) than at private training institutesgetting training is considerably lower. The indus- (26 percent).tries with the lowest figures (less than three percenttrained) include food products, beverages and tobacco, Other key external sources for both groups of work-paper andprinting, and chemicals. ers are buyers and suppliers-who provide the train-

ing to meet their product requirements or to useWhich workers are getting training? Table 2.7 pre- their equipment-and advanced skills training insti-sents estimates of the numbers trained in four tutes. As before, few workers get training at ITIs,broad occupational groups-supervisors, techni- IKMs, youth training centers and vocational schools,cians, and skilled and unskilled production work- reflecting the primary orientation of these publicers-as a proportion of the total number of emnployees training institutions to pre-employment training inin the relevant occupation. basic skills.


Factors Shaping Training Decisions labor, and whether the firm is unionized. Two-digitof Firms industry dummy variables control for other industry

differences.With this overview of training as background, wenow turn to a more formal analysis of the factors that In the discussion that follows, we summarize the ef-shape firms' decisions to provide their employees fects of the most important regressors on the likeli-with formal structured training, and whether the de- hood of the employer providing any formal training,terminants of training differ by skill group and by by skill group, and by training source. The coeffi-training source. To address these issues, we esti- cients estimated by the probit model (these are re-mate separate probit regression models for any for- ported in Annex Table 2.1) provide insights into themal training, training for production workers and statistical significance of each variable and the direc-non-production workers, and in-house versus ex- tion of its effects on training. However, they cannotternal training. be interpreted as marginal effects because of the

non-linear nature of the probit model.The likelihood of an employer providing each typeof training is hypothesized to depend on the relative To facilitate interpretation, we report instead thecosts and benefits. It equals one if the present value marginal effects of the probit model evaluated at theof training exceeds its cost, and equals zero other- sample means of each variable. The marginal ef-wise. The net benefits of training (benefits minus fects from different probit models are presented to-costs) are not directly observed, but are thought to gether in Table 2.9 to facilitate comparisons ofbe related to a set of observable attributes of the regressors across the different training measures.employer. These firm attributes include firm size;worker characteristics such as educational attainment Firm Sizeand skill mix; its level of technology as reflected in Table 2.9 confirms that training probability is stronglyits R&D expenditures and its purchases of know- related to firm size. Relative to micro firms (the omit-how; exporting, and foreign capital participation; or- ted size category), small, medium and large firms areganizational factors such as the degree of automation, 14, 35 and 53 percent more likely to provide anyuse of quality control rnethods, employment of female formal training. The importance of finn size, con-

Table 2.8 Workers Trained from External Sources by Occupation

Production Workers Non-Production WorkersExternal Source of Training Number Proportion Number Proportion

Trained Trained Trained Trained

Polytechnics 429 2.4 219 1.9Vocational/Technical Schools 379 2.1 99 0.9Advanced Skills Training Institutes 1,649 9.6 503 4.5Skills Development Centers 5,546 31.3 2,065 18.4Institute Kemahiran Mara (IKM) 219 1.2 56 0.5Industrial Training Institute (ITI) 605 3.4 228 2.0Youth Training Centers 95 0.5 2 3.6Other Government Institutes 726 4.1 876 7.8Joint Venture Partners 1,214 6.9 294 2.6Buyers/Material Suppliers 1,767 10.0 644 5.7Private Training Institutes 4,550 25.7 5,810 51.8Overseas training 466 2.6 406 3.6

Note: Estimates weighted using 1988 Industrial Survey weightsSource: 1995 MITP Survey


trolling for the other correlates of training (includ- The effects of fin size on training probability dif-ing level of technology), may reflect scale econo- fer by skill group: compared to micro firms, themies in training provision, the greater access of large likelihood of training for skilled workers in largefirmns to resources for training, and unobserved em- firms rises to 61 percent as compared to only 43 per-ployer attributes associated with improved manage- cent for unskilled workers, a trend evident in thement and training capabilities. simple tabulations reported earlier. Larger firms are

Table 2.9Marginal Effects on the Likelihood of

Formal Training Estimated from a Probit Model

Independent Any In-house External Skilled UnskilledVariable Formal Formal Training Worker Worker

Training Training Training Training

Small Firm Size 0.138 b' 0.129 b 0.059 0.168 cl 0.085(16-100 workers) (0.067) (0.065) (0.063) (0.086) (0.061)

Medium Firm Size 0.348a' 0.261 a/ 0.253 a' 0.395 a' 0.224 a(101-250 workers) (0.065) (0.063) (0.062) (0.081) (0.060)

Large Firm Size 0.529a' 0.426 as 0.488 a' 0.606 a 0.430 a'(over 250 workers) (0.070) (0.074) (0.080) (0.094) (0.071)

Mean education 0.024a' 0.028 a' 0.016 a' 0.025 a' 0.026 alof the workforce (0.007) (0.006) (0.005) (0.081) (0.006)

Percent of 0.006 a' 0.002 a/ 0.005 a' 0.003 a' 0.006 a'skilled workers (0.001) (0.0008) (0.0007) (0.0008) (0.0008)

Invests in R&D 0.140 a' 0.135 a' 0.095 a' 0.112 ' 0.151 a'(0.030) (0.026) (0.023) (0.026) (0.027)

Foreign capital 0.071 al 0.080 a' -0.007 0.019 0.080 aparticipation (0.027) (0.024) (0.020) (0.023) (0.025)

Exports 0.010 -0.004 0.008 0.031 0.001(0.027) (0.024) (0.022) (0.024) (0.026)

%Valueof 0.001 0.001 0.001 a, 0.001 a, 0.004automatic machinery (0.0003) (0.001) (0.0002) (0.0003) (0.003)

Use of quality 0.103 al 0.101 al 0.043 ' 0.128 a 0.084 acontrol methods (0.026) (0.023) (0.020) (0.023) (0.024)

Proportion of female 0.024 0.008 0.013 -0.051 0.005workers (0.047) (0.041) (0.037) (0.042) (0.043)

Unionization 0.060-c' 0.027 0.059 a' 0.066 bl 0.020(0.031) (0.026) (0.024) (0.027) (0.028)

Log (likelihood) -1133.40 -1090.86 -918.23 -970.59 -1102.68

a Significant at 1%b Significant at 5%c= Significant at 10% level

Note: Numbers in parantheses are standard errors. Omitted firm size is micro enterprises with 15 orfewer workers. Age of firm and multi-plant status included but were not statistically significant.

Source: Annex Table 2.1.


also more likely to use both in-house and external both skill groups. Thus, unskilled workers enjoy antraining sources than their smaller counterparts. externality by working in a workplace with a high

proportion of skilled workers.Education and Skill MixThe training effects of education stand out. The The Firm's Technologyresults indicate that employers are more likely to The results provide strong evidence that skill andprovide formal training for all groups and from all training requirements are shaped by the firm's tech-sources the more educated are their workers. A nology. Firms that invest in research and develop-one year increase in the education of the workforce ment (R&D) are about 10-15 percent more likely to(the mean in the MITP sample is 8.7 years of school- train fornally than firmswithoutR&D.ing) is associated with a two to three percent higherprobability of training. The significant positive rela- The results, by skill group, suggest that while R&Dtionship is strong evidence that the two kinds of hu- firms are more likely to train both production andman capital-education and training-are highly non-production workers than firms not doing R&D,complementary. Educated workers are better the likelihood of their training production workers islearners and thus benefit more than less educated actually higher.workers from training. A higher level of work forceeducation also raises the probability that the firm Workers typically require little formal instruction,will train in-house relative to sending workers for beyond some informnal OJT by co-workers, to oper-external training, a result evident from the rela- ate mature well-established technologies. When newtively larger estimated effects of education for in- technologies are being introduced, however, pro-house training. duction is no longer routinized. Under these new

and challenging circumstances, fornal structuredFirmns with a more skilled workforce are more likely training for all workers-both production and non-to train. Skill mix is measured as the percentage production-becomes critical if unanticipated prob-share of managers, engineers, technicians, supervi- lems are to be detected and fixed, and thesors, and skilled production workers in the total work productivity advantage of using new technologiesforce of the firm. Controlling for education (and over mature technologies are to be realized.8

other factors), a one percent increase in the skill mixis associated with roughly half a percent increase in Doing R&D has different effects on where employ-the probability of training. ers train their workers. The marginal effects of do-

ing R&D on training probability are larger forThe results also indicate that skill mix of the work in-house programs (13.5 percent) than for trainingforce is a more important determinant of external from external sources (9.5 percent).training than of in-house training. To the extent thatskilled worker training tends to be highly technical These results-that R&D firms are more likely to trainand specialized, employers may find it more eco- their workers in-house-are consistent with the hy-nomical to send non-production workers to external pothesis that the use of advanced technologies istraining providers than to develop these programs associated with a greater reliance on training in-in-house. house than on external.9 In part, this is because

external training providers are not well-equipped toThere is also evidence that a more highly skilled train in new technologies when extant knowledge isworkforce is associated with a greater probability so limited; and in part, because in-house experienceof training for both skilled workers and unskilled working with, and adapting, new technology devel-workers. The skill mix variable is positive for ops the firm's technological capabilities.


Exports and Foreign Ownership technology. These results suggest that automation

We hypothesize that firms can acquire relevant and will require greater efforts on the part of employersbest-practice technology through their links with to train non-production workers and to send themforeign buyers and foreign firms operating locally, for external training.and are therefore more likely to train their employ-ees. However, the results suggest that exporting is On average, employers that emphasize quality con-not associated with training. The weak result may trol methods are between four and 13 percent morebe reflecting the high correlation between exports likely to train than those firms without quality con-and other firm attributes, such as foreign capital trol. This result is significant for training provided toparticipation, which are already included in the all groups of workers and for training from both in-regression. house and external sources.

Foreign firms are in general about seven to eight A second result is suggested by comparing the rela-percent more likely to provide training for their em- tive size of the estimated marginal effects on trainingployees as compared to local firms. Note that this for each skill group and for each training source.marginal effect persists even after controlling for These comparisons indicate that employers usingother factors, many being characteristics of multina- quality control methods are more likely to train skilledtionals such as R&D, exports, and firm size. workers (13 percent) than unskilled workers (eight

percent), and to train them in-house (10 percent) asForeign firms are eight percent more likely to train opposed to sending them offsite for training (fourin-house than local firms, but not when it comes to percent).external training. This may reflect well-developedin-house training capabilities, since many are large Use of Female Labor and Unionizationmultinationals involved in technology intensive semi- We include two other variables to characterize workconductor and electronics production and assem- organization in the firm-the use of female labor andbly. Finally, while foreign firms are no more likely to unions. Use of large numbers of female workerstrain skilled workers than local firms, they are signifi- may reflect forms of organization built around simplecantly more likely to train their unskilled ernployees. assembly, manual dexterity, seasonal work, and rela-

tively low skills. However, there appears to be littleAutomation and Quality Control support for this hypothesis in Malaysia.The model included two variables-the degree ofequipment automation and use of quality control Controlling for mean education and skill composi-methods-to investigate the training effects of mod- tion, a workforce with a higher proportion of femaleem modes of production organization. Automation workers is not associated with a lower likelihood ofcan either lead to the "dumbing down" of skills, as training. This is important in the context oftightlaborsome have argued, or to increased skill requirements markets in Malaysia for it suggests that increased useto operate and maintain increasingly sophisticated of female workers to meet industry's labor needs isequipment. The results suggest that the probability unlikely to have deleterious effects on firm-levelof formal training is higher the greater is the per- productivity, provided women are similarly educatedcentage share of the firm's machinery and equip- andgiventhe same formal trainingastheirmale coun-ment that is semi- or fully automatic. terparts.

Malaysian policymakers have stressed the need for In theory, unions are thought to reduce the likeli-industry to become more automated, both to con- hood of training by negotiating higher levels ofserve on increasingly scarce labor and to deepen wages and reducing the ability of employers to lower


wages to finance firm-specific training through a With the exception of SDCs and CIAST, two in-training wage. However, when statistically signifi- stitutions that are either demand-driven or that catercant union effects on training are found, they are to higher-level skills training, the other publicinvariably positive and about six to seven percent training institutions-ms, IKMs, YTCs, polytech-higher as compared to non-unionized firms. Simi- nics, and vocational and technical schools-play alar results have been reported in several industri- very minor role in meeting the in-service trainingalized countries (see Lillard and Tan, 1992, Tan et needs of the manufacturing sector. Their primaryal, 1992). focus thus far has been on pre-employment train-

ing in basic and intermediate-level technical skills.The union effect is strongest in increasing training Given their limited role in in-service training, it isfrom external sources (six percent) and training for clear is that the private sector will have to take onskilled workers (seven percent). Unions may have greater responsibilities for meeting its growing skillthis beneficial effect on training by giving workers requirernents.an alternative to job turnover. By establishing griev-ance and arbitration procedures, unions promote The Govemment can, and is, helping facilitate in-greaterjob stability and increase incentives for fimns creased private sector-led training through the Hu-to investintraining. man Resource Development Fund, through

seed-grants to setup private sector-managed SDCsin the different states, and through subsidized credit,

Findings and Policy Implications training, and technical assistance for the populationof small and medium-scale firmns that are most likely

Manufacturingfirms in Malaysia under-invest in not to train or to tely on informal on-the-job trainingthe training of their employees. This is based on (these policies are assessed subsequently).our estimates that about 80 percent of all firms ei-ther do no training or rely exclusively on infor- However, the design and implementation of thesemal training from co-workers and supervisors, and training and related policies are rarely accompaniedthat only 21 percent of firms provide formal training. by adequate monitoring of their take-up, or by pro-

gram irnpact analyses, both of which require a sys-This conclusion is bolstered by the responses of temnatic data collection effort.employers (reported later in Chapter Four) aboutwhy they provide little or no training. Most cite The Governnent's existing systemnfor training datathe use of mature technology as the principal rea- collection and analysis isfragmented and shiould beson for doing little training. While this is not a strengthened. Data on public training institutionsmarket failure per se, a sizeable number of other are typically maintained by each responsible minis-employers, smaller firms in particular, cite other try but seldom reported, on a systematic basis to-training constraints that are-free ridership from gether with detailed cost data, to a central coordinatinghigh labor turnover, lack of knowledge about train- agency for planning and policy analysis.ing methods, and limited resources for training.

Likewise, information on private-sector training in-Firms that train meet their skill needs in-house or stitutions is only collected on an ad hoc basis. Fewthrough a variety of external training sources. Of evaluation studies of training programs-based onthe external training sources, firms rely most tracersurveys of graduates, comparisons withacon-heavily on private providers-private training in- trol group, and cost-benefit calculations-have beenstitutes, buyers and equipment suppliers, joint- conducted; evaluations comparing different publicventure partners, and overseas training training institutions are even rarer. The Nationalinstitutions. Vocational Training Council (NVTC) was designated


as the institution to coordinate both public and pri- provide DOS with the necessary resources and in-vate vocational training programs, and the Govern- centives to implement and speedily process the aug-ment should give NVTC the necessary legal standing, mented surveys on a periodic basis.resources, and capabilities to play this role more ef-fectively. Several determninas of enterprise training stand out.

First, smaller firms are much less likely to train thanLeast well developed is information on in-service larger firms, suggesting that this groups will requiretraining. Existing industrial and household surveys special attention from policymakers. Second, em-fielded by the Department of Statistics (DOS) are ployers are more likely to train when its workforcepotentially potent, but currently under-exploited, is better educated and more technically skilled sincevehicles for developing these training data bases. they benefit more from training. As such, firm incen-

tives to train should increase as education policies toA great deal of demographic and employer informa- promote higher school retention rates and more tech-tion is already elicited in these surveys. The addi- nical education are implemented.tion of a short training module to each survey thusprovides nationally representative estimates of train- Investments in new technology, automated equip-ing at the level of the enterprise and at the level of ment, and quality control are associated with in-the individual. Once institutionalized, these aug- creased training, a fact that reinforces the need formented firm- and worker-level surveys will yield continuous skills upgrading if firms are to adopttime-series data needed for policymakers to monitor more technology-intensive production. Finally,and analyze training trends. The Government local firms are in general much less likely to trainshould setup a committee to design, fund, and coor- relative to foreign firms, reflecting both their weakdinate analyses using these training modules, and training capabilities and lack of a training culture.


Annex 2.1

The following table reports the parameter estimates of probit models for different training measures,where the dependent variable is one if an employer invests in that source or type of training, and zerootherwise. While these estimates provide insights into the significance of explanatory variables and thedirection of their impact on the training outcome, they are not readily interpreted because of the non-linearnature of the dependent variable which is constrained to lie between 0 and 1. However, the marginaleffects of explanatory variables can be calculated and they are reported in the text.

Probit Estimates of the Likelihood of Formal Training

Independent Any Formal In-house Extemal Skilled UnskilledVariable Training Formal Training Training Worker Training Worker Training

Small Firm Size 0.362 b/ 0.385 b' 0.221 0.518 c, 0.246(16-100 workers) (0.176) (0.193) (0.232) (0.264) (0.177)

Medium Firm Size 0.939 -' 0.801 a 0.924 a' 1.265 ' 0.657 a(101-250 workers) (0.176) (0.192) (0.228) (0.259) (0.176)

Large Firm Size 1.446 a' 1.182 L' 1.477 a' 1.748 E' 1.172 a'(over 250 workers) (0.193) (0.207) (0.084) (0.271) (0.192)

Mean education of 0.064 aY 0.088 a' 0.062 a/ 0.081a' 0.076 a'the workforce (0.019) (0.019) (0.021) (0.020) (0.019)

Percent skilled 0.0174 a/ 0.0063 -' 0.0194 ' 0.0110a' 0.0167 a/workers (0.0023) (0.003) (0.003) (0.003) (0.002)

Invests in R&D 0.365 - 0.395-a/ 0.334 ' 0.344 a/ 0.424 a/(0.078) (0.076) (0.080) (0.079) (0.076)

Foreign capital 0.188 a/ 0.243 -' -0.029 a/ 0.064 0.233 a/participation (0.072) (0.073) (0.079) (0.075) (0.073)

Exports 0.027 -0.012 0.030 0.101 0.002(0.074) (0.077) (0.084) (0.081) (0.076)

% Value of 0.002 0.001 0.004 a' 0.004 a' 0.001automatic machinery (0.001) (0.001) (0.001) (0.001) (0.001)

Use of quality 0.272 ' 0.309 a/ 0.160 t' 0.403 a/ 0.245 a'control methods (0.070) (0.071) (0.076) (0.073) (0.071)

Proportion of 0.065 0.025 0.048 -0.167 0.016female workers (0.127) (0.128) (0.141) (0.137) (0.128)

Unionization 0.158 -S 0.082 0.215 a 0.207 b/ 0.059(0.083) (0.083) (0.086) (0.085) (0.083)

Constant term -1.808 -' -2.305 a -2.331 a -2.514 a' -2.214 a(0.326) (0.336) (0.374) (0.387) (0.329)

Log (likelihood) -1133.40 -1090.86 -918.23 -970.59 -1102.68

-Significant at 1%I = Significant at 5%c = Significant at 10% level.

Note: Numbers in parantheses are standard errors. Omitted firm size is micro enterprises with 15 or fewer workers. Age offirm and multi-plant status were also included but were not statistically significant

Source: 1995MITP Survey

CHAPTER THREE: PRODUCTIVITY AND WAGE OUTCOMrES

Inthis chapter, we turn to an empirical analysis of the rizes the complex engineering relationships be-outcomes of enterprise training-both on firm-level tween the firm's output and the inputs used to pro-productivity and on the wages of workers. We are duce that output - plant and equipment, labor,interested in finding out whether employer investments intermediate inputs and energy.in formal training are associated with higher firm-levelproductivity. Other issues of interest are whether It can be specified in many ways, but the specifi-there are productivity differences in the training pro- cation that we will use is the Cobb-Douglas pro-vided to different groups of employees, for exarnple, duction function. I The firm's output is measuredskilled or unskilled workers, and which source of as the natural logarithm of value-added, that istraining-in-plant training programs or training pro- gross output less the value of intermediate inputsvided by external institutions-has the largest im- and energy used, and this is related to the firm'spact on firm-level productivity. use of the two major factors of production - capital

(book value of physical plant and equipment) andWe also examine the relationship between training labor (total employment), both-also expressed inand monthly wages paid by employers. The issue is logarithms.whether the productivity gains from training areshared with workers in the form of higher pay and, if In this Cobb-Douglas functional form, the coeffi-so, what kinds of training have the largest wage ef- cients of capital and labor represent their relativefects and which groups of workers benefit most. This contribution to output, and they typically sum toanalysis of the productivity and wage outcomes of train- one, or roughly constant returns to scale. The pro-ing has ramifications for employers, workers and duction function that we estimate is augmented topolicymakers. Insights into the effects of training on include different training measures and a set of con-firm-level productivity are important for employers trol variables.who must mnake decisions about whether to train, whoto train, and what kinds of trainiing to sponsor. The training measures range from simple indicator

variables-whether employers provide any formalFor workers, these wage gains, if any, represent an training-to more complex ones, such as training pro-incentive for them to undertake training and a moti- vided to different groups of workers, training byvation for them to develop long-term job attachment source (in-house versus external training), and typeto the firm. This is important since highjob tumover of external training providers. These training mea-reduces employer incentive to invest in workers' sures allow us to ask whether training investmentsskills. The training outcomes are also of interest to are associated with higher firm-level productivity,policymakers concerned with issues of economic controlling for inputs of capital and labor, and for theperformance, resource allocation, design of training influences of other contemporaneous variables thatpolicies, and income distribution. also affect productivity.

The latter include the average educational attain-

Estimating the Productivity Impact ment of the firm's workforce, indicator variables forof Training firm characteristics such as whether it exports or in-

vests in technology (measured by R&D or technologyWe analyze the productivity outcomes of employer licensing agreements), and two-digit industry dummyinvestnents in formal traiingwithin aproduction func- variables to control for productivity differencestion framework. The production function summa- across industries.

PRODUCTIVITY AND WAGE OUTCOMES 25

Our production function approach takes into account function. Using the predicted, rather than the actualthe possibility of "selectivity bias" in estimating train- value of the training variable in the production func-ing outcomes. This bias may arise if finns have very tion allows us to get unbiased estimates of the pro-different underlying productivity endowments, and ductivity effects of training.the firms that choose to train differ systematically fromnon-training firms in both their observed and unob-served productivity attributes. To the extentthatwe Productivity Effects of Training forcannot fully control for these unmeasured differ- Different Firmsences, the production function may over or under-state (bias) our estimates of the productivity impact of We begin by presenting estimates of the productiv-training. We use an instrumental variable approach ity effects of fornal training provided by differentto correct for this potential "selectivity bias." groups of employers. In defining these different

groups, we rely on the findings in Chapter Two,In Chapter Two, we report the results of estimating namely, that the likelihood of training is greater inprobit models for the firmn's decision to train. Here, larger firms, in firms using new technology, in ex-we use those probit results to construct a predicted porting firms, and in foreign-owned firms. If thevalue for the training variable that, by its construc- higher incidence of training in these firms is indica-tion, is uncorrelated with the unmeasured productive of the relative profitability of investments intivity attributes (the error tern) in the production worker training, we should also expect to find rela-

Table 3.1 Production Function Estimates by Firm Size

Independent Variable Overall Small Medium Large

Log (labor) 0.576a 0.583a 0.401a 0.578a(0.039) (0.065) (0.201) (0.090)

Log (capital) 0.267a 0.256a 0.274a 0.304a(0.019) (0.026) (0.042) (0.039)

Invests in technology -0.104 -0.107 -0.139 0.024(0.071) (0.118) (0.115) (0.113)

Exports 0.034 0.187b -0.138 -0.387(0.069) (0.091) (0.124) (0.170)

Age of firm 0.007a 0.006b 0.002 0.009a(0.002) (0.003) (0.004) (0.003)

Education of workers 0.029 0.025 0.019 0.053(0.020) (0.029) (0.034) (0.039)

Predicted Training 0.325a 0.323a 0.297b 0.125(0.080) (0.104) (0.144) (0.151)

Constant 7.614a 8.130a 8.905a 7.179a(0.403) (0.505) (1.131) (0.767)

a = Significant at 1 %.b= Significant at 5%.

Note: Numbers in parentheses are standard errors.Industry dummy variables included but their estimates are not reported here.


tively high productivity outcomes from training The firm's age is positive, suggesting that the olderamong these groups of firms. firms tend on average to be more productive, re-

flecting their accumulation of production experience.In interpreting the training results, note that output The other explanatory variables-the mean educa-or value-added is expressed in natural logarithms. tional attainment of the workforce, technology, andThis allows us to interpret the coefficient of the train- exports-never attain statistical significance in the pro-ing indicator variable (or its predicted value) as the duction function estimates though, as seen in Chap-percentage change in output of being a training firm ter Two, they are very important determinants of therather than a non-training firm, controlling for the firm's decision to train.productivity effects of other variables.

For the MITP sample as a whole, Table 3.1 indicatesProductivity Effects by Firm Size that training has a positive and statistically signifi-Table 3.1 reports the production function estimates cant impact on firm-level productivity. The esti-for the MITP sample as a whole and separately by mated training coefficient is 0.325, suggesting thatthree firm sizes. Before turning to the training esti- training firms are, on average, about 32 percent moremates, we note that both labor and capital coefficients productive than firms that do not train, controllingare positive and significant, and that their magnitudes for all other factors that also influence productivityof approximately two-thirds and one-third, respec- in firms. Training effects of this magnitude are nottively, are broadly consistent with the shares of la- unusual and, in fact, are broadly similar to those esti-bor and capital in the economy. mated for other developing countries (see Box 3.1).

Box 3.1 Enterprise Training and Productivity in Developing Countries

Tan and Batra (1995) used a common production function model to estimate the firm-level produc-tivity effects of training in the manufacturing sector of Indonesia, Colombia, Malaysia, and Mexico.In all four countries, they found evidence that enterprise training is associated with higher firm-levelproductivity.

Their findings also indicated that the productivity effects of training, especially training providedskilled workers, are larger in lower-income economies (Colombia and Indonesia) as compared tothe higher-income countries in their sample (Malaysia and Mexico), possibly reflecting the relativescarcity of skills in these lower income countries.

The implication is that economic development is strongly tied to workforce skills development, andthat policies to encourage increased enterprise training will have large productivity gains for theeconomy.

Country GNP per capita Productivity Effects Productivity Effects(year of survey) US$ Any Training Skilled Training

Indonesia (1992) $ 670 0.711 1.430Colombia (1992) $1,330 0.266 0.386Malaysia (1994) $3,140 0.282 0.252Mexico (1992) $3,470 0.444 0.204

Source: Tan and Batra, Enterprise Training in Developing Countries, Private Sector Development Department,World Bank, 1995.


Do the productivity effects of training vary by firm firms, the productivity effects of training are muchsize? To address this question, separate production smaller-12 percent-and these effects are not statis-functions are estimated for three firm size groups- tically significant.small firms (up to 100 employees), medium-size firms(101-250 employees), and large firms (over 250 em- The fact (shown in Chapter Two) that relativelyployees)-and the results reported in columns two few smaller firms train despite this evidence ofthrough four of Table 3.1. large potential gains in productivity from doing

so, leads us to conclude that small and mediumThe estimated training coefficients are 0.32, 0.29 and firms under-invest in training. Such wide discrep-0.12 in small, medium and large firms. These results ancies in returns could not persist in a perfectlyindicate that worker training has large productivity competitive market since firms would train (in-benefits among small and medium-size firns-32 and crease the supply of trained workers) to equalize29 percent-productivity increases that are statisti- the returns to training across markets. The factcally significant at the five percent level. For large that they do not suggests that market failures are

Box 3.2 Technology Raises the Productivity of Training in Taiwan, China.

Using microdata from the 1986 Taiwan Census of Manufactures, Aw and Tan (1994) investigatedthe effects of training on firm-level productivity in seven industries. They were interested in whetherthe productivity effects of training varied with the firm's technology level, as measured by in-houseR&D or purchases of technology. For each industry, they estimated separate production functionsfor firms that invested in technology, termed "high-tech," and for firms that made no such technologyinvestments, or "low-tech," correcting for potential selectivity bias in firms' technology decisions.

They found clear evidence that technology had an impact on the productivity outcomes of training.First, within each industry, training provision was associated with a larger impact on firm-levelproductivity when training was accompanied by firm investments in R&D or purchased technology.Second, looking across industries, the differential impact of training in high-tech and low-techfirms is more pronounced in the technology-intensive industries such as electronics, chemicals,and plastics than in the more traditional industries like textiles and apparel. Thus, both within andacross industries, the evidence indicates that the returns to training rises with technologicalchange.

P ro d u c tiv ity E ffe c ts o f T r a in i n g T a iw a n 1 9 8 61 2

o .a0 6

0 4

0 2

a ci 0 a

r~~~~~~~~~~~~0


present, especially among small and medium firms Productivity Effects by Technology(SMIs). The level of technology used in firms may also af-

fect the productivity outcomes of worker trainingThis conclusion is bolstered by employer responses (Lillard and Tan, 1992). The characteristics of newabout why they provide employees with little or no technologies are often poorly understood, and theirtraining. The evidence, which will be presented in productivity advantages over the older technolo-Chapter Four, suggests that several market failures- gies that they replace are seldom manifested withoutfrom lack of information about how to develop and significant employer investments in learning-by-do-manage their training programs, high job turnover ing and training. In this environment, the productiv-whichmakesitdifficultforfilrmstorecouptheirtrain- ity gains from worker training can be quiteing investrnents, and limited access to finance for substantial.trhining-are iWortant reasons for why some employ-ers invest very little in training. More significantly, In contrast, older and more established technologiestheir responses also suggest that these factors pose require less training since their specific characteris-particularly severe constraints for many SMIs. tics are well-known; consequently, the productiv-

Table 3.2 Production Function Estimates by Technology Level

DoTec=O DoTec=1 HasTL=O HasTL=1

Log (labor) 0.604a 0.531a 0.624a 0.391a(0.047) (0.058) (0.041) (0.084)

Log (capital) 0.259a 0.294a 0.261a 0.287a(0.022) (0.038) (0.020) (0.062)

Conducts R&D - - -0.139 -0.218(0.083) (0.155)

Exports 0.093 -0.132 0.063 -0.084(0.079) (0.126) (0.070) (0.213)

Age of Firm 0.005b 0.009a 0.006a 0.019a(0.002) (0.003) (0.002) (0.007)

Education of workforce 0.028 0.032 0.029 0.004(0.024) (0.034) (0.021) (0.055)

Predicted Training 0.281a 0.282a 0.233a 0.554a(0.095) (0.120) (0.082) (0.181)

Constant 7.954a 7.768a 7.813a 8.798a(0.421) (0.643) (0.375) (1.035)

R square 0.619 0.635 0.636 0.626

a = Significant at 1%.I = Significant at 5%.

Note: Numbers in parentheses are standard errors.Industry dummy variables included but their estimates are not reported here.DoTec = invest in R&D or has technology license.HasTL = has technology licensing agreement(s).



ity gains from training to use older technologies are To summarize, these production function estimatesalso likely to be limited. In Chapter Two, we found show that firms can make potentially large produc-strong evidence that firms were more likely to train tivity gains of over 55 percent when new technolo-their workers if they were also investing in R&D. gies acquired through licensing agreements areThese perspectives lead us to formulate the follow- complemented with investments in training. In con-ing tests of the link between the firm's technology trast, R&D has limnited impact either on overall pro-level and the productivity outcomes of training. We ductivity levels, or on productivity of workersplit the MITP sample into two groups of firms by training. We interpret this limited impact of R&Dlevel of technology, and compare the productivity as reflecting the relatively weak R&D capabilitieseffects of training in the high and low technology of Malaysian firms; MNCs are widely believed togroups. Such an analysis can also be done for indi- have greater capabilities in conducting R&D but theyvidual industries when data on large samples of do little in Malaysia.firms are available (see Box 3.2).

Productivity Effects by Export OrientationTwo definitions of technology are used. First, we and Ownershipdefine an indicator variable, DOTEC, which takes Two other attributes of firms-export orientation andon a value of one if the firms invests in R&D or has foreign ownership-may affect the productivity out-technology licensing agreements with other firns, comes of training through the mediating role of tech-and zero otherwise. Second, we define an indicator nology and links with external markets.variable, HASTL, to distinguish between firmns withand without technology licenses. The level of technology in exporting firms may be

higher for two reasons: (a) a firm's export-orienta-This second definition recognizes that when in- tion may sinply reflect its underlying technologicalhouse R&D capabilities are weak, as is true in many capabilities and international comnpetitiveness; (b) ex-Malaysian firrms, licensing agreements can be an importing may also raise technological capabilities byportant means of accessing relatively sophisticated giving firmns access to technologies and know-howtechnologies from abroad, even if the firm does no from abroad and, through interactions with foreignin-house R&D. The production function estimates buyers, informnation about new markets and productcorresponding to these two technology measures specifications as well.are reported in Table 3.2. When the broad defini-tion of technology, DOTEC, is used, the productiv- Foreign firms-defmed here as firms with over 50ity effects of training-about 28 percent increase in percent foreign capital participation-are thought tovalue-added-are virtually indistinguishable in the embody relatively high levels of technology, know-high technology and low technology firm samples. how, and managerial expertise as compared to do-To see this, note that almost similar training coeffi- mestic firms. Their level of technological capabilitiescients of 0.28 are reported in columns one and two. is not well reflected by indicators such as local R&D

spending or technology licenses, since they are ableThe second definition, HASTL, which is based on to draw on the MNC parent's stock of technologywhether firms have technology licensing agree- and R&D.2 These are not typically located in de-ments, does a better job of discriminating between veloping countries where there is often a short sup-the high and low technology firms, controlling for ply of experienced R&D scientists and engineers.their R&D investments. Not only are average pro-ductivity levels (reflected in the constant term) much Table 3.3 reports production function estimates forhigher in firns with technology licenses, the pro- groups of firms differing in their export-orientationductivity effects of training in these firms are over and ownership. Columns one and two indicate thattwice as big as those for firms without technology the productivity effects of training are higher in firmslicenses-55 versus about 23 percent.


Table 3.3 Production Function Estimates by Export Orientation and Ownership

Exports=0 Exports>0 Foreign=0 Foreign=1

Log (labor) 0.678a 0.515a 0.571a 0.578a(0.068) (0.046) (0.048) (0.069)

Log (capital) 0.251a 0.266a 0.285a 0.207a(0.029) (0.027) (0.022) (0.044)

Invest in Technology 0.071 -0.135 -0.096 -0.099(0.151) (0.075) (0.087) (0.119)

Exports - - 0.041 -0.419(0.078) (0.215)

Age of Firm 0.004 0.010a 0.007a 0.018a(0.003) (0.003) (0.002) (0.006)

Education of workforce 0.036 0.022 0.025 0.099b

(0.034) (0.023) (0.024) (0.042)

Predicted Training 0.270b 0.333a 0.283a 0.327b(0.133) (0.095) (0.098) (0.158)

Constant 7.751a 7.784a 7.319a 7.843a

(0.579) (0.506) (0.487) (0.776)

R square 0.583 0.608 0.625 0.673

a = Significant at 1 %.b = Significant at 5%.

Note: Numbers in parentheses are standard errors. Industry dummy variables included but theirestimates are not reported here.


that export-about 33 percent-as compared with of domestic firms. As shown in Chapter Two,those that do not-about 27 percent. Columns three firms with these characteristics are more likelyand four show a similar result, that the productivity to invest in the training of their workers. The pro-outcomes of training are higher in foreign-owned duction function results reported here confirmfirms (33 percent) than in domestic firms (28 per- that the productivity effects of this increasedcent). It is also noteworthy that overall levels of training are significantly higher among firms thatproductivity (as reflected in the constant term) are export, have technology licensing agreements,much higher in the sample of foreign-owned firms and some foreign capital participation.(7.84) than in the domestic firm sample (7.32).

Taken together, these results and the findings re- Productivity Outcomes by Skill Groupported in Table 3.2 provide support for the view and Training Sourcethat employer investments in technology and train-ing are complementary in that investments in one Thus far, we have treated all forms of training as ifenhance the productivity of the other. Given cur- they had the same productivity outcomes. We nowrent weak local R&D capabilities, the results sug- consider several potential variations in the produc-gest that export-orientation, foreign technology tivity outcomes of training across different workerlicensing and joint-ventures may offer the great- groups and sources of training. As before, we con-est potential for improving the technology levels trol for selectivity bias by including the predicted

PRODUCT[VITY AND WAGE OUTCOMES 31

values of training from probit models of training pervisors, engineers, technicians and skilled produc-for different skill groups and for different training tion workers; and unskilled production workers.sources. For each skill group, we begin by estimating sepa-

rate probit models of whether employers provideWe also test a specification where training is pre- in-house or external training, including the group-dicted using a tobit model. The tobit specification specific measures of skill mix and female workers asis a mix of a probit model and a regression model the identifying variables. The estimated parametersin that it incorporates information both on the prob- are then used to calculate predicted training mea-ability of an event and, conditional on that event sures for each group in the production function.taking place, the distribution of a continuous vari-able. This tobit specification allows us to estimate A corresponding set of tobit training models is alsothe productivity effects of "training intensity," that estimated to predict training intensity of in-house andis, the proportion of workers in a specific group external training. Production function estimates us-getting training, while taking into account the de- ing these alternative probit and tobit training mea-cisions of some finns not to train. sures are reported in Table 3.4. Both the probit and

tobit measures are consistent in showing that trainingEstimating the separate effects of each type and of skilled workers has a positive and statistically sig-source of training is complicated by the high nificant impact on firm-level productivity while thecorrelation that exists between different training training of unskilled workers does not. The trainingmeasures. The correlation arises in large part estimates for the latter group never attain statisticalbecause firms that provide training tend to rely significance, and are thus interpreted as having zeroon all sources of training while employers that do ipco rdciiylittle training rarely use more than one source.

What about training skilled workers? Using theTlhis means that our probit or tobit predicted tram- prbt asur the stimaed coefie o.in-ing measures will be correlated, unless identify- - e . ' .

ing vaibescnbefud.o p dicates that provision of skilled worker training ising variables can be found to explain why g employers nmiight choose one training source over associated, on average, with a 38 percent increase inanother.Given tchooe paucityam sourc idenf var- productivity. A reasonable way of interpreting theanother. GJiven the paucity of identifying voimauri-eauaetcefiin-.2--thables for each source, we can only address this sampl mean to tevtraining variaen.2Th ppoidentification issue in a linmited way. sample mean of the trainig variable. The propor-

tion of skilled workers trained was 0.175 or 17.5 per-

For training by skill groups, we rely on variations cent. This implies that a 10 percent increase in thein the proportion of skilled occupations and un- proportion of skilled workers trained (i.e. 0.0175) isskilled workers to identify what types of training associated with a 2.1 percent increase (1.22 xare provided. For training by source, we include 0.0175 x 100) inproductivity.the presence of a training center and training staffto identify decisions to provide in-house training, The differential impact of training by skill group isand the use of joint training programs with other not surprising once it is recognized that education isfinms for external training. The limited number of the foundation of subsequent learning, and to theidentifying variables precludes a more disaggre- extent that skilled workers are more efficient learn-gated analysis of training for each detailed occu- ers, they benefit more from training. Employers ap-pational group or every training source. pear to recognize these differences in the learning

capabilities of different worker groups. In ChapterProductivity Effects by Skill Group Two, the analyses indicated that firms were moreWe consider the productivity effects of training for likely to provide all kinds of training to their skilledtwo worker groups: skilled workers, including su- employees than to their unskilled workers.


We note that these results are not unique to Malay- ing from all outside sources combined. As before,sia. Similar patterns of training outcomes, not only the probability and intensity of in-house and exter-on the productivity but also the wages of skilled and nal training measures are predicted using probit andunskilled workers, are found in other developing tobit models, respectively. In these models, we iden-countries such as Mexico, Colombia and Indonesia tify the employer's choice of each training mode by(Tan and Batra, 1995). If the experiences of these the occupational and sex mix of its workforce.countries are any guide, the differential productiv-ity impact of training is likely to result in growing The production function results with the alternativewage differentials between skilled and unskilled training measures are reported in Table 3.5, sepa-workers in the absence of training policies to up- rately for local firms (columns one and two) and forgrade unskilled workers to skilled status. foreign firms (columns three and four). Table 3.5

clearly shows that productivity outcomes by trainingProductivity Effects by Training Source source are quite different depending upon owner-Next, we compare the productivity effects of in- ship status of the firm.3

house company training programs and external train-First, consider the productivity effects of internal and

Table 3.4 Production Function Estimates with external training when training measures are pre-Predicted Training by Worker Groups dicted by a probit model (columns one and three).

Independent Probit Tobit For the sample of domestic firms, only externallyVariable Prediction Prediction provided training has a positive and significant im-

Log (labor) 0.577a 0.558a pact on productivity, averaging about 26 percent;(0.040) (0.042) no statistically significant impact of in-house training

is evident. In the case of foreign firms, the resultsLog (capital) 0o.20179a 90.201669 indicate that both in-house and external training have

(0.019) (0.019) apositive and significant effect on firmproductivity-Invests in Technology -0.102 -0.107 13 percent for in-house training and 33 percent for

(0.065) (0.071) external.

Exports 0.010 0.021(0.064) (0.069) These results are striking-on one hand, they pointto

Age of Firm 0.006a 0.006a the strong in-house training capabilities of foreign(0.002) (0.002) firms; on the other hand, they highlight the weak in-

Education of workforce 0.035 0.029 house training capabilities of local firms and the po-(0.023) (0.022) tentially important role that external training

providers can play in meeting their training needs.Predicted Skilled 0 .38 3 b 1 .220b The qualitative results using the tobit training mea-Worker Training (0.165) (0.571) sures are broadly similar in showing the importance

Predicted Unskilled -0.151 -0.680 of external training sources for local firms and in-Worker Training (0.248) (0.723) house training for foreign firms. The only differ-

Constant 7.533a 7.732a ence is that external training intensity for foreign(0.448) (0.425) firms is no longer statistically significant.

a = Significant at 1%b= Significant at 5%. What are the effects of training more intensely, at

Note: Numbers in parentheses are standard least for those external sources of training thaterrors. are statistically significant? For local firms, theIndustry dummy variables included but proportion of workers getting in-house and ex-their estimates are not reported here.

Source: 1995 MITP Survey ternal training is 0.094 and 0.026, respectively.


Table 3.5 Production Function Estimates: In-house versus External Training

IndependentVaiable Local Firms Foreign FirmsProbit Tobit Probit Tobit

prediction prediction prediction prediction

Log (labor) 0.627a 0.589a 0.559a 0.637a(0.049) (0.042) (0.077) (0.058)

Log (capital) 0.273 a 0.290a 0.201a 0.209a(0.022) (0.022) (0.044) (0.042)

Invests in Technology -0.083 -0.029 -0.120 -0.144(0.088) (0.088) (0.118) (0.116)

Exports 0.101 0.051 -0.442b -0.331(0.079) (0.076) (0.213) (0.206)

Age of Firm 0.005b 0.005b 0.017a 0.015a(0.002) (0.002) (0.006) (0.006)

Education of 0.026 0.015 0.089b 0.092bworkforce (0.023) (0.025) (0.042) (0.042)

Predicted Internal -0.038 -0.063 0.13b 0 40bTraining (0.050) (0.169) (0.068) (0.214)

Predicted External 0.256a 1.751a 0,329b 1.297

Training (0.096) (0.691) (0.151) (1.021)

Constant 7.555a 7.735a 8.494a 8.364a(0.512) (0.437) (0.847) (0.747)

a = Significant at 1%b = Significant at 5%



Evaluated at these means, the coefficient of 1.754 eral external sources-polytechnics, vocational

on external training suggests that a 10 percent intraining institutes, IKM, ITI, SDCs, Advancedcrease in the proportion of workers getting external Skills Training Institutes (e.g. CIAST and GMI),training (0.0026) will lead to a 0.5 percent increase buyers and suppliers, other private firms, and over-in productivity. For foreign firms, the correspond- seas (presumably by foreign partners). The pre-

ing proportions are 0.191 for in-house training and vious findings raise the following questions:0.032 for external training. Using the in-house train- Which of these external training providers are

ing coefficient of 0.40, a 10 percent increase in train- likely to be most important for improving produc-

ing (0.019) is associated with a productivity tivity and, given differences in in-house training

improvement of about 0.8 percent.4

capabilities by ownership status, are the same ex-

ternal training providers likely to be equally ef-

Productivity Effects of Different External fective for domestic firms as for foreign firms?

Sources of Training

The MITP survey elicited information from firms We begin to address these questions by combin-

about the number of employees trained in sev- ing the different external training providers into


Table 3.6: Production Function Estimates: Training from External Sources

Independent Variable Domestic Firms Foreign FirmsProbit Tobit Probit Tobit

prediction prediction prediction prediction

Log (labor) 0.65a 0.588a 0.546a 0.589a(0.057) (0.049) (0.078) (0.076)

Log (capital) 0.269a 0.284a 0.220a 0.208a(0.022) (0.022) (0.043) (0.043)

Invests in Technology -0.077 -0.083 -0.109 -0.095(0.088) (0.089) (0.118) (0.117)

Exports 0.158c 0.002 -0.427c .0.457b(0.088) (0.085) (0.223) (0.236)

Age of Firm 0.002 0.008a 0.024a 0.0l5b(0.003) (0.003) (0.007) (0.006)

Education of workforce 0.011 0.005 0.077c 0.105b(0.026) (0.029) (0.046) (0.051)

Predicted Internal Training -0.031 -0.132 0.147b 0.515b(0.050) (0.172) (0.069) (0.232)

Predicted Training in -0.339 -12.516b 1.466b 24.396aPrivate Institutes (0.350) (5.376) (0.602) (9.129)

Predicted Training by 0 .6 3 2 b 3 9 .099b -0.741 -31.238SDCs&Adv. Train. Ins. (0.258) (16.767) (0.456) (38.148)

Predicted Training in 0.019 11.346 -0.406 -33.619Government Institutes (0.262) (8.341) (0.441) (17.425)

Constant 8.508a 8.158a 7.325a 7.557a(0.712) (0.700) (1.196) (1.343)

a= Significant at 1%b = Significant at 5%



three groups5: (1) government-run training inof external training. The production function re-stitutions, including ITIs, IKMs, YTCs, voca- sults with predicted training measures are re-tional and technical institutes, and polytechnics; ported in Table 3.6. These results should be(2) advanced skills training centers and SDCs treated with caution since we do not have sepa-providing high-level skills training with input from rate identifying variables for each external train-the private sector; and (3) all other private sec- ing source.tor training providers, including private traininginstitutes, foreign partners, buyers and sellers, Table 3.6 shows thatextemal training providershaveand overseas training sponsored by employers. very different productivity effects depending uponWe are motivated to aggregate training into ownership. For local firms, in-house training contin-three broad categories because of the high de- ues to be statistically insignificant. Among externalgree of correlation among the different sources training sources, the results indicate that only the


training provided by SDCs and advanced skills vanced skills training centers is associated with atraining centers to local firms has a positive and sta- 1.2 percent gain in productivity. For foreign firms,tistically significant productivity impact, irrespec- the corresponding productivity gains from increas-tive of whether probit or tobit training measures are ing the intensity of in-house training is one percent,used. The probit measure indicates that this pro- that from private training institutes and overseasductivity impact is large, averaging about 63 per- training is 5.6 percent.cent. Training provided by other privateproviders is actually associated with lower pro- The different patterns of training outcomes in for-ductivity in domestic firms. eign and domestic firms suggest the following in-

terpretation. Foreign firms have well developedFor foreign firms, two sources of training are as- in-house training capabilities, and therefore maysociated with significant productivity gains-in- need to rely less on SDCs and advanced skillshouse training, and training from private training training centers for training their workers. Weinstitutes-these include local providers as well as speculate, but cannot confirm, that the relative im-overseas training. However, the latter source of portance of training from other private providerstraining has an implausibly large productivity im- may reflect their ability to send their workerspact of 146 percent, based on the probit measure. abroad to the parent company for training. InIn both groups of firms, there are no significant contrast, domestic firms have relatively weak in-productivity gains from training in government house training capabilities so that SDCs and ad-institutes. vanced skills training centers are important

sources of higher-level skills training for them.The productivity effects of increased training in-tensity are reported in Table 3.7 for statisticallysignificant sources of training. For each group of Firm-Level Wages Outcomes of Trainingfirms, the table reports the sample means of train-ing intensity, and the gains in productivity from We now turn to a second outcome of training-its ef-increasing training intensity by 10 percent. fects on the monthly wages of employees. Several

issues are of interest: to what extent are the produc-For local finns, a 10 percent increase in the propor- tivity gains from training passed on to workers in thetion of workers getting training in SDCs and ad- form of higher pay? In which types of firms are the

Table 3.7: Productivity Effects of Increased Training Intensity

Local Firms Foreign FirmsTraining source Fraction Productivity Fraction Productivity

getting impact of getting impact oftraining 10% change training 10% change

In-house training 0.094 not sig. 0.191 1.0Private training institutes 0.017 not sig. 0.023 5.6SDCs & Adv. train. ins. 0.003 1.2 0.004 not sig.Government training institutes 0.005 not sig. 0.005 not sig.

Note: The productivity impact is calculated for a 10 percent change in mean training intensity;statistically insignificant impacts denoted as "not sig."

Source: Coefficients of tobit training measures taken from Table 3.6, means of training intensity variablescalculated from the MITP data.


wage payoffs to training highest? And, which groups experience (age), and in firms that invest in newof workers benefit most from training? technology. Firms with a workforce that is more

highly educated also tend to pay higher wages, whileWe are interested in these issues for several rea- those that rely heavily on female workers pay lowersons. First, how the productivity gains from training wages.are shared has implications not only for worker in-centives to undertake training, but also for employer Both training measures indicate a positive and sig-incentives to sponsor and pay for training which may nificant effect on monthly wages. In column one,not be able to be recouped because ofjob turnover. the training indicator variable has an estimatedSecond, to the extent that higher wage payments are wage effect of 0.04 (four percent); in column two,feasible only when justified by productivity gains the estimated wage effect of the predicted train-from training, these wage analyses-by firm charac- ing variable is 0.06 (six percent). Even given thisteristics and by worker groups-provide a way of in- range of estimates, what is striking is that the wagedependently verifying the productivity outcomes of effects of training are smaller than the productiv-training identified earlier. ity gains from training of 0.32 (32 percent) esti-

mated in a production function model (see TableTo get estimates of the wage effects of training, we 3.1, column one).regressed the logarithm of monthly wages on a mea-sure of worker training and other control variables. A comparison of these estimates suggests that roughlyThe other explanatory variables are similar to those one-eighth to one-fifth of the productivity gains fromused in the production function model, and include a training are passed on to workers in the form ofquadratic measure of firm size (logarithm of employ- higher wages. By implication, the remaining seven-ment), age of the firm, indicator variables for whether eights to four-fifths of the productivity gains accruethe firm exports or invests in technology, mean to the employer as the returns to his (share of) in-schooling of the workforce, the proportions of non- vestments in training.production and female workers, and a set of two-digit industry dummy variables. We also estimated This evidence of firm-worker sharing of productiv-separate wage models for four occupational groups ity gains from training is of some policy interest, givento determine if the wage effects of training differ for proposed guidelines on linking wages to productiv-skilled and unskilled workers. ity growth currently being drafted in Malaysia by a

tripartite group representing employers, unions andOverall Wage Effects of Training the government. However, we caution that this evi-In Table 3.8, we report the results of two wage model dence is cross-sectional, when what is required tospecifications, one where training is measured by an inform the deliberations of this tripartite groups isindicator variable (column one), a second where we evidence on how gains in productivity over time areinclude the predicted value of training obtained from passed through to wages increases. This will re-a probit model to account for potential selectivity quire rigorous time-series analyses of productivitybias (colurnn two). As noted in previous sections, the and wage growth, which is beyond the scope of thiswage effects of training may be biased (either up or report.down) if the firms that train differ systematicallyfrom those that do not. Do the wage effects of training vary systematically

across different groups of firms? The previous pro-Before presenting the training results, we note that duction function results revealed a pattern of pro-mean pay levels tend to rise with firm size (employ- ductivity gains from training that was higher in finmsment) up to a point, but decline in the very large that invested in technology, that exported, or hadfirms; they are higher in firms with more production foreign capital participation. To determine if the wage


Table 3.8 Wage Model Estimates cant impact on wages in finns that do not invest inwith Training Indicator and Predicted Values technology, or in domestic firms. These weak wage

Independent Training Indicator Predicted Training effects may reflect, inpart, the much smaller produc-Variable Specification Specification tivity gains from training in these latter groups of

Log (labor) 0.098a 0.073a firms. Thus, we conclude thatthe training-wage re-(0.027) (0.029) sults by firm characteristics are broadly consistent

Log -0.013a -0.012a with the patterns of productivity gains from training(labor squared) (0.003) (0.003) found earlier.

Invest in 0.032c 0.031cTechnology (0.018) (0.019) Wage Effects of Training by Occupation

Exports -0.023 -0.023 The wage effects of training can also be analyzed(0.018) (0.018) for four occupational groups-supervisors, tech-

Age of Firm 0.004a 0.004a nicians, skilled production workers, and unskilled(0.004) 0.001) production workers. The MITP survey elicited

detailed occupation-specific information on wagesEducation of 0.023a 0.018a as well as numbers getting training by source. WeWorkforce (0.004) (0.005) exploit this rich detail by estimating separate wageAny Formal 0.038b 0.062b models for each occupation, firstusing indicatorvari-Training (0.017) (0.027) ables for whether workers in that specific occupa-

Proportion -0 034a -0.046a tion received in-house or external training, andFemale Workers (0.013) (0.014) then using their corresponding training intensity

Proportion of Non- 1 .039a 0.943a measures.production Labor (0.060) (0.079)

Constant 5.779a 5.953a Table 3.9 Wage Effects of Training by Level of(0.089) (0.119) Technology, Exports, and Ownership

a = Significant at 1%b= Significant at 5% Estimated Training Coefficient

c = Significant at 10% No investment in technology Invest in technologyNote: Numbers in parentheses are standard 0.023 0.073b

errors. (0.022) (0.030)Industry dummy variables included butnot reported here. No Has

Source: 1995 MITP Survey technology license(s) technology license(s)0.026 0.106b

(0.019) (0.049)effects of training also exhibited this pattern ofvariation, we estimated separate wagemodels for dif- o 061 Exportsferent groups of firms defined in an analogous way. (0.033) (0.020)The training effects for each group of firms are sum-marized in Table 3.9. o 022 Foreign-owned Firm

(0.022) (0.028)Like the productivity results, they show positive and (0.022) (0.028)statistically significant wage effects oftraining in the a= Significantat 1%firms that investin technology, thathave technology c Significant at 10% levellicenses, that export, and that are foreign-owned. In Note: Numbers in parentheses are standardcontrast, there is little evidence (except in non-ex- errors.porting firms) that training has a statistically signifi- So u rce: 1995 M ITP Survey


Table 3.10 Occupation-Specific Wage Effects of Training

Training Measures Supervisors Technicians Skilled UnskilledProduction Production

Workers Workers

Training IndicatorsInternal Training Indicator 0.063a 0.014 0.072a 0.029

(0.024) (0.024) (0.024) (0.020)

External Training Indicator 0.001 -0.019 0.050C -0.001(0.025) (0.025) (0.026) (0.022)

Training IntensityProportion trained internally 0.095a 0.019 0.079a -0.007

(0.030) (0.031) (0.034) (0.027)

Proportion trained externally -0.052 0.005 0.110c 0.116(0.081) (0.041) (0.061) (0.091)

a = Significant at 1 %b = Significant at 5%

-= Significant at 10%.Note: Numbers in parentheses are standard errors.Source: 1995 MITP Survey

Table 3.10 summarizes the wage effects of training pace of growth in information technology, increasedfor each occupational group. For supervisors and inflows of capital and technology, and the growingskilled production workers, the results suggest that integration of world markets is likely to create strongin-house training has a positive and significant im- demand for skilled workers far outstripping the sup-pact on wages; external training is not associated ply capacity of the existing educational and trainingwith higher pay except for skilled production institutions. Without appropriate responses from theworkers where it is marginally positive and sig- private and public sector, the outcome is likely to benificant. For technicians and unskilled produc- growing income inequality over time between skilledtion workers, there is no evidence of wage effects and unskilled groups.of training from either internal or externalsources. These conclusions are unchanged irre- This phenomenon of widening wage gap betweenspective of whether training is measured by an skilled and unskilled workers is not unique to Ma-indicator variable or by training intensity-the pro- laysia. It has been observed in industrializedportion of workers that receive training in each countries as well, and there is some evidence attrib-occupational group. uting this growing differential to skill requirements

of technological change (Katz and Murphy, 1992;These results are broadly consistent with the find- Dunne and Schmitz, 1995).ings of positive productivity outcomes of training forskilled workers (defined to include supervisors, tech-nicians and skilled production workers) and the ab- Compensation Policy andsence of productivity effects for unskilled workers Labor Turnoverreported in Table 3.4.

The previous section indicated that some employersThis training-skill complementarity has implications share part of the realized productivity gains fromfor income inequality in Malaysia. The accelerating training with their workers in the form of higher pay.


For these employers, the higher pay made possible sample size), there is a trend for quit rates to riseby improved productivity can be an important means with firm size irrespective of relative wages.of cementing job attachment without which therewould be litfle incentive either for the firnn to spon- Third, there is a negative relationship between quitssor, or for workers to undertake, training. and wages. Training firms (top panel) paying wages

above the mean are able to keep quit rates below 25Long-termjob attachment, especially in the context percent, while quit rates in firms that pay below av-of the tight labor markets and high rates of labor erage wages rise to 33 percent and 42 percent forturnover in Malaysia, is critical if employers are torecoup their investments in workers' job skills. In- Figure 3.1 Quit Rates and Wage Policies:deed, many employers cite concerns about high job Training and Non-Training Firmsturnover as a key deterrent to their provision ofworker training. We address this issue by investigat-ing the potential role that compensation policies might Training Finnsplay in reducing labor turnover, thus increasing theincentives for employers to train. 60

50 -To motivate this discussion, we begin by graphically Ipresenting data on average quit rates in Chart 3.1. ]40 -

Quit rates are defined as the ratio of total quits over 30 - _ .ha age

the past year to the level of employment prevailing 20 gl-Vbge:~20at the beginning of the year, expressed as a per- a 10

centage. Finns are categorized by their pay levels, i_M_and defined as being high-wage or low-wage rela- 0tive to the overall mean wage of the MITP sample. 2 Eo E o

They are further disaggregated by firm size to ac- 0 E

commodate size-related differences in wages andother contemporaneous factors.

The top panel of Chart 3. 1 shows quit rates in train-ing firms according to their pay levels, while the bot- Non-Training Firmstom panel shows the corresponding quit rates fornon-training firms. The chart makes several points: 60

50First, quit rates are generally lower in high-wage f_firms than in low-wage firms for both training and 40non-training firms. In firms that provide training, quit 30 Ilrates are 22 percent per annum when employerspay wages that are above the sample mean, and 34 a 10percent when wage levels are lower than the mean. 0Among non-training firms, the corresponding quit 8 X E a

E *- 2rates are 19 percent and 25 percent, respectively. (D n

Second, larger firms have higher quit rates thansmaller employers. With the exception of micro firms(these firms may be discounted because of their small


medium and large firms, respectively. Among non- elsewhere in the market, in effect by deferringtraining firms (bottom panel), quit rates are kept to current period compensation to the future, theseabout 20 percent or less by above average pay, but latter instruments can have a powerful impact onrise to 24 percent and 50 percent in medium and enhancing job retention among workers (Lazear,large firms that pay below average wages. 1995). For example, there is evidence from Ja-

pan that the low quit rates and strong job attach-However, quit rates are often higher in training fiins ment in manufacturing firms are shaped to a largethan in non-training firms. This is readily apparent extent by the use of severance pay schemes (Tan,from a comparison of similarly sized finns in the top 1989)and bottom panels. Higher quit rates in training firmsreflect shortages of trained workers in the market, Modelling Quits and Compensationand the willingness of other employers to offer We use a regression model to analyze the effects ofhigher pay to hire away these workers (or "poach") wage and other compensation policies on reducingfromtraining finrs. labor turnover. In the model, annual quit rates are

regressed on a set of explanatory variables, includ-To summarize, it appears that firms can use wage ing firm size, industry dummy variables, characteris-policies to reduce quits among employees. Ofcourse, tics of the workforce, and several measures ofnot all employers will pay higher wages to retain compensation. These include the mean monthlyworkers; the decision will depend upon whether wage, the rate of wage growth with seniority, andthe higher resulting wage bill is offset by cost-sav- indicator variables for whether the firm's compensa-ings from reduced worker turnover. tion package included a pension plan, a severance

pay scheme, or other fringe benefits.6 In definingFor employers that invest heavily in worker train- the seniority-wage growth measure, we used dataing, the cost-savings from reduced quits can be sub- on the mean monthly pay of a typical productionstantial and they will have an incentive to pay higher worker-at the entry point and at 10 years of ser-wages out of the increased productivity from train- vice with the company-and calculated the wageing. For other employers doing little or no training, increase over this period as a fraction of startinghigh labor turnover imposes few sunk costs. Conse- pay.7

quently, they will have little incentive to pay com-petitive wages to retain their workers. We estimate quit models for all workers, and for

non-production and production workers.8 If ef-Higher pay is not the only means to reduce quits. In fective, the coefficients of each compensationtheir compensation policies, employers can use a variable should be negative, implying reductions invariety of instruments to reduce quits. In addition quit rates. We also distinguish between trainingto higher overall pay, employers can retain work- firms and non-training firms. The motivation is toers by offering a variety of fringe benefits such as investigate whether these compensation policies aresubsidized housing, medical plans, meals, or trans- relatively more effective among training firms-theportation. They can also tie some parts of compen- employers most able to afford improved compensa-sation to length of service, such as (i) steepening the tion packages out of higher productivity, and forrate of wage increase with length of service; (ii) whom high job turnover is presumably the mostoffering a pension in which length of service costly in terms of sunk training costs.strongly influences retirement pay; (iii) providinga severance pay scheme linked to length of service. These quit models control for selectivity bias intro-

duced by employers' training decisions. This is doneBy making compensation grow faster with length in two steps: first, estimating a probit training modelof service relative to workers' opportunity wages similar to those reported in Chapter Two, and then


Table 3.11: Summary Statistics on Quits and Compensation Policies

Variable Means All workers Non-Production ProductionFirms not Training Firms not Training Firms not Training

training Firms training Firms training Firms

Annual quit rates % 19.8 26.7 7.2 9.5 21.5 29.3Tenure-wage growth 1.556 1.517 1.556 1.517 1.556 1.517Monthly wages 644 699 1229 1354 568 573Have pension % 33.0 47.3 29.0 45.0 31.8 45.0Have severance pay % 43.5 56.4 38.4 54.3 42.5 55.3Other fringe benefits % 0.5 2.2 0.5 1.2 0.4 1.9Sample size (firms) 1,097 707 963 697 1,095 700


using these estimates to compute a selectivity cor- The Effects of Compensation Policies onrection variable; second, including this variable in Quit Ratesthe second-stage quit models estimated separately Table 3.12 reports the regression estimates of twofor training and non-training firmns. We are particu- specifications of the quit model. The first, reportedlarly interested in the patterns and relative effects of in column one, pertains to the pooled sample of allthe different compensation policies in the two groups firms; the second specification splits firmns into twooffirms. samples by training status, and includes a selectivity

correction variable to control for any bias introducedTable 3.11 provides summary statistics on the key by separating firms on the basis of a decision vari-variables used in the regression analyses. The variable, in this case, training.able means are reported separately for training andnon-training firms, and for three worker groups-all The first model specification provides a convenientemployees, non-production and production workers. summary of the principal correlates of quits in theThe data on quits reflect the point raised in Chart 3:1, overall sample when no account is taken of training.namely, that quit rates are on average higher in train- It suggests the following broad results-quit rates riseing firms than in non-training firms; they also show with employer size as compared to micro firms (thethat non-production workers are less likely to quit as omitted group); quit rates tend to fall as overall paycompared to production workers. levels rise; and quit rates are lower in firms having a

severance pay scheme. The other explanatory vari-Two points about the compensation variables are ables are not significantly related to quits.9

noteworthy. First, contrary to our expectations, the The second specification, which takes into accountmeasure of wage growth with seniority (which re- firms' decisions to train, is more illuminating. It sug-fers only to production workers) is actually lower intraining firms than in non-training firms. Training g gfirms may be offering higher starting pay in order to . The correlation between firm size and quits dis-attract the most able new recruits, but this may be at appears when training is considered. This fol-the expense of slower subsequent wage growth with lows from the fact that larger firms are more likelyyears of service. Second, all other compensation to train (see Chapter Two), and it implies that it ismeasures-not only overall wage levels, but also pen- training, rather than firm size per se, that is thesion plans, severance pay schemes, and other fringe critical factor in shaping quits. Quit rates frombenefits-are higher among training firmns than non- training firms are high because of acute short-training firns. ages of trained workers in other firmns.


Table 3.12 Compensation Policies and Overall Quit Ratesby Training Status

Annual Overall Quit RatesExplanatory Variables Firms Training

All Firms Not Training Firms

Small firms (16-100 workers) .0514c .0249 .0812Medium firms (101-250 workers) .0771a .0211 -.0418Large firms (over 250 workers) .1482a .0469 -.0387Mean education of workforce .0003 -.0084 -.0073Proportion female workers .0264 .0129 .0553Foreign-owned firm .0379b .0299 -. 0009Wage growth 0-10 years tenure -.0112 -.0046 -.0208cMonthly wages (x RM100) -.0081a .0079a -.0131aHave pension plan .0051 .0009 -.0078Have severance pay scheme -.0516a -.0492a -.0512cIndicator for other fringe benefits .0257 .1327 -.0288Training selectivity correction d n.a. -.1124c -.1649aConstant term .2728a .2551a .6263aR-squared 0.061 0.058 0.069Sample size 1,804 1,097 707

a - Significant at 1 %;b = Significant at 5%;c_= Significant at 10%.d = The selectivity variable is the Mills Ratio computed from a probit training model.

Industry dummy variables included but not reported in table.


H Higher pay is a deterrent to quits for both training * For the same wage bill, a steeper seniority-wageand non-training firms. This is evident from the policy can reduce quits in training firns. For train-negative and significant effects of the monthly ing firms, the increment in pay over 10 years ofwage variable. To the extent that firms have the service is 1.5 times starting pay (see Table 3.1 1),higher productivity to do so, both groups of firms or an increase in pay of about 9.2 percent percan pay higher wages to reduce quits. annum. The estimated coefficientof -0.02 suggests

• Firms that train are better able to reduce quits thataninclease to 2.5 times startingpay after l 0years,through higher pay than non-training firrns. Their or 12.6 percent increase in pay per annum, willthrouh hiher ay tan nn-trinin fins. Teir Iead to a two percent fall in the quit rate.coefficients, -0.013 for training fimns and -0.008

fornon-taininngflms,suggestthataRMlooincrease Table 3.13 reports the regression results separatelyin wages is associated with a 1.3 percent decrease for production and non-production workers accord-in quits for traiiing furms (from a mean of 26.7 to ing to whether their employers provide trnig. The

25.4tpercent), and ao .8percentfall in quits for non results are broadly similar to the overall regressions,training fin-ns (from 19.8 to 19.0 per-ent). except that the tenure-wage growth is no longer sta-

* Severance pay schemes can reduce quits by tistically significant. Some differences between theabout five percent. In both groups of firms, the two groups emerge.severance pay coefficients (-0.051 for taining and-0.049 for non-training firms) are negative, and First, while quit rates in both groups are lowerthey suggest that employers can reduce quits by when their employers pay higher overall wages,about five percent by introducing such a scheme. the effects are most pronounced for production


Table 3.13: Compensation Policies and Quit Rates by Occupationand Training Status

Quit Rates of Quit Rates ofNon-Production Workers Production Workers

Explanatory Variables Firms Not Training Firms Not TrainingTraining Firms Training Firms

Wage growth with tenure .0081 -.0020 -.0059 -.0201Monthly wages (x RM100) .0014 -.0025b -.0098a -.0133aHave pension plan -.0149 -.0119 .0067 -.0035Have severance pay .0062 -.0049 -.0555a .0539cOther fringe benefits -.0352 . 0 9 0 8 b .1709 -.0355Training sel. correction -.0484 -.0612c -.1254C -.1424cConstant term -.0314 .3125a .2862a .5675bR-squared 0.039 0.077 0.063 0.070Sample size 963 697 1,095 700

a = Significant at 1 %b= Significant at 5%c = Significant at 10%.

Note: Industry dummy variables, firm size, ownership, and worker characteristics are included but arenot reported here.


workers. An increase in monthly pay of RM100 is The productivity benefits of training are particularlyassociated with a 0.25 percent reduction in quits large for SMIs, the group least likely to train, sug-among non-production workers in training firms; gesting that SMIs under-invest in training. Theiramong production workers, this same increase in use of simple technologies means that skill needs arepay results in a one percent reduction in quits in also correspondingly low; they also face severalnon-training firms, 1.3 percent in training firms. market failures-from limited finance for training, high

job turnover which makes it difficult to recoupSecond, severance pay schemes are effective in re- training costs, and weak training capabilities-whichducing quits (about five percent) for production work- deter them from training. To be effective, train-ers; however, such schemes are not effective for ing policies targeting SMIs should not be uni-di-non-production workers. mensional, focusing on just one constraint or the

other. An integrated set of policies is required,which simultaneously address a multitude of con-

Findings and Policy Implications straints involving finance, identification of train-ing needs, information about training pedagogy,

The analyses confirn thatformal training improves technology upgrading, and adoption of qualityfinn-levelproductivity. Firms that train, on average, control methods.are about 32 percent more productive than firns thatprovide no formal training. Productivity effects of The productivity effects of training are larger whenthis magnitude are not unusual and, in fact, are broadly new technologies acquired through licensing aresimilar to the effects estimated for other developing complenented with training. Compared to technol-countries such as Mexico, Colombia and Indonesia. ogy licensing, a firn's own R&D has limited effectsThis information should be widely disseminated to either on overall productivity or on the productivityprivate sector firms that do not train or provide very of worker training, suggesting that technological ca-little training because of skepticism about the pro- pabilities are relatively weak among local firms. Theductivity benefits of training. implication is that licensing may be a more important


source of new technology for most firms, and that training role in the Seventh Malaysia Plan, not onlythe productivity benefits can be quite substantive if in pre-employment training but also increasingly intechnology transfer is accompanied by training. This meeting employers' in-service skill needs.suggests that the Government should place greateremphasis on promoting technology licensing, skills The productivity results suggest ways of improvingtraining and technology transfer that accompanies the delivery of training tofirms. First, they suggestsuch agreements, than on encouraging firms to de- that in-service training provided by public trainingvelop their own indigenous technologies through institutions are not well-tailored to meet employers'R&D incentives. skill needs. Ways of making their curricula more

demand driven should be identified. Second, theyThe productivity effects of different sources of train- reveal that while SDCs are an important source ofing vary by local orforeign ownership. The evi- training for domestic firms, take-up of SDC trainingdence is consistent with local firms having weak is currently low among SMIs. The Governmentin-house training capabilites as compared to foreign should implement measures to increase SMI partici-firms. For local firms, no productivity effects from pation in the design of training programs tailoredin-house training are discernible; however, the train- more to their specific needs. Third, advanced skillsing they receive from SDCs and advanced skills training institutes are a second important source oftraining institutions are associated with large produc- training for local firms, but the supply of their gradu-tivity effects. For foreign firns, it is in-house training ates is still limited. The Government should exploreand training from private sector sources which have the feasibility of expanding the number of these in-large productivity effects. stitutions, and setting up bilateral training centers like

GMI, MFI, JMTI with countries such as Britain andThese findings suggest several training strategies the United States.targeting local firms: (i) expand their access to ex-ternal training institutions capable of delivering train- Firns pay higher wages out of increased productiv-ing in higher-level skills; (ii) expose employers to ityfrom training. Overall, training is associated withbest-practice training methods by promoting joint a six percent increase in wage levels, suggestingtraining programs with large firms and MNCs that that one-eighth to one-fifth of the productivity gainshave world-class training programs; and (iii) provide from training are shared with workers in the form ofincentives for firms to develop their own in-house higher pay. The patterns of wage increases mirrortraining capabilities, by undertaking training needs those of the productivity gains from training, beinganalyses (TNAs), training trainers, and implementing a higher in firms that invest in technology, that export,systematic training plan to upgrade worker skills. and that are foreign-owned. Similarly, skilled worker

training is more productive than training for unskilledNo significant productivity effects were discernible workers, and training of supervisors and skilled pro-for in-service training provided by public training duction workers is associated with higher pay, but isinstitutions. Ms, IKMs, YTCs, and vocational and not for unskilled production workers.technical institutes tend to focus on pre-employmenttraining, a subject which is not addressed here. None- The implication is that these productivity differen-theless, the absence of any productivity effects of the tials will lead to growing wage disparities betweenin-service training that they provide is striking, and skilled and unskilled workers in the absence ofit may suggest that the training provided by them is training policies to upgrade unskilled workers tonot well suited to employers' needs. A careful study skilled status. Technological change, with its as-of the effectiveness and relevance of training pro- sociated higher skill requirements, will also putvided by public training institutions should be con- additional upward pressure on relative pay ofducted, especially if they are to play an expanded skilled workers.


Employers can reduce quit rates through appropri- especially among firms that train. The reason is thatate compensation policies. Employers have at their training firms are better able to fund more attractivedisposal different instruments to promote long-term compensation packages out of the higher productiv-job attachment-higher pay, fringe benefits, pay in- ity resulting from training. These findings should becreases tied to length of service, severance pay and disseminated to employers, especially to the smallerretirement schemes. Of these, the analyses indicated firms unfamiliar with using compensation policy asthat higher pay, steeper seniority-wage profiles, and part of a personnel strategy to promote training andseverance pay were most effective in reducing quits, reduce quits of trained employees.

CHAPrER FouR: TRAINING POLICIES

The previous two chapters provided an overview Constraints on Training: An Employerof the incidence, correlates, and productivity out- Perspectivecomes of in-service training. They made two prin-cipal points: First, despite evidence showing that In the design of training policies, it is critical thattraining increases productivity, a substantial frac- policymakers know which firms train and whichtion of firms, SMIs in particular, provide little or firms do not, and the reasons why some firms in-no structured training to employees. Second, the vest little in the training of their employees.evidence shows that local firms have weaker in-house training capabilities than foreign firms, and The following issues are key. Is the low incidencethat most of their productivity gains from training of formal training, and the striking differences income from external training providers such as SDCs training by firm size, a reflection of the weak train-and institutes providing advanced skills training. ing and technological capabilities of MalaysianWhat little in-service training provided by most gov- firms, small firms in particular, or is it the result ofermnent-run training institutions appears to have market failure? What market failures are operative?little impact on productivity. Are firms constrained by poor access to financing for

training, or do they simply lack interest, know-howIn this chapter, we address several issues raised by or capability to design and implement training pro-these findings. Why do so many employers, SMIs grams? How important a constraint is "labor poach-in particular, not train and what constraints do they ing", the hiring away of employees trained at theface? If market failures are responsible, what kinds employer's expense which prevents firns from re-of training policies are effective in addressing these couping the returns to their sunk investments inmarket failures? In addressing these issues, we draw training?on the unique perspectives provided by employerresponses in the MITP survey, both about why they Without an adequate understanding of these issues,provide liffle or no training, and about their use of well-intentioned incentives may needlessly benefittwo training schemes-the Double Deduction Incen- firms that already train, while doing little to encour-tive for Training (DDMI) and the Human Resource age other employers to initiate or increase training.Development Fund (HRDF). Both policies weredesigned to encourage firms to play a greater role in Insights into some of these questions are providedmeeting their own skill needs, but through very dif- by firm respondents in the MITP Survey. Theyferent means. were asked to rank, on a scale of one (not impor-

tant) to five (very important), the relevance of eachFor DDIT, we discuss the incidence of its use one of seven statements to their decision to provideacross different firm sizes and industries, and rea- little or no training.' These statements included:sons for its limited use by firms. For HRDF, wediscuss several issues not readily investigated us- * Training is not affordable because of limiteding administrative data-non-compliance in regis- resourcestering with the HRDC; the extent of training * Training is costly because of high labor turn-claims among firms registered with the HRDC; overnew schemes introduced by HRDF; and some ten- * The firm lacks knowledge about training tech-tative insights into whether HRDF has increased niques and organizationtraining in firms. We conclude with some les- * The firm uses a mature technology, so learning-sons and policy recommendations. by-doing is sufficient

TRAINING POLICIES 47

* Skilled workers are readily hired from other important constraint, especially if there are no ex-firms temal training providers capable of meeting employ-

* Skills provided by schools are adequate ers' particular skill needs. Finally, resources to* We are skeptical about the benefits of training finance training may not be forthcomning because of

imperfect capital markets.To facilitate comparison of the relative importance These same three factors were cited by firms of allof these factors, we coded firm responses to each T s an e proportiondofysmirms ofdallstatement as being "very important" if it was assigned sizes About an equal proportion of small, mediuma high score of either four or five, and as "not very and large firms (36-37 percent) ranked high laborimportant" otherwise. These responses are tabulated turnover as an important constraint, as compared toin Table 4.1 for the MITP sample as a whole, and micro firmns (29 percent). The differences across firmseparately by four firm sizes. size in the importance accorded the other factors are

more pronounced-a higher proportion of micro and

For the overall sample, the use of mature technol- small firms (28-30 percent) ranked lack of knowl-ogy was the most commonly cited reason (56 per- edge about training methods as important relativecent) for why firms provide little or no training. In to large firms (22 percent); they were also morewell-functioning markets, this was the response that likely to rank limited resources for training as im-we expected to find from theory. portant (25 percent) as compared to large firms (10

percent).

The productive attributes of mature technologies are These findings are not peculiar to Malaysia.2 In awell-established and there is typically little scope recent study, we compared employer responses tofor improving upon existing production techniques. recet study, we other coyeriesponesiaAs such, no additional training is required and work- amilar questions in two other countries, Indonesiaers quickly become proficient at their jobs through and Colombia. Like Malaysia, manufacturing firmslearning-by-doing. Furthermore, employers may not in the other two countries identified the use of ma-need to train when mature technologies are widely ture technology, lack of knowledge, high labor turn-diffused since there is a plentiful supply of skilled over, and hrnted resources among the top reasonsworkers with experience using the older technolo- for little or no traininggies in the external labor market, and they are readily Firms in both Malaysia and Colombia ranked thehired from other firms. use of mature technology and high labor turnover

as the most and second most important reasons forThis linterpretationl iS consistent with the evidence liie netet ntann.Releci toein Chapters Two and Three, namely, that incentives ted livestments In traing gtS lowe

relative income level, Indonesia ranked mature tech-to train are dimiinished among firms not investing nology a close second to limnited resources, and lackin technology in which the productivity outcomes of knowledge about training as number three. Inof training are relatively low. Colombia, limited resources tied with lack of

knowledge for third place, while it was rankedWhat is significant in Table 4.1 is the importance fourth in importance in Malaysia.attributed by a sizable number of firms to three otherfactors: high labor turnover, lack of knowledge about Thus, while markets are generally well functioning intraining methods, and limited resources for train- Malaysia, there is evidence that market failures poseing. High labor turnover can inhibit training by pre- important constraints on training for many employers,venting employers from recouping their investments especially SMIs, and they justify government inter-in workers' skills. Lack of information on how to vention. What kind of policy intervention is appropri-train or to organize training programs can also be an ate depends upon the nature of the market failure.


Table 4.1 Reasons for Little or No Training, Overall and by Firm Size

Reason for NotTraining Overall Micro Small Medium Large

Limited resources for training 20.2 25.5 25.3 20.8 10.2High laborturnover makes training costly 36.0 29.4 35.7 36.9 36.8Lack knowledge about training 26.8 28.1 29.7 27.2 21.6Mature technology requires little training 56.3 42.5 57.4 58.3 55.1Skilledworkersreadilyhiredfromothers 16.5 18.3 19.7 14.0 16.6Skillsprovidedbyschoolsareadequate 14.4 11.1 18.0 14.1 11.1Skepticalaboutbenefitsoftraining 9.3 7.8 10.2 8.2 10.7


High rates of labor turnover suggest that there are In the following sections, we use these employerexternalities in training, and to the extent that firms insights to assess the efficacy of these two trainingare unable to internalize the benefits of training be- policies. Even though the scope of DDIT is now re-cause skilled workers are hired away by other finns, duced, an assessment of its implementation and lim-there will be under-investment in training. The ap- ited take-up is still irnportant for the lessons it offerspropriate policy response is to institute a payroll levy policymakers. HRDF now provides the whole in-to provide incentives for all firms to train, or if firms frastructure of training incentives for employersdo not train, to contribute to the cost of training pro- and a network of public and private training pro-vided by others in the industry. viders to support their in-service training efforts.

Our focus here is on providing insights into the useWhen poor information is the constraint, the appro- of existing and new HRDF schemes by private sec-priate policy response is to widely disseminate best tor fimns.practices in training know-how, as well as informa-tion about the availability and cost of services that Double Deduction Incentive forexternal training providers can offer. Training Scheme

Finally, when lack of finance for training is the The objective of DDIT was to encourage firms toconstraint, policymakers can provide incentives train-especially in skill areas related to new prod-by subsidizing the costs of training through the ucts and processes, and productivity and qualitytax system. Care in the design of training subsi- improvements-by permitting employers to deductdies is needed to ensure that they reach only those double the amount of allowable training expensesfirms that need it. on their tax returns.3

Some elements of all three policy responses are The DDIT scheme could be used in two ways.reflected in the DDIT and HRDF training First, employers could send their workers to ap-schemes, though both policies had different ob- proved training institutions, of which there werejectives and were implemented in different ways. 12 in 1994, including SIRIM, NPC, CIAST, Ger-The DD1T, introduced in 1987, was the principal man-Malaysia Institute (GMI), SDCs in Penangtraining policy until 1993 when HRDF was iniple- and Perak, and various public training institutesmented; thereafter, it was retained for smaller such as MIs and IKMs. Second, employers couldmanufacturing firms with less than 50 employ- apply directly to MIDA (the Malaysian Industrialees, and HRDF became the principal training Development Authority) for approval of theirpolicy instrument for larger firms. planned training programs. Firms sending their


workers to approved training institutions were au- 1992, over 60 percent of applications were filedtomatically qualified to claim the double deduction by MNCs or majority foreign-owned firms; theincentive directly from the Department of Inland remaining 40 percent were principally joint-Revenue.4 ventures, and a small number of wholly Ma-

laysian-owned firms.Administrative Records on DDIT UseAItnistwidelyrativeReordso DDIt use ofthe DDIT * Finally, take-up of DDIT by small companiesIt is widely acknowledged that use of the DDIT has been very low.' Crude estimates suggest thatincentive has been limited, though the reasons for less than seven percent of all applications tothis are not well-established. Data on DDIT use tha were frcent f all less tothrough the first route-approved training provid- MIDA were from small firms with less than 50ers-are not available since there was no require- over 500 emsployees.ment for firms to notify MIDA of this training .However, administrative records from MIDA areavailable on applications filed with it in the pe- DDIT Use Among MITP Firmsriod between 1987 and 1993. They provide some Table 4.2 presents estimates of DDIT use reportedinsights, albeit incomplete, into the take-up of that in the MITP Survey. These figures are more broad-part of the DDIT scheme:5 based since they include use of DDIT through both

routes-from approved training providers and from- Take-up of the DDIT scheme has been quite M1DA-thoughtheydonotdistinguishbetweenthem.

limited. In the period between 1987 and 1993, The figures refer to DDIT use in the entire 1987-MIDA approved a total of 591 in-house train- 1993 period when it was still the principal policying programs, involving 3,253 trainees and instrument for training.costing a total of just under RM 32.5 million.About 35 percent of applications for in-house Table 4.2 reveals the following patterns of DDITtraining were rejected for being incomplete use. First, the overall use of the scheme has beenor inadequate. quite low since its inception. Only 8.3 percent of

the firms in our sample (183 firms) used DDIT be-ther 199I Tschenumberof expandicatscoverae a fore 1993. This figure falls to 4.3 percent when the

from 37 in 199 1, to 214 in 1992 and to 392 in data are weighted to reflect the over-sampling of large

1993. This was due in large part to an ex-pansion in types of training covered,6 an in- Second, take-up of DDlTamong fimshas been verycrease in the number of approved training pro- uneven. Use by small companies has been very low,viders, a simplified application process, and averaging three percent for micro firms, and justreduced rejection rates for applications. under 20 percent for large firms. Across industrial

* Take-up of DDIT has been very uneven across sectors, the primary users of DDIT were electricalsubsectors. Firms in the electric and electron- machinery, fabricated metals, chemicals, and trans-ics industry were the primary beneficiaries, portation equipment; use was low in food, woodaccounting for over half of training programs and furniture, and textiles. Thus, DDIT use wasapproved and over half of all workers it higher in industries characterized by a higher per-trained; in contrast, no training programs centage of firms investing in R&D, having technol-were approved in the beverage and food in- ogy licenses, more foreign capital participation, anddustry. exports (see Chapter Two).

* DDIT use has been dominated by the MNCs, Finally, MNCs and firms with some foreign capi-and few domestic firms have benefited. In tal participation were more likely to use it than


Table 4.2 Participation in DDIT by or firms in traditional sectors that typically doIndustrial Sector little or no training.

Industry MITP firms % of firmsreporting use by size This uneven take-up of DDIT raises questions about

of DDIT or industry the design of the scheme, and its effectiveness in

Overall Sample 183 8.32 encouraging firms to train. Arguably, most large and

FIRM SIZE majority foreign-owned firms would train evenMicro (<=15 workers) 4 2.61 without the DDIT incentive, given the high-techSmall (16-100 workers) 14 2.17 subsectors in which they operate and their produc-Medium(1 01 -250 workers) 74 7.84 tion for export markets (see Chapter Two). In do-Large (>250 workers) 91 19.83 mestic-oriented subsectors and in the larger

INDUSTRY population of Malaysian-owned firns and SMIs,Food 14 5.28 where skill levels and technological capabilities areOther Food, generally low, the low take-up of DDIT suggests thatBeverages, Tobacco 5 39.00* this scheme has generally been ineffective in encour-

Apparel 2 1.72* aging training among firms that were not trainingWood & Furniture 16 5.23 before.Paper& Printing 10 7.94Chemicals 13 14.44 Malaysia's experiences with DDIT are not unique.Rubber 12 9.16 Many countries have used similar training subsidiesPlastics 8 6.02 gGlass & Pottery 16 11.19 or tax write-offs of training expenses to encourageIron & Basic Metals 8 11.27 firms to train, including Argentina, Brazil, Chile,Fabricated Metals 18 16.36 Fiji, Pakistan, and the Philippines. The limited evi-Machinery 4 4.65* dence suggests that they often needlessly subsidizeElectrical Machinery 10 125.82 well-run firms that already train, while poorly man-

Other Industries 6 8.32 aged firms either do not respond or respond by es-tablishing training designed more to maximize

*Fewerthan 5 observations ftiancial gains than to develop needed skills. Evalu-

Source: 1995 MITP Survey ations of these programs in Chile and Brazil indi-cate that the main beneficiaries are large firms in

domestic firms. Before 1993, less than six percent the most dynamic sectors of the economy.I The fol-of all domestic firms used it as compared to 14 per- lowing section provides some insights into whycent of joint-ventures and wholly foreign-owned DDIT take-up has been so low.firms.

These MITP figures, though more broad-based in Reasons for Not Using DDITcovering both types of DDIT use, are nonetheless The MITP survey elicited information about whyremarkably similar to patterns revealed by admirn- firms did not use DDIT prior to 1993, from 1,504istrative data from MIDA. The strong implica- finms. We classified their responses into fifteen maintion is that both avenues of DDIT are being categories-not aware, don't need training, don'tutilized by the same group of employers-namely, know details, bureaucratic procedures, non-avail-the MNCs, joint-ventures, and larger firms in ability of appropriate training, don't meet require-skill-intensive sectors-who typically already ments, no training capabilities, no training, smalltrain. DDIT approved training institutions are scale, high cost, no time, confusion with HRDF, la-apparently not relied on more heavily by other bor turnover, no permission from management, andgroups of small and medium-size local companies several other minor reasons.


Table 4.3 Reasons Given by Firms For Table 4.4 breaks out these responses by firm size.Not Using DDIT They clearly show that lack of information about

Reason for Not Using DDIT # of % of DDIT was pervasive in all firm sizes, even amongFirms Firms the large firms (40 percent).

Not Aware 682 45.4Don't need training 260 17.3 Among small-scale firms, a higher proportion didDon't meet requirements 163 10.8 notuse DDIT because they did not need it or didDon'ttrain 110 7.3 not train (25 to 28 percent), as compared to largeDon't know details 83 5.5 firms (13 percent). This is consistent with the lowSmall scale of operations 60 4.0No training capabilities 29 1.9 skill requirements of SMIs associated with their useOthers 26 1.7 of relatively mature technology (see Chapter Two).

Source: 1995 MITP Survey A reason for not using DDIT, unique to large firms

and not cited by SMIs, is the bureaucratic applica-tion procedures for DDIT. These findings suggest

3The sevenprincipal reasons cited are listed inTable several lessons and recommendations for policy-4.3. The remaining reasons are collapsed into an makers:"other" category since fewer than one percent of firmsreported each of them. First, the lack of awareness about DDIT, and its re-

quirements, has been the principal reason for theThe most commonly cited reason (45 percent) was limited use of this incentive scheme. While this lackthat many employers were not even aware of the of information was most acute among small firms, itDDIT scheme. To this lack of information prob- was also the principal reason cited by large firms.lem should be added another reason cited by firms,namely, that they did not know the details of the The key lesson for policymakers is that any policyDDIT scheme (over five percent). Thus, more or incentive, whether in training or in other areas, isthan 50 percent of firms did not use the DDIT not likely to be effective if targeted beneficiariesscheme because they were not aware of it or knew are unaware or inadequately familiarized with theits details only imperfectly, this is despite great program. The Government should thus widely dis-effort on the part of the Government to publicize seminate information about training incentives andits availability. programs, especially to SMIs, the group for whom

informational constraints are most severe.Another cluster of reasons for not using DDIT isthat employers did not need training (17 percent), Second, the DDIT incentive scheme has generallywere not currently training (seven percent), or not proved effective in inducing firms to train. Itlacked training capabilities (two percent). Col- has been used primarily by MNCs, joint-ventures,lectively, they suggest that over one-quarter of the and larger firms who, arguably, were training al-firms did not train because they did not need it ready. For these firms, the DDIT scheme has meantor did not know how. sizable windfall gains.

Finally, about 11 percent of firms did not use the For the vast majority of small local firms, the DDITDDIT scheme because their existing training ef- scheme has failed to induce them to begin, or in-forts did not meet the requirements and standards crease provision of, training. Other more proac-established by the DDIT scheme, or because DDIT tive approaches are needed for SMIs. Thesewas not attractive because of the small number of should be designed to affect a change in their at-trainees involved (four percent). titudes towards training, and address their weak


Table 4.4 Reasons for Not Using DDIT by Firm Size

Micro Small Medium LargeProblem Percentage Problem Percentage Problem Percentage Problem Percentage

Notaware 50.8% Notaware 48.3% Notaware 44.1% Notaware 40.5%

Don'tneed 20.2% Don'tneed 18.5% Don'tneed 17.7% Don'tmeet 17.4%requirements

Don'tknow 8.1% Don'ttrain 10.1% Don't meet 1.8% Don'tneed 12.7%details requirements

Don't train 6.5% Don't meet 7.1% Don't train 7.2% Don'tknow 6.2%requirements details

Don't meet 6.5% Small Scale 5.6% Don't know 5.0% Bureaucratic 5.0%requirements details procedures

Small scale 5.7% Don't know 5.2% Small scale 2.8% Small scale 3.5%details

Note: Number of firms: Micro = 124, Small = 466, Medium = 655, Large = 259


training and technological capabilities. Examples 50 employees. While MIDA is no longer involvedof proactive SMI training and technical assistance in approving DDIT applications, it is unlikelypolicies include Mexico's CIMO and Chile's that many eligible small firms are using DDITPROFO programs. These are discussed in Chapter through the approved institutions.six.

Firms with less than 50 employees should be broughtThird, bureaucratic requirements were a major under the HRDF umbrella and registered. Thisconstraint on DDIT use. Its low initial take-up, would simplify administration since the inevitableand its rapid expansion after 1991 when several growth and shrinkage of firms above or below theprogram modifications were introduced and re- 50 worker cutoff would be seamlessly accommo-jection rates reduced, indicates that an onerous dated by universal coverage of all manufacturingapplication process can discourage take-up of infirms under HRDF.centives.

However, the issue of payroll contributions fromCompanies may not find the incentive attractive smaller firms needs to be resolved-one possibility isbecause the benefits of doing so are exceeded by for the government to match their payroll levy con-application costs, including personnel time and tribution; another is for it to provide a block grantaudit fees to certify training expenditures. Strin- to HRDF from general revenues to cover the costs ofgent application requirements and high rejection their use of training services.rates can also reduce interest in the incentive sinceexpected benefits of applying must now be dis- Human Resource Development Fundcounted by a high probability of rejection.

The HRDF was established with a matching grantFinally, the Government should eliminate the re- from the Government.9 The Act created a councilmaining DDIT coverage of firms with less than (HRDC), with representatives from the private sec-


tor and from responsible government agencies, and at least 10 percent of the company's workforcea Secretariat to administer the HRDF schemes. and 15 percent of junior level employees. In ad-

dition, HRDC supported efforts of employers toUnlike DDIT, the HRDF is not a subsidy scheme. develop training plans through the JURUPLANEmployers who have contributed a minimum of six scheme.months are eligible to claim a portion of allowabletraining expenditures up to the limit of their total In 1995 and 1996, the HRDC introduced severallevy (one percent of payroll) for any given year. additional schemes, many with a focus on theThe HRDC has set rates of reimbursement, varying needs of small and medium-size companies. Theby type of training and generally being lower for PERLA Scheme (Training Agreement Scheme) islargercompanies. designed to lower firms' training cash outlays by

enlisting ATP training providers as their agents,In 1993, the HRDC introduced three basic training to collect from users only that portion of fees forschemes that offered firms a great deal of flexibility which firms are responsible and claim the reim-over their training programs. In the ATP scheme, bursable balance directly from HRDC. The SBLemployers can freely send employees for approved Pre-Approved Scheme gives time-tested in-planttraining courses offered by registered training pro- training courses an official pre-approved designa-viders without the prior approval of the HRDC, and tion, which not only allows training providers tosubmit claims on completion of the course. market this training but also simplifies employer

claims for reimbursement.In the SBL scheme, employers submit plans toHRDC for approval of ad hoc in-plant or extemal The HRDC has also targeted SMIs with several train-training courses offered by non-registered training ing schemes. HRDC organizes Training Needsproviders. These plans must include specific objec- Analysis (TNA) workshops and clinics to answertives, areas of training, duration, number of train- questions about different schemes; provides assis-ees, instructors, and means of assessment. tance in the purchase of training aids and setup of

training rooms; and most recently, introduced JointIn the PLT scheme, which is designed for firms with Training Schemes (JTS) to promote group traininglong-term and predictable training needs, employ- of SMIs, and on a pilot basis, a Group Trainingers submit detailed annual training plans covering Scheme (GTS) to encourage employer associations

to play a greater role in developing training programsfor their members.

Table 4.5 Use of HRDF By MITP Firms, 1994

Schemes # of % of These most recent HRDF initiatives are not capturedFirms sample in the MITP Survey, which was fielded in 1994 and

1995. The following section focuses on the HRDFFirms Registered with HRDFa 1048 7213 schemesthatexistedpriorto 1995, andwe defer dis-ATP Scheme (approved programs) b 468 44.7 cussion of recent schemes to a subsequent section.SBL Scheme (ad-hoc programs) b 493 47.0PLT Scheme (annual plan) b 99 9.5 Use of HRDF SchemesFirms not claiming under HRDF 362 34.5 Table 4.5 tabulates the responses of MITP firms

a = Of the 1,450 firms eligible, the proportion regarding the HRDF. It shows the numbers of firmsregistered with the HRDF that were eligible for HRDF, those that said they were

b = Of the 1048 eligible and registered, number registered with the HRDF, those that reported filingclaiming reimbursement. claims under the different SBL, ATP and PLT schemes,

Source: 1995 MITP Survey andthose notclaiming under any of these schemes.


Table 4.6 Eligible Firms Not Registered with this regulation appears to be significant, even recog-HRDF by Size and Industry nizing the possibility that both response and coding

Firm Size and # of Eligible Firms errors and the crude definition of eligibility usedIndustry eligible Not Registered may be partly responsible for this high figure.

with HRDF

# % Table 4.6 presents crude estimates of the severity of

Firm Size non-compliance by firm size and industrial sector.Small (50-100 workers) 461 226 49.0 The estimates are intended to be illustrative because

(101-250 workers)535 140 26.2 the MITP sample is not completely representative.Large (>250 workers) 454 36 7.9 Small firms (with 50-100 workers) are more likely

to be in non-compliance (49 percent) than largeIndustryfms(ihpecn)Food 145 60 41.4 firms (eightpercent).

Other Food, Beverages, 27 51.9 Across industries, non-compliance is higher in the

Textiles 74 14 18.9 food, beverages and tobacco, wood and furniture,Apparel 97 25 25.8 glass and pottery industries (one-third to one-halfWood & Furniture 208 104 50.0 of firms), than the electrical machinery, chemicals,Paper & Printing 89 22 24.7 and textile industries where rates of non-complianceChemicals 62 7 11.3Rubber 113 21 18.6 rates are lower (10 to 20 percent). As such, it ap-Plastics 87 23 26.4 pears that non-compliance is concentrated amongGlass & Pottery 80 27 33.8 small firms and firms operating in the traditional,Iron & Basic Metals 42 9 21.4 domestic-oriented industries.Fabricated Metals 71 16 22.5Machinery 39 8 20.5Electrical Machinery 198 21 10.6 To look at this issue in greater depth, we estimate aTransport 48 10 20.8 probit model to identify the factors associated withOther Industries 45 8 17.8 non-compliance. Underlying this analysis is an eco-

Source: 1995 MITP Survey nomic model in which firms make cost-benefit cal-culations, weighing the probability and cost of being

Wedefine "eligible fimns" as those employing 50 ornore caught in non-compliance against the benefits of notworkers, broadly following the 1995 Guidelines from registering with the HRDF.HRDF, Human Resouce DevelopmentCouncil.'0 Onthe basis ofthis roughdefinition, 402 films (about27.7 We hypothesize that the probability of appre-percent) out of the total of 1,450 eligible reported that hension is lower for smaller firms, who are lessthey were not registered with the HRDF. visible, and for firms that are located in more re-

mote areas. Benefits of non-compliance are two-Of those that were registered with the HRDF, 45 fold: the firm avoids payment of payroll levies,percent claimed reimbursements under ATP, 47 per- and the potentially high fixed cost of setting up acent under SBL, and less than 10 percent under the formal training program if one does not alreadyPLT scheme. However, 34.5 percent of registered exist.firms reported that they did not claim reimburse-ments under any of the three schemes. The results of the probit analysis are reported in

Table 4.7, and they suggest the following points.

Non-Compliance with HRDF First, compared to small fimis, medium size and largeThe Human Resource Development Act of 1992 firms are less likely to be in non-compliance, possi-made it mandatory for eligible finms to register with bly because they believe that the probability of theirthe HRDC. The 27 percent non-compliance with being caught is high, given their higher profile.


Table 4.7 Probit Estimates of Non- The HRDC is aware of thenon-compliance issue butCompliance with HRDF it has few resources to devote to enforcement. It

currently has a skeleton team-an administrative of-Independent Variable Estimate Standard ficer and a clerk-working on developing lists of the

error underlying population of firms that are eligible butMedium size firms 0.532a 0.086 not registered with the HRDC.

Large size firms(>250 workers) -1.323a 0.114 It has established a panel of lawyers but their au-

Only internal thority is limited to civil cases regarding non-pay-informal training 0.254a 0.083 ment of levies by registered firms, not prosecution

No training 0.759a 0.148 of non-registered firms. Only legal officers canRegion: Western

corridor states -0.767a 0.141 pursue the latter cases, and the HRDC is seekingRegion: Pahang, to fill several of these positions. Until such time

Trengganu, Kelantan 0.254 0.184 as the HRDC develops the capability to identifyRegion: Perlis and Kedah -0.421 a 0.187 and prosecute non-registered firms, the threat ofIncreased production

over the last 3 years -0.241 a 0.081 prosecution will not be credible and the non-com-Constant 0.420a 0.153 pliance problem will persist.Log likelihood -684.46 -684.46

The following steps should be taken to address thisa = Statistically significant at 1% level. The omitted re- issue. First, the Government should expeditiously

gion is Sabah and Sarawak; the omitted size is provide HRDC with the necessary manpower andsmall firms with 50-100 workers; the omitted train-ing group is firms providing formal training. legal resources to identify and prosecute firms in

non-compliance, and to recover back levies andS,ource: 1995 MITP Survey otherpenalties.

Second, firms that do not train or that rely only on Second, with additional resources in hand, the

informal training, are significantly more likely to HRDC should also mount an information campaign,

be in non-compliance as compared to those provid- on television and in newspapers, to encourage eli-

ing formal training. This is consistent with the pres- gible firms to register with HRDC. It should an-

ence of high-fixed costs of developing and setting nounce its intention to vigorously enforce

up training programs, and incorporating the new compliance with the HRDF Law and, to ensure that

skills into existing production. this threat is credible, it should publicize its in-

Third, there are systematic regional variations, creased enforcement capabilities as well as its pros-

with non-compliance being much higher in the ecutions of selected firms.

eastern states of Pahang, Kelantan and Trengganu, Finally, this campaign should be accompanied by a

and in East Malaysia as compared to the west coast time-limited amnesty program for firms to come for-

states. ward, register with the HRDC, and pay their back

levies without civil or criminal penalties. SimilarFinally, we attempted to control for the firmts amnesty programs have been effectively used in

growth experience in the past three years, on the the United States.

grounds that finns that are not growing are more

likely to fail and thus have little incentive to reg- Claims under HRDF

ister with the HRDF. The estimated results-that A second issue is that a sizable number (362) of reg-

firms with stagnant or declining sales are less istered firms do not claim reimbursements for train-

likely to register-is consistent with this hypothesis. ing expenditures despite contributions of payroll


levies to the HRDF. Their claims for training under line-to June 1995-granted by the HRDC. 12 This isany of the schemes are a crude indicator of how ef- supported by HRDC data showing an increase infective HRDF has been in encouraging firms to be- the ratio of funds approved to levy collected, fromgin or increase training. 64.7 percent in 1994 (the time period covered by

the MITP survey) to 88.9 percent in 1995.Tabulations suggest that small firms are less likelyto claim as compared to their larger counterparts- We estimate probit models to get insights into why50.2 percent for small firms with 50-100 employ- these registered firms did not implement trainingees, 41.3 percent for medium size firms with 101-250 programs and claim reimbursements. The depen-employees, and 19.4 percent for large finms with over dent variable is an indicator variable with a value250 workers. This is a key issue since firms paying of one if the firm does not claim, and zero other-into the system, but not claiming reimbursements, wise.in effect pay a tax of one percent of payroll withoutgetting any tangible benefits from doing so. Two models specifications are considered. In the

first model, we include several measures of firm size,Who are these non-claimant firms and why are they reported training status, an indicator variable fornot training? Table 4.8 reports the distribution of whether they have a training plan, and industryregistered firms that do not claim according to dummy variables. In the second specification, wetheirtraining status-no training for workers, only replace the actual training variables with the firms'informal training on-the-job, and training formally. own responses about why they provided little or no

training. Table 4.9 reports the estimated probit re-

Table 4.8 Registered Firms Not Claiming from sults for these two models.HRDF by Training Status

T he results of the first model indicates that the firmsTraining Status Registered Distribution least likely to claim from HRDF are small firms, and

not claiming training firms providing no training or only informal train-status ing. This result was already evident in the simple

Firms not training 22 6.1 tabulations. Having a training plan, however, is as-Firms training informally only 196 54.1 sociated with a greater likelihood ofa claim to HRDF,Firms training formally 144 39.8 though not necessarily through the PLT (annual

Source: 1995 MITP Survey trainiing plan) scheme."

This is not surprising. A training plan is indicativeInterestingly, only about six percent of these non- of an awareness, on the part of the employer, of itsclaimants do no training. The majority of firms (54 skill needs, as well as a commitment to a strategy ofpercent) are those that only provide informal OJT systematically training employees to meet these skillfrom co-workers and supervisors. l l Thus, about 60 needs either in-house or through services of exter-percent of these firms are not eligible for reimburse- nal training providers.ments because they either provide no training or areonly training infonnally. The remaining 40 percent The results of the second model provide insights intoreport providing formal training yet do not claim why firms contribute but do not claim. The statisti-reimbursements for these expenditures. cally significant responses are that employers have

limited resources for training, they use mature tech-It is possible that some (unknown) fraction of these nology with low skill requirements which arelatter firms submitted claims subsequent to the readily met by school graduates, and skilled work-MITP survey, based upon an extension of the dead- ers are readily hired from other firns.


Table 4.9 Probit Estimates of Not Claiming from HRDF

IndependentVarable Model 1 Standard Model 2 StandardEstimates Errors Estimates Errors

Mediumfirms (101-250 workers) -0.123 0.107 -0.196 0.105Large firms (>250 workers) -0.630a 0.114 -0.836a 0.110Limited resources for training 0.242b 0.115No knowledge about training 0.067 0.099Mature technology 0.147c 0.084Get skilled workers from others 0.285b 0.119Skeptical about training benefits 0.097 0.139Skills from schools are adequate 0.283b 0.124Firm only trains informally 0.463a 0.091Firmdoesnottrain 0.511a 0.216Firm has a training plan o0.493a 0.089Constant -0.24 8b 0.104Log Likelihood -623.06 -623.06

a = Significant at I %b =Significant at 5%c = Significant at 10% levelNote: The omitted size category is small firms with 51 -100 workers; and the omitted training

category in model 1 is firms that provide formal training.Industry dummy variables included but are not statistically significant.


These responses are consistent with the weak In the JTS scheme, groups of small firms reaptraining and technological capabilities of small scale economies by banding together to engageand local firms shown in Chapter Two, as well as training providers who run specific training pro-the financial constraint on funding training iden- grams for them on a joint basis. In addition totified in earlier sections of this chapter. They lower per trainee cost, the JTS provides an addedexplain why most of them do little or no formal incentive for the firm that organizes the jointtraining on their own despite the financial incen- training.tive of the HRDF levy, relying instead on the edu-cational system or on trained workers hired from In the GTS scheme, now being implemented on aother firms. pilot basis,"4 employer associations are encour-

aged to take the initiative in providing trainingRecent HRDF Initiatives to members, with HRDF providing funds to setThe HRDC recognizes the funding and training up training facilities, and paying the salary of adifficulties faced by small local firms, and it has training coordinator for three years. The coordi-introduced several schemes and initiatives to ad- nator conducts a skill needs survey, and organizesdress these constraints. One set of initiatives seeks training program for its members.to assist both large and small firms, in develop-ing a training infrastructure-the JURUPLAN for In this section, we provide insights into these ini-developing a training plan, and a scheme to pur- tiatives using data reported in the MITP survey.chase training aids and set up training rooms. The We report the incidence among firms of in-housesecond set of initiatives-the Joint Training Scheme training centers, training plans and how they were(JTS) and Group Training Scheme (GTS)-is de- developed, and the different types of joint train-signed to encourage group training for smaller ing programs, focusing in particular on differencesfirms. in these measures by firm size.


Table 4.10 Training Centers and Training Plans in MITP by Firm Size

Training System Conditional on Having a Training PlanFirm Size % firms % firms % firms % firms % firms

with with Training Plan Training Plan Training PlanTraining Training developed at developed by funded byCenters Plans own cost consultants JURUPLAN

Small 3.1 10.9 83.2 23.7 7.6Medium 9.7 42.4 76.7 33.5 7.1Large 19.2 69.2 79.3 35.7 8.6


Table 4.11 Joint Training Programs in MITP by Firm Size

Source of Joint Training Programs Firm SizeSmall Medium Large

% Firms with joint training programs 3.9 9.9 18.9Conditional on a joint training program:

Through Industry associations 23.4 30.2 37.2Organized by government institutions 27.7 24.5 30.2Ad hoc programs with other firms 34.0 49.1 52.3Through specialized companies 21.3 35.9 40.7Organized by suppliers 31.9 34.0 17.0Organized by buyers 14.9 17.0 27.9


Table 4.10 reports the distribution of firms with How were these training plans financed? Columnstraining centers and training plans by three firm sizes. three through five report the sources of their fund-Small, medium and large sizes are defined as em- ing to develop training plans, conditional on havingployers with less than 100 employees, 101-250 em- one. Multiple responses are permitted so that theployees, and over 250 employees, respectively. numbers do not sum to 100.

Column one shows that firm size is an important Two points emerge. First, there do not appear to bedeterminant of whether an employer has a training major differences by size in how employers developcenter-only three percent of small firms have train- training plans. Second, the figures reveal that mosting facilities as compared to 19 percent of large firms. training plans (over 70 percent) are developed byAs such, considerable potential exists for HRDC to employers at their own cost, followed next by train-extend the training aids scheme to small firms. ing plans developed with the assistance of consult-

ants. The proportion of training plans funded byColumn two shows the proportion of firms with a the JURUPLAN scheme is low, about seven totraining plan. As before, the presence of a training eightpercent. HRDC should ascertain the reasonsplan is highly correlated with size, though a much for why employers prefer to fund their own train-higher number of finns of all sizes report having a ing plans when an alternative source of finance istraining plan as compared to training facilities. To available through the JURUPLAN scheme.the extent that employers can send workers outsidefor training, it reduces the necessity for them to have Table 4.11 shows the incidence and types ofjointa training facility in-house. training reported by employers in the MITP survey.


Firms were asked whether they had joint Such ad hoc arrangements arise when firms,trainingprograms with other finrns to provide work- which otherwise compete with each other, seeers with training, and if so, how these training pro- acollective interest in jointly investing in a com-grams were organized. Finns indicated one or more mon good, in this case, worker training. The im-of six types of programs organized through: portance of other types of joint programs varies

markedly by firm size.* industry or professional associations* government or public institutions For small firms, programs organized by suppliers* ad hoc arrangements with other firms and government agencies are cited most often after* specialized training companies ad hoc arrangements. Unfortunately, the govern-* programs organized by suppliers ment and public agencies involved are not identi-* programs organized by buyers fied. For medium size firms, suppliers are also

important as are specialized training companies. ForThe first row of Table 4.11 indicates thatjoint train- large firms, most commonly cited after ad hoc jointing programs are relatively rare in Malaysian indus- programs are specialized training companies andtry. When they occur, these programs are most employer or professional associations. The latter'scommonly found among large firms (19 percent) focus on large employers would be reoriented to-rather than among smaller firms (four percent) who, wards supporting smaller firms under the HRDF'sit may be argued, need them most. Unlike larger new GTS scheme.firms, individual small employers are often unableto assemble a large enough group of employees to Has HRDF Increased Training by Firms?warrant the fixed costs of hiring an outside provider It is too early to make judgments about the efficacyto deliver a tailored training program. Joint train- of HRDF in promoting training and skill upgrad-ing programs, such as those envisaged by the ing. Additional years of accumulated informationJTSscheme, would allow groups of small firms to (panel data) will be needed to do that. However, areap the economies of scale. crude test is possible using retrospective responses

from employers about how their level of trainingGiven the obvious benefits of such programs, espe- has changed-increased, stayed the sarne, ordecreased-cially for smaller firms, it is unclear why more joint over the past three years, a period spanning the yearprograms are not found among them. Is it due sim- prior to the introduction of HRDF in 1993, to theply to the low skill requirements of small firms, present (1995). We will do this by comparing theor are there collective failures-no tradition of col- training experiences of two groups of firms: thoselaboration among small firms, or absence of em- registered with the HRDF, and those who were eli-ployer associations to represent the collective gible but did not register. In principle, the regis-interests of small business-which prevent them from tered group would have increased incentives to trainworking together? This issue should be studied by so as to recover theirpayroll levy contributions. InHRDC to determine if incentives alone are suffi- contrast, the non-registered group would not facecient to encourage joint training. these same incentives since they do not contribute

to HRDF. We recognize that these two groups ofThe remaining columns of Table 4.10 show how ex- firms are different, not only in terms of their mea-isting joint training programs are organized. When sured characteristics but also in terms of their unob-firms have one, the single most important type of served (to us) productivity attributes.joint training program for all firm sizes is throughad hoc arrangements with other firns-the fractions Table 4.12 cornpares the training experiences of theseciting this range from 34 percent for small firms to two groups of firms. Of those registered with the52 percent for large firms. HRDF, about 50 percent said that they had increased


Table 4.12: Probit Estimates of Increased Training Under HRDF

IndependentVariable Combined Purely Firms withInteractions Between Size Sample of Domestic Foreignand HRDF Firms Firms capital

Small Firm HRDF 0.160 .141 0.179(0.122) (0.151) (0.215)

Medium Firm HRDF 0.312a 0.171 0.435a(0.101) (0.129) (0.179)

Large Firm HRDF 0.788a 0.839a 0.753a(0.102) (0.147) (0.175)

Introduced new technology 0.428a 0.499a 0.365ain last 3 years (0.076) (0.103) (0.114)

Constant -0.574a -0.616a -0.516a(0.083) (0.099) (0.160)

a= Significant at 1 % level

Note: Industry dummies were included butwere not statistically significant.Source: 1995 MITP Survey

Table 4.13 Changes in Training Levels Over the Past Three Years:Firms Registered with HRDF and Unregistered Firms

Registration Status Increased Training Training is the Same Decreased Training

Eligible Registered Firms 522 412 12(49.8) (39.3) (1.2)

Eligible Unregistered Firms 109 190 21(27.1) (47.3) (5.2)

Note: The percentages do not sum to 100.Close to 10 percent of registered firms and 20 percent of unregistered firms said they did not know.


training over the last three years, 39 percent firms creased training over the past three years in regis-said that their training had remained the same, and tered HRDF firms versus non-registered firms. Theonly one percent said that their training had de- effects of HRDF are allowed to vary by firm sizecreased. In contrast, of the eligible firms not regis- using a set of interaction terms between firm sizetered with the HRDF, 27 percent said that their dummies and an indicator variable for being regis-training had increased, 47 percent firms said that tered with HIRDF.their training had remained unchanged, and fivepercent said that their training had decreased over The model includes a set of industry dummy vari-the lastthreeyears. Thus, itappears thatHRDF may ables to control for possible differences inhave played a role in increasing training provision industrycomposition of registered and non-regis-among registered firms. tered firms. We include a measure of whether the

firm introduced new technologies over the past threeWe test this hypothesis formally using a probit years. The intent was to net out the confoundingmodel. This model compares the likelihood of in- effects of increased training due to new technology


that is independent of HRDF. Finally, separate sponding increase in demand for training amongmodels are estimated for pure domestic firms and such firms.firms with foreign capital to see if the training ef-fects of HRDF varies by foreign ownership. The DDIT incentive scheme has generally notproved

effective in inducingfirms to train. It has been usedTable 4.13 reports the results of this exercise. They prinarily by MNCs, joint-ventures, and larger firmnsdemonstrate that HRDF has had a significant role in who, arguably, were training already. For theseincreasing training among medium and large firms firms, the DDIT scheme has meant sizable wind-registered with the HRDF, but not small firms. This fall gains; for the firms that provided little or noresult continues to hold for the sample of firms with training, the DDIT scheme has failed to inducesome foreign capital participation. Among purely them to begin, or to increase provision of train-domestic firms, HRDF has only been effective in ing. Lack of awareness about DDIT, and its re-increasing the training of large firms with over 250 quirements, has been the principal reason for itsemployees; the HRDF incentive was not effective limited use. A second factor was the heavy re-in increasing training among small and medium-size quirements of applying for DDIT, and the corre-local firms. These results were not affected by dif- sponding high rates of rejection, both of whichferences in industrial composition of the two groups, reduced interest in using the incentive. The keywhich we control for using industry dummies. lesson for policymakers is that any policy or in-

centive, whether in training or in other areas, isHowever, whether or not finms had introduced new unlikely to be fully effective if targeted benefi-technology in the recent past made a difference. In- ciresy y- clarles ~~~~~~~~are unaware of or inadequately familiar-creases in training and introduction of.new technol- ized whe program other les iat, where

ogy oer th pas thre year aresigniican lized with the program. Another lesson is that, whereogy over the past three years are significantly feasible, filing requirements should be streamlinedcorrelated, a result consistent with that findings in to improve take-up of incentives.Chapter Two that technological change is accompa-nied by higher skill requirements. The DDIT incentive is currently restricted to small

firms with less than 50 employees; all other firmsFindings and Policy Implications are covered by HRDF. The Government should

eliminate the remaining DDIT coverage entirelyMarkets are generally well functioning in Malay- on several grounds. First, it is likely that fewsia, but there is evidence that mnarketfailures pose small firms are using the incentive today. Sec-important constraints on trainingfor many employ- ond, bringing all firms under the HRDF umbrellaers, especially the small and medium-size compa- greatly simplifies administration, since universalnies. These include high labor turnover, which coverage of all firms would seamlessly accommo-prevent employers from recouping investments in date the growth or shrinkage of firms above ortraining; poor information on training methods, below the 50 employee cutoff. Finally, HRDF isespecipecially how to train or what kinds of training developing new schemes to support the trainingto provide; and inadequate finance for training, es- activities of SMIs, and the 50 employee cutoffpecially among SMIs. These market failures justify would arbitrarily restrict access of small firms togovernment intervention. Whilenotamarket failure these programs. The issue of payroll contributionsper se, the use of mature technologies with low skill for these smaller firms needs to be resolved. Theneeds was the principal reason for little or no training government might consider a waiver of the payrollboth among local firms and SMIs. Increased take-up levy for small firms, and provide HRDF with a blockof incentives to adopt new technology or improve qual- grant from general revenues to cover their use ofity, such as ITAF schemes, should lead to a corre- training services.


Non-compliance in HRDF appears to be significant. deadline granted by HRDC, it is likely that the un-The MITP survey indicates that as many as 27 per- derlying problem remains, especially for smallercent of eligible firns with 50 or more employees are firms. About 60 percent of them provide no train-not registered with, and contributing to, the HRDF. ing or only unstructured, informal, on-the-job train-It is concentrated among smaller firms, firms in traing that is not eligible for reimbursement under theditional and domestic-oriented industries, firms in HRDF. Some of their constraints-poor knowledgethe states on the east coast and in East Malaysia, and about training, not having a training plan, or inad-among firms providing little or no structured train- equate training facilities-are being addressed throughing. While there may be good reasons to downplay HRDF's TNA workshops, the JURUPLAN schemeenforcement in the early gestation period, policymak- to develop training plans, and schemes to fund pur-ers will eventually have to make a strong effort to chase of training aids. Other factors which limitaddress the issue of non-compliance. HRDC cur- demand for training, such as use of mature technol-rently has few personnel or legal officers to devote ogy, are under the purview of other governmentto enforcement. The Government should expedi- agencies, and policies to address them are discussedtiously provide HRDC with the necessary man- in Chapters Five and Six.power and legal resources to identify and prosecuteeligible but non-registered firms. HRDChas introduced two new schemes-JTS and

GTS-to encourage group trainingfor smaller em-The HRDCshould also mount an information cam- ployers, either initiated by groups of small firmspaign, on television and in newspapers, to encour- themselves, or organized by employer associations.age eligiblefirms to register with HRDC. It should The MITP survey indicates that such joint trainingannounce its intention to vigorously enforce com- programs between firms are rare in Malaysia. Theypliance with the HRDF Law and, to ensure that this are commonly found not among SMIs, but amongthreat is credible, it should publicize its increased large firms. When they occur, most are ad hoc ar-enforcement capabilities as well as its prosecutions rangements. Joint training programs organized byof selected firms. This campaign should be accom- suppliers and by government agencies are more im-panied by a time-limited amnesty program for firms portant for small firms; joint programs organizedto come forward, register with the HRDC, and pay by specialized companies are cited by many mediumtheir back levies without civil or criminal penal- and large firms; industry associations are also cited,ties. Similar time-limited amnesty programs have but primarily by large firms. These industry as-been used effectively in several states in the U. S. to sociations will have the responsibility, under theimprove compliance. pilot GTS scheme, for organizing training for

SMIs. These group-oriented approaches are po-As of year-end 1994, over one-third of registered tentially potent policy instruments for fosteringfirms had not claimed any reimnbursementsfor train- training among SMIs. Variants of both policiesing through the HRDF. This figure was especially have been used, with some success, in a numberpronounced for small and medium size firms, about of developing countries (see Chapter Six) and thehalf of whom did not claim. While claims have risen progress of these initiatives in Malaysia shouldsince them, in large part due to an extension of the be carefully monitored.

CHAPTER FivE: TECHNOLOGY, QUALITY AND SKILLS

Malaysian policymakers have identified low levels past three years, and its consequences for changingof technology and product quality as a bottleneck to skill needs and employment.sustained growth and competitiveness. They areactively encouraging finns-through fiscal incentives This wealth of firm-level data, from such a largeand the activities of technology support institutions: sample of firms, provides an unprecedented oppor-

tunity to analyze enterprise decisions to invest in* to increase firms' spending on research and de- technology, to examine the quality control efforts of

velopment, firms, to identify the effects of introducing new tech-* to accelerate technology transfer from the MNCs nology on future skill requirements and productiv-

to domestic firms, to invest in automated pro- ity, and to draw out their implications forduction technologies to economize on scarce policymakers.labor,

- to modernize small and medium-scale industries Technological Characteristics of Firms(SMIs),

* to adopt quality control systems and raise prod- We begin by using the MITP survey to character-uct quality to meet exacting international stan- ize the technology level of firms in Malaysia, thendards for exports (MTI, IMPReview, 1994). their efforts to improve quality.

The 1992 National Survey of Research and Devel- Employers can develop their technological capabili-opment provides some information on industrial train- ties in several different ways. First, they can de-ing in Malaysia.I It indicates that overall the research velop technology in-house through investments inand development efforts in Malaysia-about 0.37 per- research and development. Since few local enter-cent of GDP-are lower than projected in the 1990 prises have the requisite scientific, engineering andAction Plan for Industrial Technology Development technical capabilities to conduct cutting-edge R&D,(APITD), while private sector R&D spending-0. 17 much of this expenditure may reflect relatively mod-percent of GDP-is higher than projected. However, est engineering and product R&D activities.the scale of private sector industrial R&D is Malay-sia is relatively modest by international standards Second, when in-house R&D capabilities are lim-(WorldBank, 1996).2 Much of R&D is concentrated ited, technology transfer is an alternative way forin the electrical and electronics industry and among local firms to acquire new technology, either throughMNCs, and private R&D spending in Malaysia total licensing and know-how agreements with otherRM 125.4 million spread overjust 97 firms. firms, or through joint-ventures with foreign firms.

Finally, firms can acquire new production technol-In this chapter, we use the MITP survey to provide ogy embodied in new vintages of capital, throughadditional insights on the technology level, quality investments in automatic machinery, computer-as-control systems, and associated skill needs of enter- sisted production, and testing and quality controlprises. The survey elicited detailed firm-level infor- equipment.mation on R&D expenditures as a percentage ofsales; technology and know-how licensing agree- Table 5.1 begins by providing a broad overview ofments; investmnents in automation and quality control the incidence of these technology indicators by fourequipment, as well as the vintage (age) of machin- firm size categories and by ownership-domestic firms,ery; quality control methods; ISO-9000 certification; joint-ventures, and wholly foreign-owned firms.whether new technology was introduced over the Three broad sets of indicators are considered:


* Research and development -- whether the mosthalf of this R&D (RM 4.812 million) is concen-firm does R&D, and R&D expenditures as a trated in the electronics industry.percentage of sales

* Technology transfer -- does the firm have When these sample estimates are multiplied by theany technology or know-how licensing agree- population weights, total R&D spending almostments, from foreign or domestic sources doubles--to RM 1,908 million in 1994. Thus, com-

* Sophistication of machinery and equip- pared to the 1992 R&D survey, the 1994 MITP sur-ment -- percent of equipment that is fully auto- vey finds four times as many firms reporting R&Dmatic, whether the firm has quality control or expenditures, higher per firn R&D spending, and iftesting equipment, and percent of equipment the weighted estimates are to be believed, almost 15that is more than 10 years old. times as much private sector R&D spending as re-

ported in the 1992 R&D. Some part of this gap isFirst, consider the R&D expenditures reported by undoubtedly due to differences between surveys infirms in the M1TP survey. Out of a sample of 2,200 the definition of R&D, and to the two years separat-firms, 435 firms or 19.8 percent had positive R&D ing the surveys. The important point to note is thatexpenditures in 1994. Based on reported R&D to even with this more expansive R&D measure, lev-sales figures, we estimate that these firms spent a els of private R&D in Malaysian industry are stilltotal of RM 1,030 million on research and develop- relatively low in comparison to other Asian NICsment, or approximately RM 2.4 millionper firm. Al- and industrialized countries.

Table 5.1 Technology Characteristics by Firm Size and Ownership

Percent of Firms Mean ValuesOwnership Type Do Have Have QC R&D Equipment Automaticand Firm Size R&D Technology & Testing % of over 10 yrs equipment

license(s) Equipment sales % %

Domestic FirmsMicro 4.4 1.5 4.4 1.31 39.7 3.1Small 9.2 2.8 17.1 0.08 34.2 11.6Medium 23.9 5.6 38.4 1.40 25.1 20.6Large 31.4 12.8 50.0 0.38 22.0 27.3

Joint-VenturesSmall 16.7 10.0 36.7 0.27 28.2 17.4Medium 23.4 22.5 44.0 0.93 20.9 22.3Large 38.8 33.8 60.4 1.64 19.3 32.9

100% ForeignSmall 15.4 7.7 46.2 0.49 11.4 23.4Medium 20.7 15.7 47.9 0.43 7.2 25.9Large 26.9 23.1 67.5 2.09 7.5 38.3

Notes: Micro = less than 16 workers, Small = 16-100 workers, Medium = 101-250 workersLarge = over 250 workers.Micro firms not reported for joint-ventures or 100% foreign firms because of small sample sizes.


TECHNOLOGY, QUALITY AND SKILLS 65

Table 5.1 shows a striking positive relationship be- Finally, Table 5.1 reveals striking differences in thetween firm size and the likelihood of R&D. Among types and vintage of capital equipment by firm sizelocal firms, just over four percent of micro firms re- and ownership. Compared to larger firms, micro,port R&D spending; this figure rises to 24 percent small and medium size firms are less likely to havefor medium firms and to over 30 percent for large quality control and testing equipment, a smaller frac-firms. A similar size-R&D trend is found amongjoint- tion of their equipment is made up of numericallyventures and wholly foreign-owned firms. Except controlled automatic machinery, and a higher frac-for local firms, R&D spending as a ratio of sales gen- tion of their capital equipment is over 10 years old.erally rises with firm size especially among large Furthermore, for any given firm size, the table showsfirms with foreign capital. that a progression to more intensive use of testing

equipment, greater automation, and younger vin-A second, intriguing result are the differences by tages of equipment as the fraction of foreign equityownership status. For any givenfirm size, medium increases in the firm.and large local firms and joint-ventures are morelikely to report R&D spending than wholly foreign- Table 5.2 reports these technology indicators by two-owned firms. For example, among large firms with digit industrial sector. The figures reveal consider-over 1,000 employees, over 31 and 39 percent of able cross-industry variation by foreign equity, bylocal firms andjoint-ventures reported R&D spend- capital intensity, and by export orientation. Indus-ing, respectively, as compared to just 27 percent of tries with high levels of foreign direct investmentwholly foreign-owned firms. Plausibly, the latter firms (FDI) and joint ventures, such as electrical machin-have few incentives to conduct R&D locally since ery and chemicals, are more likely to have high pro-they can draw on the parent MNC's stock of tech- portions of firms with R&D and technology licenses,nology and R&D laboratories; these typically are using quality control and testing equipment in pro-not located in developing economies. duction. Capital intensive industries with heavy do-

mestic ownership, such as iron and basic metals andA similar pattern of technology licenses by firm size transport equipment, are also relatively technology-and ownership is also apparent. In general, small intensive. A high proportion of firms in iron andfirmns irrespective of their ownership are less likely basic metals have technology licenses and qualityto report technology licenses than larger firms, re- control equipment, while many firms in the transportflecting lower levels of technological capabilities in sector (primarily Proton) report R&D spending. Elec-SMIs. Joint ventures are more likely to report tech- trical machinery, along with plastics, rubber and ap-nology licenses than comparably-sized wholly for- parel are also export-oriented industries, and a higheign firms. For example, amnong small firms, 10percent proportion of these firms have quality control andofjoint ventures have technology licenses versus testing equipment to produce for export markets.eight percent of foreign fimls; the differential wid- The remaining industries -- food products, bever-ens among large firms, with 34 percent ofjoint ven- ages and tobacco, textiles and apparel, and generaltures and 27 percent of foreign firms reporting machinery -- generally show low overall levels oftechnology licenses. the technology indicators.

This pattern of licensing by ownership status may Quality Control and Precision inreflect a conscious strategy by MNCs to recover the Productioncosts of developing new technologies from its joint To become competitive in world markets, Malaysianventures. This incentive to license its technology is firns will need to produce more and better productsdiminished when the enterprise in question is a that meet intemational standards for price and qual-wholly-owned subsidiary. ity. It is not adequate merely to introduce new tech-


Table 5.2 Technology Characteristics by Industry

Percent of Firms Mean ValuesIndustrial Sector Do Have Have QC R&D Equipment Automatic

R&D Technology & Testing % of over 10 equipmentlicense(s) equipment sales years% %

Food 3.7 1.2 7.9 0.99 19.2 7.0Beverages & tobacco 1.9 0.3 2.3 0.09 46.3 2.2Textiles 5.3 0.6 10.2 0.15 12.5 5.9Apparel 8.1 0.5 11.2 0.51 23.2 4.8Wood &furniture 4.6 1.4 5.6 0.74 20.3 3.6Paper & Publishing 4.8 0.7 6.5 0.45 66.9 10.0Chemicals 30.4 27.0 42.4 1.11 23.1 6.5Rubber 11.8 5.1 33.0 0.79 29.6 16.0Plastics 8.3 11.4 14.4 0.08 15.2 16.5Glass & Pottery 11.1 4.8 17.7 0.30 28.2 7.5Iron & Basic Metals 3.8 59.5 66.3 0.02 56.4 5.1Fabricated Metals 11.6 3.3 19.4 0.90 18.7 5.7Machinery 3.7 3.4 18.7 0.80 51.4 6.3Electrical Machinery 18.9 11.8 76.1 3.23 19.6 27.1Transport 50.7 2.9 11.9 1.24 82.7 2.2Other 52.8 2.5 3.0 0.77 11.3 2.5


nologies to reduce production costs. Enterprises will process control, quality control circles and precisionalso need to introduce new forms of work organiza- testing instruments in production, and only 16 per-tion that emphasize product quality, precision in cent rely on visual inspection to verify accuracy inproduction, consistency of quality, and continuous production. These figures stand in sharp contrast toquality improvement. Such organizational features those for SMIs. Less than a quarter of micro andinclude the introduction of quality control circles small finms report using either statistical process con-(QCC), the use of statistical process control (SPC), trol or quality control circles. Less than one-fifth ofand reliance on quality control and testing equip- them use precision measuring equipment to verifyment rather than visual inspection to meet the high accuracy in production; in fact, 64 percent of microlevels of quality demanded by increasing sophisti- firms and 43 percent of small firms rely exclusively

cated users and consumers. on visual inspection to verify accuracy. This sharpsize differential in quality highlights an area of pri-

Table 5.3 reports the incidence, by firm size and ority for policymakers. There is a clear need forownership, of several variables thatreflect firms' em- policies to instill quality consciousness among SMIsphasis on product and process quality: through information dissemination, subsidized QC

training, and incentives to use precision measuring* whether it relies on statistical process control and testing equipment.* whether it has quality control circles* how it verifies accuracy in production Second, local firms are less likely than joint ventures* whether it provides training in quality control. and wholly foreign-owned firms to have a quality

control system. Between 20 and 25 percent of localFirst, firm size isanimportant determinant ofwhether firms use SPC or QCC techniques, and about fivean enterprise has a quality control system in place. percent provide QC training. The comparable fig-Among large firms, about 50 percent use statistical ures for joint ventures are 31-46 percent for use of


Table 5.3 Quality Control and Precision in Production

Quality Control System Verifying Accuracy in ProductionFirm Size and Statistical Quality Quality Precision Simple VisualOwnership Type Process Control Control Measuring Measuring Inspection

Control Circles Training Equipment DevicesFirm SizeMicro 8.1 4.1 1.6 8.1 23.7 63.8Small 16.5 25.1 4.7 19.9 31.6 42.8Medium 30.8 41.7 9.2 34.8 29.9 26.4Large 49.6 53.5 13.7 52.9 24.5 16.3

OwnershipDomestic 20.0 25.4 5.5 20.5 30.0 42.9Joint-Ventures 31.5 46.0 9.9 43.0 28.9 22.3100% Foreign 44.7 47.9 12.3 49.2 25.6 19.4


these QC techniques and 10 percent of JVs provide control training variable was constructed from in-QC training. The incidence of QC and QC training formation provided by employers on the main typesis highest among wholly foreign-owned firms. of training provided to different occupational

groups-technicians, supervisors, skilled productionWhile part of this result reflects differences in size workers and unskilled production workers. A firmcomposition by ownership, it is also consistent with was coded as providing QC training if any one ofthe notion that the use of new technology requires these four occupational groups reported QC trainingnew forms of work organization and quality control. as being the most important training type provided. 3Are local firms less likely to have quality controlsystems than joint ventures or foreign firms, once It very likely understates the incidence of qualityaccount is taken of size? The answer appears to control training, since it excludes QC training frombe yes. external sources and QC training that was a second-

ary area of training. By this definition, 160 firmsFigure 5.1 graphically shows the incidence of these provided QC training to one or more occupationalquality control indicators by firm size and foreign groups. Not surprising, the industries with high pro-ownership. The vertical bars represent the percent portions of firms providing training in quality con-of local firmns, joint ventures (JVs), and wholly for- trol-elecial machinery, plastics and chemicals-wereeign owned finrs reporting each QC indicator. The also the industries where QC methods are common.four panels clearly show that controlling for firm size,a higher proportion ofjoint ventures and wholly for- To summarize, the MITP data show that the scope ofeign firms use statistical process control, quality con- private R&D in Malaysia is relatively low by interna-trol circles and precision measuring instruments to tional standards. Furthermore, there are large dif-verify accuracy in production as compared with do- ferences in technological capabilities-as measuredmestic firns. Across all size categories, a much higher by R&D, technology licensing, sophistication of ma-proportion of domestic firms rely on visual inspec- chinery, and quality control systems-between localtion to verify accuracy in production as compared and foreign-owned firms, and between SMIs andwith foreign firms. large firms. These size and ownership differences

in technological capabilities mirror those involvingFinally, the table confirms that the introduction of training, which is not surprising, given the strongquality control systems increases the requirement for linkages between training and technology revealedtraining in quality control techniques. This quality by the analyses in previous chapters.


Figure 5.1 Quality Control Systems by Firm Size and Ownership

A. Statistical Process Control B. Quality Control Circles

70 60

60 50

50 40

4030 30

20 20

70 1 0

0 0o E E, a) 2 E

E 0, 2c| 11Loal LVo aFoeiE

E E

C. Precision Measuring Equipmt. D. Visual Inspection Only

70 70

60 60

50 50

40 40

30 30

20 20

10 10

0 0

0 E a 2 2 aE a E, 2' 2 a2

E E

*gLocal *JVs 0 Foreign

If frms with weak technological capabhiiies are also First, the overwhelming demand from firms is forthe same ones with weak training capabilities, it imn- diffusion services for known technologies ratherplies that policies should be designed to address both than support to develop wholly new technologies.sets of firm-level weaknesses and constraints since For Malaysia, this means focusing support on tech-the target population is the same. A recent study of nology transfer, licensing agreements, dissemi-technology-support institutions in six countries sug- nation of information, standards and testing, andgests some broad directions for the design of tech- skills training rather then public R&D or R&Dnology policies to address these firm size and incentives for firms. Support for theownership differences in capabilities (see Box 5.1). prioritization of technology diffusion, rather than


Box 5.1 Use of External Sources of Technical Support by Firms

A recent World Bank study looked at six economies--Japan, Korea, China, India, Mexicoand Taiwan--to examine the key characteristics of technology support institutions (Tls)and their use by industrial firms (Goldman, 1995). Tls were broadly defined to include allpublic and private sources of technology and training support used by firms, including (i)national technology and standards institutes, industry associations, and productivity cen-ters; (ii) private sources such as foreign technology licensors and contract laboratories;and (iii) technical assistance from suppliers and buyers. Over two thousand firms wereinterviewed, including both small and large firms and covering six sectors. Several of theprincipal findings and conclusions are summarized below.

The overwhelming demand by firms, both large and small, is for services related totechnology diffusion, i.e. the transfer and application of known technology. Firms mostcommonly use basic services related to acquisition of information, standards and test-ing, trouble shooting, and technology-related training. And when firms use R&D servicesof public Tls, they tend to contract for answers to specific technology questions, ratherthan for development of new technologies.

While larger firms tend to use Tls more intensively than smaller firms, use is also shapedby whether firms have in-house laboratories or technical departments. This is especiallypronounced among small firms, where those with in-house resources use Tls at nearlytwice the rate of other small firms. This highlights the difficulty of reaching and servingsmall firms, particularly those without internal technological capabilities. Large firms arealso three times more likely than the overall sample to have received grants, tax incen-tives or soft loans for technology; only assistance directed at technology diffusion--suchas help in developing standards, or subsidies for training--seem to be taken up by smallas well as by larger firms.

A high proportion of firms reported using a public TI at least once, though long-termcustomers, followed by suppliers, were the most commonly used external sources oftechnology. The survey found that small firms require special TIs dedicated to them;otherwise, they obtain little or no support. Tis focusing on small firms need to workproactively to expose them to the benefits of change if demand is to be generated fortechnology improvement and assistance. The Japanese approach--support directed atindustry clusters in a region and focusing on technology diffusion--is quite effective inreaching a large number of small firms. The Taiwanese approach--productivity centerswhich develop generic expertise with applicability to small firms in a wide range of indus-tries--is also effective, but reaches a lower fraction of the target population.

development, was provided in Chapter Three ized support institutions working actively to de-which showed licensing to have greater produc- liver technology support services to hard-to-reachtivity effects than R&D. SMIs, especially those with limited in-house ca-

pabilities, to expose them to the benefits of changeSecond, larger firms use support services more in- and create demand for technology improvementtensively, and their take-up of technology incen- and assistance. For Malaysia, this means restruc-tives is more common, than smaller firms. SMIs turing the way public institutions deliver supporthave special needs, and these are seldom met by to firms, SMIs in particular-from one that reliesbroad-based institutions. They require special- on firms to take-up incentives, to one in which


technology support services are delivered systems and the structured training programs neededproactively to firms. These policies are discussed to attain those standards.4 The Brazilian experiencein greater length in Chapter Six. indicates that adoption of ISO-9000 certification and

total quality management standards (TQM) in pro-

ISO-9000 and Quality Assurance duction has led to productivity and quality gains(see Box 5.2). ISO-9000 certification also provides

It is increasingly recognized that standards and me- a strong signal to clients that a firm is prepared totrology can be an important policy instrument for attain and maintain high and exacting quality stan-improving and diffusing modem production meth- dards (Frischtak, 1995). As such, it has become theods and quality control systems (Dahlman, 1992), and prerequisite for doing business in many sectors ofupgrading product quality to meet the exacting in- the European Community (EC) and, increasingly,temational standards for export markets. One such is being extended to the East Asian Trade area aroundvoluntary standard, the ISO-9000 series introduced Japan.in 1987 by the International Standards Organization,represents the international consensus on how best the MITP survey provides insights into the adop-to operate and assess quality management systems. tion of ISO-9000 standards in Malaysia. There is

growing interest in implementing ISO-9000 amongThe publication of ISO-9000 standards provides Malaysian firms. The Standards and hidustrial Re-firms with a benchmark of what constitutes best search Institute of Malaysia (SIRIM), which is re-practice in their specific area, and thus incentives to sponsible for metrology and standards, is theput in place both the quality control and assurance registration body for ISO-9000. It has awarded 700

Box 5.2 Diffusion and Impact of ISO-9000 in Brazil

Many observers attribute the recent productivity and quality gains in Brazilian industry to producers'commitment to total quality management (TQM) and adherence to international quality standards ofthe International Standards Organization (ISO) 9000 series. The diffusion of ISO-9000 among in-dustrial firms has been rapid--between 1990 and 1994, the number of certified firms increased from18 to 577, an average annual growth rate of 138 percent. The industries with the greatest number ofcertified firms are electrical equipment and instruments, chemicals, basic metals and fabricatedproducts, and general machinery.

A number of factors were responsible for the rapid diffusion of TQM and ISO-9000 certification--industrial restructuring during the 1990-92 recession, major trade reforms beginning in 1990, andgrowing awareness of the increasing importance of quality control to meet client needs, reducecosts, and improve competitiveness viz. a viz. international producers. The Government has playedan active diffusion role through the Brazilian Program for Quality and Productivity (PBQP). The PBQPprovides (1) analysis of the market environment, (2) assessments of systemic and internal con-straints to competitive behavior and the diffusion of TQM (3) establishment of sectoral and globalbenchmarks in terms of productivity and quality indicators, (4) dissemination of information on TQMand provision of TQM training, and (5) subsidizing adoption of TQM practices.

A recent survey of 93 major Brazilian enterprises indicates that adoption of new managerial methodsfor quality control, ISO-9000 certification in particular, has had beneficial effects on the firm--55percent cited increases in productivity, 35 percent improved standardization of processes, 31 per-cent increased employee participation in quality control, 25 percent in product quality improvement.and over 20 percent cited increases in client satisfaction (Frischtak, 1995).


Table 5.4 ISO-9000 Status and Quality Control Systems

Systems of Quality Control With ISO-9000 Seeking ISO-9000 No ISO-9000and Verification of Accuracy Certification Certification plans

Firms % Firms % Firms %

Total Sample of Firms 229 10.4 731 33.2 1,240 56.4Quality Control System

Statistical Process Control 130 56.8 268 36.7 171 13.8Quality Control Circles 123 53.7 329 45.0 268 21.6

Verifying Accuracy in ProductionPrecision Instruments 141 61.6 299 41.0 198 16.0Simple measuring devices 47 20.5 216 29.6 370 29.9

Visual Inspection 22 9.6 154 21.1 609 49.1


foreign and local firms with some level of ISO-9000 and implemented systems of quality control andcertification, and is reportedly in the process of as- quality assurance. A much higher proportion ofsessing another 600 firms. ISO-9000 certified firms use QCC and SPC to en-

sure quality in production and precision instrumentsThe survey elicited information from firms about to ensure accuracy in production, followed by firmnswhether they had any ISO-9000 series certification, seeking certification within the next three years.and if they did not, whether they expected to gain Firms with no plans for ISO-9000 certification areISO-9000 certification within the next three years. much less likely to report use of QCC or SPC, andThis second question was designed to identify firms are significantly more likely to rely on visual in-that were preparing for certification, a process that spection to verify accuracy in production.can take as long as three years. Firms that did notcurrently have ISO-9000 certification, or were not Table 5.5 reports the distribution of ISO-9000 sta-expecting it within three years, were classified as tus by firm size and ownership. It indicates thathaving no plans for ISO-9000 certification. while the number of ISO-9000 certified firms is

small, interest is growing. Currently, over 30 per-Table 5.4 reports the number of firms with ISO- cent of large firms are certified, but the proportion9000 certification, those seeking certification and of micro, small and medium firms with ISO-9000those without certification. Out of 2,200 firms in certification is relatively low-less than one percentthe MITP survey, 229 firms (10.4 percent) had among micro firmns, four percent among small firms,ISO-9000 certification; 731 firmns (33.2 percent) ex- and eight percent among medium-size firms. Thepected ISO-9000 certification within the next three trend in the number of firms expecting certificationyears, and 1,240 firms (56.4 percent) did not have is more optimistic, with 27 percent of small firmscertification and did not have any plans to become and 48 percent of medium firms expecting ISO-9000certified. Industries with the highest fraction of firms certification within the next three years. This trendwith ISO-9000 included the most technology-inten- implies that within three years, over three-quarterssive industries such as electrical machinery and of large firms will have ISO-9000 certification.chemicals as well as the export-oriented industriessuch as rubber and plastics. Nonetheless, the total coverage of ISO-9000 among

micro firms will still be below seven percent at theThe bottom panel of Table 5.4 show the distribu- lowest end of the size spectrum. This highlights ation of quality control systems by firms' ISO-9000 potentially important area of focus for Malaysianstatus. ISO-9000 certifies firns to have documented policymakers. Another important area of policy fo-


Table 5.5 ISO-9000 by Firm Size and Ownership

Firm Size and % Firms With % Firms Seeking % Firms With NoOwnership Type ISO-9000 ISO-9000 ISO-9000

Certification Certification Plans

Firm SizeMicro 0.8 6.5 92.7Small 4.2 26.9 68.9Medium 8.2 48.2 43.5Large 31.3 43.6 25.1

Ownership TypeDomestic 5.6 29.6 64.8Joint-Ventures 14.3 43.9 41.8100% Foreign 27.8 35.3 36.9


cus should be local firms. Compared to joint ven- ISO-9000. According to SIRIM, the SMI sectiontures (14 percent) and foreign-owned firms (28 per- has provided QIP consultancies to a cumulative to-cent), only about five percent of local finns currently tal of 162 SMIs by the end of 1995.have ISO-9000 certification. However, this appearsto be changing. A growing number of local firms QIPs developedjointly by SIRIM and leading MNCsappear to realize the importance of total quality man- in specific sub-sectors, are a potentially powerfulagement and quality standards for improving com- policy instrument for assisting groups of SMIs topetitiveness and meeting the increasingly high upgrade their quality and to foster increased link-standards demanded in intemational markets--thirty ages with MNCs and other larger firmns. For manypercent of them expect to get ISO-9000 certification MNCs, a major obstacle to developing supplier rela-within the next three years. tions with local SMIs is the low and uneven quality of

their products (Fong, 1991). SMIs may not know whatSIRIM can play a greater role in disseminating quality standards are required to become part sup-international best practices in production and qual- pliers, so that few are willing to invest the necessaryity control to employers and, through their adop- resources to upgrade quality practices on the chancetion of ISO-9000 standards, improve the ofbecomingasubcontractor. TotheextentthatQlPscompetitiveness of local firms. The recent can establish clear, and certifiable, quality standardscorporatization of SIRIM in 1996, and the reorga- acceptable to leading firms in a given sub-sector,nization of the institution that is now in progress, they provide tangible incentives not only for SMIsshould allow it to respond more flexibly to the to improve and upgrade their quality control prac-dramatic growth in private sector demand for ISO- tices, but also for MNCs and other larger employers9000 certification.5 to accept QIP-certified SMIs as part suppliers.

Not all firms, SMIs in particular, can afford the high Sub-sectoral QIPs, when developed, are amenablecost and time required (about three years on aver- to group provision of assistance to SMIs in terms ofage) to meet ISO-9000 standards. SIRIM can play an consultancies, training, finance, as well as technicalexpanded role in helping SMIs to improve quality assistance from leading firms in the industry.control by building on its existing, but thus far lim- SIRIM should pursue this program in collaborationited, consultancies on Quality Improvement Prac- with other govemDent agencies-such as the Nationaltices (QIP), which are less expensive to attain than Productivity Corporation (NPC), HRDC, and


MMI's SMI agency-andwith leading private sector countries, Australia, and New Zealand. All otherfirms. countries, primarily those in ASEAN and the

Middle East, are included in the developing mar-ket category.

ISO-9000 and Export OrientationTable 5.6 shows the proportion of firms exporting

There is considerable anecdotal evidence linking to different markets by their ISO-9000 status.quality certification to producers' efforts to penetrate They suggest two points. First, among firms with-developed markets in the US, the EEC, and Japan out plans for ISO-9000 certification, a lower pro-(see Frischtak, 1995). In Malaysia, we also observe a portion export to industrialized markets (20strong correlation between ISO-9000 certification percent) as compared to developing country mar-and the export status of firms. About 82 percent of kets (24 percent). Second, firms with ISO-9000,ISO-9000 certified firms currently export, while or in the process of certification, are more likelyexport-orientation is 69 percent among firms seek- to export to industrialized country markets-47 anding certification within the next three years, and 36 percent, respectively-than to developing coun-just 44 percent among those without certification try markets, where the corresponding fractionsand not planning to do so in the near future. While of exporting firms is 36 and 33 percent. The thirdit is difficult to establish a causal relationship be- column, which is conditioned on exporting, rein-tween getting ISO-9000 certification and in- forces these points, namely, that the relative impor-creased exports, these figures suggest that the tance of exports to industrialized markets increasesfirms which already have ISO-9000 certification, with firms' efforts to get ISO-9000 certification.or those in the process of being certified, are bet-ter able to compete in export markets. Figure 5.2 shows the proportion of firms exporting

to each market type in each of 16 industries whereExporting is clearly not precluded for firms without industries are sorted in ascending order (from left tocertification. However, it may be more difficult with- right) by the share of firms with ISO-9000 certifica-out ISO-9000 certification to break into industrial- tion. The percentage share of certified firms in eachized country markets, where quality requirements industry is represented by the heights of bars. Thetend to be higher, than it is to export to developing dark shaded area shows the percent of firms export-countries. To determine if ISO-9000 certification ing to industrialized country markets, the light shadedmakes a difference, we distinguished between in- area the corresponding figure for exports to devel-dustrialized country markets and developing oping country markets.country markets on the basis of firms reportedprimary export market. The industrialized mar- In all industries, a higher proportion of firms ex-kets include the United States, Japan, the EEC port to developing country markets than to indus-

trialized markets, as is evident by the light shaded

Table 5.6 ISO-9000 and Export Orientation

% Firms that Export Exporting Firms

ISO-9000 Status To To % exporting toindustrialized developing industrialized

countries countries countries

No certification plans 20.3 23.5 46.6Seeking ISO-9000 certification 36.0 33.3 51.8With ISO-9000 certification 46.5 36.0 56.3



Figure 5.2

IS O -9000 a nd Ex ports

90

8 0

7 0

6 0 L

5 0

4 0

3 0

2 0

1 0

0(U n

co CD 0 a) -a

(U 05 a U

in d u s tr ia liz e d d de ve lo p in g IS 0- 9000 L

area being above the dark area. Witi some excep- with an "umbrella" subcontracting scheme where, intions, the general trend is for the export-orientation return for a commiission, a large firm helps Malayof industries to rise with the share of firms having subcontractors market their products and providesISO-9000 certification in the industry. them with a wide range of technical, management,

training and financial services. Guthuie aggressivelyThe exceptions-apparel, textiles, wood and furnii- entered the export market in 1991 with a furnitureture-are highly export-oriented industries where fuinishing plant in Port Klang.other factors may play a more important role thanformal ISO-9000 certification. We estimated regression models to explain firms'

export propensities to industrialized and develop-In textiles and apparel, a very high proportion of ing country markets by their ISO-9000 status, con-firms give formal tranding to ehployees-58 percent trolling for firm size, foreign ownership andand 37 percent, respectively-as compared to the industry. Two kinds of models are used. The firstoverall sample average of about 21 percent (see is a probit model, to explain the probabilities that aChapter Two). These two industries also stand out firtn exports to industrialized markets and to devel-in the proportion of firms that have quality control oping country markets. The second is an orderedand testing equipment. The wood and fumriture in probit model to explain three potential export out-dustry, in contrast, has a relatively low proportion of comes-do not export, export to developing countryfirms that give formal training (11 percent). Its ex- markets, and export to industrialized narkets-whichport orientation may reflect its abundant resource are ranked in ascending order of difficulty. Thisbase (lumber), or the presence of a marketing agent model provides a direct test of the hypothesis thatsuch as Guthrie Furiture. It is unique in being one exporting to industrialized markets is more diffi-of two industries (the other is the food industry) cult than exporting to developing country markets.


Table 5.7 ISO-9000 and Export Propensity by Principal Markets

Probit Ordered Probit

Independent Variables Export to Export to Export todeveloping industrialized different countrycountries countries groups

Small (16-100 workers) 0.413a 0.766a 0.589a(0.148) (0.279) (0.143)

Medium (101-250 workers) 0.582a 1.457a 1.251a(0.147) (0.275) (0.142)

Large (>250 workers) 0.411a 1.786a 1.609a(0.162) (0.282) (0.155)

Joint Ventures 0.436 a 0.183b 0.426a(0.076) (0.082) (0.069)

100 % foreign firms 0.072 0.840a 0.978a(0.095) (0.098) (0.088)

Seeking ISO-9000 Certification 0.109 0.251a 0.264a(0.071) (0.077) (0.064)

With ISO-9000 certification 0.105 0.283b 0.296a(0.112) (0.115) (0.137)

pA for exports toDeveloping Countries 0 0975a

(0.244)

p for exports toIndustrialized Countries - - 1 999a

(0.246)

Log likelihood -1202.484 -976.543 -1787.01

a= Significant at 1 %b = Significant at 5 %Note: Numbers in parentheses are standard errors.

Industry dummy variables were included in the models but are not reported here.


Table 5.7 reports the results of estimating the two ond, foreign ownership is not generally a key deter-kinds of models, probits in columns one and two minant of exports to developing countries (exceptand the ordered probit in column three. Several for joint-ventures) but it is a critical factor in ex-results are suggested by the probit estimates. First, ports to industrialized markets. In this regard,compared to micro firms (the comparison group), being a wholly foreign-owned firm is more im-larger firms are, with one exception, more likely portant than being a joint-venture, not surprisingto export to both industrialized and developing since many local subsidiaries produce for exportcountry markets. This is attributable to the strong to MNC parents. Finally, ISO-9000 is not sig-association between size and a wide variety of mea- nificant in explaining exports to other develop-sures of productivity (see previous chapters). Sec- ing countries. However, having ISO-9000


Table 5.8 Introduction of New Technology Since 1992

% of Firms % Share Type of New Technologya

Firm Attributes Any new Live Newtechnology? Computerization Automation Machinery

All firms 42.4 24.5 17.7 57.7

Firm SizeMicro firms 13.4 12.1 15.2 66.7Small firms 31.5 21.5 11.6 65.9Medium firms 51.2 24.1 19.0 55.8Large firms 68.9 26.5 23.0 50.5

OwnershipDomestic firms 35.5 23.3 15.2 60.9Joint-ventures 53.5 23.6 21.5 51.3100 % foreign firms 58.3 20.0 21.1 57.2

a= Conditional on introducing newtechnology since 1992.Source: 1995 MITP Survey

Table 5.9 Effects of New Technology on Skill Needs and Employment

Firms Introducing NewTechnology Since 1992a Overall Micro Small Medium Large

Resulting Employment Change% reporting increases 46.3 35.7 43.3 46.4 48.1% reporting decreases 35.2 35.7 34.3 34.4 31.3

Resulting Skill Needs Change% reporting increases 78.6 64.3 75.6 78.7 80.7% reporting decreases 14.6 21.4 18.9 13.9 13.0

a = conditional on introducing new technology in the past 3 years.Source: 1995 MITP Survey

certification, or actively seeking it, is important function shifts for exports to the two differentfor exporting to industrialized country markets. markets. The g values are positive, indicating that

exporting is more difficult than not exporting.The ordered probit estimates reported in column However, the 1t value for industrialized countrythree of Table 5.7 reinforce the principal find- exports is larger than the m for exports to devel-ings of the previous probit models. Conceptu- oping country markets, suggesting that industri-ally, it is the more rigorous model in that it takes alized country markets are more difficult tointo account all three export outcomes, rather than penetrate. In such a competitive global market, atreating the two export markets as being indepen- firm commitment to improve quality control,dent of each other. Clearly, they are not. The or- adopt TQM methods, and seek ISO-9000 certifi-dered probit model yields two extra parameters, cation can help Malaysian firms penetrate the mar-.t, which measure how the export propensity kets of industrialized countries.


New Technology and Changing percent) did not, and the rest (three percent) didSkill Needs not respond or did not know. Of those introducing

technology, the most common was new productionHow will skills and training needs change as firms machinery (58 percent), followed by computers suchintroduce new technologies, and what are the effects as CAM and CIM (25 percent), and then line auto-on productivity growth? While time-series data are mation (18 percent).needed to address this question, some insights areprovided by the MITP survey. This table also shows striking differences in the

introduction of new technology across firm sizeFirms were asked whether they had introduced new and ownership groups. In general, large firmstechnology in the past three years, the nature of the were more likely to have introduced new technol-technology, and whether its introduction was accom- ogy, especially computerization and line automation,panied by increased, unchanged, or decreased em- while smaller firms were more likely to have intro-ployment and skill requirements. Unfortunately, duced new production machinery. In terms ofthose not introducing new technology were not ownership, a higher proportion of firms with for-asked to respond to questions about changes in eign capital introduced new technologies as com-their employment and skill needs. As such, there pared to local firms. As before, new productionis no way to determine if employment and skill machinery was the most common form of technol-needs might have changed for reasons unrelated ogy introduced in all three ownership categories.to whether or not the firms introduced new tech- The second ranking type of technology intro-nology. However, allfirms were independently duced was computerization in domestic firms andasked about whether their worker training had joint-venture firms; however, wholly foreign-ownedincreased, stayed the same, or decreased in the past firms emphasized line automation.three years. We combine both responses to look athow introduction of new technology has affected Employment and Skill Needsskills and training needs, whether its effects differ Employers were asked whether introduction ofby firm size, and how it affects productivity. new technology had an impact on employment and

the skill content ofjobs. Table 5.9 tabulates theirTable 5.8 provides some summary figures on the responses. Of the 925 firms that reported newproportion of firms introducing new technology over technology, 428 firms (46 percent) reported that itthe past three years, and the nature of the new tech- led to increases in employment while a smallernologies put in place. Out of the 2,200 firms in the number, 324 firms (35 percent), reported a fall inMITP sample, 922 firms (42 percent) said that they employment. Thus, contrary to concerns that newhad introduced new technologies, 1,214 firms (55 technology displaces jobs, the figures suggest that

Table 5.10 New Technology and Changes in Training since 1992

Introduced New Technology No New TechnologyTraining Since 1992 Training Since 1992

Firm Size Increased Same Decreased Increased Same Decreased

Overall 50.7 37.6 1.4 21.6 56.6 3.6Micro 33.3 48.5 9.1 7.7 64.6 2.9Small 35.1 48.3 1.7 17.0 57.8 4.1Medium 50.7 38.7 0.7 31.4 55.0 3.7Large 67.4 25.6 1.0 48.5 40.8 2.3



two-thirds of firmns reported either no decrease in However, it is weakened by not having a benchmarkemployment or an actual increase in employment to compare reported changes in skill needs amongas a consequence of introducing new technology. firms not introducing new technology. The issue is

whether respondents are able to accurately attributeWhat is even more striking are the figures on skill changes, or for that matter employmentchanges in skill content from the introduction of changes, to introduction of new technology rathernew technology. Fully 79 percent of firms re- than to other contemporaneous factors. These fac-ported that the new technologies led to an increase tors might include tightening labor markets orin skill content, while only 15 percent of firms re- changes in work organization. We address this is-ported a decrease in skill needs. These results are sue in Table 5.10, by comparing employers' inde-essentially unchanged when the figures are broken pendent responses on how their training has changeddown by firm size. However, there is a percep- over the past three years, separately for firms thattible trend by firm size- among firms introduc- introduced new technology and for those that diding new technology, the proportion of firms not. The employer responses in Table 5.10 makereporting employment increases and higher skill three points:needs rise with firm size. The converse is alsotrue, that as firm size increases, the proportion First, introduction of new technology is accompa-reporting declines in employment and skill con- nied by increased training. Among firms that in-tent falls. troduced new technology over the past three

years, a higher proportion of them also reportedThis evidence is consistent with analyses in previ- increasing worker training (51 percent), as com-ous chapters that link adoption and assimilation of pared to the firms that did not introduce new tech-new technologies to skills upgrading and training. nology (22 percent). Furthernore, firms with new

Table 5.11 Impact of New Technology on Training

Firm Size Marginal Effect Standard % Firms Currently % Firms withof Introducing Deviation of with Formal 1 std. dev.New Technology New Training or Increase in

In Past 3 Years Technology Increasing Training Introduction ofVariable over Last 3 Years New Technology

Probability of Currently Providing Formal TrainingSmall 0.089 a 0.448 23 27Medium 0.093 b 0.500 51 56Large 0.039 0.463 71 73

Probability of Increasing Training Over Past 3 YearsSmall 0.104a 0.448 20 25Medium 0.160a 0.500 41 49Large 0.082 0.463 62 66

a = Significant at 1 %b = Significant at 5%.Note: Column 4 = column 3 + (column 1 x column 2 x 100)

Source: Annex 5.1, Tables A5.1 and A5.2.


technology were less likely to report no changes els of training for firms already investing in thein the level of training (38 percent) as compared skills of their workers.to those without new technology (57 percent).

The training probit models contain a comprehen-Second, largerfirms are more likely to have in- sive set of explanatory variables on current attributescreased training in the recentpast, especially if they of the firm(ownership, industry, and export-orien-also introduced new technology. Compared to tation), its workforce including mean educationsmaller firms, a higher proportion of large firms re- and both skill and sex mix, work organization andported increasing training in the past three years ir- quality control, as well as its contemporaneousrespective of whether or not they introduced new investments in R&D. These control variables al-technology. However, those with new technology low us to isolate the training effects of past in-were more likely to increase training than those that vestments in new technology, holding constant thedid not. effects of other influences.

Finally, introduction of new technology reduces the We estimated separate probit models for each onetraining gap between small and largefinns. Among of three firm sizes-micro and small firms with lessfirms that did not introduce new technology, a very than 100 workers, medium firms with 101-250 em-low fraction of small firms increased training as com- ployees, and large firms with over 250 employ-pared to large firms-17 versus 48 percent. Among ees. This approach allows us to confirm thefirms that introduced new technology, the corre- previous finding that introduction of new tech-sponding figures for small and large firms are 35 and nology has a greater impact on small firms than on67 percent, respectively. Thus, new technology is large firms.associated with a doubling in the probability of-in-creased training for small firms (from 17 to 35 per- The probit results are reported in Annex 5.1. Forcent) as compared to the smaller increase for their ease of interpretation, the marginal effects of ex-large counterparts (from 48 to 67 percent). planatory variables are shown in column one.

To see if these trends are robust, we estimate Panel A for current provision of formal training,probit models to analyze the impact of new tech- Panel B for increases in training over time. Thesenology introduced in the past on two measures of probit results confirm the principal trends revealedtraining. by simple tabulations of the data. Controlling for

other influences, introduction of new technology hasThe first-whether the firm increased training since positive and statistically significant effects both on1992-does not distinguish between formal training the probability that employers currently provide for-and informal OJT. Furthermore, since the survey mal training to their employees, and on the likeli-asks whether training increased, stayed the same, hood that they increased levels of training over theor decreased since 1992, it implicitly assumes that past three years. However, this finding only holdsemployers were already doing some informal OJT for micro, small and medium-size firms; in large firms,or formal training during this period.6 The second these training effects are not statistically significantmeasure-whether the finn currently provides for- possibly because most are already doing a great dealmal structured training-is less subject to these ca- of training.veats. Taken together, the two measures providea crude decomposition of the potential training These probit results can be used to simulate whateffects of new technology introduced in the past: training would be like, if the proportion of firms in-(i) increasing the probability that a firm provides troducing new technology is increased by oneformal training today, and (ii) increasing the lev- standard deviation (column two). Column three


Table 5.12 Impact of New Technology on Productivity by Firm Size

Firm Size Estimated Mean (std. dev.) % Productivity changeParameter from of new technology with 1 std. dev. increase inProd. Function indicator variable new technology use

Overall 0.229 bl 0.420 (0.494) 11.3Small 0.507 0.278 (0.448) 22.7Medium 0.301 0.512 (0.500) 15.1Large 0.338 0.689 (0.463) 15.6

a = Significant at 1%I = Significant at 5%

Note: Column 3 = column 1 x std. dev of column 2 x 100

Source: Annex 5.2, Table A5.3.

shows the existing distributions of the two train- Impact on Firm-Level Productivitying variables, and column four what the new train- The final question we address is whether introduc-ing distributions would be if a higher proportion tion of new technology in the recent past affects cur-of firms introduced new technology. To illustrate, rent levels of productivity. We do this within thecolumn four is calculated for small firms in Panel production function framework used in ChapterA by multiplying columns two and three by 100 to Three. The model specification adopted here isget the percentage increase in small firms provid- broadly similar, and needs no additional explana-ing formal training, and adding this increase to tion (see Annex 5.2). The only difference is the in-the existing percent of small firms already train- clusion of an indicator variable for whether theing. These simulations suggest the following out- employer introduced new technology in the pastcomes of higher rates of new technology three years. We want to see whether past invest-introduction: ments in new technology explain current levels of

firm-level productivity, even after controlling for• The number offirms that provide fonnal train- contemporaneous investments in training and R&D.

ing rises. The proportion of firms that train in- Furthermore, we ask whether the productivity ef-creases from 23 to 27 percent for smaller firms, fects from the past introduction of new technologyfrom 51 to 56 percent for medium firms, and varies across firms of different size.from 71 to 73 percent for large firms.

Column one of Table 5.12 reports the estimatedsmTherfimpac on f propormtraionig firs gr ater fo production function parameters of the new tech-smarentlletrafir Ase increasproort ofa, f dirms t nology variable for the overall firm sample, andcurrently tirasin, th7icras for smand28percelle separately for small, medium and large firms. Sinceand large firms is 17.4, 9.8 and 2.8 percent the dependent variable (value added) is ex-respectively, pressed in natural logarithms, the parameter of

* The number offirms increasingfornal train- the new technology variable can be directly in-ingorinformal OJTrisesdramatically. This terpreted as the percentage change in value-is most pronounced among small firms--com- added from introducing new technology.pared to those that increased training over Controlling for firm and worker characteristics andthe past three years, the rate of expansion is 25 contemporaneous investments in worker traing andpercent for small finns, 19.5 percent for medium R&D, two conclusions are suggested by the regres-firms, and 6.5 percent for large firms.


sion results. First, for the overall sample of firms, ownership. Given the weak R&D capabilities ofthe introduction of new technology over the past most domestic firms, it may be more promising forthree years is associated with a 23 percent increase firms to acquire technology through licensing agree-in productivity levels. Second, for micro and small ments and know-how embodied in new equipmentfirms, the productivity impact of introducing new than to develop in-house indigenous technology.technology is even larger-about 50 percent. When accompanied by formal training, these exter-

nal sources of technology, but not R&D, are associ-How would mean levels of productivity in each firm ated with large productivity gains (see Chaptersize group change if a higher proportion of employ- Three).ers introduced new technology? Column three in-dicates that a one standard deviation increase in the The policy implication is that greater emphasisfraction of firms introducing new technology is as- should be placed on facilitating technology andsociated with much larger productivity improve- know-how diffusion to local firms. This may bements among small finms, about 23 percent, than the done through dissemination of information on ap-15 percent average increase in productivity among propriate technologies, expedited processing oflarger firms. technology licensing applications by MIDA, in-

centives for firms to adopt new technology andThis implies that high priority should be given to purchase new equipment, and greater links be-encouraging SMIs to adopt new technology. New tween local firms and MNCs.technology which has a direct impact on produc-tivity, as well as an indirect productivity effect from A growing number offinns have implemented qual-training, coming through increased training to ity controltechniques and introducedprecision mea-meet the skill requirements of new technology. suring instruments. This concern with quality is

concentrated amnong foreign-owned and larger firms-Findings and Policy Implications halfofthemuse SPC andprecisionmeasuringequip-

ment, as compared to about one-fifth of micro andPrivate sectorR&D investments in Malaysia are rela- siall firms; most SMIs rely on visual inspection totively low compared to other developing countries. verify accuracy. Not surprisingly, training in qualityThe MITP estimates are broadly consistent with control is more prevalentin larger firms thanin SMIs,MASTIC 's 1992 National Survey ofR&D and they and in foreign firms than in local firms. Policymak-confirm that very few SMIs engage in R&D activi- ers should devote greater efforts to raising qualityties. The number of firms doing R&D rises with consciousness among SMIs and local firms if theysize, and between 30 and 39 percent of large firms are to remain or become internationally competitive.report some R&D expenditures. Of note is the find- Interest is also growing in implementing ISO-90ing that wholly foreign-owned firms are less likely to standards, especially amongjoit ventures and localreport R&D as compared to local finns orjont-ven- fims. While their numbers are currenfy low in coi-tures of similar size. MNCs source their technology psnto MC subsides 30percentof local firmsfrom abroad, and it is unclear, given the small local and 44 percent ofjoint ventures report that they ex-scientific and engineering base, whether financial p t

incenivesalon willindue MNs to ocat R&D pect to get ISO-9000 certification within three years.This trend is encouraging since many industrialized

activities in Malaysia. country buyers are now requiring exporters to have

Other indicators of technology--licensing agree- ISO-9000 certification. Our analyses showed thateqieof automated equip- while exporting to industrialized country markets is

ments, equhoment age, and use ofautomair dsizemore difficult than exporting to developing coun-ment--show similar patterns by firm size and tries, exporting to the former is greatly facilitated for


ISO-9000 firms and to a lesser extent, for firms in eign capital were more likely to have introducedthe process of getting certification. However, ISO- new technology, especially computerization and9000 is apparently not critical for exporting to other labor saving line automation. When they in-developing countries. troduced new technology, SMIs placed greater

emphasis on new machinery which is encour-SIRIM can play a critical role in the Government's aging since many of them continue to rely onexport promotion strategy by encouraging finns to older vintage, less automated machinery.adopt ISO-9000 standards. Its reorganization, nowunderway, should allow SIRIM to respond more Introduction of new technology has an ambigu-flexibly to the dramatic growth in private sector ous impact on employment, but a clear cut im-demand for ISO-9000 certification. However, pact on raising skill requirements. Fully 79ISO-9000 certification may be beyond the finan- percent of firms reported an increase in the skillcial reach of SMIs, and interest in it remains low content of jobs, while only 15 percent reported afor the majority of SMIs. fall in skill needs. These reported changes in skill

requirements from new technology are reflectedFor SMis, consultancies in Quality Improvement in their training activities, both in terms of whetherPractices (QIPs), a lower cost alternative to wSO employers currently train, or whether they increased9000 certification. SIRIM should work with training provision over the past three years. TheseMNCs and leading companies to develop and ex- training impacts of introducing new technologytend the sectoral coverage of QIPs for SMIs. QIPs are most pronounced for SMIs, increasing the

hstablish clear-cut quality standards towards probabilities that they provide training or in-which SMIs can work to obtain certification, and creased training over the past three years by ninewhich MNCs and other anchor firms can accept to ten percent, as compared to just four to eightas an assurance of quality. percent for large firms.

The sub-sectoralfocus of QIPs makes them ame- On average, technology introduced in the recent pastnable to group provision (and funding) of assis- is associated with a 23 percent increase in produc-tance to SMIs. Once QIPs are developed, SIRIM tivity today, even taking into account investmentscan draw upon resources of other government in training and R&D. For micro and small firms,agencies and the private sector to deliver the productivity impact is even larger--about 50 per-consultancies, training and finance, and technical cent. This fact, coupled with the finding that newassistance to interested groups of SMIs. The out- technology increases training more in small firmscome is not only more quality upgrading among than in large firms, implies that the GovernmentSMIs but also development of greater supplier should give high priority to encouraging SMIs tolinkages between SMIs and both MNCs and lead- introduce new technology. Several incentives al-ing firms in the industry. ready exist that target SMIs--the Industrial Techni-

cal Assistance (ITAF) for consultancies, productThe MITP survey indicates that about 42 percent design and development, quality and productivityoffirms have introduced some kind of new tech- iinprovement, and marketing; and the Soft Loannology over the past three years. The most com- Scheme for Modernization and Automation to up-mon technologies new machinery, followed by grade SMI equipment--but their take-up thus farcomputerization and then line automation. Mir- has been limited. A more proactive approach tororing the patterns of R&D, technology licensing promoting SMI use of these incentives should beand quality control, larger firms and firms with for- explored.


Annex 5.1Introduction of New Technology and Training

This annex reports the probit results of the impact of introduction of new technology over the past three yearson two different measures of training. The probit parameters are useful for ascertaining the significance anddirection of effects of explanatory variables on the outcome of interest, but are difficult to interpret because ofthe nonlinear nature of the probit model. For ease of interpretation, we report the marginal effects thatcorrespond to the estimated probit parameters. Table A5.1 reports the marginal effects of the probit modelfor formal training, 1 if the firm provides formal training and 0 otherwise. Separate probits were estimated foreach firm size--micro and small firms with less than 100 workers, medium firms with 101-250 employees,and large firms with over 250 employees.

Table A5.1 Probability of Formal Training by Firm SizeMarginal Effects of Probit Model

IndependentVariable Small Medium Large(100 or (101-250 (over 250

less workers) workers) workers)

Mean education of the 0.028a' 0.018 0.011workforce (0.008) (0.014) (0.014)

Proportion of skilled workers 0.492 -' 0.705 a' 0.407 b/

(0.089) (0.234) (0.213)

Invests in R&D 0.086 b 0.158 PI 0.087 c,(0.037) (0.056) (0.050)

Foreign capital participation 0.128 0.034 0.019(0.034) (0.051) (0.051)

Exports 0.004 -0.001 0.001(0.027) (0.057) (0.069)

% Value of automatic machinery 0.001 PI 0.001 0.001(0.0003) (0.007) (0.001)

Use of quality control methods 0.091 b/ 0.093 c' 0.073(0.030) (0.053) (0.050)

Proportion of female workers 0.106-b -0.057 -0.009(0.051) (0.101) (0.085)

Unionization 0.060 0.049 0.026(0.044) (0.058) (0.051)

Introduced New Technology 0.089-a' 0.093-nb 0.039in the past 3 years (0.029) (0.049) (0.052)

Log (likelihood) -534.44 -320.61 -1102.68

Sample size 1197 528 448

a = Significant at 1 %b= Significant at 5%c = Significant at 10% level.Note: Numbers in parentheses are standard errors.

The model also included age of firm, multi-plant status and industry dummy variables.Table A5.2 reports the corresponding marginal effects of introduction of new technology fortheprobability of increasing training over the past three years.



Table A5.2 Probability of Increasing Level of Training Since 1992Marginal Effects of Probit Model by Firm Size

IndependentVariable Small Medium Large(100 or less (101-250 (>250workers) workers) workers)

Mean education of 0.038-' 0.022 0.004the workforce (0.008) (0.013) (0.016)

Proportion of 0.035 0.535 b' 0.641a'skilled workers (0.088) (0.222) (0.268)

Invests in R&D 0.102 -' 0.039 0.055(0.037) (0.054) (0.055)

Foreign capital participation 0.032 0.108 b -0.046(0.031) (0.049) (0.056)

Exports 0.051-' -0.091 -0.019(0.026) (0.056) (0.076)

% Value of automatic machinery 0.001 -0.001 0.001(0.001) (0.001) (0.001)

Use of quality control methods 0.002 0.040 0.045(0.028) (0.052) (0.054)

Proportion of female workers -0.037' -0.056 -0.018(0.049) (0.098) (0.095)

Unionization 0.080-2' 0.059 0.033(0.042) (0.057) (0.056)

Introduced New 0.104-a' 0.160-a' 0.082Technology in the last 3 years (0.028) (0.047) (0.056)

Log (likelihood) -528.63 -323.719 -265.90

Sample size 1197 528 448

Notes: a = Significant at 1 %b = Significant at 5%c = Significant at 10% level.Numbers in parentheses are standard errors.The model also included age of firm, multi-plant status and industry dummy variables.



Annex 5.2Introduction of New Technology and Firm-Level Productivity

A Cobb-Douglas production function was estimated to measure the impact of introduction of new technologyover the past three years on productivity. The dependent variable--value added in logarithms--was re-gressed on the logarithms of capital and labor and a vector of explanatory variables including the newtechnology indicator variable. These variables are defined in the text of Chapters Three and Five. Theproduction function model was estimated for the pooled sample of firms and the results reported below inTable A5.3. In results not reported here, production functions were also estimated separately by three firmsizes to investigate the possibility that the productivity effects vary by employer size; they are available onrequest from the authors.

Table A5.2 Impact of New Technology on ProductivityPooled Sample of All Firms

IndependentVariable Parameter Estimate(standard error)

Constant 7.283a(0.300)

log (labor) 0.621 a(0.043)

log (capital) 0.289 a(0.018)

Mean Education 0.051aof the Workforce (0.016)

Proportion of 0.337aFemale Labor (0.126)

% Skilled Worker 0.173cTraining -- current (0.101)

% Unskilled Worker -0.052Training -- current (0.120)

Exports -- current 0.069(0.061)

Invests in R&D -- current -0.113(0.070)

Technology licensing 0.103agreement -- current (0.089)

Introduced new technology 0.229bin the past three years (0.098)

a = Significant at 1%,b = Significant at 5%,c= Significant at 10%.Note: Cobb-Douglas production function, dependent variable= log

(value added).

CHAPTER Six: FIRM EFCIENCY AND ITS DISTRIBUTION

Previous chapters looked first at training and its im- Another high priority area is improving the techno-pact on finn-level productivity, and then at the tech- logical capabilities of domestic firmns, both large andnology of firms and associated skill requirements. In small. While the manufacturing sector has demon-this chapter, we bring these two strands of analyses strated impressive growth both in the production andtogether to study their joint effects on technical effi- exports of technologically sophisticated products,ciency of firms in the Malaysian manufacturing much of this has been driven by the MNCs. Localsector. firms lag behind MNCs in their use of modem equip-

ment, in the training provided to workers, and inFirm-level efficiency-howffarafirmis from bestprac- their capabilities to undertake R&D and developtice technology-is a key measure of how its competi- indigenous technologies.tiveness compares to the most efficient firms in theeconomy, many of which are world-class MNC sub- To address these perceived weaknesses in the do-sidiaries. Our focus is on comparing and explaining mestic sector, policymakers have introduced a widethe relative levels of efficiency in firms by size and range of policies. These include incentives for R&D,by local and foreign ownership-two dimensions along building allowances, and tax exemptions; funds towhich pronounced differences in training and tech- promote public sector R&D, venture capital; match-nological capabilities are found-and on drawing out ing grants for product design, quality improvementtheir implications for policymakers. and market development under the Industrial Tech-

nical Assistance Fund; HRDF to promote upskilling;A high priority concern of policymakers is improv- development of high-tech parks; and measures toing the competitiveness of SMIs who make up the foster linkages between SMIs and larger firms andmajority of establishments in Malaysian manufactur- between local firms and MNCs (Malaysia, 1994).ing. In 1989, out of a total of 28,335 manufacturingestablishments, SMIs accounted for 26,238 or 92.6 Analyses of firms' productive efficiency can pro-percent. However, SMIs contributed only 19.6 per- vide insights into both policy concerns. We will es-cent of the manufacturing sector's total value added, timate frontier production functions to help identifyand 40.2 percent of its employment (MITI, 1994). the most important technological and workforce fac-Policymakers project SMIs will contribute 40 per- tors that are associated with higher firm-level effi-cent of manufacturing value added and 50 percent ciency. This methodology yields a firm-specificof employment by the year 2000, a goal which will index of efficiency which we will use to compare therequire significant upgrading of SMI productivity. efficiency levels of different groups of firms.

To this end, policies have been initiated to improve We will use this index to examine the distributions ofSMI access to finance, provide them with incentives efficiency within each firm size category, and iden-to train workers, give them technical assistance, fa- tify the attributes of highly efficient firms which lesscilitate their links to larger firms, and extend to them efficient firms may emulate to improve their own ef-marketing and export promotion services. I Hith- ficiency levels. We will estimate separate frontiererto fragmented responsibilities for SMIs have also production functions for firms varying by owner-been recently consolidated into one single agency- ship, to gain insights into the efficiency differencesthe Small and Medium Industry Development Cor- between foreign and local firms, and whether theporation (SMIDEC-to coordinate support for SMIs efficiency levels of domestic firms are improved byand to spearhead SMI development. the presence of foreign firms.

FIRM EFFICIENCY AND DISTRIBUTION 87

Measuring Technical Efficiency Equation (2) relates firm-level inefficiency to a setof attributes that reflect the firm's technological and

The methodology we adopt to analyze firm-level workforce capabilities. Because it is more conve-efficiency is the stochastic frontier production func- nient to think about firm efficiency rather than ineffi-tion. See Annex 6.1. The production frontier is the ciency, we will reverse signs and discuss thesetheoretical maximum output that can be achieved explanatory variables and their hypothesized effectsusing every possible combination of inputs. As such, on firm-level efficiency:the frontier can be thought to represent "best prac-tice" technology. In practice, many firms operate Technological Capabilities. Employers can ac-inside that frontier because of inefficiency. For given quire technological capabilities and attain higher ef-input levels, this level of inefficiency (the existing ficiency levels in several ways: (i) investments inoutput) can be measured relative to the theoretical in-house R&D or in technology or know-how li-maximum output, so thata value of 1 represents best censing agreements from others; (ii) contacts withpractice technology and values between 0 and 1 foreign firms, either through exporting relation-measure how far finns' efficiency levels are from ships with buyers or by setting up joint-venturesbest practice. with foreign partners; and (iii) production expe-

rience.The model is made up of two equations, a frontierproduction function equation and an equation relat- R&D Investments. In developing countries whereing firm-level inefficiency to a set of firm attributes. enterprise capabilities in basic research are oftenSimilar models have been used in the literature to limited, indigenous R&D efforts are usually orientedestimate firn-level efficiency and investigate its cor- towards reverse engineering and modification ofrelates. Examples include Pitt and Lee (1981) on existing product and process technologies. Whileweaving firms in Indonesia; Little, Mazumdar and R&D is associated with higher firm efficiency in in-Page (1987) on five industrial sectors in India; and dustrialized countries, the evidence for developingCortes, Berry and Ishaq (1987) on metal working economies is mixed.and food firms in Colombia. Our empirical approachdiffers from the other studies.2 We use maximum Know-how licenses. Many firms may not have thelikelihood methods tojointly estimate the stochastic capabilities to do their own R&D. For these firms,frontier production function and the model relating technology and know-how agreements with bothfinm-level inefficiency to the explanatory variables. foreign and innovative domestic firms can be a sub-

stitute for own R&D investments to develop indig-Model Specirication enous technologies.Equation (1) is specified as a two-factor Cobb-Dou-glas production function. The dependent vari- Exporting. International contacts offer exportingable is the logarithm of value added, calculated as firms opportunities for acquiring new technology andthe difference between the firm's value of output improving their technical capabilities. Foreign buy-and the sum of its expenses on raw materials, en- ers play a critical role in this technology transmis-ergy and electricity. The two factors of produc- sion-by providing firms with crucial informationtion-capital and labor-are expressed in logarithms, relating to product specifications and, in many cases,with labor measured by total employment and capi- offering free technical assistance.tal by the value of net assets. Other explanatoryvariables include industry indicator dummy vari- Foreign capitalparticipation. Foreignjoint-ventureables to control for industry effects and the firm's partners can bring new technology not availablerate of capacity utilization, a measure of how fully domestically. This technology transfer, and the ac-the two inputs are used. companying management expertise and training to


use the technology effectively, can lead to signifi- Determinants of Firm-Level Efficiencycant improvements in firm efficiency. In estimating this two-equation model, we assume

initially that there is a common production frontierAge of the Firm. Start-up firms often go through an for the whole manufacturing sector and that cor-initial period of developing expertise in production, relates of inefficiency are invariant across firmmanagement, and marketing. If these learning-by- size and ownership, and control for sectoral differ-doing effects are important, older firms with longer ences in technology level by using industryproduction experience are likely to be more effi- dummy variables.cient than their younger counterparts.

Table 6.1 reports the coefficient estimates of the fron-Workforce Capabilities. A firm's efficiency is tier production frontier (top panel) and the ineffi-also dependent upon production know-how and ciency equation (lower panel). The estimated laborexperience embodied in the human capital of its and capital coefficients are positive and statisticallyemployees. The productivity effects of human significant, and they correspond roughly to factorcapital-reflected in the mean education of employ- shares of capital and labor in the economy. To giveees, the formal training provided to skilled and the results a more intuitive interpretation, signs ofunskilled workers, and the use of female work- the inefficiency model are reversed so that explana-ers-have been previously studied, but it is useful tory variables relate to efficiency rather than to inef-to summarize their hypothesized effects on effi- ficiency.ciency again.

Of all the measures of the finn's technological capa-Education of employees. Educated workers are not bilities, only exporting status and firm age are associ-only more productive in performing given tasks, but ated with statistically higher levels of efficiency. Itthey are more adept at critically evaluating new in- appears that there are strong efficiency-enhancingformation and learning from it. Firms with a more benefits from operating in export markets, possiblyeducated workforce are likely to be more efficient because of exposure to information about new prod-because of their greater capability to absorb and ucts and technologies, and interactions with foreigneffectively utilize new technology. buyers. This export-efficiency relationship also re-

flects the presence of highly efficient MNCs pro-Worker Training. Like education, training provides ducing for export markets. Firm age has a positiveworkers with the skills to perform a wide variety of and significant effect, which is consistent with effi-tasks, to improve quality, and to upgrade job skills as ciency gains from longer production experience andnew technologies are introduced. Training plays a learning-by-doing. Holding constant the effects ofkey role in adaptation and modification of new tech- exports and age, none of the proxy measures ofnology, without which its superior productivity lev- technology are significantly associated with higherels over the older technologies that it replaces are firm efficiency.seldom realized.

There are two ways to interpret these weak tech-

Use offemale workers. Use of large numbers of nology results. First, and most obvious, they mayfemale workers may reflect formns of work organiza- reflect the weak technological capabilities of mosttion built around simple assembly, manual dexterity, local firms to conduct R&D or to effectively useseasonal work, and low pay. Efficiency levels are new technologies acquired through licensing andlikely to be low in such organizations where low know-how agreements, a point that is widely rec-skills and high job turnover inhibit learning and the ognized by policymakers. Second, the resultsretention of production know-how within the firm. may be driven by different technology strategies of


Table 6.1 Stochastic Frontier Production Function local and foreign firms. As noted in Chapter Five,Estimates highly-efficient foreign firms tend to do little R&D in

IndependentVariables Estimate Malaysia, in large part because R&D is conducted(standard error) elsewhere by their MNC parents. In contrast, local

Frontier Production Function firms who tend to be less-efficient must get new tech-Constant 6. 383594 a nology from licensing agreements or by relying on

(0.339) ttheir own in-house R&D. The differing technologyLog (labor) 0.681 a strategies can introduce a negative correlation be-

tween R&D and licensing on one hand, and firm-Log (capital) 0.319 alevel efficiency on the other.

(0.021)

Efficiency Equation In a subsequent section, we account for the poten-

(0.211) tially confounding effects of foreign ownership by

Invest in R&D -0.206 estimating separate production frontiers for domes-(0.118) tic firms, joint ventures, and firms with 100 percent

Has technology license(s) 0.089 foreign ownership. These analyses reveal the pres-(0.136) ence of efficiency gains from technology licensing

for domestic firms and from R&D forjoint ventures.Foreign ownership 0.002

Consistent with Chapter Three, workforce charac-Exports 0.187 b teristics are important determinants of firm-level effi-

ciency. The mean level of education of employeesAge of firm 0.01°1 a is positively related to efficiency, indicating that more

educated workers are better at learning and re-Education of workforce (0.020)a sponding to new information. Only training provided

to skilled workers-including supervisors, technicians,Skilled worker training 0.414 b and skilled production workers-is significantly cor-

(0.211) related with higher efficiency; training of unskilledUnskilled workertraining -0.105 production workers does not appear to have any

(0.226) impact on firm efficiency. This result parallels ear-

Proportion of female labor -0.5155 her results showing differential productivity and wage

effects of training for skilled and unskilled workers.s2, 0.969 a Finally, the intensive use of female workers tends to

(0.019) be associated with lower firm efficiency, possibly9 0.014 because of the low-skill nature of high-volume as-

(0.055) sembly operations.Mean efficiency 0.74

x2 37.79 Overall, these results have two important implica-

Log likelihood -2124.096 tions for policymakers. First, when technologicalcapabilities are limited, as is true for many local firms

Significant at 1% in Malaysia, exports and links with foreign buyersb: Significant at 5%,c Significant at 10%. and foreign firms may be a more important source ofNote: Industry dummy variables included. technology than investments in own in-house R&D

Numbers in parentheses are standard errors to develop indigenous technologies.3 Second, ir-

Source: 1995 MITP Survey respective of where new technology is acquired,

employer efforts to assimilate and effectively use


new technology require an educated, well-trained First, consider the broad results suggested by theworkforce. Their investments in training, especially production frontier estimates for Malaysia and sev-of skilled workers, are associated with gains both in eral other developing economies as reported inproductivity and in firm-level efficiency. Table 6.2. The mean efficiency level estimated for

all Malaysian firms is 0.74 in 1994, a figure that has

Distribution of Efficiency by beenadjusted to accommodate forthe over-sarnplingFirm Size of large firms inthe MITP sample. This estimate is

close to levels estimated for Taiwan in 1986 (0.76),The frontier production function approach yields and is higher than the efficiency levels estimated forestimates of a firm-specific efficiency index that Mexico (0.59), Colombia (0.55) and Indonesia (0.39)ranges from 0 to 1. Firms with an index of 1 operate in 1992. If the production frontier in all five econo-at the production frontier "best-practice technology" mies is defined, to a large extent, by the operationswhile those firms with a value between 0 and 1, for of MNCs using international "best-practice" tech-example, 0.5, are producing at 50 percent of best- nologies in each economy, then these figures pro-practice. vide a measure of the relative ranking of the overall

manufacturing efficiency of the five economies.In this section, we use this unit-free index to in- Some support for this interpretation is found in thevestigate the distributions of efficiencies in firms broadly similar rankings of economies by efficiencyof different sizes, and to address several key levels andbyper capital income.4

policy issues: how competitive are SMIs, whatdetermines how efficient they are, and are there Second, Table 6.2 indicates that SMIs in Malaysiaattributes of highly efficient firms that can be emu- are on average less efficient than their larger coun-lated by less efficient firms, SMIs in particular, to terparts. Defining micro, small, medium and largeimprove their efficiency levels? For purposes of firms as those with less than 16, 16-100, 101-250,comparison, we draw upon similar studies con- and over 250 employees, the mean efficiency lev-ducted for Indonesia, Mexico, Colombia, and els by firmsize are 0.72,0.74,0.79 and 0.84 for micro,Taiwan, China (Tan and Batra, 1995). We stress small, medium and large firms, respectively. Thisthat these analyses pertain to conditions prevail- result is not unique to Malaysia, but is found in all theing at different points in time- 1992 for Indonesia, other economies considered. Sample means, how-Mexico, and Colombia, and 1986 for Taiwan, ever, canbe deceptive. For policymakers, the moreChina-and were determined by data availability. important issue is whether SMIs are all uniformly

Table 6.2 Distribution of Efficiency by Firm Size and Economy

Colombia Indonesia Malaysia Mexico TaiwanMean Efficiency (1992) (1992) (1994) (1992) (1986)(weighted)All firms 0.55 0.39 0.74 0.59 0.76

Micro firms 0.53 - 0.72 0.46 0.74Small firms 0.54 0.36 0.74 0.58 0.77Medium firms 0.55 0.35 0.79 0.61 0.81Large firms 0.54 0.43 0.84 0.61 0.82

Notes: Micro = less than 16 workers; Small = 16-100 workers,Medium = 101 -250 workers; Large = over 250 workers.

Source: Tan and Batra (1995), Chapter 4


inefficient, in which case there is little possibility Three striking results emerge from a close examina-that SMIs can play the vastly expanded role planned tion of the distribution of efficient firms in Figure 6.1.for them to the year 2000, or whether micro and small First, in Malaysia as well as in the other economies,firms exist that are either efficient or with efficiency there is considerable variance in firm-level efficiencylevels can be raised, and that have potential for grow- in every size category. This suggests that micro anding into larger firms. small enterprises are not inherently inefficient. On

average, across economies, at least 35 percent ofEfficiency within Size Groups firms in these micro and small firm size categoriesTo address this question, we classify firms in each are classified as being efficient. Their lower aver-size group as "efficient" and "inefficient" relative to age efficiency noted earlier is attributable to the factthe overall mean efficiency estimated for the that there are fewer efficient firms in these sizeeconomy. Figure 6. 1 graphs the percent of effi- groups, and not to uniformly lower efficiency levelscient firms in each size group for Malaysia and inall finms.for each of the other economies. Note that thepercent of efficient firms summed across all size This point is illustrated in Figure 6.2 using the Malay-categories can be more or less than 50 percent sian case. The figure shows that 41 percent of microbecause firm efficiency is being compared to the firms have efficiency levels that place them in themean, rather than the median. Means and medi- first (bottom) quartle of efficiencyintheMlTP sampleans can differ greatly depending upon the shape and only nine percent in the fourth (top) quartile ofof the efficiency distribution, a fact that we exploit efficiency. Among large firms, in contrast, the cor-in comparing efficiencies both across size catego- responding figures are reversed with 14 percentries and across economies. and 42 percent in the first and fourth quartiles, re-

Figure 6.1 Distribution of Efficiency by Economy

Percent of

efficient firms

90

80

70

60

50

40

30 V

Colombia Indonesia Malaysia Mexico Taiwan

* Micro * Small n Medium E LargeSource: 1995 MITP Survey


spectively. If many SMIs are inefficient because the large-size category, with most micro and smallthey invest little in worker skills or technology, as is enterprises being of below average efficiency. Thus,the case in Malaysia, these results suggest potential while Malaysian development policies (includingfor upgrading their efficiency levels through train- special tax incentives) to attract large multinationalsing and technical assistance directed at SMIs. appear to have generated growth, they have done

relatively little to improve the efficiency of domesticSecond, comparing economies, there is consider- SMIs.able variation in the proportion of efficient firms ineach size category. In Malaysia and Mexico, about To summarize, these cross-national comparisonsa third of micro and small firms are classified as being highlight two points. First, they reiterate the needefficient. In contrast, almost half are in Colombia and for Malaysian policymakers to place greater empha-Indonesia, and three-fifths in Taiwan. At the other sis on improving the efficiency of SMIs. This is criti-end of the size scale, the proportion of efficient large cal if SMIs are to play a greater role, in the Seventhfirms is approximately 60 percent in Colombia and Malaysia Plan and thereafter, in contributing to manu-Indonesia, 70 percent in Malaysia and Mexico, and facturing value added, employment and exports.88 percent in Taiwan, China. Second, the presence of highly efficient SMIs sug-

gests that policies which target SMIs can be effectiveThese efficiency distributions by size, and their in upgrading the productivity and competitiveness ofvariation across economies, suggest somewhat dif- SMIs, provided they are properly designed andferent areas of focus for policymakers in each place. delivered. Design of these policies should reflectIn Colombia and Indonesia, the proportion of effi- an understanding of the many interrelated constraintscient firms is relatively low in all size groups, suggest- that SMIs face-in training and technology.ingproblemsendemictoall fims. Assuch, broad-basedpolicies to improve overall industrial productivity A Profile of Efficient Firms by Sizeshould be the focus in these two economies.

In Malaysia as well as in Mexico, there are large We turn now to a discussion of some correlates ofsize differennals in the share of efficient firms This firm-level efficiency to highlight the critical elementsfinding suggests that gove fments should focus on in an integrated policy approach to SMI assistance.

improving productivity at the smaller end of the en- We compare the attributes of fms with efficiencyterprise scale. The Malaysian Government has al-ready recognized the importance of assisting SMIs Figure 6.2 Malaysia-Distribution of Efficiencyto upgrade their competitiveness. by Firm Size

Third, the high overall levels of efficiency were at-tained very differently in Taiwan, China (sample ---------- 50

mean of 0.76 in 1986) and Malaysia (sample mean of 0

0.74 in 1994). In Taiwan, there is a high proportion(60 percent and above) of efficient firms in all size l0groups. In addition to higher levels of education 0and strong external links to buyers and suppliers, s medi.m

small and medium enterprises in Taiwan benefited a micro

from publicly supported R&D and technology ex- 2 a

tension services directed to SMIs. In contrast, N_X

Malaysia's overall efficiency level is driven by the Source: 1995 MITP Surveyrelatively high proportion of highly efficient firms in


levels above the MITP sample mean ("efficient") * whether the firm conducts R&D in-house;with those of firms having below average efficiency * whether the firm has technology or know-how("inefficient"). Three broad groups of attributes are licensing agreements;considered-technology, workforce skills, and orga- * whether it exports;nizational factors. * whether it has any foreign capital participation.

These analyses are presented graphically using bar It was noted earlier that in-house R&D is less impor-chart-efficient firms denoted by dark bars and inef- tant than having technology or know-how licensesficient firms by light bars. Each comparison, done in discriminating between efficient and inefficientseparately by firm size, typically involves asking firms. Panels A and B in Figure 6.3 lend support towhether a particular attribute is more or less likely to this finding. With the exception of micro firms, effi-be found among the efficient firmns as compared to cient firms of all sizes are actually less likely tothe inefficient firms so as to help characterize the report doing R&D as compared to inefficienthighly efficientfirms. Furthermore, the importance firms. In Panel B, this relationship is reversedof that specific attribute to each firm size can be de- and efficient firms in all size categories are moreterminedby asking how well itdiscriminates between likely to have technology or know-how licensesefficient and inefficient firms in that size category than inefficient firms. This result is consistent withversus other firm size categories. the view that technology transfers through licens-

ing agreements is a more important new technol-TechnologyFactors Figure6.3 showsthechar- ogy source than own R&D when firms'acteristics of efficient and inefficient firms as mea- technological capabilities are weak (see Tan andsured broadly by their sources of technology: Batra, 1995).

Figure 6.3 Technology Attributes of Efficient and Inefficient Firms

A. Do R&D B. Technology Licenses

60 30

50 25

40 2-30 15

20 10

10 5

0

E E r E! fi E E,E I 0~~~~~~~~~~~~~ E 0 ~~~0 'E

E

C. Exports D. Foreign-owned

90 7050 60 170I50 50So 40 _-.40 30 +30 20 j _ *

20~~~~~~~~~~~~~~~~2

0 0 0

ES E 1



Figure 6.4 Training Attributes of Efficient and Inefficient Firms

A. Do R&D B.Technology Licenses

60 - 3050 25

40 20

30 15

20 10

100~~~~~~~~~~~~~~~~~~~~~

e2E E e -o E E u,

_1 66~~~~~~~~~~~~~~~~~~~~~

C. Exports D. Foreign-owned

90 7080 ~~~~~~~~~~~~~~6060 ~~~~~~~~~~~~~~5050 ~~~~~~~~~~~~~~4040 ~~~~~~~~~~~~~~30

2 E 27<E


Panels C and D indicate that exports and foreign Panels A and B contrast the degree of formality ofcapital participation are important characteristics two polar strategies-informal training on-the-jobof efficient firms, especially micro, small and me- from supervisors or co-workers, or structured for-dium size firms. For these fmns, contacts with formal training within a needs-based training plan. Ineign buyers and foreign partners through general, SMI employers tend to provide only infor-exporting and FDI are potentially important means mal on-the-job training as compared to large firms;for firms to acquire technology and know-how, however, within each size category efficient firmsand to improve efficiency. However, in the larg- are much less likely to rely exclusively on infor-est firm size category, efficient and inefficient mal training than inefficient firms.firms are not distinguishable on the basis ofwhether a firm exports. In contrast, larger firms are more likely to have de-

veloped a training plan than smaller firms (no microWorkforce Skills Figure 6.4 compares efficient firms reported having a training plan), and in eachand inefficient firms along several dimensions of size category, efficient firms are always more likelyemployers' training strategy: to have a training plan. Thus, there appears to be

payoffs to conscious efforts by employers to identify* whether the firm provides only informal train- their critical skill needs and develop a structured

ing to its employees; training plan to address these skill shortfalls.* whether it has a developed training plan;* the proportion of skilled workers getting formal Such efforts are supported by the HRDF through

structured training; regional seminars on training needs analysis (TNAs)* the proportion of unskilled workers getting for- and support for the development costs of training

mal training. plans under the JURUPLAN scheme. HRDC has


Figure 6.5 Quality Control in Efficient and Inefficient Firms

A. Statistical Process Control B. Quality Control Circle

60 60501- 50+40+ 40+301 30

Z0 oE 13o 0 _ aE 1 1micro s mall medium large micro small medium large

C. Precision M easuring Equipm ent 0. ISO 9000

60- 3550 3040 2530 1

2 L0 20 { __ _

micro small medium large micro small medium large


also recently introduced new schemes to promote local firms-from reliance on informal OJT to moregroup training for SMIs, and these new incentives formal, structured training programs. Both HRDCshould be coordinated with SMIDEC and other pub- and other agencies with SMI responsibilities shouldlic agencies to promote more group or joint training play a greater role in disseminating this information.among SMls.

Organizational Factors Employer decisions

Panels C and D show the percent of workers re- about technology and skills development are deter-ceiving formal training in skilled and unskilled occu- mined by, and in turn shape, the organization of pro-pations, respectively. They make two points. First, duction and work. Organizational modes can varynot only are efficient firms significantly more likely markedly by size, and have different efficiency im-to have a training plan than inefficient firms, but they plications for small and large firms.also tend to provide formal training to a much higherproportion of their workforce. Second, compared Figure 6.5 show four variables which reflect the firm'sto inefficient firms, efficient finns are always associ- concern with product and process quality, including:ated with greater intensity of training for both skilledand unskilled workers. Both skilled and unskilled * whether it relies on statistical process control;workers play a critical role in the adoption and as- * whether it has quality control circles;similation of new technology, and this last point high- * whether it verifies accuracy by using precisionlights the importance of not neglecting the training wemeasurnng instruments;of unskilled workers. *whether the firm has received ISO0-9000

certification.

The HRDF provides the framework for promoting Panels A and B indicate that, with the exception ofchange in the training approach of most SMIs and micro firms, efficient firms are slightly more likely


than less efficient firms to use statistical process con- * whether the firm's compensation package in-trol (SPC) and quality circles (QC) to monitor quality. cludes profit-sharing or bonuses.Panel C highlights the importance of accuracy inproduction-in all firmn size categories, efficient firms First, consider quit rates in efficient and inefficientare significantly more likely to verify production ac- firms. Panel A shows that while quit rates increasecuracy using precision measuring instruments rather with firm size for both groups of firms, they are in-than through visual inspection. Finally, Panel D variably lowerinefficientflrmsespeciallyinthelarg-shows that ISO-9000 certification is associated with est firm size category. As noted in Chapter Three,higher efficiency, since it signals the firm' s high level high rates of job turnover can have a detrimentalof quality and commitment to ongoing improvement. effect on incentives to provide training since quits

prevent employers from recouping their investmentsIn Figure 6.6, we turn to several measures of in worker skills. As such, HRM practices-compen-workforce organization or human resource manage- sation policies that reduce quits, facilitate training,ment (HRM) that affectjob retention and employee and provide employees with incentives to use theirincentives, and thus ultimately, firm-level efficiency. new skills and share their accumulated knowledgeThese include: with co-workers-can have a powerful impact on firm

efficiency.• the firm's annual quit rate, a variable defined

previously in chapter three; Panel B compares the starting monthly pay of the- the stardng monthly pay of a typical, newly-hired typical production worker in efficient and inefficient

production worker; firms. In all size categories, efficient finns appear towhether the firm has a severance pay scheme; pay a mean wage premium of RM 30-40 over startiing

Figure 6.6 Quits and Compensation in Efficient and Inefficient Firms

A. Quit Rates B. Starting Monthly Pay (RM)60 - 3680-

50~ 360 -40 340-

20 320-

10 300

micro small medium large micro small medium large

C. Severance Pay System D. Have Profit-SharinglBonus

70 100 60 805040 -- 6030 40

20 20

micro small medium large small medium large



pay in inefficient firms. Except for micro firms, the employees a stake in the higher productivity thatpremiumpersists as the typical production worker gains results. Such compensation policies are already inexperience. In graphs not shown here, the premium use among larger employers and MNCs, and theireither remains the same or gets larger by the tenth experiences with these HRM practices should beyear on thejob, to the RM 100-140 range in small and widely disseminated to SMIs and local firms with poormedium size firms. This high-wage policy allows em- personnel practices.ployers to attract and retain higher quality produc-tion workers; it is sustainable because of their higher Technology and Training in the Last Three Yearsoverall productivity, including the large productivity Finally, we relate current efficiency levels to pastgains from training reported onealier. investments in technology and training. Figure 6.7

shows the percentage of efficient and inefficientFinally, Panels C and D show two components of firms that:compensation that are thought to have incentive ef-fects for workers. Severance provides workers with * introduced new technology (including comput-a lump-sum tied to years of seniority at the time of erization, line automation, and new productionseparation from the firm. To the extent that it re- machinery) in the past three years;wards long seniority, it can enhance job attachment * increased provision of training over the lastand encourage skill acquisition. Panel C shows that three years;severance pay schemes are more common in effi- * neither invested in new technology nor in-cient firms. Especially in the small and medium size creased training;category. * both introduced new technology and increased

training.A similar pattern is found in Panel D, with efficientfirms being more likely than inefficient ones to have Panels A and B in Figure 6.7 reveal a strong rela-profit-sharing and bonuses to reward effort and give tionship between size and the introduction of new

Figure 6.7 Technology and Training in Past 3 Years

A. New Technology Last 3 Yrs B. Increase Training Last 3 Yrs

B0 70

340

m icro sm all m edium large m icro sm all m edium large

C. Nelther Tech Nor TraIning D. Both New Tech & TraIning

8 0~~~~~~~~~~~~6

2 0 04 D 240 |1iIh0 1 0 li

m icro sm all m edium large m ico sm all m edium large

Source: 1995MITP Survey


technology or increased training provision over the These findings, together with those from previouspast three years. Within each size category, effi- chapters, suggest the following two implications forcient firms were more likely to have done so than the design of SMI policies.inefficient firms. Panels C and D, which combineresponses from both questions, are mirror images of First, they indicate that most SMIs face multiple con-each other. Firms that neither invested in new tech- straints, and the way that these constraints interactnology nor increased training are primarily micro implies that no single policy alone-focusing just onand small firms, while those that did both are found training or just on technology-is likely to be effec-mainly among larger firms. In each size category, tive. To be effective, policymakers must deliver anefficient firms were less likely to have done neither, integrated package of incentive and services spe-and more likely to have done both, as compared to cifically designed for SMIs covering consulting,inefficient firmns. training, technology upgrading, finance, quality con-

trol, and personnel management. Responsibility forThese trends are consistent with the results reported delivering services to SMIs is currently spread overin Chapter Five that introduction of new technology multiple agencies, with little coordination or inforna-has consequences for rising skill requirements, in- tion sharing among them despite their targeting, orcreased training, and higher productivity. often even serving, the same firmns.

Second, to the extent that poor access to informationSummary' and employer resistance to change are important

The results indicate that highly efficient firms, both constraints for SMIs, responsible public or private

large and small, have specific technological, work sector agencies must workproactively with SMIs tofetcan in expose them to the benefits of change, and create

fricie and organizationayless charcteristfic t demand for support services. The current policyprinciple be emulated by less efficient firms. approach-providing SMIs with incentives and leav-

• They do not necessarily invest in R&D, but in- ing the responsibility for take-up with them-ignoresstead tend to acquire technology through licens- these informational and cultural impediments toing and know-how agreements, others through change, and the result has been low take-up of mostforeign capital participation and exports. incentives targeting SMIs.

* They have structured training programs that A rethinking of the whole approach to SMI supportreach a greater number of their employees, both is required, and one part of a proactive strategyskilled and unskilled. should be an initial diagnostic of constraints facing

SMIs, so that a tailored program of assistance can be

* They have modes of work organization that em- developed and delivered. Mexico's CIMO programphasize quality control, including statistical pro- is an example of a proactive strategy of deliveringcess control, quality control circles, and use of integrated training and technical assistance to SMIsprecision measuring equipment. that has proved to be cost-effective. See Box 6. 1.

* They have HRM practices that provide incen- A first step in this direction has been taken with thetives for job retention, skill acquisition and creation of the Small and Medium Scale Develop-greater worker effort. ment Corporation (SMIDEC) within Mm to coordi-

nate planning and delivery of SMI services by other* And they are more likely to have introduced government agencies. SMIDEC should undertake

both new technologies and increased training a careful review of existing SMI incentives to deter-in the recent past. mine why their take-up is so low, and how existing


incentives and services can be delivered more ef- all the attributes of 100% foreign firms? We do thisfectively. Such a review should study the potential by assigning local firms and JVs the mean values offor increased coordination of both training and tech- attributes (from the efficiency model) of the 100 %nology support services from implementing agen- foreign firms, and then scaling the efficiency levelscies such as HRDF, SIRIM, NPC, MOSTE andMiTI. of local firms and JVs to that of the numeraire group.It should also consider the feasibility of implement-ing a mechanism for active promotion and delivery Table 6.3 reports the estimated parameters of theof integrated services to hard-to-reach SMIs, one production frontier and efficiency model. The re-that will require devolution of service delivery to sults of the efficiency models for each ownershipthe state or local areas, and the creation or expan- group are of particular interest, and they make foursion of existing regional institutions. This would be principal points. First, regarding technology invest-consistent with Mm's recent adoption of the indus- ments, they clearly indicate that R&D and efficiencytrial cluster approach to development. are significantly related among JVs, but not local or

100% foreign firms. Technology licensing, how-

Ownership, Efficiency Differences and ever, is weakly associated with improved efficiencyFDI Spillovers fordomestic firms. Second, exporting-an informal

source of foreign technology and know-how-is im-

Domestic and foreign firms have different profiles portant for efficiency only among JVs, but again notof the attributes associated with efficiency. Chapter among local finms or 100% foreign firms. Third, theFive showed that domestic firms were less likely to intensive use of female workers is associated withdo R&D, have technology licenses, provide worker lower efficiency in local firms and JVs, presumablytraining, emphasize quality control and have ISO- because of the low-skill nature of their assembly op-9000 certification, and export than eitherjoint-ven- erations, but not in 100% foreign firms. Finally, atures or wholly foreign-owned firms. Since all are more educated work force and the provision of for-attibutes associated with highly efficient firms, it raises mal training have statistically significant and positivetwo questions-how far do local firms lag behind, firms effects on efficiency levels of JVs and 100 % for-with foreign capital participation in their efficiency eign firms, but not local firms. This last result mirrorsand what can be done to improve their efficiency the production function results on training in Chap-levels? ter Three.

Efficiency Differences How efficient would local firms be if they wereWe examine differences between local firms, joint- more like foreign firms? As noted earlier in Chap-ventures (JVs), and wholly foreign-owned firms ter Five, domestic fimns lag behind MNCs in R&D(100 % foreign) by estimating separate frontier pro- investments and technology licensing, in workerduction functions for each of the three ownership training, and in export orientation. Adjusting forcategories. First, we are interested in whether the differences in mean efficiency attributes of thesecorrelates of efficiency are the same in each owner- three groups of firms, we find that foreign firms areship category. In the preceding analyses, estimated more efficient than domestic firms and, among thisparameters of the efficiency model were constrained fonmer group, fimns with 100 percent foreign owner-to be the same for all firms, irrespective of ownership are the most efficient. I The mean efficiencyship. Second, we want to compare overall efficiency levels of domestic firms, joint-ventures, and whollylevels of the three groups, controlling for mean foreign-owned finns are 0.74,0.76 and 0.81, respec-group differences in these efficiency attributes. This tively. These estimates suggest that even if do-allows us to ask the counter-factual question: how mestic firms had the same attributes as the whollyefficient would local firms or JVs be if they adopted foreign-owned firms-intenmsofexportstatus, R&D,


Box 6.1 Mexico's Pro-active Approach to SMI Support

The Mexican Secretariat of Labor has developed a pro-active approach to SMI support-the CIMOprogram-that has proved to be effective in reaching and assisting SMIs upgrade worker skills, improvequality, and raise productivity. CIMO, initiated in 1988 as a pilot project to provide subsidized training toSM Is, quickly evolved when it became apparent that lack of training was only one of many inter-relatedfactors which contribute to low productivity among SMIs. By 1994, it had expanded the scope of itscoverage and was providing an integrated package of training and industrial extension services to over23 thousand SMls per annum and training to 150 thousand of their employees. Since 1988, CIMO hasassisted a cumulative total of over 70 thousand SMIs and trained a quarter million workers. Privatesector interest has also grown, and over 300 business associations now participate in CIMO, up from72 in 1988.

The CIMO program is operated by 49 CIMO units in most major urban to be close to their targetpopulation. All states and the Federal District of Mexico have at least one CIMO unit, each staffed by3-4 promoters, and most units are housed in business associations which contribute office and supportinfrastructure. These CIMO promoters organize workshops to disseminate information about trainingand technical assistance services, identify potential local and regional training suppliers and consultingagents forthe program, but most importantly, they actively seek out the SMIs to deliver assistance ona cost-sharing basis. They work with interested SMIs to conduct an initial diagnostic of the firm, whichforms the basis on which training programs and other consulting assistance are recommended anddelivered. CIMO is currently expanding SMI support in two directions-assisting groups of SMIs (acluster approach) according to their specific sub-sectoral needs, and providing an integrated packageof services including information on technology, new production processes, quality control techniques,and marketing as well as subsidized training.

An evaluation study showed that CIMO is a cost-effective way of assisting SMIs. The study trackedtwo groups of SMIs over three years, one that participated in CIMO in 1991 or 1992, another a broadlycomparable control group of enterprises that were not related to the CIMO program. While CIMOfirms tended to have lower performance indicators than the control group prior to participation in theprogram, by 1993 their levels of labor productivity had either caught up or exceeded those of thecontrol group. Other performance indicators showed similar improvements-increased profitability,sales, capacity utilization rates, and wage and employment growth; and reduced rejection rates forproducts, laborturnover, and days of absenteeism. The most dramatic impacts of the CIMO interventionswere among micro and small size firms and, to a lesser extent, among medium-size firms.

Source: STPS, La Capacitacion yAsistencia Tecnica en la Micro, Pequena y MedianaEmpresa: Evaluacion de/programa CIMO, Mexico, 1995.

technology licenses, workforce education, train- firms have more of the measurable attributes noting, and reliance on female workers-they would included in the efficiency model-including qual-still only be 8.6 percent (i.e. 1 -0.74 / 0.8 1) less effi- ity control methods, training systems, intensity ofcient than the wholly foreign-owned finns, andjoint- training, and capital equipment-but that are asso-ventures would be 6.1 percent less efficient. ciated with higher firm efficiency. They may also

have unmeasured productivity attributes-includ-The residual difference in efficiency between the ing superior technological capabilities, technicalthree groups of firms are due to several factors. As know-how, high quality of its personnel, and or-shown in earlier sections, wholly foreign-owned ganizational forms-not readily captured in our


data. To some extent, local firms may acquire Interfirm Linkages and FDI Spilloversthese intangible kinds of "knowledge and human hiterfirm linkages are ongoing relationships betweencapital" through joint venture arrangements with firms, with repeated mnsactions thatrange from annsforeign firms, or through increased interactions length transactions, to contractual buyer-supplierand linkages with them as suppliers or subcon- relationships, to licensing andfranchising, andto joint-tractors. We turn to some of these interfirm link- ventures (Wong, 1991). Such linkages are poten-ages below. tially beneficial to all parties involved. For some

Table 6.3 Stochastic Frontier Production Function Estimates by Ownership

IndependentVariables Domestic JointVentures 100 percent foreign

Constant 6.021 a 7.113a 7,304a(0.329) (0.491) (0.950)

Log (labor) 0.566 a 0.613a 0.599a(0.058) (0.044) (0.088)

Log (capital) 0:427 a 0.358a 0.339 a(0.026) (0.031) (0.071)

Efficiency EquationConstant -0.278 -2.368a -3.504 a

(0.193) (0.679) (1.501)

Do R&D 0.194 1.429a -0.272(0.137) (0.418) (0.344)

Technology License(s) 0.168 C 0.357 0.387(0.089) (0.412) (0.385)

Exports 0.059 2.502 0.316(0.058) a (0 579) (0.611)

Proportion female labor -0.486a -2.275a -0.399(0.177) (0.597) (0.325)

Mean Education 0.026 0.558 a 0.330a

(0.022) (0.102) (0.143)

Provide Formal Training 0.022 1.379 a 0.614b

(0.074) (0.438) 0.246)

s2,, 1.393 a 2.216a 0.759a

(0.082) (0.353) (0.092)

9 0.030 0.604a 0.103

(0.039) (0.090) (0.141)

Mean Efficiency 0.74 0.76 0.81

Log likelihood -1552.01 -412.08 -222.177

a = Significant at 1 % level,

b= Significant at 5% level,

c Significant at 10% level.

Note: Industry dummy variables included. Numbers in parentheses are standard errors.



large firms and MNCs, such relationships make it mies, the exception being HRDC's efforts to intro-possible for them to achieve cost reductions by con- ducejoint and group training schemes targeting SMIscentrating on their core lines of business and sub- (see Chapter Four).contracting certain production activities tospecialized local firms. Where these vertical in- Numerous questions remain about interfirm linkages,terfirm linkages develop, they can provide small how they develop, the nature of their benefits, thesubcontractors not only with new markets but also way in which technology transfer takes place, andexposure to new forms of production and man- what role public policy can play in enhancing inter-agement organization, and access to technical as- firm linkages. As currently structured, the MITPsistance and training support to upgrade their survey is ill equipped to answer these questions sincetechnological capabilities. Horizontal linkages no information was elicited on interfirm linkages.among groups of smaller firms can also, in principle, Nonetheless, it can provide limited insights into theallow them to take advantage of scale economies-in importance for domestic firms of "spillovers" fromhiring high-calibre group managers, joint training, the presence of MNCs and joint-ventures. We dotechnology upgrading, finance, and exporting-not this by asking the following questions: Is the effi-available to them as individual firms. The initial evi- ciency level of domestic firms improved by the pres-dence on efforts to promote SMI enterprise net- ence of foreign firms operating in the sameworks in some industrialized and developing subsector? If so, what is the nature of these spilloverscountries is encouraging (see Box 6.2). and are technology and skill spillovers from foreign

finrs important?Policymakers in Malaysia have recognized the po-tential importance of interfirm linkages as a means We extend the stochastic frontier production func-of fostering technology transfer from foreign to do- tion model to test for the presence of efficiencymestic small and medium-scale enterprises.6 To date, spillovers to local firms from foreign firms. Threegovenmmentpolicies to promote linkages have been proxy variables for the magnitude of the foreignmodest. firm presence are developed: (1) the employment

share of foreign fims in total employment of the sub-The Subcontracting Exchange Scheme (SCX), a sector, (2) the foreign firm share of total R&D spend-MITI-operated computerized clearing house to ing in the sub-sector, and (3) the corresponding sharematch SMIs with larger firms, has thus far gener- of workers getting formal training. We further dis-ated few solicitations. According toFong (1991), the tinguish between the spillovers fromjoint-venturesmain obstacle to SMI supplier development is the and from wholly foreign-owned firms. To see iflow and uneven quality oftheirproducts, and limited spillovers are important, we re-estimated the localtechnological and skill capabilities. Vendor devel- firms' frontier production function without the indus-opment programs such as Proton reportedly entry dummies, augmenting it to include the differentcountered similar problems-low quality, little spillover proxy variables. If they are, we shouldappreciation for the importance of quality imnprove- expect the spillover variables to be positively re-ment, and limited use of testing equipment, and im- lated to firm-level efficiency.precise pricing (Meyanathan, 1994). However, inelectronics, an increasingly dense network of inter- Table 6.4 reports the augmented frontier produc-firm linkages have begun to develop, as local capa- tion function estimates for the sample of domesticbilities have improved and MNCs such as Intel have firms. The first model specification-the simple em-spun off suppliers (Rasiah, 1994). To date, there ployment share measure-indicates that there are ef-have been no efforts made to promote SMI enter- ficiency spillovers for local firms from the presenceprise networks to take advantage of scale econo- of both joint-ventures and wholly foreign-owned


Box 6.2 Promoting SMI Networks in Chile

In 1990, the Chilean SMI promotion agency, SERCOTEC, introduced the PROFO program to fosterthe creation of networks among SMIs and, through promoting links among SMIs and with largecustomers, to upgrade their competitiveness. It also saw this as a strategy for increasing the limitedtake-up of other services provided by SERCOTEC, and for using these networks as focal points forstimulating industrial development through increased participation by private sector firms and publicsector agencies in the locality.

PROFOs are developed in three steps. In the first step, SERCOTEC identifies potential groups ofSMIs (usually between 10 and 30) in a particular locality where the basis for collaboration exists,conducts a diagnosis of individual and group problems, and works with SMIs to establish its credibilityand to begin addressing their problems. The second stage is to facilitate consolidation by hiring agroup manager, whose salary is financed 70 percent by SERCOTEC in the first three years andwholly thereafter bythe SMI group. The group manager has several tasks: (i) increasing take-upsubsized training and support services by members through SERCOTEC's Technical AssistanceFund; (ii) coordinating delivery of these services by public and private sector bodies; and (iii) promotingcooperation through visits to each other's factories, workshops and group travel, and developingjoint initiatives to address common problems and pursue common objectives. The third and finalstage is graduation from SERCOTEC support as PROFOs become self-sustaining.

The early results of this PROFO SMI initiative has exceeded SERCOTEC's expectations. Of the 16PROFOs operating in Chile in 1993, a number had expanded market shares, gaining new access tomarkets in Chile and abroad, and especially important, developing supplier linkages to large firms.Several groups of metalworking SMIs were able to improve their performance to such an extent thatthey began supplying state mining companies with local inputs that had originally been imported orsourced in other regions. Despite initial concerns, many PROFOs have demonstrated that SMIshave the capacity for collective action and, with appropriate support from public sector institutions,for initiating collective efforts on product design, process development, human resource developmentand training, sales and finance. These early results were sufficiently encouraging that SERCOTEChas introduced a new PROFO program directed at helping SMI groups enter export markets.

Source: Humphrey and Schmitz, Principles for Promoting Clusters and Networks of SMEs, reportprepared for UNIDO, 1995

firms. Efficiency levels in domestic firms are sig- These results suggest that the presence of foreignnificantly higher the larger are the employment capital in the same industry, whether joint-venturesshares ofjoint ventures and foreign firms in the in- or wholly-owned foreign MNC subsidiaries, hasdustry. The second model specification-R&D and beneficial effects on the productive efficiency oftraining shares-reveals an interesting pattern of effi- local firms. The efficiency spillovers to local fmnsciency spillovers to local firms varying by whether appear to come through the R&D activities ofjoint-FDI is in the form ofjoint-ventures or wholly for- ventures, and the training efforts of wholly foreign-eign-owned firms. The results indicate, first, that owned firms. We speculate, but cannot confirm, thattraining by wholly foreign-owned firms, but not these efficiency spillovers operate through subcon-joint-ventures, has a significant positive impact on tracting relationships between local and foreign firms,local firms' efficiency. The results are reversed for throughjob turnover of trained personnel from for-R&D, with positive R&D spillovers fromjoint-ven- eign firms, through diffusion of information abouttures but not 100 percent foreign-owned firms. new production techniques and new products gen-


Table 6.4 Stochastic Frontier Production Functions Estimateswith FDI Spillovers

Independent Varables Model 1. Model 2.FDI Employment FDI Training & R&D

Share SharesProduction FunctionConstant 6.688a 6.645S

(0.232) (0.081)

Log (labor) 0.589a 0.576 a(0.043) (0.042)

Log (capital) 0.381 a 0.390 a(0.021) (0.022)

Efficiency EquationConstant -0.226a -0.301 a

(0.020) (0.017)

Exports 0.093c 0. 054a(0.048) (0.011)

Has technology license(s) 0.200 0.133(0.228) (0.189)

Education of workforce 0.010 0.005(0.070) (0.054)

Proportion of female labor -0.381 -0.200(0.687) (0.831)

Own Training 0.031 0.066(0.033) (0.123)

Own R&D -0.167 -0.105(0.115) (0.067)

Measures of FDI SpilloversEmployment share of joint ventures 0.234b

(0.117) n.a.

Employment share of 100% foreign firms 0.334 b(0.167) n.a.

Training share of joint ventures n.a. 0.167(0.139)

Training share of 100% foreign firms n.a. 0.331 a(0.019)

R&D share of joint ventures n.a. 0.263b(0.048)

R&D share of 100 % foreign firms n.a. 0.179(0.293)

s2, 1.413a 1.471a(0.059) (0.067)

9 0.001 0.022(0.002) (0.010)

Mean Efficiency 0.73 0.74Log likelihood -1559.97 -1551.14

a= Significant at 1% level, b= Significant at 5% level, c= Significant at 10% level.Note: Numbers in parentheses are standard errors.Source: 1995 MITP Survey


erated by R&D conducted by joint ventures and, Highly-efficientfirms, both large and small, haveto a limited extent, wholly foreign-owned firms, several identifiable attributes: (i) they do not neces-and possibly through other demonstration effects. sarily invest in R&D, rather acquiring know-howThese efficiency effects are over and above the and technology through licensing agreements,direct benefits that FDI are thought to provide throughjoint-ventures, and through exports; (ii) theythe local economy-investment capital and foreign sponsor structured training programs for their em-technology, employment creation, foreign ex- ployees, both skilled and unskilled, in in-house pro-change, tax revenues, and higher productivity grams or through external training providers; (iii)and wages. their work organizations emphasize quality control,

including statistical process control, quality controlPolicymakers will need to have a better understand- circles, and use of precision measuring equipment;ing of these linkages-both the vertical links between (iv) they have human resource practices that pro-local firms and MNCs on which we have reported, vide incentives for job retention and skill acquisi-and horizontal links among groups of SMIs on which tion; and (v) they are more likely to have introducedless is known-to design effective policies to foster new technologies and increased training in the re-increased interfirm linkages and promote industrial cent past. Employers should benchmark themselvesdevelopment. To this end, a second round of the against these attributes and, where feasible, emulateMITP survey will be fielded to elicit additional infor- them so as to improve their productivity levels. Thesemation about the nature of firm linkages, the flows of "best-practices" may not be familiar to many firms,technology, knowhow, training, and technical assis- and policymakers, state development authorities, andtance that takes place between firms, and the effec- industry associations should widely disseminate thistiveness of different policies in overcoming informnation to the private sector, SMIs inparticular.constraints to the development of these interfirm net-works. The design of SMlpolicies should have severalfea-

tures. First, because SMIs face multiple constraints,Findings and Policy Implications focusing assistance just on training orjust on tech-

nology is likely to be effective. To be effective,The empirical evidence indicates that while SMIs policies should deliver an integrated package of in-as a group are generally less efficient than larger centives and services designed including consult-firns, there is considerable variability in the effi- ing and technical assistance, training, technologyciency of individual SMIs in Malaysia. This find- upgrading, finance, quality control, and managernent.ing is important for policymakers: if SMIs are all Responsibilities for delivering services to SMIs areuniformly inefficient, there would be little that currently spread over multiple agencies, and therepolicymakers can do to upgrade their levels of should be greater coordination and infornation shar-efficiency. Nonetheless, the efficiency gap being among them. Second, because poor informationtween the majority of SMIs and larger firms is quite and employer resistance to change are importantpronounced in Malaysia (as in Mexico) unlike constraints for SMIs, responsible public or privateTaiwan, China. The high overall efficiency level sector agencies must work proactively with SMIs toof Malaysia's manufacturing firms is primarily the expose them to the benefits of change, and createresult of a high proportion of highly efficient firms demand for support services.. Malaysia's currentin the large-size category, principally MNCs, with policy approach-providing SMIs with various incen-most micro and small firms being of below aver- tives and leaving the responsibility for take-up withage efficiency. A primary focus of policy should them-ignoresthese cultural and informationaimpedi-therefore be on improving productivity at the ments to change, with the end result that take-up ofsmaller end of the enterprise scale. most incentives targeting SMIs has been low.


A rethinking of the whole approach to SMI support The evidence reveals that the efficiency levels ofis required. The first priority of SMIDEC should be domesticfirns are improved by the presence offor-to conduct a careful review of all existing SMI in- eignfirms. These efficiency spillovers to local firmscentives, and identify why incentives work or do appear to come from R&D activities ofjoint-ven-not, and why. Part of this review should consider tures, and the training efforts of wholly foreign-the feasibility and desirability of alternative ap- owned firms. They suggest that supplier linkagesproaches to SMI support, one being an incremental between firms and job turnover of trained workersimprovement in the coordination of services deliv- may be important routes through which theseered by other agencies, the other being a more pro- spilovers operate. Future rounds of the MITP sur-active system of outreach, promotion, diagnoses, and vey should elicit information about these linkages-delivery of tailored services to SMIs. Examples of the flows between firms of technology, know-how,proactive SMI policies exist in both industrialized training, finance, and assistance in such matters asand developing countries, including Mexico's CIMO production, quality control, and marketing-to pro-program of training and technical assistance to SMIs, vide policymakers with insights on how to facilitateand Chile's PROFO program to foster enterprise technology transfer to local firms, building on exist-networks among SMIs, and study tours to glean les- ing incentives (such as ITAF) and vendor develop-sons appropriate to Malaysia should be undertaken ment and "umbrella" schemes to promoteas part of this review. subcontracting and development of SMI clusters.


ANNEX 6.1Stochastic Frontier Production Functions

Following Aigner, Lovell and Schmidt (1977), the stochastic frontier production function is expressed as:

exp(xJ3 + Vi - ui)(1)

where subscript i indexes thefirm, y is the maximum outputobtainable from a vectorof inputs,x,, and /l is

an unknown parameter vector to be estimated. The vi s are random errors which are assumed to be

independently distributed of the one-sided error term ui where ui <0. The non-positive one-sided error term

U1 reflects the fact that each firm's output must lie on or below its stochastic frontier, f(xi; , + vi).

To investigate the presence of systematic influences on firm-level inefficiency, we incorporate firm

characteristics into the model by expressing ui as:

ui = Zi8 + wi

(2)

where inefficiency is assumed to be a linear function of a systematic component zi, and a random

component w; . The systematic component includes a vector of firm attributes zj, e.g. the firm's

technology and workforce capabilities, which are related to efficiency by the parameters, (5. The random

variable wi follows the same truncated normal distribution as the one-sided error term, Ui, where the point

r Iof truncation is -Zi such that wi 2--z 6.

Equations (1) and (2) are estimated jointly using maximum likelihood techniques to obtain consistentestimates of the parameters of the production frontier (equation 1) and the inefficiency effects (equation 2).From these results, an index of firm-specific efficiency can be calculated as

TEi = exp(-ui) = exp(-zi8 - wj) which ranges between 0 and 1. The joint estimation of these twoequations is necessary to obtain consistent estimates of technical efficiency. Some studies, using a two-

stage procedure, have simply regressed the efficiency estimates on zi . This yields inconsistent parameter

estimates of equation (2) because ordinary least-squares treats w, as being normally distributed in the

second-stage regression when it should follow the same distribution as ui .

CHAPTER SEVEN: CONCLUSIONS AND RECOMMENDATIONS

Human resource development will underpin sights, from the employer perspective, of what fac-Malaysia's strategic vision of attaining industrialized tors constrain their training, and the efficacy ofcountry status by the year 2020. For policymakers, alternative incentive schemes to promote in-servicethe HRD challenges are how best to increase the training.supply of skilled workers to meet current acute la-bor shortages as well as projected growth in skills Summary of Main Findingsdemand; upgrade skills and technical competenciesof the workforce to facilitate technological and orga- Chapters Two through Six used a variety of ana-nizational change by enterprises to compete in in- lytic methods to investigate several key topics, in-ternational markets; and address the associated cluding the incidence, determinants, andproblems of sharply rising relative pay for skilled productivity and wage outcomes of training; train-workers and high labor turnover, both of which ing constraints faced by firms and policies to ad-threaten Malaysia's wage competitiveness and abil- dress them; the links between technology, qualityity to continue attracting foreign direct investment. control, and skill needs; and the technical efficiencyFor policymakers, it is evident that growth in skills of firms by size and ownership. Analyses of thesedemand over the past decade has outstripped the topics yielded a large number of findings, whichsupply capacity of public sector training institutions, confirm, reinforce and complement each other.and that the private sector will have to play an in- Taken together, they suggest the following broad con-creasingly greater role in training and meeting its clusions:own skill needs.

Firms under-invest in training. ManufacturingThis report is concerned with in-service training and firms in Malaysia under-invest in the training ofits role in raising firm-level productivity and promot- their employees. This is based on our estimates thating technological change in Malaysian industry. As about 80 percent of all firms either do no training orpart of this study, a broad-based survey of 2,200 rrianu- rely exclusively on informal training from co-work-facturing enterprises was fielded in 1995 to obtain ers and supervisors, and that only 21 percent of firmshitherto unavailable information on the incidence of provide formal training.in-service training, provided in-house and by dif-ferent public and private training providers. This conclusion is supported by employer responses

about why they provide little or no training. MostAnalyses using this survey yielded new insights into cite mature technology, which has low skill require-employer's use of training from different sources, ments, as the principal reason for doing little train-variations in the productivity and wage outcomes ing. While this is not a market failure per se, a sizableof training of different types of training provided number of other employers, smaller firms in particu-different groups of workers, and how these rela- lar, cite other training constraints that are-free rider-tionships are influenced by employer investments ship from high labor turnover, lack of knowledgein technology and organizational change. It also about training methods, and limited resources forrevealed marked differences in the training and training.technological capabilities of firms varying by sizeand ownership, with most local firms and SMIs oper- Employers play a key role in skills develop-ating at significantly lower technical efficiency lev- ment. The MITP survey revealed that many em-els as compared to joint ventures and MNC ployers can, and do, meet their skill needs throughsubsidiaries. The survey also provided unique informal, structured programs of in-service training.

CONCLUSIONS AND RECOMMENDATIONS 109

In fact, notwithstanding the conclusion that firms skill requirements, and hence creates little de-under-invest in training, employers provide in-ser- mand for training.vice training to more workers than traditional voca-tional and technical institutions. The MITP survey Training raisesfirm-levelproductivity. Firmsshowed that employers sponsored formal training that train, on average, are about 32 percent more pro-courses for about 196,000 workers in 1994. In com- ductive than firms that provide employees with noparison, all public training institutions combined formal training. The productivity effects of train-produced a total of 145,000 skilled and semi-skilled ing are more important, both in terms of the magni-graduates over the five years of the Sixth Malaysia tude of its impact and in a statistical sense, forPlan. in-house formal training than for training from ex-

ternal sources, and for the training of skilled work-The private sector is the most important source ers than for unskilled worker training.of training. Finns that train meet their skill needsin-house or through a variety of external training The productivity effects of training are larger whensources. Of the external sources, firms rely most new technologies acquired through licensing areheavily on private sector providers-private training complemented with employee training. Reflectinginstitutes, buyers and equipment suppliers, joint- the weak R&D capabilities of local firms, a firm'sventure partners, and overseas training institutions. own R&D spending has limited effects either onWith the exception of SDCs and advanced training overall productivity or on the productivity of workerinstitutes (such as CLAST or GMI), both of which training. Similar results on the productivity effectsare either demand-driven or cater to higher-level of training and its links with technology adoptionskills training, the other public training institutions- are found in other developing countries such asMTs, IKMs, YTCs, polytechnics, and vocational and Mexico, Colombia and Indonesia.

technical schools-play a minor role in meeting thein-service training needs of industrial firms. Their SMIs will benefit most from formal training.primary focus thus far has been on pre-employment The analyses revealed that formal training has par-training in basic and intermediate-level technical ticularly large productivity effects for SMIs, theskills. group least likely to train or only to provide infornal

on-the-job training to employees. For small and me-Technology shapes the skill requirements of dium size firms, the productivity impact is about 32employers. The MITP survey showed that firms and 29 percent, respectively, as compared to 12 per-are more likely to train when they are large, employ cent for large firms.an educated work force, invest in R&D, possess tech-nology or know-how licenses, have foreign capital It is clear that SMIs under-invest in training. This isparticipation, use quality control methods, and ex- attributable to their use of simple technologies, whichport to foreign markets. Many of these factors are means that skill needs are also correspondingly low;related to the ways firms acquire technological ca- and to several market failures-from limited financepabiities-through own R&D, or through contacts for training, high job turnover which makes it diffi-with foreign firmns and buyers. Employers invest- cult to recoup training costs, and weak training capa-ing in these technological capabilities are more bilities-whichdeterthemfromtraining.likely to use highly-educated workers, who aremore adept at working with new technologies and Local and foreign firms have different train-new production methods, and to train them. For ing needs. The productivity effects of differentfirms that provide little or no training to their em- sources of in-service training vary by local or for-ployees, the single most important reason was eign ownership. For local firms, no productivitytheir use of mature technology, which has low effects from in-house company training are discern-


ible; however, the training they receive from SDCs schemes to promote longer-term job attachmentand advanced skills training institutions such as among emnployees, and hence training. Of these, theCIAST or GMI are associated with large productiv- analyses in Chapter Three indicated that higher pay,ity effects. For foreign firms, it is in-house com- steeper seniority-wage profiles, and severance paypany training and training from private sector were most effective in reducing quits, especiallysources that have large productivity effects. among firms that train. The reason is that firms that

train are better able to fund more attractive com-These findings suggest that most local firms have pensation packages out of the higher productivityweak in-house training capabilities, and they will need that results from training, as compared to firms thatto rely more on SDCs and other institutions provid- do not.ing advanced skills training. In both groups of firmns,no significant productivity effects were discernible The DDIT is ineffective in inducing training.for in-service training provided by public training It has been used primarily by MNCs, joint-ventures,institutions such as lTIs, IKMs, YTCs, and vocational and larger firms who, arguably, were training al-and technical institutes. They focus mainly on pre- ready. For these firms, the DDIT scheme has meantemployment training, and it appears that the little in- sizable windfall gains; for the firms that providedservice training they provide is not well suited to little or no training, the DDIT scheme has failed toemployers' needs. induce employers to begin, or increase provision of,

training.Firms that train also pay higher wages. Em-ployers that provide training pay wages that are 6 Lack of awareness about DDIT, and its requirements,percent higher on average, suggesting that one- has been the principal reason for its limited use. Overeighth to one-fifth of the productivity gains from train- half of all firms in the MITP survey reported noting are shared with workers in the form of higher using it because they were unaware of the incen-pay. The patterns of wage increases mirror those of tive, or knew its details only vaguely. Another fac-the productivity gains from training, being higher in tor was the heavy bureaucratic requirements offirms that invest in technology, that export, and that applying for DDIT, and the corresponding high ratesare foreign-owned. The evidence confirms that train- of re the DDIT covers only small employers withing of supervisors and skilled production workers is less than 50 workers, and few (if any) of these firmsassociated with higher pay, but not training for un- are likely to be using the DDIT incentive.skilled production workers.

HRDF is effective but non-compliance isIf these trends are extrapolated, they imply that pro- significant. The HRDF provides firms with dif-ductivity differentials from training will lead to grow- ferent schemes to flexibly organize their training ef-ing wage disparities between skilled and unskilled forts and upgrade their training systems. The HRDCworkers in the absence of training policies to up- has actively sought to reduce application and report-grade unskilled workers to skilled status. Techno- ing requirements, expedite processing and reim-logical change will also put additional upward bursements of training claims, disseminatepressure on relative pay of skilled workers. information on training through TNA workshops

and clinics, and support hard-to-reach SMIs throughCompensation policies can deter quits and group-based training initiatives.encourage training. High labor turnover detersemployers from training. However, they can reduce However, non-compliance remains an important is-quit rates through different compensation policies sue, with as many as 27 percent of eligible firms notsuch as higher pay, fringe benefits, pay increases tied registered with, or contributing to, the HRDF. Theto length of service, severance pay and retirement problem is concentrated among smaller firms, firms


in traditional and domestic-oriented industries, in trial country buyers now require exporters tothe states on the east coast and in East Malaysia, and have ISO-9000 certification. The analyses indi-among firms providing little or no structured train- cated that exporting to industrialized countrying. While there are good reasons to downplay en- markets is greatly facilitated for ISO-9000 firmsforcement in the early gestation period, policy and to a lesser extent, for firms in the process ofmakers will eventually have to make a strong effort getting certification.to address the issue of non-compliance.

New technology raises skill and trainingLocal firms and SMIs have weak techno- needs. The MITP survey showed that about 42logical capabilities. The MITP survey elicited percent of firms have introduced some kind ofdata on a number of technology indicators which new technology over the past three years. Largerconfirm the main findings of MASTIC's 1992 Na- firms and firms with foreign capital were moretional Survey of R&D. They show that private sec- likely to have introduced new technology, espe-tor R&D spending in Malaysia is relatively low cially computerization and labor saving line au-compared to other developing countries. They con- tomation; when they introduced new technology,firm that the number of firms doing R&D rises with SMIs placed greater emphasis on new machineryfirm size, with 30-39 percent of larger firms report- to replace their older vintage, manual machinery.ing some R&D spending, while few SMIs engage inR&D activities. Of particular note is the finding Introduction of new technology had an ambiguousthat wholly foreign-owned firms are less likely to impact on employment, but a clear cut impact on rais-report R&D activities as compared to local firms or ing skill requirements. Fully 79 percent of firms re-joint-ventures of similar size. The other technology ported an increase in the skill content ofjobs, whileindicators-technology licensing agreements, equip- only 15 percent reported a fall in skill needs. Thesement age, and extent of automation-show sirnilar changing skill requirements were reflected in theirpatterns by firm size and ownership. training activities, both in terms of whether employ-

ers currently train, or whether they increased train-Quality consciousness is growing among larger ing provision over the past three years.firms. The MITP survey showed that some firms New technology is associated with higherhave introduced quality control systems and imple-mented QCtraining programs to tecome intemnation- productivity. Firms introducing new technol-mentedlQC cptiveam d exprog sto bcowver, tathios i ogy in the past three years had productivity lev-ally competitive and export. However, this is els today that were, on average, 23 percent higher

concntrtedamog frei-owed nd argr fnns--- even taking into account their current investmentshalf of them use SPC techniques and precision mea- in training and R&D. For micro and small firms,suring equipment, as compared to about one-fifth the productivity impact was even larger-about 50of smaller firms; most SMIs continue to rely on percent.visual inspection to verify accuracy. Not surpris-ingly, QC training is more prevalent in larger firms This productivity impact comes on top of anotherthan in SMIs, and in foreign firns than in local firms. outcome of new technology, namely, its effects on

increasing training which, in tum, is associated withInterest is also growing in implementing ISO-9000 increased productivity as shown in Chapter Three.quality standards, especially among joint ventures The effect of introducing new technology on tram-and local firms. While their numbers are currently ing is most pronounced for SMIs, increasing thelow, 30 percent of local firms and 44 percent of probabilities that they provide training or in-joint ventures report that they expect to get ISO- creased training over the past three years by 9-109000 certification within three years. Many indus- percent, as compared to just 4-8 percent for large


firms. Together, they highlight the potentially Localfirms lag behindforeign firns in tech-large productivity gains from encouraging SMIs nical efficiency. The analyses showed that, onto adopt new technology. average, local firms were less efficient than joint

ventures and, in turn, joint ventures were less effi-SMIs are not inherently inefficient. The tech- cient that MNCs. Some part of these differences isnical efficiency of SMIs is central to the debate about due to group differences in R&D, technology licens-the role of small scale enterprises in economic de- ing, worker training, and export orientation.velopment. SMIs are unlikely to be an importantsource of growth and employment generation if However, even if local firms andjoint ventures werethey turn out to be relatively inefficient, with lim- given all the attributes of MNCs, such as export sta-ited ability to compete, survive and grow into tus, R&D, technology licenses, workforce educa-larger firms. The analyses in Chapter Six revealed tion and training, the efficiency ranking bythat while they are on average less efficient than ownership status would still remain. The adjustedtheir larger counterparts, a significant number of mean efficiency levels of local firms, joint-ventures,SMIs are actually more productive than many andMNCsare 0.74, 0.76 and 0.81, respectively, sug-large firms. gesting that local firms would still be nine percent

less efficient than MNCs, andjoint ventures wouldFor policymakers, the latter is the more important be six percent less efficient than MNCs.finding. It indicates that SMIs are not inherentlyinefficient as conpared to large firms, and that well- Spillovers from foreign to local firms aredesigned policies targeting SMIs can be effective in important. The efficiency levels of domestic finmsraising their productivity and efficiency levels. are higher in industries with a large foreign direct

investment (FDI) presence. These efficiencyHighly efficient firms have identifiable "spillovers" to local firms appear to come from thecharacteristics. Efficient firms, both large and R&D activities ofjoint-ventures, and the training ef-small, have several technological, work force, and forts of wholly foreign-owned firms. These findingsorganizational characteristics that can, in principle, suggest that local firms benefit from FDI throughbe emulated by less-efficient SMIs. supplier linkages withjoint ventures and MNCs, and

through the job turnover of trained workers fromThey have better access to new technology through such firms. These findings need to be confirmed bylicensing agreements, joint-ventures with foreign further study. To the extent that these inter-firmpartners, and export contacts with foreign buy- linkages can be encouraged, they provide a poten-ers and suppliers, and have introduced new tech- tially important source of technology transfer fromnology in the recent past. Theyhave a more educated MNCs to local firms.workforce, and they sponsor structured training pro-grams for employees, both skilled and unskilled. Policy RecommendationsTheir work organization emphasizes quality control,including statistical process control, quality con- These empirical findings have implications not onlytrol circles, and precision measuring equipment for training institutions and training policy, but alsorather than visual inspection, and they have HR for technology and SMI policies. Our policy recoin-management and compensation practices that pro- mendations are grouped under the following fivevide incentives for job retention and skill acqui- topic areas:sition. The wide dissemination and adoption ofthese best practices are likely to have significant I Collection and Dissemination of Trainingproductivity-enhancing benefits for SMIs. Information


II. Expanded Role of Education and Training veys can be compared, to monitor and analyzeInstitutions private sector training efforts and the efficacy of

Im. More Effective Training Policies public policies in promoting worker training,IV. Technology Diffusion and Promotion technological change, and productivity growth.V Better Coordinated and Proactive SMI Our recommendations to strengthen data collec-

Policies tion for monitoring worker training, both at theIColoof Training level of the firm and the individual, are as fol-

I. Collection and Dissemination oTring lows:Information

The Government's existing system for collecting, The Govemment should develop andfield nation-

analyzing, and dsisseinating information about tran- ally representative enterprise and household sur-ing in Malaysia is fragmented and uncoordinated, veys oftraining on aperiodic basis, building onand should be strengthened. the existing survey capabilities of the Department

Data on public training institutions are typically of Statistics (DOS) A great deal of policy-relmaintained by each responsible ministry but seldom evant information is already collected in the pe-reprtaied,o a sematich rs sibl msthery buth setaled riodic industrial and household surveys fieldedreported, on asystematic basis together withidetailed by DOS, and these are readily augmented with acost data, to a central coordinating agency for plan- core module of questions about training. Oncening and policy analysis. Likewise, information on coreumonale these aut firmn andprivate-sector training institutions is only collected institutionalized, these augmented firm- andpriat-seto triringintittins s nlycoleced household-level surveys will yield time-serieson an ad hoc basis. Few evaluation studies of train- daaneded forveysakers yied ana-ing programs-based on tracer surveys of its gradu- data needed for policymakers to monitor and ana-ates, comparisons with a control group, and lyze traing trendscost-benefit calculations-have been conducted toensure that public resources are being used cost pn Inter-agency steernng committee should be seteffectively; evaluations comparing different public up to design, find, and coordinate this survey ef-

-ranig nsittinsare even rarer. fort with DOS. This committee should identify thetratining institutions are even rarer. priority training or related issues to be addressed in

The National Vocational Training Council (NVTC) each industrial or household survey, and work withwas designated as the institution to coordinate public DOS to augment the core modules with questionsand private vocational training programs. The Gov- designed to address these issues. The steering com-emnment should give NVTC the necessary legal mittee should also have responsibilities for commis-standing, resources, and capabilities to play this role sioning studies using the survey data, and for themore effectively, publication and wide dissemination of information

on training and training trends.

Little effort has been made to make training in-formation widely available to the final consumer- Timely turnaround of survey results is critical.individuals and the private sector. They can only As such, DOS should be corporatized to give itmake informed training decisions if they pro- the flexibility and the resources to respond to thisvided with periodic, timely publications and new mandate. It should be given the standing toanalyses on both public and private sector train- bill appropriate costs to the government agenciesing. One constraint is that little data exist on how for which the augmented firm and household sur-much and what types of training employers are veys are being conducted. Several national sta-providing and individuals are receiving. The tistical agencies play this role, including StatisticsMITP Survey provides a benchmark on in-ser- Canada and Mexico's INEGI, and they offer po-vice training against which future firm-level sur- tentially useful lessons for DOS.


II. Expanded Role of Education and stitutes, which focus on pre-employment training.Training Institutions This suggests that the in-service training they pro-

In-service training is one, albeit key, part of the vide are not well-suited to firms' needs. In contrast,process of human resource development. Em- depending upon the firms concerned, it found largeployers' decisions to train, and the productivity and positive productivity effects of in-service train-and wage outcomes of that training, depend criti- ing provided by SDCs, advanced skills training cen-cally upon the stock of education and technical ters, and private sector training institutes. Ourskills that individuals bring with them into the la- recommendations, based on these findings, are as fol-bor market. lows:

The MITP study clearly showed that pre-employ- An in-depth study of the effectiveness and relevancement education and in-service training are comple- of training provided by public training institutionsments, not substitutes, for each other. Employers should be conducted, especially if they are to playare more likely to train, and provide more training, an expanded training role in the Seventh Malaysiawhen the schooling attainment of its workforce is Plan. The study should assess the relative efficacyhigher because educated employees are better of different institutions within each ministry and acrosslearners and thus benefit more from investments in ministries, identify weaknesses that should be cor-training. A one year increase in the mean years of rected and strengths that can be built on, field tracerschooling attainment leads to a two to three percent studies of their graduates that are comparable acrossincrease in the probability that employers will pro- training institutions, and conduct cost-benefit analy-vide workers with formal structured training. If the ses using these data. The study should also takeaverage years of schooling rises to 11 years, from stock of the structure, organization, staffing, and cur-the MITP sample average of 8.9 years, this would ricula of these institutions with an eye to exploringraise the percent of firms training by four to six per- their potential for playing a greater role in in-ser-cent. In addition to facilitating in-service training, vice training. The World Bank has helped designeducation also has an independent effect on raising and conduct similar program impact studies of VETsfirm-level productivity and wages. in other developing countries and can provide the

Government with technical assistance.Existing low rates of continuation intofurthereduca-tion orpost-secondary education cannot sustain de- On the basis ofsuch an exercise, the Governmentsired rates of post-employment training and should identify selected public training institutionsretraining. Continuous learning and skills upgrad- as candidates for corporatization. Theseing required are for moving into higher technology corporatized training institutions should be given theproduction. The Government has recognized the flexibility and incentives to design and deliver cus-need to address this issue, and it is embarking on an tom training courses tailored to the needs of indus-ambitious program to expand secondary and tertiary try, with input from industry and institutes of highereducation with an emphasis on sciences, engineer- learning, and to compete with other public and pri-ing and technical subjects. vate training providers for training resources. Pub-

lic training institutions in several Latin AmericanFirms' use of different external training providers countries, including Brazil's SENAI, have been suc-appear to accurately reflect the relative productivity cessfully corporatized to make them more demand-of the training courses offered by these training pro- driven, and lessons for Malaysia's trainingviders. The MITP study could not find any discern- institutions can be gleaned from their experiences.ible productivity effects for the in-service trainingprovided by public training institutions, such as the SDCs have demonstrated their effectiveness in de-ITIs, IKMs, YTCs, and vocational and technical in- livering demand-driven training to both MNCs and


domestic firms, especially in Penang where the application requirements to obtain the incentive, asPSDC has been in existence since the late 1980s. well as high rejection rates.Nonetheless, large firms and MNCs are the most in-tensive users of SDC training. The Government The key lesson for policy makers is that any policyshould ascertain why take-up of SD C training is so or incentive, whether in training or in other areas, islow among small and medium size domestic compa- unlikely to bejfully effective if targeted beneficia-nies, and implement measures to increase theirpar- ties are unaware or inadequatelyfamiliarized withticipation in the design oftraining programs tailored the program. Information about policy initiativesmore to their specific needs. should be widely publicized and, for hard-to-reach

SMI firmns, disseminated proactively through localAdvanced skills training centers such as CIAST, the public offices and employer associations. AnotherATC in Sepang, and GMI are another important lesson is that, to the extent feasible,filing require-source of higher-level skills training for domestic ments for incentives should be streamlined to im-firms, and the analyses showed that this training was prove take-up. Implementation of policies shouldassociated with productivity gains. However, the be closely monitored to quickly identify and resolvesupply capacity of these institutions is limited rela- the inevitable problems that firms face in respond-tive to the revealed demand for their services, a ing to, or taking up, new incentives.point that the Government has recognized and willaddress in the Seventh Malaysia Plan. It plans to The Govemment should eliminate the remainingestablish nine new skills training institutes offering coverage of the DDIT incentive for smallfirmsadvanced courses, some with input from MNCs. with less than 50 employees. The justificationThese include two new ATCs in Johor Bahru and for doing so is as follows: First, it is likely thatKlang, the JMTI in Penang, and the Japanese Ma- very few small firms are using the incentive today.laysia Institute in Kulim. In addition to the bilateral Second, bringing all firms under the HRDF um-training institutes that have been set up with Ger- brella greatly simplifies administration, since uni-many, France, and Japan, the Government should versal coverage of all firms would seamlesslyalso explore thefeasibility of setting sinilar bilateral accommodate growth or shrinkage of firms abovetraining institutes with Britain and the United States. or below the 50 employee cutoff. Finally, HRDF

is developing new schemes to support the train-

III. More Effective Training Policies ing activities of SMIs, and the 50 employee cut-The MITP study used employers' responses to off would arbitrarily restrict access of small firmsthe DDIT and HRDF to assess the efficacy of to these training programs.these two training policies in encouraging firms toprovide structured training programs to their em- The issue of payroll contributions for these smnallerployees. firms needs to be resolved. Two options are avail-

able for finding this recommendation. First, the gov-The DDIT incentive scheme has generally been inef- ernment may consider a waiver of the payroll levy forfective. It was usedprirnarily by MNCs, joint ventures, small firms, and provide a block grant to HRDF fromand larger domestic firms who were training already. general revenues to cover the costs of their use ofFor these firms, the DDIT scheme has meant sizable training services. The drawback is the potential dis-windfall gains and a loss of tax revenues to the Govern- incentive effects for SMIs to remain small so as not toment. Among firms doing litde orno tainig, the lack of contribute to the HRDF. An alternative is to requireawareness about DDIT was the principal reason for SMIs to register and contribute at a reduced rate,its limited use. Other factors which reduced interest perhaps half a percent of payroll, with the govern-in the incentive were the time-consuming and costly ment matching the SMIs contribution. This govern-


ment contribution could be time-limited, and fall The HRDCshould also mount an infonnation cam-over time, as SMIs begin to develop a training con- paign, on television and in newspapers, to en-sciousness and training capabilities. courage eligiblefirms to register with HRDC. It

should announce its intention to vigorously en-The HRDF, in contrast, has made considerable force compliance with the HRDF Law and, to en-progress in creating an enabling environment for sure that this threat is credible, it should publicizetraining. It has done this by providing firms with its increased enforcement capabilities as well asalternative schemes for training through in-house train- its prosecutions of selected firms. This campaigning programs and external training providers, stream- should be accompanied by a timne-limited amnestylining reimbursement of training expenditures, program for firms to come forward, register withreducing adrinistrative and reporting burdens on em- the HRDC, and pay their back levies without civilployers, widely disseminating information about or criminal penalties. Similar time-limited am-training through TNA workshops and clinics, help- nesty programs have been used very effectivelying fund the development of training plans and es- in several states in the United States to improvetablishment of training facilities in firms, and tax compliance.introducing new group-based training schemes tar-geting SMIs. A sizable number offirms in the MITP survey had

not claimed any reimburenementsfor training throughThe design and delivery of schemes have been flex- the HRDFas ofyear-end 1994. While claims haveible and imaginative. The HRDF has also created a risen since then, in large part due to an extension oflarge and growing market for both public and pri- the deadline granted by HRDC, it is likely that thevate sector training providers, which augurs well for underlying problem remains, especially amongincreased training supply in the near and medium smaller firms who provide no training or only un-term. In short, HRDF has developed a market-re- structured, informal on-the-job training that is notsponsive framework for encouraging in-service eligible for reimbursement.training. Our recommendations are intended tocomplement these efforts. Some of their constraints include poor knowledge

about training, not having a training plan, or inad-Non-compliance in HRDFappears to be significant. equate training facilities. These are being addressedThe MITP survey indicates that as many as 27 per- by TNA workshops, the JURUPLAN scheme to de-cent of eligible firms with 50 or more employees are velop training plans, and schemes to fund purchasenot registered with, and contributing to, the HRDF. of training aids, and HRDC should continue to vig-Non-compliance reduces funds available to the orously promote these activities. Other factors whichHRDF, and it circumvents the very intent of the limit demand for training among SMIs, such as use ofpolicy-to induce firms to invest in training and skills mature technology, are under the purview of otherupgrading, and to reduce free-riding on the training public agencies. To address these SMI constraints,of other firms which, in turn, has a chilling effect on HRDCshould closely coordinate its efforts with thoseoverall employer incentives to train. HRDC iS aware planned under SMIDEC, the new SMI agency.of this problem but currently has few resources todevote to enforcement. It only has a staff officer and HRDC has introduced the JTS and GTS schemes toclerk to develop the necessary databases to identify encourage group training for smaller employers,firms that are eligible but not registered, and no legal either initiated by groups of small firms themselvesofficers with the power to prosecute non-compliance. (JTS), or organized by emnployer associations (GTS).HRDC has submitted a staffing request which should The MITP survey indicated that such joint trainingbe acted on expeditiously by the Government. programs between firms are rare in Malaysia, and


they are most commonly found among large firms, these dimensions, as compared to large domesticnot among SMIs. HRDC, in collaboration with companies and MNCs.SMIDEC, should conduct a study to identify theimpediments to collective action by SMIs to ben- The strong message for policymakers from all theefitfrom scale economies in training. Some lessons analyses is that increasing R&D spending alone-may be gleaned from Chile's experience in foster- through incentives-is not the solution. More impor-ing SMI networks forjoint activities, or Mexico's tant than inadequate funding for R&D, the criticalCIMO program for supporting training among clus- issue among most local firms is weak in-house tech-ters of SMIs. nological capabilities. For most firms (exceptjoint

ventures), R&D spending had no significant impactThe MITP survey also showed the principal ways in on firm-level productivity or efficiency, while tech-whichjoint training activities are organized. Suppli- nology licensing agreements were associated withers and government agencies play key roles in organiz- large productivity gains, especially when accompa-ing joint training courses for small firms, while nied by training programs. The R&D injoint ven-specializedtrainingcompaniesaremoreimportantfor tures is more productive because of transfer ofmedium and large firms. hdustry associations are also technology and know-how, technical assistance, andcited, butprimarily by large firms. Industry and em- training from foreign partners. These findings sug-ployerassociaons shoiuldassmegreaterandmoreprm- gest the following recommendations:active responsibiliiiesfor organizing taining among itsSMI members. The GTS scheme currently being pi- For most localfirms, technologies acquired throughloted by HRDC is one avenue for doing so. licensing and know-how agreements are a more im-

portant source ofproductivity gains than in-houseRegional offices of the HRDF should be established R&D efforts to develop indigenous technologies.closer to the principal regional clusters of industries. This implies that, at least in the near term, the Gov-The HRDC has submitted a working paper to the emient should make technology licensing and theGovernment proposing the establishment of regional wide diffusion of existing technologies to local firmsoffices in Penang, Johor, Sabah and Sarawak. the principal focus of technology policy. ActionsOperationalization of these regional offices should to facilitate technology transfer and diffusion ofspeed up HRDF response to firms' training applica- know-how include the wide dissemination of in-tions, facilitate closer interactions with employers formation on appropriate technologies, expeditedand provision of advisory services, and promote reg- processing of technology licensing applications byistration of, and payment of levies by, employers MIDA, incentives for firms to adopt new technol-not yet registered with the HRDC. HRDC should ogy and purchase new equipment, or expanded ac-also consider co-locating its staff in local offices of cess for SMIs to incubators or to joint-use facilitiesother agencies with SMI responsibilities, so as to with testing and precision measurement equipment.better coordinate delivery of integrated training andother support services to SMIs. The wide gap in the productive efficiency of local

firms and SMIs as compared toforeignfinns is anIV. Technology Diffusion and Promotion added argumentfor emphasizing technology diffu-The MITP survey revealed marked differences sion over R&D spending. The efficiency gap isacross firms in a wide range of technology indica- much less pronounced in Taiwan, China, which hastors-industrial R&D spending, technology licensing, actively promoted technology diffusion and skillsquality control systems, use of precision measuring upgrading to its large population of SMIs. Given theequipment, vintage and sophistication of machinery. evidence of large productivity gains from adoptingLocal firms and SMIs in particular fare poorly on all new technology and training by SMIs, a similar strat-


egy in Malaysia of promoting diffusion of known productivity, quality control circles (QCC), and to-technologies to the majority of local finns and SMIs tal quality management (TQM). However, as theshould go a long way towards narrowing this effi- MITP survey shows, quality consciousness amongciency gap and, through upgrading their technol- SMIs is still low, and most SMIs have no qualityogy, raising overall efficiency growth in the control systems in place. This suggests that NPCmanufacturing sector. will need to take a mnore active approach to promote

quality and productivity among SMIs, and to pro-Technology transfer can also occur through links vide them with technical assistance and training.with MNCs and efficient domesticfirns. The MlIPstudy indicated that the technical efficiency of local SIRIM can also play a critical role by encouragingfirms was improved by the presence of foreign firms, firns to adopt ISO-900 )standards. The analyses in-though it was silent on the inter-firm relationships dicated that exporting to industrialized country mar-through which these transfers occurred. A careful kets was greatly facilitated by having, or workingstudy should be conducted to better understand the towards, ISO-9000 certification. The recent surgenature of these inter-firm linkages-both vertical inof interest in IS0-9000 has reportedly exceededvolving an anchor MNC or large domestic firm and SIRIM's capacity to respond in a timely fashion,its subcontractors or suppliers, and horizontal in- and many firms are seeking certification throughvolving groups of SMIs cooperating for mutual ben- foreign institutions. SIRIM's reorganization, nowefit-and the nature of the flows of technology, underway, should allow it to respond more flex-technical assistance and skills between firms. Such ibly to this growth in private sector demand fora study, which is currently planned by the World ISO-9000 certification.Bank and EPU as part of a second round of MITPsurveys, will identify the extent to which such firm ISO-9000 certification may be beyond the financiallinkages and networks exist, and where they are reach of SMIs, and interest in it remains low for thepoorly developed, assess whether such networks can majority of SMIs. SIRIM has provided some SMIsbe created or fostered by public policy. with consultancies in Quality Improvement Practices

(QIIPs), a lower cost altemative to ISO-9000 certifi-Standards and metrology is an important policy in- cation. It should work with MNCs and leading com-strumentfor diffusing modem production methods panies to develop and extend the sectoral coverageand quality control systems tofirms, and upgrading of QIPs for SMIs. QIPs establish clear-cut qualityproduct quality to meet the exacting standardsfor standards towards which SMIs can work to obtainexport markets. The experiences of several devel- certification, and which MNCs and other anchor firmsoping countries, such as Brazil, indicates that adop- can accept as an assurance of quality. MNCs andtion of ISO-9000 and TQM has led to strong quality other leading firms should find this approach attrac-improvements and productivity gains. The MITP tive since they would bear few of the costs of identi-study indicated that a similar phenomenon is oc- fying and developing a pool of local suppliers thatcurring among Malaysian firms. While their num- meet their quality standards. The Government shouldbers are currently low in comparison to MNCs, a approach the principal foreign employer associa-growing number of local firms and joint ventures tions in Malaysia to encourage their active participa-expect ISO-9000 certification within three years. tion in this exercise.

The National Productivity Corporation (NPC) cur- The sub-sectoralfocus of QIPs makes them ame-rently provides a wide range of services to SMIs nable to group provision andfunding of quality up-through regional quality centers operated jointly with grading for SMIs. Once QIPs are developed, SIRIMSIRIM. It disseminates booklets on the importance can leverage its limited staff in the SMI section byof quality, and runs subsidized training courses on drawing upon rnanpower and financial resources of


other government agencies (the QC training of NPC tive approach to assisting SMIs. Such an approachand HRDF's new JTS and GTS schemes) and the would actively seek out and deliver to individualprivate sector, to expand delivery of associated SMIs or groups of SMIs a package of integrated ser-consultancies, technical assistance, training, and vices-including consultancies, training and technol-funding needed to upgrade SMIs to these new QIP ogy information and incentives, and technicalstandards. The outcome is not only more quality assistance in production, quality control, and mar-upgrading among SMIs, but also the development keting.of greater supplier linkages between SMIs and bothMNCs and other leading firms in the industry. This implies a fundamental restructuring of poli-

cies toward SMIs. A first step in this direction hasV. Better Coordinated and Proactive SMI been taken with the recent creation of the Small and

Policies Medium Scale Development CorporationA consistent theme running throughout the report (SMIDEC) within MM. Its objective is to consoli-is that SMIs have weak training and technologi- date the hitherto fragmented planning and coordi-cal capabilities, and low levels of productivity nation of SMI policies that are administered andand efficiency relative to larger firms and MNCs. implemented by other government agencies. Our

recommendations for improving SMI support areasMost SMIs do not train, and those that do rely on follows:informal OJT. They face a variety of training con-straints from high labor turnover, poor information, SMIDEC should begin by undertaking a carefil re-and finance. Most SMIs use older vintage, manual view of existing SMI incentives to determine whyequipment, they rarely have quality control systems, their take-up is so low. Firm-level information perti-and most they tend to rely on visual inspection rather nent to such a review has already been collected astmanprecision measuring instruments to verify qual- part of the 1994 SMI study. This SMI survey re-ity. Reliance on these outdated technologies and pro- sembles the MITP survey, but it is more compre-duction methods creates littde demand for high-level hensive in its coverage of SMIs, their constraints,skills or training. These weak training and techno- and use of a wide range of policies that target them.logical capabilities interact to create a vicious cycle Similarities between the two surveys suggest thatof low levels of investments in human or knowledge many analytic approaches used in the MITP studycapital, low levels of productivity, and with limited can be fruitfully applied here.resources, few incentives to train or adopt new tech-nology. As part of this review, SMIDECshould address the

issue of how existing incentives and services can beThe strong implication is that simply providing fi- delivered nore effectively to SMIs. When new in-nancial incentives-whether for training as in DDIT, centives are introduced, such as DDIT or the differ-or for technology upgrading as in ITAF-is inad- ent ITAF schemes, the principal responsibility forequate. It ignores the reality that funding is seldom take-up rests with the firms; implementing agenciesthe only constraint that impedes SMIs from invest- typically focus on reviewing and assessing applica-ing in training or technology. Given their weak ca- tions, or delivering a service, only when firms takepabilities, most SMIs may not even recognize that the initiative to approach them. Many SMIs have athey have a problem, or if they did, most would not poor grasp of their limitations, or their training orknow what incentives were available, or how they technology needs, so that many incentives designedwould apply for, and effectively use, these incen- for them are not taken up. As such, a proactive ap-tives. To the extent that their problems are systemic, proach needs to be adopted, with active promotionas our analyses suggest, what is required is aproac- and delivery of services to hard-to-reach SMIs.


SMIDEC must either ensure that the implementing Outreach and closer interactions with SMIs will re-agencies adopt a more proactive approach in ser- quire devolution of service delivery to states or lo-vice delivery, or alternatively, it must take on some cal areas, and new or existing regional institutionsof these outreach functions, and coordinate service need to be expanded. Many agencies are already indelivery from other implementing agencies. theprocessofdoing so. TheMalaysian hnial Tech-

nology Information Center (MISTIC) has been des-SMIDEC can draw lessonsfrom the experiences of ignated the one-stop-shop for information aboutother countries with proactive approaches to SMI technology and related incentives. SIUM and NPCsuppon. Examples close to home include the indus- jointly operate Regional Quality Centers throughtrial extension services provided to SMIs in whichtheydeliverproductivity and quality training.Singapore and Taiwan, China; farther afield, The HRDC has proposed setting up regional HRDFChile's SMI agency has actively promoted the offices to provide information about training, organizecreation of SMI networks to increase take-up of TNA workshops and clinics, and interact with firms.policies, with professional managers to coordinate SMIDEC should ensure that regional and local officesmembers' use of a wide range of training and other of all agencies provide information not only on theirSMI support services (much like HRDF's GTS own programs but also those of other agencies, inscheme); Mexico has setup CIMO offices in all states effect, turning each office into a first-stop for all otherto proactively reach and deliver integrated training, services. Integrated provision of these services mayconsulting, and marketing support to SMI groups. also be improved by co-locating staff from differentSMIDEC should visit these countries to glean and agencies in the same office.adapt lessons more appropriate to Malaysia's insti-tutional context. While the details of each approach A wide range of SMI incentives and programsmay vary, they all share similar features. already exist and SMIDEC's challenge is to inte-

grate the efforts of different agencies to reachAll interventions in the context of service deliv- the maximum number of SMIs. For example, inery should involve a diagnostic or audit of the expanding its vendor development programs toSMI's capabilities and constraints, either individu- other groups of SMIs and sub-sectors, SMIDECally or as a group. This is to ensure that an inte- can draw not only on SIRIM to develop the rel-grated set of services, both financial and evantQIPs, butalsoonNPCtodeliverqualitytrain-non-financial, can be designed that are appropri- ing and productivity upgrading, and on HRDF toate to that SMI or group of SMIs' real, rather than fund group training activities. An expansion ofown perceived, needs. This diagnostic serves other QIP certification may also revive interest in itsfunctions: it has a pedagogic role, in helping SMIs moribund subcontracting exchange (SCX)-whichlearn about their strengths and weaknesses; it is in- has few entries and few queries-by providing po-formational, in informing them about the range of tential foreign buyers and MNC users with theservices available to address their constraints; and quality assurance that SMI subscribers have QIPit serves to establish the credibility of the imple- certification. Similarly, because HRDC does notmenting agency and overcome the aversion that have the staff resources to catalyze and identifymany SMIs have to dealing with government what kinds of joint training courses are neededagencies. For SMIDEC, the issues to address are by SMI groups, SMIDEC should ensure that otherwhich agency should conduct the audit, how to agencies provide these functions and the auditminimize duplicative efforts, and design ways to needed to maximize the training benefits of theshare this information among agencies. JTS scheme.

NOTES 121

NoTEs

Chapter One

The linkage between the 1988 MLFS and 1994 MITP surveys was not completed in time to beincluded in this report, but the panel data will be analysed in future work on the MITP project.

2 Department of Manpower, Government of Malaysia (1991), Survey of Industrial Skill Needs, regionalreports, Kuala Lumpur.

Chapter Two

The sampling weights from the 1994 industrial survey would have been preferable but they were notavailable at the time of this study.

2 The proportion of firms investing in technology in chemicals is 45.13 % , iron and basic metals(62.84 %), electrical machinery (25.37 %), transport equipment (52.61 %), plastics (19.12%).

3 The average export shares of these industries are 37.86% in electrical machinery, 41.68% in apparel,41 % for rubber, and 20% for plastics.

4 This evidence is based on empirical studies of wage outcomes from training in three industrializedcountries (Lillard and Tan, 1992, Tan et al, 1992), and the productivity outcomes of training in produc-tion function studies of four developing countries including Malaysia (Tan and Batra, 1995).

5 SIRIM and NPC are the Standards and Industrial Research Institute of Malaysia and National Produc-tivity Corporation, respectively. Unfortunately, the data did not permit a more disaggregated break-down of training by these two agencies.

6 Estimates of the total number of workers trained are obtained by multiplying the number of reportedtrainees in each firm by its sampling weight. These weights, representing the number of firms repre-sented by each firm in a given industry-firm size category, were calculated from the frequencydistributions of firms in the 1988 industrial survey. The resulting training figures under-estimate howmuch training actually occured in 1994 given the growth in manufacturing since the 1988 survey wasundertaken. Changes in the composition of firms since 1988 are another source of imprecision in theseestimates.

7 The recommendation to make it mandatory for employers to train 10 percent of their workforce wassuperceded by the enactment of the Human Resource Development Fund in 1993. Nonetheless, it isof some interest whether this target of 10 percent training has been achieved.John Enos, "Invention and Innovation in the Petroleum Refining Industry", in Kenneth Arrow (ed.),The Rate and Direction of Inventive Activity, Princeton University Press, 1962.

9 See Hong Tan, Human Capital and Technological Change, Ph.D thesis, Yale University, 1980; andLee Lillard and Hong Tan, "Private Sector Training: Who Gets It, How Much, and Why", in R.Ehrenberg (ed.), Research in Labor Economics, JAI Press, 1993.


Chapter Three

The constant returns to scale Cobb-Douglas production function was selected for its flexibility andease of interpretaton. Studies have demonstrated its superiority over other more complex functionalforms, like the trans-log, in the presence of missing data and measurement error problems such asthose common to developing country data sets (Tybout, 1992).

2 We note that foreign-owned firms are not homogeneous, and that the country of origin of foreigncapital may be associated with differences in their propensity to train or to invest in R&D. While apotentially interesting issue, analyses based on separate production function estimates by country ofownership was precluded by small sample sizes.

3 When a common production function was estimated for all firms, the results indicated that only externaltraining had a positive and significant effect on firm-level productivity; in-house training had nodiscernible effects on productivity. This counter-intuitive result was driven by the ownership composi-tion of firms in the MITP sample, and it led us to estimate separate production functions by ownershipsat1s.

4 For local firns, the productivity impact of external training is calculated as (0.0026 x 1.751 x 100); forforeign firms, the productivity impact of internal training is (0.019 x 0.4 x 100).

3 To identify which external training source had the important impact on productivity, we experimentedwith alternative grouping schemes such as dividing external sources by public or private trainingproviders, or by whether the institution provided advanced versus basic skills training. In general,they reveal that private training providers are more important than public-run institutions, and thatadvanced skills training is more important than basic skills training. However, in each case, the resultwas driven by the productivity effects of training from SDCs and CIASTs.

6 Infornation was also collected on profit-sharing, but this variable proved to be highly correlated withsome of the other components of comnpensation and was dropped.

7 To be more precise, if starting pay is W(0) and pay after 10 years of service is W(10), then the wageincrease is calculated as [W(10) -W(0)] / W(0).

8 A more disaggregated analysis by specific occupational groups was precluded by the availability ofinfonnation on the components of compensation by only two broad groups, non-production workersand production workers.

9 A set of industry dummy variables were included in all regressions but their estimates are not reportedhere in the interest of brevity.

Chapter Four

Respondents were actually asked to rank the relevance of eight statements. One of these, thatinformal training is sufficient, was deemed to be similar to another statement on the adequacy oflearning by doing because of mature technology used. As such, the eighth statement was droppedfrom consideration.

2 See Tan and Batra (1995), Enterprise Training in Developing Countries, Private Sector DevelopmentDepartment, The World Bank.

3 Employers already deduct training expenditures as a business expense, but the DDIT scheme allowsthem to deduct an additional amount equal to eligible training expenditures.

NOTES 123

4 In 1994, there were 12 approved training institutions: the National Productivity Corporation,SIRIM, Mara Institute of Technology, Malaysian Agricultural Research and Development Institute,Forest Research Institute of Malaysia, Centre for Instruction in Advanced Skill Training, PenangSkill Development Centre, IKM, Industrial Training Institutes, German-Malaysian Institute,Malaysian Timber Industry Board, and Perak Entrepreneur and Skill Development Centre.

5 This discussion draws upon an analysis of MIDA's administrative data on DDIT reported in theWorld Bank study (1994), Malaysia-Meeting Labor Needs: More Workers and Better Skills, ChapterFour.

6 Originally, training programs had to be directed at either (1) development of craft, supervisory andtechnical skills for the manufacture of new products or processes, or (2) upgrading of craft, supervi-sory and technical skills in existing products and processes. In 1991, two broader categories wereadded: (3) production-related training for productivity improvements, and (4) training for qualityimprovements in production. This expansion in the scope of eligible training programs resulted in adramatic increase in the number of production workers getting training.

7 MIDA's administrative records did not contain employment size information on applicants. Thiswas determined by matching MIDA's list of DDIT applicants with a master list of all manufacturingfirms in operation in 1992 which contained data on employment size. Only 70 percent of applicantscould be matched so that the estimates are necessarily tentative.

8 World Bank, Vocational Education on the Threshold of the 1990s, commissioned study byCINTERFOR and 1LO, volumes I and 11, 1991.

9 The Government contributed R48.9 million to match projected company levies in the first year; ineach of the following three years, it will add an additional R16 .3 million to the HRDF.

1 0 We note that our definition of "eligibility" is partial. The HRDC defines firms as eligible if they have50 or more employees that are citizens of Malaysia, not including foreign workers or workers sup-plied by labor contractors. Some firms meeting the employment size criterion were subsequentlyderegistered when the nationality of their employees was taken into account.On-the-job training is eligible for HRDF reimbursement provided that the OJT is structured, with aclearly defined program objective, training plan, and identifiable trainers. These structured trainingprograms are excluded from our definition of informal OJT.

12 Many firms were reportedly unable to meet the original deadlines because of missing receipts andturnover of firms' human resource officers; this prompted the HRDC to grant these firms dispensa-tion to file claims as late as June 1995 for training expenditures incurred in the third quarter of theprevious year.

13 In fact, administrative data from HRDC indicates that use of the PLT scheme-both in terms ofworkers trained and training expenditure-is lower than that of the SBL scheme. Even firms with atraining plan rely on the SBL scheme because of its flexibility.

14 Five employer associations have been selected to participate in the pilot GTS. They include theMalay Chamber of Commerce and Industry, the Chinese and Indian Chambers of Commnerce andIndustry in Selangor state, the Bumiputra Manufacturer's Association, and the Malaysian Iron andSteel Federation.

Chapter Five

l Malaysian Science and Technology Information Centre (1994), Ministry of Science, Technology andthe Environment, Kuala Lumpur.


2 By way of comparison, overall R&D as a percent of GDP is 1 percent in Singapore, 1.7 percent inTaiwan, and 2.1 percent in Korea, while the corresponding private sector contribution to R&D is 0. 6,0.8 and 1.7 percent, respectively. World Bank, Strengthening Industrial Technology Developmentfor Sustainable Growth 1996, Table 1.3.

3 The eleven training categories included (1) technical (2) managerial (3) machine operation (4) qualitycontrol (5) machine maintenance (6) skills upgrading (7) safety and health (8) team work and motivation(9) technology upgrading (10) on thejob training, and (1 1) production systems and procedures.

4 In interviews, SIRIM confirmed that having a structared training program with instruction in qualitycontrol techniques is one imnportant criterion for ISO-9000 certification.

5 According to SIRIM, the recent growth of interest in ISO-9000 has been so great that it has exceededSIRIM's capacity, and many firms have turned to foreign certification bodies despite their substantiallyhigher fees.

6 A check reveals that, for the most part, firms that are not currently training tend to respond "don'tknow" or "reduced training", though there are a few cases when firms said they increased traininglevels but were not currently providing training.

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