title economic analysis on grain market integration …...economic analysis on grain market...

Title Economic Analysis on Grain Market Integration and StructuralChange in Guizhou, China( Dissertation_全文 )

Author(s) Chen, Shuning

Citation Kyoto University (京都大学)

Issue Date 2017-05-23

URL https://doi.org/10.14989/doctor.k20585

Right 許諾条件により要旨は2017-07-31に公開

Type Thesis or Dissertation

Textversion ETD

Kyoto University

Economic Analysis on Grain Market Integration and

Structural Change in Guizhou, China

Shuning CHEN

2017

i

Abstract

In the western inland provinces of China, grain sector development is crucial for solving

the issues of regional economic backwardness and severe rural poverty. In the last 15 years, the

southwestern inland province of Guizhou has faced difficulties in improving regional economic

development and increasing rural income. The objective of this study is to analyze the role and

performance of the grain sector in the transition of the Guizhou economy. In particular, this

study focuses on policy impacts and government intervention on the grain sector in this

province.

First, the results of an input–output analysis on the economic structural change of the

Guizhou economy from 2002 to 2007 indicate that inland regional economic development is

mainly promoted by government investment. Capital-intensive primary resources and energy

industries have become the leading industrial sectors. The provincial government has

emphasized ecological/environmental issues rather than economic prospects that consider

improvements in rural welfare. Because of the strict political concerns about food security and

grain self-sufficiency, the crop sector, of which grain is the major product, is the only

agricultural sector strongly supported by the government in recent times. However, the

production in crop sector has become self-sufficient oriented, thus, production in the crop

sector has become less important for regional development during the economic transition. A

large amount of the surplus labor force has retrained in the crop sector. Moreover, production

in the crop sector is very inefficient and has consequently become less efficient during the

economic transition.

Following this, an intra-provincial market integration estimation provides informative

detail of regional grain-market performance. Because the provincial government monitors rice

distribution and state-owned enterprises (SOEs) has taken control of the rice-marketing channel

in Guizhou, market integration for rice is the most efficient of all grains in this province. Market

integration for other grains, which are promoted by private participants, is rather inefficient.

Market integration structures are less favorable for the distribution of local grain products.

ii

Oversupply and a less developed processing industry are associated with the market integration

structure of the major cash grain, rapeseed, in Guizhou.

In accordance with these results, this study suggests that crop production in Guizhou is

unprofitable and that government support for production has become a heavy finical burden

rather than a means to improve the development of the crop sector. Biased regional policy and

the development of the crop sector have contributed to make Guizhou province fall behind

national rapid economic growth and have so far impeded economic development in this

province. Local farmers cannot get out of poverty. However, judging by the crop sector’s

importance for domestic consumption, the strong employment effects within the economy, and

the growing potential in the crop-related sector, the promotion of this sector is especially crucial

for solving poverty and the depressed situation in Guizhou province.

In order to improve crop sector development, this study provides several implications.

First, the biased policies that work against the situation in Guizhou province should be revised.

Production in the grain sector should be adjusted to meet different demands from other sectors.

Second, the improvement of technological levels in crop production and the development of

less labor-intensive production are essential for sustainable grain production. Third, the

development of the local agroindustry can absorb the surplus rural labor force. Grain

production can be maintained because farmers can generate nonagricultural income without

migration. Last, the marketing channel needs to be improved and private participation in grain

processing and distribution needs to be supported. Market integration between the producing

market and the consumption market is crucial for grain sector development.

Keywords: Guizhou China, economic structural change, grain market, uneven regional

development, rural poverty.

iii

Table of Contents

Abstract ....................................................................................................................................... i

Table of Contents ......................................................................................................................iii

List of Tables .............................................................................................................................. v

List of Figures ........................................................................................................................... vi

Chapter 1 Introduction ............................................................................................................... 1

1.1 Background ...................................................................................................................... 1

1.2 Research Objectives ......................................................................................................... 4

1.3 Dissertation Organization ................................................................................................ 7

1.4 References ........................................................................................................................ 9

Chapter 2 The Grain Industry in China’s Transitional Economy ............................................ 10

2.1 The transitional economy of China and its grain autarky policy ................................... 10

2.2 Grains’ demand–supply pattern and changes in China .................................................. 14

2.3 The market oriented grain reserve system and the participants in the grain marketing

channel ................................................................................................................................. 21

2.4 References ...................................................................................................................... 27

Chapter 3 Economic Structural Change and the Agricultural Sectors in Guizhou .................. 28

3.1 Introduction .................................................................................................................... 28

3.2 Generation of agriculture-based regional IO tables ....................................................... 31

3.3 The input–output analytical framework ......................................................................... 33

3.4 Industrial structural change in Guizhou and the role of the crop sector ........................ 41

3.5 Predictions of industrial development potential through the RAS method ................... 54

3.6 Summary ........................................................................................................................ 56

iv

3.8 References ...................................................................................................................... 58

Chapter 4 The Grain Industry and Regional Market Integration in Guizhou .......................... 59

4.1 Introduction .................................................................................................................... 59

4.2 Grain markets in Guizhou .............................................................................................. 61

4.3 Methods and data sources .............................................................................................. 65

4.4 Results and discussion ................................................................................................... 74

4.5 Summary ........................................................................................................................ 82

4.5 References ...................................................................................................................... 86

Chapter 5 Conclusions and Policy Implications ...................................................................... 88

5.1 General summary ........................................................................................................... 89

5.2 Concluding remarks and policy implications ................................................................ 91

5.3 Limitations of the study ................................................................................................. 95

Acknowledgements .................................................................................................................. 97

Appendix .................................................................................................................................. 98

v

List of Tables

Table 3.1(a) The production inducement coefficients of the Guizhou economy in 2002 ........ 44

Table 3.2(b) The production inducement dependency of the Guizhou economy in 2002 ....... 46

Table 3.3 The employment influence and sensitivity coefficients in 2002 and 2007 .............. 48

Table 3.4 The net backward and forward linkages in 2002 and 2007 ..................................... 49

Table 3.5 (a) Supplementary table for the Guizhou economy in 2002 .................................... 51

Table 3.5 (b) Supplementary table for the Guizhou economy in 2007 ……………………….51

Table 3.6 Substitution effects and fabrication effects from 2002 to 2007 ............................... 55

Table 4.1 Basic features of three cities in Guizhou.................................................................. 64

Table 4.2 ADF test for the order of integration ........................................................................ 75

Table 4.3 Granger causality Wald test and selected lag length in the VAR model .................. 76

Table 4.4 Rank test for long-run co-integration ....................................................................... 77

Table 4.5 Granger causality test for market pairs .................................................................... 78

Table 4.6 VECM estimates regarding four grain products ...................................................... 82

vi

List of Figures

Figure 1.1 Entire framework of the dissertation and the links among chapters ........................ 8

Figure 2.1 The growth of three sectors in China from 1978 to 2014 ....................................... 10

Figure 2.2 Grain production in China from 1978 to 2014 ....................................................... 12

Figure 2.3 Annual production of five grains from 1995 to 2014 ............................................. 13

Figure 2.4 Thermodynamic charts of regional rice areas in China in 1996 and 2014 ............. 15

Figure 2.5Thermodynamic charts of regional wheat areas in China in 1996 and 2014 .......... 17

Figure 2.6 Thermodynamic charts of regional soybean areas in China in 1996 and 2014 ...... 18

Figure 2.7 Thermodynamic charts of regional rapeseed areas in China in 1996 and 2014 ..... 20

Figure 2.8Thermodynamic charts of regional maize areas in China in 1996 and 2014 .......... 21

Figure 2.9 Marketing channel for rice circulation ................................................................... 23

Figure 2.10Marketing channel for wheat circulation ............................................................... 24

Figure 2.11Marketing channel for soybean circulation ........................................................... 25

Figure 2.12Marketing channel for rapeseed circulation .......................................................... 26

Figure 3.1 Location of Guizhou in China ................................................................................ 28

Figure 3.2 The gross output growth in Guizhou from 1996 to 2014 ....................................... 30

Figure 3.4 The total output of each industrial sector in Guizhou in 2002 and 2007 ............... 42

Figure 3.5 Skyline chart for Guizhou in 2002 and 2007.......................................................... 53

Figure 3.6 Indices of substitution and fabrication effects ........................................................ 55

Figure 4.1 Map of Guizhou and its three main cities ............................................................... 61

Figure 4.2 The major grain areas in Guizhou from 1995 to 2014 ........................................... 62

Figure 4.4 Prices series of rice and soybeans in three regional Markets in Guizhou (1/2008–

3/2016) ..................................................................................................................................... 74

Figure 4.5 Primary grain products price transmission direction with time lags in markets .... 79

Figure 4.6 Processed grain product price transmission direction with time lags in markets ... 80

1

Chapter 1

Introduction

1.1 Background

China has tried to attain grain autarky during its economic transition. While economic

development of the country has become world-market oriented and its manufacturing sectors

have shifted to reliance on the world market for foreign investment and primary material supply

in recent decades, its grain sector has concentrated on supplying the domestic market and

maintaining a high level of self-sufficiency. Central government actively intervenes in the grain

market because of significant concerns about food security, grain market stability, and grain

self-sufficiency. Several grain production support policies were implemented in the early 2000s.

Moreover, China’s total grain production has continued to grow since 2004. This most

populous country is the largest grain producer and consumer in the world.

Significant changes in the grain demand–supply situation have been observed in China

in recent years (Hu et al. 2016). One of the most obvious changes is the variety of the regional

demand–supply pattern. Several southern coastal provinces that used to be the central grain-

producing area have been changed by industrialization and urbanization. Now, regional grain

production in these provinces is not sufficient to meet their own demand. Farmers from inland

regions have left their homes and moved to coastal urban regions as migrant workers. Labor

migration from inland regions to coastal regions has resulted in a grain production decrease in

the inland labor-supplying area. Grain production has become concentrated in the central and

northeastern regions, both of which are suitable for large-scale agriculture and grain production.

Such a development has been supported by the government.

In addition to regional change, there has been a shift in the demand–supply pattern of

grain types in China (Gandhi & Zhou 2014). As staple grains, rice and wheat production

increases in recent decades have been stable. However, production of feed grain such as maize

2

has increased sharply. Maize output exceeded rice output and become the largest grain in terms

of volume in 2011(Fu et al. 2012). The importation of grain in China is relatively small

compared with domestic production, although soybean imports are a special case because these

have increased exponentially in the last 20 years. Indeed, soybean consumption depends highly

on imports.

Structural and policy factors have contributed to the changes in China’s grain sector.

Rapid economic growth, regional development disparity, and production condition conversion

are structural reasons that have influenced the grain demand–supply situation. Government

intervention, such as a grain production support policy, regional market regulation, and other

agriculture-related policies, have altered the activities of farmers and other entities in the grain

market. Grain production sustainability and domestic market development in China have

attracted great research interest. It is particularly necessary to emphasize the importance of

economic analysis of grain sector development in the western inland provinces.

Economic development in the backward western inland China provinces depends on

the domestic market. Grain farming is still the main income resource for western inland farmers,

most of whom are suffer from poverty. Economic development in the western inland provinces

has been left behind in favor of the coastal provinces during the last two decades. Thus, western

farmers are the poorest in the entire country. Even worse, in recent years, grain deficiency in

many western inland provinces has become a serious problem. Both grain deficiency and rural

poverty indicate the underdevelopment of the grain sector in western inland China. However,

even though grain is the most important agricultural product and is essential for rural residents

in the western inland provinces, few studies have conducted economic analyses of the grain

sector in this region because of research preference bias and data limitations. Researchers and

central government have focused on the grain sector in terms of its production sustainability

and self-sufficiency situation at the national level and have exhibited less interest from the

economic perspective. The relation between the grain sector and regional economic

development has been neglected. The underdevelopment of the grain sector in the western

inland provinces is related to the most important economic issues in modern China.

3

When China experienced great economic growth in the early 2000s, the economic

development oriented to the world market led to deep regional disparity between coastal

provinces and inland provinces. Most of poor farmers are living in the western inland provinces

and reliant on grain farming. The rural–urban income gap has contributed to significant income

inequality in China, inequality that is among the world’s worst. The western inland farmers

found it difficult to generate income because of the underdeveloped grain production. The

rural–urban income gap has enlarged in the early 2000s. Further, when the supply–demand

pattern changed and the western inland provinces became grain deficient, even though the

demand of these provinces could be met though the interprovincial grain trade, the backward

inland provinces needed to pay high costs in order to meet the primary demand for grain instead

of investing in the industrial sector for high value-added production; thus, sustainable economic

growth has been impeded. Briefly, the development of the regional grain sector should satisfy

a variety of demands from the transitional economy, and grain production should contribute to

an increase in rural welfare. The failure in the grain sector to contribute to economic

development and rural welfare generation directly links to sluggish economic growth and rural

poverty in the western inland provinces.

Economic analysis of grain sector development in the western inland provinces in

China is important. First, the role of the grain sector in the western inland provincial economy

needs to be distinguished in the context of the economic transition. Regional grain production

is supported by policies that aim to meet national grain demand. In this regard, each province

is asked to maintain its own food self-sufficiency. However, the grain sector contributes to

regional economies in a variety of ways. Further, the demands on the grain sector from the

economy change during economic transition (Johnston & Mellor 1961). A biased grain policy

misleads grain production, a situation that is unsuitable for regional demand. Especially when

the economy is continuing to grow, the industrial structural changes and grain demand alters

profoundly. The performance of the grain sector in the overall regional economy and the

question of whether or not production in the grain sector can meet the demands of the

transitional regional economy are crucial problems that need to be clarified.

4

Second, intra-provincial market efficiency, which is directly linked to local farmers’

profits, should be investigated. Growing market deregulation and liberalization in China has

fluctuated since the 1990s. Deregulation and government intervention occur alternately in

China’s grain market. Prior analytical studies of China’s grain market are ambiguous. Although

several researchers have explained the improvement of interprovincial grain market efficiency

in China (Park et al. 2002, Huang & Rozelle 2006, Awoke 2007), some other studies refer to

the significant provincial trade barriers (Poncet 2005). Most of these studies are based on

provincial-level data and do not consider differences in market structure or data paucity. There

is a need to explore local market efficiency through intra-provincial market integration

estimation. Further, the influence of government intervention and the participation of state-

owned enterprises regarding specific grain crops are unclear. A more informative study of the

regional grain market situation is needed.

The background of China’s grain sector requires an economic analysis of the grain

sector in the western inland provinces. This research contributes a downscale analytical

economic study for grain sector development in a western inland province in China that has

rarely been considered before. The general and specific context of this study has been

previously discussed in detail. Some of the methodologies used in this study have been widely

applied, but no thorough assessment of regional grain sector development has been attempted.

This study intends to provide useful findings and constructive policy implications for grain

sector development in the western inland provinces of China.

1.2 Research Objectives

The main objective of this study is to explore the status of the grain sector in the

transitional provincial economy of Guizhou, which is a southwestern inland province in China.

This study uses quantitative research based on an econometric methodology. Grain is the largest

agricultural product in Guizhou and is mainly supplied for local consumption. Grain production

in Guizhou was at its highest levels in the early 2000s; however, since 2010, grain production

has decreased significantly. Several regional development programs were implemented in

5

Guizhou province in the early 2000s. In the same period, the most important grain policies of

the last 20 years were launched in China, some of which were specific to Guizhou. It is

important to explore the production–demand situation in the grain sector and analyze the

sector’s role in this regional economy, taking account of the simultaneous impact of

development programs on industrial structural change and the impact of grain policies on the

regional grain sector.

Since grain production in Guizhou focuses on supplying the provincial grain market,

this study aims to estimate regional grain market distribution efficiency. The efficient

distribution of local production indicates that farmers are active producers and follow market

demand signals and participate in the grain market. The measurement of the intra-provincial

market integration structure is necessary to explain regional grain distribution efficiency.

Thus, the main objective of this research is decomposed into three elements of

assessment and estimation as follows.

1. To investigate the importance of the grain sector for the regional economy in the

context of economic structural change in Guizhou in terms of production and trade.

2. To explain the policy impact on regional grain sector development in terms of input

and output efficiency.

3. To measure the regional grain market structure and distribution efficiency for each

grain.

Economic development in Guizhou in recent 15 years can be divided into two stages.

From 2000 to 2007, most of the important regional development programs and grain-related

polices were launched in Guizhou. Since 2007 to the present, economic growth in Guizhou has

exceeded the pace of national growth. However, regional grain deficiency has worsened in the

last eight years.

The first and second objectives discuss the grain sector in Guizhou in the

macroeconomic context. An overview of the grain sector in Guizhou is provided in the first

part of this dissertation. The regional economy is composed of sectors that include the

agricultural, industrial, and service sectors. These sectors are related to each other through the

6

flow of production. Employment and capital transfer from one sector to another. Change in one

part of the economy influences the rest of the economy through direct and indirect relationships.

Input–output (IO) analysis encompasses all products and industries (Miller & Blair 2009).

Moreover, it provides systematic descriptions of the IO structures between industries; thus, it

is useful for analyzing the grain sector in the transitional Guizhou economy and the event

impact on the economic structure of Guizhou, such as development and grain production

support policies. This study utilizes the regional IO tables of 2002 and 2007 to analyze the role

of the grain sector in terms of economic transition. Further, it is possible to identify specific

policies that influence the grain sector in this period.

In order to meet the third objective, intra-provincial spatial market integration is

measured by utilizing time-series data of the grain prices from regional markets. This study

focuses on the present status of the market structure in Guizhou. Time-series econometrics

capture and examine the dynamics of the data in order to understand the structure of the

economy and test the hypothesis. Agricultural production covers a wide area and involves

costly transport. The trade procedure and demand–supply situation are presented in terms of

price signals; thus, the process of price signal transmissions through different markets includes

information about the relationship between the markets. The estimation of market integration

consists of a test of long-run spatial market integration, short-run adjustment for market shock,

and causal flow from one market to another. In China, marketing channels for grain circulation

vary in accordance with the grain. Moreover, specific regional production protection policies

exist in different provinces. In order to investigate the market integration structure for grain in

Guizhou province, one vital approach is to distinguish between the diversity of the marketing

channels and related policies for diverse grains in this provincial market. The price data of four

major grains are employed to compare the influence of different determinants on market

integration structure. Moreover, this study tries to provide a comparison of market efficiency

for primary grain products and processed grain products. The results enable a discussion on the

regional agricultural industrialization condition.

7

1.3 Dissertation Organization

This dissertation is organized as follows. Figure 1.1 depicts the entire framework of the

dissertation and the links among the chapters.

Chapter 1 introduces the research background and provides the justification and

objectives of the research. This is followed by a discussion of the economic analysis of the

grain sector in Guizhou.

Chapter 2 first introduces the development of China’s grain sector in terms of its

economic reform. Grain-related policies in different periods and changes of the grain demand–

supply situation in transitional China are then explained. The marketing channel for major

grains and the participants in China’s grain market are also described.

Chapter 3 and chapter 4 are the most important parts of this study. Chapter 3 follows a

classical IO analytical framework to develop an understanding from the macro perspective of

the economic structural change in Guizhou province and the role of the grain sector in the

regional economy. By utilizing crop sector as agent of grain sector, agriculture-based IO tables

for 2002 and 2007 are generated to investigate the impact of regional development strategy on

economic development and the role and performance of the crop sector. This chapter tries to

provides an analysis of (1) changes in the Guizhou economy’s industrial structure under

regional development programs, (2) the role and performance of the crop sector under grain

policy influence, and (3) the development potential of the crop sector in the future.

The conclusion in the structural change analysis triggers further analysis. Chapter 4

focuses on the estimation of an intra-provincial market integration structure for grain

distribution. Market integration for the four most commonly consumed grains in three regional

wholesale markets is discussed. Of the cereals, two are primary grain products and the other

two are processed grain products. The time-series model is described following a discussion of

the co-integration test procedure. First, this study classifies the order of the data carefully by

testing the unit roots. In accordance with the results, a vector autoregression (VAR) model,

which is suitable for determining the relationship among grain markets, is selected. In this study,

time-trend transaction cost is considered. The VAR model is transferred to a vector error

8

correction model (VECM) format, and long-run co-integration and short-run adjustment are

estimated by following maximum likelihood estimation. The causality relation between

markets is also discussed.

Finally, Chapter 5 summarizes the main concluding remarks of the entire dissertation

and gives reasonable suggestions for promoting the grain sector’s adaption to regional

economic development. Further, the limitations of the current approach and recommendations

for future research are presented.

Figure 1.1 Entire framework of the dissertation and the links among chapters

9

1.4 References

Awoke, T.O., 2007. Market reforms, spatial price dynamics, and China's rice market

integration: a causal analysis with directed acyclic graphs. Journal of Agricultural and

Resource Economics, pp. 58-76.

Fu, W., Gandhi, V.P., Cao, L., Liu, H. and Zhou, Z., 2012. Rising consumption of animal

products in China and India: national and global implications. China & World Economy, 20(3),

pp.88-106.

Gandhi, V.P. and Zhou, Z., 2014. Food demand and the food security challenge with rapid

economic growth in the emerging economies of India and China. Food Research

International, 63, pp.108-124.

HU, T. JU, Zs. and ZHOU, W., 2016. Regional pattern of grain supply and demand in China,

71(8), pp. 1372-1383. (Chinese paper)

Huang, J. and Rozelle, S., 2006. The emergence of agricultural commodity markets in China.

China Economic Review, 17(3), pp. 266-280.

Johnston, B.F. and Mellor, J.W., 1961. The role of agriculture in economic development. The

American Economic Review, 51(4), pp. 566-593.

Miller, R.E. and Blair, P.D., 2009. Input-output analysis: foundations and extensions.

Cambridge University Press.

Park, A., Jin, H., Rozelle, S. and Huang, J., 2002. Market emergence and transition: arbitrage,

transaction costs, and autarky in China's grain markets. American Journal of Agricultural

Economics, 84(1), pp. 67-82.

Poncet, S., 2005. A fragmented China: measure and determinants of Chinese domestic market

disintegration. Review of International Economics, 13(3), pp. 409-430.

10

Chapter 2

The Grain Industry in China’s Transitional Economy

2.1 The transitional economy of China and its grain autarky policy

China started market-oriented economic reform in the late 1970s. This economic reform

began in agricultural sector. In the early stages of the economic reform, deregulation in the

agricultural market and the adoption of a household-responsibility system promoted

agricultural production (Lin 1992). Output in the agricultural sector increased significantly and

China became self-sufficient in food.

As the economic reform continued, growth in the nonagricultural sectors accelerated.

In particular, China has experienced dramatic growth in the manufacturing sector in the first

decade of the 2000s. The share of the agricultural sector in China’s gross domestic product

(GDP) declined from 27.9% in 1978 to less than 10% in 2016 (NBC of China 2016). Figure

2.1 shows the growth of three sectors in China’s economy from 1978 to 2014. The agricultural

sector has become a minor part of China’s overall economy.

Figure 2.1 The growth of three sectors in China from 1978 to 2014

Source: NBC of China 2016

The agricultural sector contributes substantially to overall economic development in

China, although its proportion has shrunk in China’s economy. The agricultural sector provides

11

various supplies to meet the growing demand that has resulted from economic development.

Sharp growth in the manufacturing sector is driven by significant migration of the rural labor

force from the agricultural sector to the nonagricultural sector. In recent decades, agricultural

production in China has profoundly changed because of rapid industrialization and

urbanization. Moreover, the sustainability of agricultural production has been doubted by

researchers (Brown 1995, Chen(b) 2007, Simelton 2011). In particular, grain sufficiency and

food security has attracted the greatest concern both domestically and worldwide (Brown 1997,

Yang & Li 2000, Chen(b) 2007). Tremendous grain demand in China, which has a large

population, is hard to satisfy through the world’s grain market. Food security in China is crucial

not only for China but also the world’s grain market. In order to meet grain self-sufficiency,

the Chinese government pays great attention to such self-sufficiency and to food security. Most

studies of sustainable grain supply in China assume that production condition restraints, such

as arable land loss, a shortage of water resources, and soil degradation, are the main threats that

China’s grain sector faces (Huang & Rozelle 1995, Brown 1998, Yang & Li 2000, Xu et al.

2006, Khan et al. 2009). However, the recent changes in China’s grain sector indicate a much

more complicated relation between production in the grain sector and overall economic

development in China.

Figure 2.2 indicates the total annual production of grain in China from 1978 to 2014.

In 1992, when China abolished its grain quota system, grain production increased from 1992

to 1999. Further reform of the grain sector was proposed by central government in the late

1990s. A market-oriented national grain reserve system was established in 1999, and the

government procurement system was abolished in the early 2000s. However, successful

nationwide harvests soon brought down the prices of grains, and less profitable grain

production prompted Chinese farmers to reduce grain farming in the early 2000s. The decline

of grain production from 2000 forced the Chinese government to launch a more influential

minimum price procurement and stockpiling policy for rice and wheat in 2004 (NDRC 2004).

This policy enabled regional governments and SOEs to intervene in the grain market for the

temporary procurement and stockpiling of grain in order to set prices.

12

Figure 2.2 Grain production in China from 1978 to 2014


Under the minimum price procurement and stockpiling policy, the Chinese government

divided provinces into key grain production areas, consumption areas, and self-sufficient areas

depending on their regional grain production conditions. Floor price procurement and

stockpiling was implemented in key production areas. The policy began with 11 provinces

defined as key production areas for rice and six provinces defined as key production areas for

wheat.

Apart from this specific regional policy, another grain production support policy was

implemented in all provinces in 2005. This latter policy is a direct grain production subsidy to

grain farmers. The grain production subsidy aims to reduce production costs such as the seed

and fertilizer costs incurred by grain farmers. Direct production subsidies vary between the

provinces. Some provinces provide a subsidy per unit of land that is used for grain production.

Other provinces provide a subsidy per unit of production sold to the SOEs.

Because of this grain production support policy, total grain output has kept increasing

in China since 2004. China has produced over 0.5 billion tons of grains (the five major grains

are rice, wheat, maize, soybeans, and rapeseed) annually in recent years, which ranks China as

the highest producer in the world. Further, China has maintained 90% self-sufficiency for staple

food grains such of rice and wheat.

When the floor price procurement and stockpiling policy started in 2004, it was only

implemented for staple food grains such as rice and wheat. However, later changes in the grain

market forced China’s central government to extend this policy from 2007 to support other

13

agricultural products; for example, maize, soybeans, and rapeseed. In the early 2000s, when

China experienced rapid economic growth and urbanization, non-staple food grain demand

increased dramatically. Increases in meat consumption and manufactured food are the driving

forces that have prompted the strong demand for non-staple food grains. These grains are

primary materials in industrial sectors. Maize and rapeseed production has continued to

increase from 2007 (see figure 2.3). Indeed, in 2013, maize production exceeded rice

production to become the most widely produced grain in China. However, this policy did not

improve soybean production because of the market competitiveness of domestic soybeans. The

demand for soybeans, which are used in the feed and oil-crushing industries, has increased

nearly exponentially in the last two decades, but domestic soybean production cannot meet this

growing demand. Moreover, soybeans in the worldwide market are much cheaper than China’s

domestic soybeans. Soybean imports keep rising and presently account for 85% of soybean

consumption in China. In this context, China stopped its soybean production support policy in

2014.

Figure 2.3 Annual production of five grains from 1995 to 2014


Because of the significant employment level in the grain sector (about 0.25 billion

people), grain productivity in China is extremely low. However, economic development has

led to an increase in production costs. In order to maintain grain production so as to meet self-

0

50

100

150

200

250

19951996199719981999200020012002200320042005200620072008200920102011201220132014

Rice wheat Maize Soybean Rapeseed

14

sufficiency and stabilize the grain market, the Chinese government continues to raise its

artificial floor prices for grain procurement to maintain profitable prices and thereby encourage

grain farmers to continue production. This artificial floor price policy has distorted market

signals in China’s domestic grain market and insulated the market from that of the world. All

major grain prices in China’s domestic market have kept increasing and exceeded the world’s

market prices in 2010. Highly priced grains are hard to sell in the consumption market. Yet the

national stock-sale ration for grain has reached a consistently high level in China. Because of

the recent changes in the grain sector, the Chinese government has reconsidered its grain

autarky policy. According to a statement by grain officials, China’s grain price-formation

mechanism needs improvement, resource allocation is distorted, and the grain industry is

plagued by high inventories, high imports, and high costs. As a result, in 2016, in order to

replace the minimum price for procurement and stockpiling policy, China adopted a direct

payment grain subsidy policy tied to the number of planted acres. Further, a more recent policy

has been proposed. This policy is called the “grain supply-side structural reform.” (People’s

Daily Online 2016). The new policy launched in 2017; however, the details are not clear yet.

2.2 Grains’ demand–supply pattern and changes in China

The grain demand–supply situation has changed profoundly in China in the last two

decades. Grain production depends on conditions that are geographically related. In this regard,

grain production support policies vary across the regions. Provincial governments have

different interests regarding grain production in terms of economic growth. Specific regional

grain production support policies have influenced the grain production plans of local grain

farmers. Moreover, the regional disparity of economic growth among provinces in China

increased in the early 2000s. Thus, the grain production and consumption situation in China

has varied regionally. It is necessary to distinguish the regional patterns of the demand–supply

situation by grains.

(1) The regional rice market demand–supply pattern

15

Figure 2.4 shows the thermodynamic charts of the regional rice areas in China in 1996

and 2014. Rice is the foremost staple grain in southern China. In the past, southern China was

the main rice-producing area (see the left-hand side of Figure 2.4). However, by 2014, the

southern coastal provinces became a rice-deficient market due to rapid urbanization and

industrialization. As indicated in Figures 2.4, China’s rice production has concentrated more in

the farmland of the resource-rich central and northeastern areas. These areas have implemented

a minimum support price policy in 11 provinces. In the two areas, five provinces (Hunan, Hubei,

Anhui, Jiangxi, and Heilongjiang) account for almost half of the total rice production in China

(NBSC 2016). Moreover, rice production in the two areas differs. Rice farmers in the

northeastern area produce the Japonica rice variety and in the central area, farmers produce the

Indica rice variety. Indica rice has a higher yield, accounts for 60% of the total rice output in

China, and is preferred in southern China. Approximately 40% of rice production in China

consists of the Japonica variety. The consumption of Japonica rice is increasing in the southern

area.

Figure 2.4 Thermodynamic charts of regional rice areas in China in 1996 and 2014(Units:

Thousands of hectares)

Source: The author created the charts in accordance with data from NBC of China 2016

Southern China is the main rice consumption area. The geographical concentration of

rice production varies between rice surpluses and rice deficits in the regional markets.

16

Developed coastal provinces with rice shortages have strengthened their connections to rice

producing areas in central and northern China. Interregional market connections can be

established regardless of distance and border restrictions because of transportation

improvements. Coastal provinces benefit from interregional market integration because their

consumption markets are eager for rice. However, in the fragile western inland provinces, rice

farmers find it difficult to access the market. Most of the local rural population live in remote

areas and rely on agriculture for a living. Because of the poor traffic infrastructure, western

inland rice farmers are less competitive than their peers in key producing areas. The isolation

from markets may push these local rice farmers into poverty.

In recent years, southwestern inland provinces such as Sicuan, Guizhou, and Yunnan

have become grain deficient markets. Labor migration from these provinces to developed areas

and the abandonment of grain production may be the reasons for such deficiency. These

backward regional economies need to pay high amounts for rice imports from markets with

surplus rice in competition with the developed eastern coastal provinces.

(2) The regional wheat market demand–supply pattern

Wheat is the second most important staple food grain in China and accounts for 30%

of food grain consumption. Figure 2.5 shows the thermodynamic charts of the regional wheat

areas in China in 1996 and 2014. In recent years, annual wheat output in China has remained

at approximately 120 million tons in order to meet domestic demand. The Chinese central

government supports wheat production in five northern provinces though floor prices. These

provinces in the north (Henan, Shandong, Hebei, Anhui, and Jiangsu) produce over 80% of

total domestic wheat (NBSC 2016). In accordance with harvesting, wheat production is divided

into winter production and summer production. Over 90% of wheat output is winter production.

Very little wheat production is found in southern areas and is produced in the summer. Southern

wheat is unproductive, and its quality is poorer than that of northern wheat.

17

Figure 2.5Thermodynamic charts of regional wheat areas in China in 1996 and 2014(Units:



Northern China is the major wheat producing and consumption area, with wheat

production concentrated in the coastal area (see Figure 2.5). In the last 20 years, wheat has

become the most commercialized grain in China. Of total wheat production, 90% is shipped to

markets and processed into wheat flour. Agglomeration of the wheat processing industry has

been observed in developed provinces in eastern coastal and central areas such as Shandong,

Jiangsu, Henan, and Anhui provinces. Wheat flour production in these areas is distributed

widely in the northern China market. The northwestern inland provinces are the major wheat

consumption markets.

Xinjiang province is the only northwestern inland province where wheat production has

increased in the last 20 years. In most northwestern inland provinces, wheat production has

declined because of changes in consumption preferences. Many western provinces have shifted

their consumption patterns from wheat to rice.

(3) The regional soybean market demand–supply pattern

Soybeans are grown in almost every province in China and serve several functions in

the food chain. Soy oil is the most consumed edible oil in China and accounts for almost half

of edible oil consumption. Figure 2.6 shows the thermodynamic charts of the regional soybean

areas in China. The northeast is the traditional soybean producing area and accounts for 50%

of domestic soybean production. Before the 2000s, more than 60% of total domestic soybeans

18

were crushed into soy oil and soymeal. The rest were used in the food-processing and seasoning

industries.

When China’s soybean market was completely opened, soybean consumption increased

almost exponentially from the late 1990s. The Chinese government prohibited imported

soybeans to be sold in food markets and promoted soybean production in the northeastern

provinces through a temporary procurement and stockpiling policy. However, imported

soybeans are now distributed by large-scale foreign enterprises, are cheaper than the domestic

product, and are easily traded. Imported soybeans have become dominant in the crushing sector.

In contrast, domestic soybeans have been gradually driven away from the crushing sector and

are now sold primarily in food markets.

Figure 2.6 Thermodynamic charts of regional soybean areas in China in 1996 and

2014(Units: Thousands of hectares)


Soybean demand from the food market has grown consistently in southern China in

recent decades. Moreover, domestic soybeans are preferred in the food-processing sector.

However, the spatial connection between the production and consumption areas is possibly

insufficient. Many soy farmers have decided to abandon soybean planting in key soybean

producing areas that are remote from the main domestic soybean consumption market in the

south. Soy farmers in the northeast are unable to sell their products to the southern consumption

market for profitable prices. As a result, soy farmers have changed their planting options to

19

lucrative grains such as rice and corn, the prices of which are supported by government, instead

of soybeans.

(4) The regional rapeseed market demand–supply pattern

Rapeseed oil is the second most consumed edible oil in China. The watershed of the

Yangtze River is the main rapeseed growing area and accounts for 90% of total output in China

(see Figure 2.7). Some northwestern provinces such as Inner Mongolia also plant rapeseed. In

the past, five provinces (Hubei, Hunan, Anhui, Jiangxi, and Henan) in the middle reaches of

the Yangtze produced 50% of rapeseed output. However, since these provinces are also the

main producing region for staple grains (rice and wheat) in China, rapeseed production

competed with rice and wheat. When staple grain production was promoted through

government policy, the acreage for rapeseed shrank by nearly 50%. In order to increase

rapeseed production, the government has implemented a rapeseed production support policy to

the watershed of the Yangtze River area. The upper reaches of the Yangtze River, which are in

the western inland area of China, have now become the key rapeseed-producing area.

The southwestern inland area is the traditional rapeseed oil consumption area; however,

because it is restricted by less favorable transportation conditions and trade barriers, local

rapeseed production is only for intra-regional consumption. At the same time, other oil products

have encountered sales difficulties in this area. Even though the area has seen temporary

purchase and stockpiling policies for rapeseed implemented, the high costs and poorer

competitiveness of rapeseed in the market has meant that such support policies were stopped

in 2016.

20

Figure 2.7 Thermodynamic charts of regional rapeseed areas in China in 1996 and

2014(Units: Thousands of hectares)


Compared with domestic rapeseed, imported rapeseed is cheaper and of high quality.

In 2016, 3.8 million tons of rapeseed were imported, accounting for 30% of rapeseed

production in China. Canada and Australia are the largest and second-largest rapeseed exporters

to China. Large-scale rapeseed crushing plants have been established in the eastern coastal

region, utilizing imported rapeseed as the primary material. The rapeseed crushing enterprises

in the southwestern inland region are generally small-scale. China also directly imports

rapeseed oil. Because edible oil demand in China’s market will continue to be strong and

because domestic production is limited, the importation of rapeseed oil is predicted to grow in

the future.

(5) The regional maize market demand–supply pattern

Maize production in China exceeded rice production in 2013 and became the most

produced grain. This situation has been caused by the strong demand for maize as a feed for

the livestock sector. Because of rapid urbanization in the coastal regions, food consumption in

these areas has shifted to a preference for a meat-based diet. Most maize is used to feed pigs,

chickens, and cows. Prompted by demand, maize production has jumped nearly 125% over the

past 25 years. As shown in Figure 2.8, in 1996, except for north and northeastern China, the

southwestern inland provinces were the producing area. In 2014, maize production was

concentrated in north and northeastern China. In the north and northeast, Inner Mongolia is the

21

only major province in the comparison of Figure 2.8 in which maize is dominantly irrigated.

In the northeast, since there is just one crop per year, maize production needs to compete with

soybeans and rice, which are also dominant crops in this region.

Figure 2.8Thermodynamic charts of regional maize areas in China in 1996 and 2014(Units:



Since 2007, the Chinese government has run a maize production support policy though

government procurement and stockpiling. However, this policy stopped in 2016 because there

was an excess supply of domestic maize production and a significant inventory that was held

by SOEs and could not be sold in the market.

2.3 The market oriented grain reserve system and the participants in the grain marketing

channel

In the late 1990s, as part of its aim to maintain food self-sufficiency and grain price

stabilization, China started to establish a market-oriented grain reserve system and

commercialize SOEs. In 2000, central government removed the grain procurement system and

opened the early rice market to private traders (NDRC 2002). In 2004, the government released

an ordinance regarding grain circulation that announced the qualifications that private traders

needed in order to enter the grain procurement market. Small-scale private traders, with trade

volumes less than 50 tons annually, can freely access the grain market without

22

registering(NDRC 2004). After almost five years’ preparation, a system of monitoring and

forecasting the market price was introduced in 2005(NDRC 2005). This system serves the

central and provincial governments and releases information on grain demand–supply, grain

quality, and grain price. Since these changes in the grain market, the structure of China’s grain

marketing channel has gradually altered.

At present, rural brokers and private traders are allowed to enter the grain procurement

market officially. Private enterprises that qualify can contract with the government for national

grain reserves. However, the level of liberalization and deregulation for grain marketing varies

for different grains and in different regions, thus affecting the structure of the grain-marketing

channel. This study describes the marketing channel for four key grains and explains the major

participants based on information collected from prior research and official statements.

(1) Rice

The grain-marketing channel comprises grain farms, assemblers, processors,

wholesalers, and retailers. Figure 2.9 shows the general marketing channel for rice circulation

in China. Since rice is the most important grain, SOEs control rice procurement. There are three

SOEs dominant in the staple grain market: SINOGRAIN, COFCO, and Chinatex Corporation.

In particular, SINOGRAIN is the most active participant in the rice-marketing channel because

this SOE and its subsidiaries belong to the official reserve system. SOEs can use social

networks and have access to the marketing information system that is provided by central and

local governments, whereas private participants are limited in their purchase capacity and

information collection. SOEs are responsible for policy-oriented business and protective

buying. Moreover, in some cases, private participants contract with the government or SOEs

for rice procurement. Small-scale private merchants/brokers sell their rice to SINOGRAIN.

Rice that is shipped to market is transported by railway, road, and water. Railway

accounts for 80% of rice distribution, especially for long-distance transport. Short-distance rice

distribution mainly depends on road transport.

23

Figure 2.9 Marketing channel for rice circulation

Source: The author created the chart in accordance with multiple sources

(2) Wheat

Wheat is the most commercialized grain. Of total wheat production, 90% is processed

into wheat flour. Figure 2.10 shows the general marketing channel for wheat and wheat flour

circulation in China. The wheat-flour processing industry is relatively developed and

agglomerates in the northern China wheat-producing area. Similar to rice, wheat is the second-

largest imported grain and the staple grain for the northern Chinese. SOEs control wheat

procurement, and SINOGRAIN is the most active participant in the wheat-marketing channel

at the procurement stage. Private participants are limited in their purchase capacities and

information collection in the wheat procurement market. Small-scale private

merchants/brokers sell their wheat to SINOGRAN. SOEs are responsible for policy-oriented

business and protective buying. However, private participants are active in wheat processing

and distribution in the consumption market. Wheat flour transportation depends on railway,

road, and water transport. Compared with rice, more wheat flour is distributed though road

transport.

24

Figure 2.10Marketing channel for wheat circulation


(3) Soybeans

Figure 2.11 shows the marketing channel for soybean distribution in China. It is

necessary to differentiate between the domestic product and imports in the soybean market.

The Chinese government has prohibited imported soybeans to be sold in the food market.

Domestic soybeans are mainly used in the food-processing sector. Private participants are

active in soybean distribution in the food market; however, such participants are usually small-

to medium-sized and face difficulties in collecting soybeans from households and villages.

Imported soybeans are distributed by large-scale foreign enterprises, are cheaper than the

domestic product, and are easily traded. Imported soybeans dominate the oil-crushing industry.

Domestic soybean distribution depends on railway and road transport, whereas imports are

transported by sea.

25

Figure 2.11Marketing channel for soybean circulation


(4) Rapeseed

Figure 2.12 shows the marketing channel for rapeseed distribution in China. Private

participants in the grain market are mostly active in the rapeseed channel and focus on local

markets. Farmers sell rapeseed in two main ways. They can choose to sell their products to

local, small rapeseed processing millers, or they can sell to local private merchants/brokers.

Most small mills are operated by farmers in their homes and provide a rapeseed processing

service for farmers’ home rapeseed oil consumption. If there is not too much surplus rapeseed,

most smallholders like to sell the rapeseed to small mills close to their homes. Most local agents

are nearby farmers and know exactly who has rapeseed to sell. Thus, they go to farmers’ homes

to bargain with them. After reaching an agreement over price, local agents drive or rent a small

truck to transport the rapeseed to their own storage facility or to other agents for direct sale.

Smallholders with small production facilities have bargaining disadvantages. Local agents can

force down the price of rapeseed to obtain greater profit through asymmetric market

information and because farmers are eager to sell.

26

Figure 2.12Marketing channel for rapeseed circulation

Author made graph according to multiple sources

27

2.4 References

Brown, L.R. and Halweil, B., 1998. China’s water shortage could shake world food

security. World Watch, 11(4), pp. 10-21.

Chen(a), J., 2007. Rapid urbanization in China: a real challenge to soil protection and food

security. Catena, 69(1), pp. 1-15.

Chen(b), L., 2007. Grain market liberalization and deregulation in China: the mediating role

of markets for farm households in Jiangxi province.

Huang, J. and Rozelle, S., 1995. Environmental stress and grain yields in China. American

Journal of Agricultural Economics, 77(4), pp. 853-864.

Khan, S., Hanjra, M.A. and Mu, J., 2009. Water management and crop production for food

security in China: a review. Agricultural water management, 96(3), pp. 349-360.

Lin, J.Y., 1992. Rural reforms and agricultural growth in China. The American Economic

Review, pp. 34-51.

People’s Daily Online 2016, The centrall work meeting of rural issues was held in Beijing,

viewed 21 December 2016, http://politics.people.com.cn/n1/2016/1221/c1024-28964629.html

National Development and Reform Commission (NDRC) P. R. of China, 2002. The opinion of

accelerating reform on state grain trading companies (Guanyu jiakuai guoyou liangshi gouxiao

qiye gaige he fazhan de jianyi). Document No. [2002] 677.

National Development and Reform Commission (NDRC) P. R. of China, 2004. Measures on

qualifications of enterprises for national grain reserves (Zhongyang chubeiliang daichu zige

rending banfa). Document No. [2004] 20.

National Bureau of Statistics of China (NBSC), 2014. China Statistical Yearbook. China

Statistics Press, Beijing.

National Development and Reform Commission (NDRC) P.R. of China, 2005. The notification

of further collecting statistics of grain circulation system (Guanyu jingyibu zuohao quanshehui

liangshi liutong tongji gongzuo de tongzhi). Document No. [2005] 376.

Yang, H. and Li, X., 2000. Cultivated land and food supply in China. Land use policy, 17(2),

pp.73-88.

Xu, Z., Xu, J., Deng, X., Huang, J., Uchida, E. and Rozelle, S., 2006. Grain for green versus

grain: conflict between food security and conservation set-aside in China. World

Development, 34(1), pp. 130-148.

http://politics.people.com.cn/n1/2016/1221/c1024-28964629.html

28

Chapter 3

Economic Structural Change and the Agricultural Sectors

in Guizhou

3.1 Introduction

This chapter examines the economic structural change in Guizhou by utilizing an IO

analysis framework. In particular, this analysis focuses on the role of the crop sector in the

Guizhou economy. In the 2000s, the Chinese government implemented several development

programs in its western inland area. During the same period, China started a new reform of the

grain sector. The most influential of the grain production support policies in the last 15 years

was implemented in this period. However, the influence of program policies on the provincial

economy has not been clarified. The research in this current study provides (1) an overview of

the Guizhou economy’s industrial structural changes, (2) the identification of the role and

performance of the crop sector under the influence of policies, and (3) insights into the

development potential of the crop sector in the future.

Figure 3.1 Location of Guizhou in China

29

Guizhou province in the southwestern inland region of China is one of the most

economically closed provinces. The location of Guizhou in China is indicated in Figure 3.1.

Compared with its area, Guizhou is a populous province with the densest population in

southwestern China. Guizhou is a multi-ethnic region with 40% of population are composed of

various minority groups. Most minority groups live in rural areas. The agricultural sector

employs the largest amount of the population of Guizhou province. In 2000, over 75% of the

population lived in rural areas. This proportion has consistently declined; however, rural areas

still accounted for 60% of the total population in 2015.

In the 1990s, the grain area accounted for 80% of the total agricultural area in Guizhou

province. In recent years, the grain area has sharply declined but still accounts for 60% of the

agricultural area. Most local farmers work in grain production.

Before the economic reform era, the Chinese central government asked every province

to establish its own industrial system. Guizhou has followed this plan and established industries

such as the metal, chemical, textile, tobacco, and food industries. Some machine-

manufacturing industries transferred from coastal regions to Guizhou province in the 1960s.

The Guizhou economy depends on the national market and is less open to the world.

Economic growth in Guizhou in the last two decades can be divided into two periods. Figure

3.2 shows Guizhou’s gross output and growth rate from 1996 to 2015 compared with China’s

gross national product (GNP) growth rate. Before 2007, even with some fluctuations, the

regional gross output growth rate of Guizhou was nearly equal to the growth rate of China’s

GNP. Since 2007, the gross output growth rate in Guizhou has exceeded China’s GNP growth

rate. Although both the regional output and China’s GNP growth rate have declined in the last

five years, growth in the Guizhou economy is faster than in China’s economy.

30

Figure 3.2 The gross output growth in Guizhou from 1996 to 2014

Source: Created by the author with data from the National Bureau of Statistics of China

This study is based on regional economic structural change from 2002 to 2007. The

most important policies related to grain production and regional development programs in the

last 20 years were launched in Guizhou during this period. In 2002, grain procurement was

abolished in Guizhou. The central government asked the provincial government to reform its

grain reserve system through a reduction of employees and commercialization of SOEs. Since

then, SOEs have not had a monopoly over grain procurement. The government allows

contracting with other commercial enterprises for grain reserves. In 2004, a minimum purchase

price policy, which supports rice and wheat production, was implemented in key grain

production areas. Guizhou was defined as having a grain deficit and being a seller’s market. In

2005, the government paid subsidies to encourage grain production to start in Guizhou. In the

same year, the tax on agricultural products was abolished. In 2007, the government’s policy on

the procurement and stockpiling of rapeseed, which aims to support oilseed grain production,

started in Guizhou province.

Apart from the policies related to grain production, several regional development

programs have been implemented in Guizhou between 2002 and 2007. The first was an energy

transmission project, which aims to send electrical power from the western inland to the east

coastal region. Guizhou is a resource-rich province. It has abundant coal, gas, hydropower, and

metal ore (most of which rank among the highest amounts in southern China). Construction of

several hydroelectric and coal-fired power plants started in Guizhou in 2001, and a power

transmission system, which connects Guizhou to the coastal Pearl River Delta, was established

0

2

4

6

8

10

12

14

16

0

20

40

60

80

100

120

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Guizhou gross output Growth rate(China) Growth rat(Guizhou)

31

in 2005. The second project involved two ecological conservation programs, which were the

grain-to-green program and the forest conservation program. Guizhou is at the upper reaches

of the Yangtze River. Land degradation and karst rocky desertification are the most serious

ecological problems in Guizhou. The grain-to green program aimed to enable farmers to retire

farmland that is susceptible to soil erosion and transfer such land into woodland or grassland.

This program started in 2000 in Guizhou and was mostly implemented from 2002 to 2005.

Another forestry conservation program in Guizhou started in 2000 and was formally carried

out in the mid-2000s. The content of this program included the cessation of logging, the

banning of free grazing, and the expansion of forest coverage. The third project was a

continuation of an infrastructure and transport system construction project. Infrastructures such

as roads and bridges started construction in 2001. Several roads and bridges have been built in

the mid-2000s. The rest are expected to finish in the 2010s. A highway system in Guizhou was

proposed in 2004.

In the rest of this chapter, the generation of agriculture-based regional IO tables is first

explained. The second part of the chapter describes the IO analytical framework. A multiplier

and RAS analysis are applied in order to analyze economic structural change in Guizhou and

the situation in the agricultural sector. In the third part of this chapter, the IO analytical results

are presented and discussed. The last part is a summary that includes implications.

3.2 Generation of agriculture-based regional IO tables

The first task of this research is to generate regional IO tables for Guizhou with detailed

classification of the agricultural sector.

In the early 1980s, the national statistical bureau (NBS) of China completed the first

national IO table. Soon afterward, regional IO tables for each province were completed by the

regional governments. In the early stages, China created IO tables through the “direct

decomposing method” (Ichimura &Wang 2003). In this regard, the IO tables belonged to the

material product system (MPS). In 1989, NBS transferred China’s national economic accounts

from the MPS to the system of national accounts (SNA), which is widely used. After intensive

32

human, monetary, and time costs, 30 regional IO tables were completed through a grass-roots

survey scheme in the early 1990s.

Since 1987, China has created benchmark IO tables every five years. China has already

released five national benchmark IO tables (1987, 1992, 1997, 2002, and 2007). In addition,

China has created four extended IO tables (1990, 1995, 2000, and 2005). Depending on the

level of industrial classification, two levels of IO tables exist and include 46 and 144 industrial

sectors. The Chinese IO table is created in terms of a producer price “competitive import type.”

Input values in processes such as deliveries and commercial services are classified into the

delivery sector and service sector. Imports are not separated in each industry and are only

summarized as imports in a column in the final demand section.

Each province in China has its own regional IO tables. The Chinese central government

demands that all provincial governments prepare regional IO tables in accordance with a

uniform format that is specified by the central government. The formats of regional IO tables

and national IO tables are identical. In regional IO tables, the inflows from other provinces to

regional industries do not differentiate imports from other countries. Imports and inflows are

indicated together as import items. Further, the outflows to other provinces do not differentiate

exports to other countries. Exports and outflows are indicated together as export items.

Regional IO tables are not completed in some backward provinces and it is hard to find the

available data. In Guizhou, the IO tables with 144 industrial sectors can only be found in printed

versions for 2002 and 2007.

Apart from the 144 industrial sectors, the original IO tables of Guizhou included five

categories of final demand and four value-added items. Final demands include domestic

consumption, government expenditure, investment, exports, and import(inflows). In this

research, we aggregated the 144 industrial sectors into 21 major industrial sectors with

emphasis on agricultural industries (see the appendix, Guizhou input–output for 2002 and

2007). The major sectors are as follows: 1 crop farming; 2 forestry; 3 livestock; 4 fisheries

(freshwater); 5 other agric. services; 6 mining; 7 food, beverage, tobacco; 8 leather, knitwear;

9 timber, paper printing; 10 chemical, medicine, nonmetal; 11 metal; 12 industrial machinery;

33

13 electricity and energy; 14 construction; 15 transport; 16 postal and information; 17 retail

and wholesale; 18 lodging and catering; 19 finance and real estate; 20 social services; 21

education and government.

3.3 The input–output analytical framework

The IO analytical framework was developed by Professor Wassily Leontief in the 1930s.

Nowadays, IO analysis is one of the most applied methods in economics. IO analysis

encompasses all products and industries. It also provides systematic descriptions of the IO

structures between industries. Quantitative analysis can be constructed to investigate the

industrial structure of an economy. Moreover, such analysis can find out the impact of any

changes in one part of the economy on the rest of the economy through direct and indirect

effects.

According to the statement by Miller and Blair (2009), if an industry is considered a

producer, the products in this industrial sector flow to other sectors and also to itself. Further,

the industrial sectors that receive products are considered consumers. In order to present inter-

industry transaction information among industries in a matrix form, the rows of the matrix must

describe the distribution of a producer's output throughout the economy. The columns of the

matrix must describe the composition of the input required by one particular industry in order

to produce its output. This matrix is the intermediate IO matrix.

The final demand records the sales of each sector to the final market for production

(e.g., domestic consumption and government procurement). In IO tables, the final demand for

each sector is represented as a row vector at the right side of the intermediate IO matrix. The

final demand is the exogenous part of IO tables. Any change in whatever part of total demand

for sectoral output is matched. The endogenous intermediate demand for sectoral output and

the endogenous demand for primary inputs, such as value added and imports, are determined

by intermediate demand and final demand.

Based on the arrangement of the importing part of an industry, there are import

competitive type and import in-competitive type IO tables. The IO tables of Guizhou are the

34

import competitive type. Import competitive type IO tables are indicated in Figure 3.3.

Competitive type IO tables make consumption statistical data the proportion of imports in each

industry, which is constant as one form of demand, expressed in one form of data and labeled

as the final demand column.

The foundation of IO analysis is an IO table that includes a shaded demand–supply

relationship among industries. Apart from the conventional demand-driven IO model, which

was developed by Leontief, the supply-driven IO model, which was developed by Ghosh, is

applied to deal with the supply-side problem (Ghost 1958).

Intermediate

Demand

(j = 1, 2, … n)

Final Demand

Imports Domestic

Production Domestic

Demand Exports

Intermediate

Input

（i = 1, 2, …

n）

Xij Yi Ei Mi Xi

Value added Vj

Total Input Xj

Figure 3.3 Competitive import type input–output framework

From the demand side, when the final demand increases in one industry, it needs to be

met by production from all industrial sectors directly and indirectly. The production

inducement coefficient represents the sum of the total amount of induced production by one

unit increase in one industry. This coefficient indicates the relative magnitude of the production

impact.

As indicated in Figure 3.3, for each industry, the value of total production (total output

X) is the sum of the intermediate demand X𝑖𝑗 plus the final demand 𝐹𝑖. Essentially, there is

equilibrium between supply and demand in IO tables, with the following equation needing to

be satisfied.

𝐴𝑋 + 𝐹 = 𝑋 (3.1)

35

Since the IO tables of Guizhou are the competitive import type, domestic final demand

for domestic products is under the same assumption. The imports (inflows) (M) input ratio is

supposed to be constant in all sectors and not linked directly to exports. Thus, equation (3.1)

becomes:

𝐴𝑋 + 𝐹 − M = 𝑋 (3.2)

The final demand F can be divided into domestic final demand Y and exports (outflows)

E. Hence:

𝐹 = Y + 𝐸 (3.3)

When this is substituted into equation (3.2), the result is

𝐴𝑋 + 𝑌 + 𝐸 − 𝑀 = 𝑋 (3.4)

Direct input coefficients for domestic intermediate goods are defined as 𝐴 = {𝑎𝑖𝑗} =

X𝑖𝑗/𝑋𝑗 . The ratio of imports (inflows) in each sector can be calculated as m𝑖 =𝑀𝑖𝑗

∑ 𝑎𝑖𝑗𝑋𝑗𝑗 +𝑌𝑖 ,

which transforms equation (3.2) into matrix form as follows:

[

𝑎11 ⋯ 𝑎1𝑗

⋮ ⋱ ⋮𝑎𝑖1 … 𝑎𝑖𝑗

] ∙ ⌊𝑋1

⋮𝑋𝑖

⌋ + [𝑌1

⋮𝑌𝑖

] + [𝐸1

⋮𝐸𝑖

] = [𝑥1 + 𝑖𝑚1

⋮𝑥𝑖 + 𝑖𝑚𝑖

] (3.5)

When the definition of m𝑖 =𝑀𝑖𝑗

∑ 𝑎𝑖𝑗𝑋𝑗𝑗 +𝑌𝑖 is substituted into the element equation (3.5),

the result is

𝑋𝑖 = (1 − 𝑚𝑖)(𝑎𝑖1𝑋1 + 𝑎𝑖2𝑋2 + ⋯ + 𝑎𝑖𝑗𝑋𝑖) + (1 − 𝑚1)𝑌𝑖 + 𝐸𝑖 (3.6)

By putting the import (inflow) coefficients as diagonal elements of the diagonal

matrix M̂ = ⌊𝑚1 ⋯ 0

⋮ ⋱ ⋮0 … 𝑚𝑖

⌋, we can obtain

[𝐼 − (𝐼 − �̂� )𝐴]𝑋 = [ (𝐼 − �̂� )𝑌 + 𝐸 (3.7)

Further, from equation (3.7), the following equation is derived:

𝑋 = [𝐼 − (𝐼 − �̂� )𝐴]−1[ (𝐼 − �̂� )]𝑌 + 𝐸] (3.8)

We denote the Leontief inverse matrix as B = {b𝑖𝑗} = [𝐼 − (𝐼 − �̂� )𝐴]−1. This is the

alternative type of Leontief inverse matrix. The term (𝐼 − �̂� )𝐴 indicates the input ratio of

domestic products when the imports (inflows) input ratio is assumed to be constant in all sectors,

36

whether these sectors relate to intermediate demand or final demand. The term (𝐼 − �̂� )]𝑌

indicates domestic final demand for domestic products under the same assumption.

(1) Production inducement coefficients analysis

The Leontief inverse matrix is usually referred to as the output multiplier matrix

because it shows the direct and indirect requirements of output per unit of sectoral final demand.

Domestic production induced by individual final demand items can be derived from the

Leontief inverse matrix. In order to keep matters simple, under the Hawkins–Simon condition,

to transfer equation (3.8) with the substitution of the Leontief inverse matrix, the induced

production value that derives from each domestic final demand item is presented as

𝑋𝑘 = 𝐵 (𝐼 − �̂� )𝑌𝑘, k = (1, 2, … n) (3.9)

Here, 𝑋𝑘 represents the induced production value derived from the respective domestic

final demand and Y𝑘 represents the domestic final demand items (domestic consumption,

government expenditure, investment, etc.). Specifically, the production value induced by

exports E can be expressed as follows:

𝑋𝐸 = 𝐵 𝐸 (3.10)

Here, the production inducement coefficients for each industry by individual domestic

final demand are the elements of 𝑋𝑘 and written asX𝑖𝑘

∑ 𝑌𝑗𝑘𝑛𝑗=1

. The production inducement

coefficients for each industry by exports (outflows) are the elements of 𝑋𝐸and written as X𝑖,𝑁+1

∑ 𝐸𝑗𝑛𝑗=1

.

The production inducement dependencies by individual final demand items are written asX𝑖𝑘

𝑋𝑖

and X𝑖,𝑛+1

X𝑖.

The calculations of production inducement coefficients for 2002 and 2007 are presented

in Table 3.1. Production inducement dependencies are presented in Table 3.2.

(2) Employment effect analysis

With an additional table of employment data, which derives from the statistical

yearbook for Guizhou (see appendix), it is not difficult to calculate labor inducement

coefficients. The ratio of employment is defined as the labor input of each sector divided by

37

the domestic production of the sector: 𝑙′𝑖𝑗 =𝑙𝑖𝑗

𝑋𝑗 . The elements of this equation can be

represented in a diagonal matrix L̂′𝑖 as follows.

𝐿𝑖 = [

𝑙𝑖1

⋮𝑙𝑖𝑗

] , L̂′𝑖 = [𝑙′𝑖1 ⋯ 0

⋮ ⋱ ⋮0 … 𝑙′𝑖𝑗

]

Moreover, with the substitution of the Leontief inverse matrix, the result is

𝐿𝑖 = L̂′𝑖𝑋 = L̂′𝑖𝐵[(𝐼 − �̂� )]𝑌 + 𝐸] (3.11)

Each column of the matrix L̂′𝑖𝐵 indicates the size of labor demand that is required

directly and indirectly in each sector when one unit of final demand is generated for each sector.

The elements of matrix L̂′𝑖𝐵 are commonly referred to as “employment inducement

coefficients.” In this regard, the Leontief inverse matrix indicates the influence impact of one

sector on the whole economy and the sensitivity of fluctuation in the whole economy. With

regard to labor inducements, employment influence and the sensitivity coefficient can also be

obtained from the employment inducement coefficient matrix L̂′𝑖𝐵. Consider 𝐶 = �̂�𝑖′ 𝐵 = [𝑐𝑖𝑗];

thus, the “employment influence coefficient” can be calculated as 𝐶𝑗

�̅� , where c𝑗 = ∑ 𝐶𝑖𝑗𝑖

and 𝐶̅ =1

𝑛∑ 𝐶𝑗𝑗 . The “employment sensitivity coefficient” can be calculated as

𝐶𝑖

�̅�, where c𝑖 =

∑ 𝐶𝑖𝑗𝑖 and 𝐶̅ =1

𝑛∑ 𝐶𝑖𝑖 . The calculation of these two coefficients for 2002 and 2007 are

presented in Table 3.3.

(3) Net backward and forward linkage analysis

Linkage analysis tries to capture the two-sided nature of sector dependence by taking

the ratio of the dependence of the rest of the economy on the sector at hand to the dependence

of that sector on the rest of the economy (Temurshoev & Oosterhaven 2014). These two

dependencies, which are called backward linkage and forward linkage, are derived from the

Leontief (1936, 1941) demand-driven IO model and the Ghosh (1958) supply-driven IO model

respectively. Several methods have been used to measure backward and forward linkage. In

this current research, the net backward linkage and net forward linkage measurements have

been applied for the Guizhou economy.

38

According to the definition of an IO table, some of the elements in a Leontief inverse

matrix deliver a row vector of total output multipliers. This result indicates the output-to-output

relations; thus, it can be applied to estimate net backward linkage.

The supply-driven IO model, which originates from Ghosh, is used to derive the effect

of change in intermediate input prices or labor costs from the output side, in which the sectors

sell their own products to be used by other sectors for production. From the input side, there is

the following relationship:

𝑋′𝐷 + 𝑉′ = 𝑋′ (3.12)

Let 𝐷 = {𝐷𝑖𝑗} = x𝑖𝑗/𝑥𝑖. The Ghosh inverse matrix can be derived as

𝐺 = {𝑔𝑖𝑗} = [𝐼 − 𝐷]−1 (3.13)

The row sum of the Ghosh inverse matrix delivers a column with a total input multiplier.

Because X’ indicates the input-to-input relation, this multiplier is used to estimate forward

linkage. Thus, with the aforementioned output multiplier, these total output and total input

multipliers define the total net backward and total forward linkages as, respectively,

𝑏𝑖𝑡 = ∑ bij

nj=1 , and 𝑓𝑖

𝑡 = ∑ bijni=1 (3.14)

The superscript t refers to both of the linkages as the “total linkage” of industry i.

Oosterhaven and Stelder (2002) proposed the net multiplier concept. Many researchers

(De Mesnard 2002, 2007, Oosterhaven 2004, 2007, Dietzenbacher 2005) have agreed that the

net multiplier should be interpreted and labeled as a net backward linkage. The net backward

linkage is defined as

𝑏𝑖𝑛 = 𝑏𝑖

𝑡 𝑦𝑖

𝑥𝑖 (3.15)

In order to obtain a complete set of forward and backward linkages, Temurshoev and

Oosterhaven (2014) defined, and added to the literature, the corresponding net forward linkage

as

𝑓𝑖𝑛 = 𝑓𝑖

𝑡 𝑣𝑖

𝑥𝑖 (3.16)

Equation (3.15) equals the i-th column sum of 𝐵Y divided by the i-th row sum of 𝐵Y.

Net backward is the measurement of the strength of demand from the rest of the economy for

39

one industry compared with production, which is generated in the industry itself. The

measurement equals the output generated in all industries due to the final demand of industry i

normalized by the output generated in industry i due to the final demand of all industries. Thus

𝑏𝑖𝑛 > 1 implies that the rest of the economy (RoE, including industry i) is more dependent on

production in industry i than industry i is dependent on the RoE (including industry i). When

𝑏𝑖𝑛 < 1, this indicates the reverse situation.

Net forward linkage is the measurement of the strength of primary input in the RoE

from one industry compared with the primary input that this industry absorbs from the whole

economy. Similarly, it is easily shown that equation (3.16) equals the i-th row sum of V𝐺

normalized by the i -th column sum of V𝐺; namely, it equals the gross input of all industries

that utilize the primary input of industry i normalized by the gross input of industry i that

absorbs/embodies the primary inputs of all industries. This finding implies that 𝑓𝑖𝑛 > 1 is an

industry that is less dependent on the primary inputs of the RoE than the RoE is dependent on

its primary inputs. Net linkage measures the two-sided nature of sectoral dependency. Both net

backward and forward linkage can be negative. There is a rare case whereby if the final demand

of an industry is negative, the net backward linkage would be negative. There is also a rare case

whereby if the total primary input of industry i is negative because of large subsidies or

economic losses, backward linkage would be negative.

The net backward and forward linkage analysis results are present in Table 3.4.

(4) Skyline analysis

Skyline analysis is a measurement that analyzes the balance of domestic production

compared with supply by utilizing IO tables. The ratio of domestic production that satisfies

domestic demand is the self-sufficiency rate. In order to calculate the self-sufficiency rate, the

imports (inflows) are supposed to be partly endogenously determined by domestic demand,

and the indirect inducement effect from imports (inflows) needs to be suppressed. Equation

(3.2) can be rewritten as

(I − A)X = F − M And X = [I − A]−1F − M (3.17)

40

Here, let B′ = {B′𝑖𝑗} = [I − A]−1 . The production amount decision model can be

obtained as

X = 𝐵′[Y + E − M] (3.18)

Production inducement by domestic demand, exports (outflows), and suppression by

imports (inflows) can be obtained from equation (3.18). Significantly, there is equilibrium of

demand and supply as follows:

X + B′M = 𝐵′Y + 𝐵′E (3.19)

Here, X is domestic production, B′M is inducement suppression by imports (inflows),

𝐵′Y is production inducement by domestic demand, and 𝐵′E is production inducement by

exports (outflows). In order to convert the symbol, the equation is:

X = 𝑋𝑌 + 𝑋𝐸 − 𝑋𝑀 (3.20)

The self-sufficiency rate, export ratio, and import ratio can be obtained by converting

the elements value to the ratio against the inducement amount of each sector’s domestic

demand. Let a horizontal line indicate the domestic demand ratio of each sector. Let a vertical

line indicate the balance of demand (the domestic demand ratio and the export ratio) and supply

(the self-sufficiency ratio and the import ratio). A skyline chart can then be drawn. The

calculation results for the skyline analysis are presented in Tables 3.5(a) and (b). The skyline

chart is shown in Figure 3.5.

The skyline chart provides information not only on the production and trade structure,

but also the scale of production and demand. Developing countries or regions may have large

agricultural sectors. Alongside economic growth, the manufacturing and service sectors may

also expand.

(5) RAS analysis

Generally, RAS is used in the early work of Stone (1961) in predication of the input

coefficient. Deming and Stephan (1940) applied this non-survey method to the update of IO

tables from existing IO tables. Because the RAS analytical procedure is carried out iteratively

on row sums and column sums, it is also known as the proportional matrix balancing technique

(Miller and Blair 2009). Kagatsume (2005 and 2006) suggested that the elements of R indicate

41

the substitution change in each sector, and the elements of S indicate the processing change in

each sector. The substitution and processing effect change can be used to predict the future

potential of each industrial sector.

In RAS analysis, R refers to a diagonal matrix of elements that modify rows, A is the

coefficient matrix being modified, and S refers to a diagonal matrix of column modifiers. In

order to write the matrix, the following equation is needed:

[

𝑎11 ⋯ 𝑎1𝑛

⋮ ⋱ ⋮𝑎𝑛1 ⋯ 𝑎𝑛𝑛

]

𝑡=𝑇+𝑚

= [𝑟1 ⋯ 0⋮ ⋱ ⋯ ⋮0 ⋯ 𝑟𝑛

] ∙ [

𝑎11 ⋯ 𝑎1𝑛

⋮ ⋱ ⋮𝑎𝑛1 ⋯ 𝑎𝑛𝑛

]

𝑡=𝑇

∙ [𝑠1 ⋯ 0⋮ ⋱ ⋮0 ⋯ 𝑠𝑛

] (3.21)

In equation (3.21), matrix A is the original input coefficient matrix in the base year T

and matrix A’ is the coefficient matrix in the predicted year (T + m). By solving the equation

with the RAS method, matrices R and S are derived. Matrix R is a row-wise correction matrix

of the original input coefficient matrix A and indicates the substitution effect. Similarly, matrix

S is the column-wise correction matrix of A and indicates the processing degree change known

as the fabrication effect. The substitution and fabrication factors are computed using the

iterative RAS procedure. The results are shown in Table 3.6 and illustrated in Figure 3.6.

Substitution effect factors represent the rate of increase of intermediate demand for

sector i from the other sectors. The fabrication effect factors represent the rate of increase of

intermediate input in sector i from the other sectors. Thus, the sectors with the combination of

R larger than one and S smaller than one can be considered the potentially growing sectors.

Moreover, the sectors with the combination of R less than one and S greater than one can be

considered the potentially declining sectors.

3.4 Industrial structural change in Guizhou and the role of the crop sector

An overview of the Guizhou economy can be obtained by simply analyzing the total

output of each industrial sector (see Figure 3.4). In 2002, output from the crop sector accounted

for 9% of total gross production in Guizhou. The livestock sector was the second-largest

agricultural sector and accounted for 3% of total gross production in Guizhou. Apart from the

agricultural sector, the construction sector; food, beverage, and tobacco sector; chemical and

42

medicine sector; and the metal sector account for the largest industrial proportions in the

Guizhou economy.

In 2007, the share of the crop sector in total gross production declined to 6%. Similarly,

the share of the livestock sector declined to 3.6%. In contrast, mining, electricity and energy,

and the education and government sectors significantly increased their shares of output. The

increases in these sectors were directly linked to development programs that were implemented

in Guizhou. It is worth noting that the fisheries sector in Guizhou refers to freshwater fish

farming. Guizhou is rich in water resources. In the 2000s, since the start of the “sending

electrical power to the east” program, several artificial lakes have been constructed in Guizhou

for hydropower stations. Artificial lakes have also been constructed for freshwater fish farming,

leading to the emergence of the fisheries sector in Guizhou during this period.

Further analyses of the crop sector and economic structural change in Guizhou are

discussed in the next part.

Figure 3.4 The total output of each industrial sector in Guizhou in 2002 and 2007

(Notes: 1 crop farming; 2 forestry; 3 livestock; 4 fisheries (freshwater); 5 other agric. services;

6 mining; 7 food, beverage, tobacco; 8 leather, knitwear; 9 timber, paper printing; 10 chemical,

medicine, nonmetal; 11 metal; 12 industrial machinery; 13 electricity and energy; 14

construction; 15 transport; 16 postal and information; 17 retail and wholesale; 18 lodging and

catering; 19 finance and real estate; 20 social services; 21 education and government. Unit:

million yuan.)

3.4.1 Production inducement effect in the crop and other agricultural sectors

(1) Production inducement coefficients

Through dividing the direct and indirect domestic products induced by individual final

demand items by the total for the corresponding final demand, we obtained production

43

inducement coefficients. The production inducement coefficients for each final demand item

of each industrial sector in 2002 and 2007 are presented in Tables 3.1(a) and (b).

In 2002, the largest production inducement coefficient of total final demand is in the

construction sector (0.186). This result indicates that the construction program that was

launched in this year promoted production in the construction sector in Guizhou. However, the

largest production inducement coefficient of domestic consumption is in the crop sector

(0.2455). The second-largest production inducement coefficient of domestic consumption is in

the livestock sector (0.0972). These results indicate that the agricultural sectors were still the

most important in terms of domestic consumption.

In 2007, the largest production inducement coefficient of total final demand is in the

electricity and energy sector. This result indicates the impact of the power station establishment

program, because several hydropower and thermal power stations were established in the mid-

2000s. The largest production inducement coefficient of domestic consumption is still in the

crop sector (0.1464). Similarly, the production inducement coefficient of domestic

consumption in the livestock sector is still the second largest (0.0934). These results show that

economic transition has not changed the importance of the crop sector in terms of domestic

consumption.

From 2002 to 2007, the production inducement effect of government consumption in

most industries increased, led by an increase in inducements related to tertiary industries and

the public service. In contrast, the production inducement coefficients of investment demand

decreased in all agricultural sectors, except the crop sector. In addition, the production

inducement coefficients of export demand declined in all agricultural sectors. These results

indicate that the government’s interest has been biased toward the crop sector and that other

agricultural sectors have been neglected. At the same time, production in the agricultural sector

became oriented to the domestic market.

44

Table 3.1(a) The production inducement coefficients of the Guizhou economy in 2002

Consumption Government

expenditure Investment

Export

(outflows) Sum

1 Crop 0.2455 0.0080 0.0119 0.1142 0.110

2 Forestry 0.0036 0.0014 0.0039 0.0153 0.008

3 Livestock 0.0972 0.0021 0.0208 0.0609 0.054

4 Fisheries

(freshwater) 0.0062 0.0004 0.0001 0.0016 0.002

5 Other agric. 0.0050 0.0472 0.0017 0.0045 0.008

6 Mining 0.0145 0.0035 0.0221 0.0861 0.041

7 Food, beverage,

tobacco 0.1082 0.0026 -0.0140 0.2122 0.102

8 Leather, knitwear 0.0128 0.0003 0.0009 0.0108 0.008

9 Timber, paper

printing 0.0082 0.0073 0.0065 0.0174 0.011

10 Chemical,

medical, nonmetal 0.0620 0.0212 0.0778 0.1998 0.112

11 Metal 0.0082 0.0016 0.0649 0.2633 0.115

12 Industrial

machinery 0.0129 0.0074 0.0598 0.1231 0.065

13 Electricity,

energy 0.0397 0.0152 0.0263 0.1192 0.062

14 Construction 0.0032 0.0033 0.6642 0.0140 0.186

15 Transport 0.0478 0.0149 0.0468 0.0821 0.056

16 Postal &

information 0.0366 0.0096 0.0070 0.0249 0.022

17 Retail &

wholesale 0.0698 0.0130 0.0727 0.0435 0.055

18 Lodging &

catering 0.0406 0.0196 0.0088 0.0684 0.040

19 Finance & real

estate 0.0587 0.0088 0.0400 0.0447 0.043

20 Social services 0.0208 0.0391 0.0183 0.0217 0.022

21 Education &

public administration 0.0551 0.6316 0.0033 0.0286 0.092

45

Table 3.1(b) The production inducement coefficients of the Guizhou economy in 2007



Exports

(outflows) Sum

1 Crop 0.1464 0.0144 0.0164 0.0671 0.072

2 Forestry 0.0043 0.0011 0.0014 0.0098 0.005

3 Livestock 0.0934 0.0004 0.0060 0.0387 0.043

4 Fisheries (freshwater) 0.0040 0.0006 0.0001 0.0012 0.002

5 Other agric. 0.0038 0.0417 0.0005 0.0041 0.007

6 Mining 0.0394 0.0094 0.0427 0.1718 0.083

7 Food, beverage, tobacco 0.0564 0.0028 0.0009 0.1448 0.067

8 Leather, knitwear 0.0033 0.0002 0.0002 0.0028 0.002

9 Timber, paper printing 0.0071 0.0040 0.0043 0.0128 0.008

10 Chemical, medical,

nonmetal 0.0846 0.0318 0.0573 0.2203 0.119

11 Metal 0.0064 0.0031 0.0640 0.2696 0.112

12 Industrial machinery 0.0161 0.0091 0.0489 0.1099 0.056

13 Electricity, energy 0.0927 0.0436 0.0593 0.2736 0.142

14 Construction 0.0079 0.0022 0.3827 0.0080 0.105

15 Transport 0.0532 0.0367 0.0425 0.0973 0.064

16 Postal & information 0.0529 0.0137 0.0058 0.0239 0.027

17 Retail & wholesale 0.0725 0.0182 0.0403 0.0496 0.051

18 Lodging & catering 0.0395 0.0228 0.0044 0.0427 0.030

19 Finance & real estate 0.0796 0.0278 0.0239 0.0561 0.052

20 Social services 0.0297 0.0723 0.0192 0.0350 0.033

21 Education & public

administration 0.0802 0.6766 0.0037 0.0381 0.103

Source: Author’s estimation

(2) Production inducement dependency

Tables 3.2(a) and (b) indicate the production inducement dependency of final demand

items. Production inducement dependency is defined as the proportion ratio of induced

production value derived from the respective final demand item. It indicates the degree of

influence or weighting of respective final demand items of domestic production in industries.

According to the results in Tables 3.2(a) and (b), in both 2002 and 2007, production

inducement in the agricultural sector mostly depends on domestic demand except in the forestry

sector. Domestic consumption demand accounts for nearly 60% of production inducement in

the crop sector in both 2002 and 2007. In 2002, less than half of the production in the livestock

sector was induced by domestic consumption. The dependency from domestic consumption

46

increases sharply in 2007 because nearly 65% of production in the livestock sector was induced

by domestic consumption. Dependency on domestic consumption accounts for over 70% of the

production inducement effect in the fisheries sector, which is the largest of the agricultural

sectors.

From 2002 to 2007, the production inducement dependency of export demand declines

in all agricultural sectors. Moreover, the production inducement dependency of governmental

and investment demand increases only in the crop sector. A similar implication of biased

governmental interest in the crop sector and orientation toward the domestic market for

agricultural production can be deduced from this result.

Table 3.2(a) The production inducement dependency of the Guizhou economy in 2002



Exports

(outflows) Sum

1 Crop 0.5912 0.0076 0.0291 0.3722 1

2 Forestry 0.1247 0.0197 0.1376 0.7181 1

3 Livestock 0.4823 0.0041 0.1047 0.4089 1

4 Fisheries (freshwater) 0.7140 0.0168 0.0150 0.2542 1

5 Other agric. 0.1579 0.5915 0.0566 0.1940 1

6 Mining 0.0935 0.0088 0.1450 0.7527 1

7 Food, beverage,

tobacco 0.2832 0.0027 -0.0373 0.7514

1

8 Leather, knitwear 0.4511 0.0035 0.0323 0.5132 1

9 Timber, paper printing 0.1995 0.0698 0.1593 0.5714 1


nonmetal 0.1479 0.0198 0.1883 0.6440

1

11 Metal 0.0190 0.0015 0.1530 0.8264 1

12 Industrial machinery 0.0530 0.0119 0.2499 0.6852 1

13 Electricity, energy 0.1699 0.0256 0.1144 0.6901 1

14 Construction 0.0046 0.0019 0.9663 0.0272 1

15 Transport 0.2254 0.0276 0.2240 0.5230 1

16 Postal & information 0.4510 0.0465 0.0876 0.4150 1

17 Retail & wholesale 0.3361 0.0245 0.3557 0.2837 1

18 Lodging & catering 0.2712 0.0513 0.0598 0.6177 1

19 Finance & real estate 0.3597 0.0211 0.2490 0.3702 1

20 Social services 0.2474 0.1828 0.2208 0.3490 1


administration 0.1596 0.7187 0.0097 0.1120

1

47

Table 3.2(b) The production inducement dependency of the Guizhou economy in 2007



Exports

(outflows)

Su

m

1 Crop 0.6003 0.0193 0.0594 0.3210 1

2 Forestry 0.2469 0.0200 0.0722 0.6609 1

3 Livestock 0.6485 0.0009 0.0368 0.3138 1

4 Fisheries (freshwater) 0.7103 0.0320 0.0135 0.2442 1

5 Other agric. 0.1673 0.6026 0.0193 0.2107 1

6 Mining 0.1405 0.0109 0.1342 0.7144 1

7 Food, beverage,

tobacco 0.2484 0.0040 0.0034 0.7442

1

8 Leather, knitwear 0.4806 0.0117 0.0299 0.4778 1

9 Timber, paper printing 0.2613 0.0481 0.1393 0.5513 1


nonmetal 0.2099 0.0258 0.1257 0.6385

1

11 Metal 0.0169 0.0027 0.1493 0.8311 1

12 Industrial machinery 0.0846 0.0156 0.2266 0.6732 1

13 Electricity, energy 0.1937 0.0298 0.1094 0.6671 1

14 Construction 0.0223 0.0021 0.9493 0.0263 1

15 Transport 0.2459 0.0554 0.1737 0.5250 1

16 Postal & information 0.5854 0.0496 0.0564 0.3086 1

17 Retail & wholesale 0.4215 0.0347 0.2071 0.3368 1

18 Lodging & catering 0.3920 0.0740 0.0386 0.4955 1

19 Finance & real estate 0.4542 0.0519 0.1203 0.3736 1

20 Social services 0.2676 0.2127 0.1523 0.3674 1


administration 0.2297 0.6335 0.0094 0.1273

1


3.4.2 Employment influence and sensitivity coefficients of agricultural sectors

Table 3.3 indicates the employment influence and sensitivity coefficients of each sector

in Guizhou in 2002 and 2007. The employment influence coefficient indicates when one unit

of final demand increases in a sector and shows the direct and indirect effects of employment

inducement, including in the self-sector. The employment sensitivity coefficient compares

employment inducement effects received at different sectors from one unit of final demand

generated at each sector.

48

In both 2002 and 2007, the employment influence coefficient of the agricultural sectors

is by far the largest among all industries. In particular, the crop sector is higher than all other

industries. The extreme high employment influence coefficient in the crop sector indicates the

low productivity of this sector in Guizhou. The second-largest employment influence

coefficient is in the livestock sector.

Table 3.3 The employment influence and sensitivity coefficients in 2002 and 2007

Employment influence

coefficient

Labor-related

sensitivity coefficient

Sector 2002 2007 2002 2007

1 Crop 4.95 5.29 8.98 9.84

2 Forestry 1.89 2.46 2.15 2.71

3 Livestock 4.53 4.05 3.95 3.22

4 Fisheries (freshwater) 0.89 0.39 0.70 0.21

5 Other agric. 0.55 1.30 0.02 0.01

6 Mining 0.28 0.20 0.23 0.12

7 Food, beverage, tobacco 1.41 1.04 0.04 0.08

8 Leather, knitwear 0.37 0.78 0.08 0.09

9 Timber, paper printing 0.38 0.35 0.07 0.07

10 Chemical, medical, nonmetal 0.55 0.40 0.12 0.14

11 Metal 0.17 0.18 0.06 0.04

12 Industrial machinery 0.21 0.21 0.10 0.11

13 Electricity, energy 0.11 0.12 0.08 0.05

14 Construction 0.23 0.36 0.05 0.23

15 Transport 0.26 0.53 0.29 0.71

16 Postal and information 0.16 0.15 0.04 0.05

17 Retail & wholesale 1.05 1.00 1.76 1.68

18 Lodging & catering 1.25 1.18 0.52 0.68

19 Finance & real estate 0.16 0.17 0.13 0.13

20 Social services 1.10 0.47 1.23 0.50


administration 0.49 0.38 0.43 0.34


From 2002 to 2007, in the agricultural sector, the employment influence coefficient of

the crop and forestry sectors increases. This result implies the enhancement of the employment

effect in the crop sector and the decrease of productivity in the crop sector. The increase of the

employment effect in the forestry sector may relate to the grain-to-green program and the

49

reforesting program, both of which aimed to increase the forestry area in Guizhou and

encourage farmers to engage in reforesting.

The employment sensitivity coefficient of the agricultural sector is also the largest

among all industries. The crop sector especially is higher than all other industries. The second-

largest labor-related sensitivity is in the livestock sector. From 2002 to 2007, the employment

sensitivity coefficients of the crop and forestry sectors increased. The employment sensitivity

coefficient of the crop sector increased the largest (from 8.98 to 9.84). When the employment

effect is enhanced in the crop and forestry sectors, such employment becomes unstable and

sensitive to fluctuations in the economy.

3.4.3 Net backward and forward linkage in each sector

Table 3.4 shows the net backward linkage and net forward linkage in each sector in

2002 and 2007. Net backward linkage shows the relation to the demand side. If net backward

linkage in one sector is larger than 1, this result implies that the RoE (including the sector) is

more dependent on this sector than production in this sector, which depends on the RoE. Net

forward linkage shows the relation to the supply side. If net forward linkage in one sector is

larger than 1, this result implies that the sector is less dependent on the primary inputs of the

RoE than the rest of economy is dependent on the primary inputs of this sector.

Table 3.4 The net backward and forward linkages in 2002 and 2007

Net backward

linkage

Net forward

linkage

Sector 2002 2007 2002 2007

1 Crop 0.81 0.79 1.23 1.18

2 Forestry 0.38 0.33 3.08 3.30

3 Livestock 1.48 1.41 0.84 0.67

4 Fisheries (freshwater) 0.90 0.89 1.14 1.29

5 Other agric. 0.89 0.96 1.20 0.77

6 Mining 0.51 0.62 2.14 1.32

7 Food, beverage, tobacco 1.79 1.65 0.48 0.65

8 Leather, knitwear 3.19 9.63 0.72 1.73

9 Timber, paper printing 1.06 2.06 1.21 1.82

10 Chemical, medical, nonmetal 0.91 1.32 0.81 0.75

11 Metal 1.36 1.78 0.52 0.49

12 Industrial machinery 2.34 3.11 1.31 0.85

13 Electricity, energy 0.44 0.57 1.75 1.05

50

14 Construction 1.60 2.46 0.24 0.26

15 Transport 0.53 0.60 1.87 1.47

16 Postal & information 0.84 1.06 0.85 0.82

17 Retail & wholesale 0.41 0.64 1.31 1.81

18 Lodging & catering 1.22 1.58 0.49 0.88

19 Finance & real estate 0.60 0.68 2.48 2.61

20 Social services 1.19 0.92 2.42 1.64

21 Education & public administration 1.71 1.52 0.80 0.79


The net backward linkages of the crop sector are smaller than 1 for both 2002 and 2007,

which indicates that production in the crop sector is demanded less by the overall economy

than its production. The livestock sector is the only agricultural sector that has net backward

linkages larger than 1, which indicates that the demand strength of the livestock sector from

the overall economy is larger than production in this sector. The net backward linkages in all

agricultural sectors decline from 2002 to 2007, which indicates that the decrease of agricultural

demand from the overall economy when the economy is growing continues.

The net forward linkages of the crop and fisheries (freshwater) sectors are larger than 1

in both 2002 and 2007, which indicates that the primary inputs from the overall economy to

these sectors is less than the primary inputs that these sectors supply for the whole economy.

The net forward linkages of the livestock sector are smaller than 1, which indicates that this

sector needs more primary inputs from the RoE than the primary inputs that it contributes to

the RoE. From 2002 to 2007, the net forward linkages weaken in the crop and livestock sectors,

which indicates that the need for primary inputs from these two sectors has increased.

3.4.4 Skyline analysis

A skyline chart indicates the demand–supply balance in the Guizhou economy. Tables

3.5(a) and (b) show the self-sufficiency ratio, imports (inflows) ratio, and exports (outflows)

ratio in each industry in 2002 and 2007. In accordance with Tables 3.5(a) and (b), let the width

of the horizontal line indicate the domestic demand ratio of each industry, and the height of the

vertical line indicate the self-sufficiency ratio, exports (outflows) ratio, and imports (inflows)

51

ratio. The red line indicates the 100% supply of domestic demand. The skyline chart is shown

in Figure 3.5.

Through the supplementary tables and the skyline chart, it can be seen that in 2002, the

crop sector and the livestock sector maintain high levels of self-sufficiency (above 100%). In

2007, the high self-sufficiency levels in these two sectors continue; in particular, the self-

sufficiency level in the livestock sector sharply rises from 111% to 134%. In contrast, the

import and export rates in the crop and livestock sectors decline from 2002 to 2007, which

implies a decrease of interprovincial trade in agricultural products.

Table 3.5 (a) Supplementary table for the Guizhou economy in 2002

Sector

Self-

sufficiency

ratio

Imports Exports Domestic

demand

1 Crop 110 42 52 100

2 Forestry 61 127 88 100

3 Livestock 111 42 53 100

4 Fisheries (freshwater) 107 26 33 100

5 Other agric. 109 17 26 100

6 Mining 65 145 110 100

7 Food, beverage, tobacco 192 66 158 100

8 Leather, knitwear 33 105 38 100

9 Timber, paper printing 46 115 61 100

10 Chemical, medical, nonmetal 86 100 86 100

11 Metal 98 119 117 100

12 Industrial machinery 34 115 49 100

13 Electricity, energy 84 114 98 100

14 Construction 96 7 3 100

15 Transport 72 98 70 100

16 Postal & information 108 50 58 100

17 Retail & wholesale 88 50 38 100

18 Lodging & catering 160 52 112 100

19 Finance & real estate 64 85 49 100

20 Social services 32 103 35 100


administration 71 40 11 100

52

Table 3.5 (b) Supplementary table for the Guizhou economy in 2007

Sector

Self-

sufficiency

ratio

Imports Exports Domestic

demand

1 Crop 109 33 42 100

2 Forestry 45 128 73 100

3 Livestock 134 9 43 100

4 Fisheries (freshwater) 82 47 29 100

5 Other agric. 99 28 27 100

6 Mining 96 107 103 100

7 Food, beverage, tobacco 168 70 137 100

8 Leather, knitwear 8 112 20 100

9 Timber, paper printing 21 126 47 100

10 Chemical, medical, nonmetal 64 106 70 100

11 Metal 84 117 100 100

12 Industrial machinery 33 109 43 100

13 Electricity, energy 110 85 95 100

14 Construction 66 36 2 100

15 Transport 72 92 64 100

16 Postal & information 120 22 42 100

17 Retail & wholesale 60 80 40 100

18 Lodging & catering 75 77 52 100

19 Finance & real estate 61 86 47 100

20 Social services 56 84 40 100


administration

83 31 14 100


53

Figure 3.5 Skyline chart for Guizhou in 2002 and 2007

54

3.5 Predictions of industrial development potential through the RAS method

In order to extend the discussion on economic structural changes in Guizhou, we use

the RAS method to make predictions about future industrial changes. Table 3.6 indicates the

indices of the substitution and fabrication effects of each sector from 2002 to 2007. Figure 3.6

presents a chart that refers to these results.

In Figure 3.6, each number in the chart corresponds to the industry of the same number

in Table 3.6. The substitution effect (R) is on the horizontal axis, and the fabrication effect (S)

is on the vertical axis. The origin of the chart is point (1, 1) not point (0, 0) because the basic

criteria to be compared with the average level is 1. The sectors plotted in Domain II have

increased in intermediate input and decreased in intermediate demand. This change implies a

decrease in value added; thus, these sectors are grouped as potential shrinking industries. The

sectors plotted in Domain IV have increased in intermediate demand and decreased in

intermediate input, a situation that is equal to an increase in value added; thus, these sectors are

grouped as potentially growing industries.

According to the RAS analysis, the crop sector cannot be classified as a potentially

growing or shrinking industrial group. The forestry and livestock sectors are supposed to be

shrinking in Guizhou. In contrast, the fisheries (freshwater) sector is supposed to be in the

group of growing industries. The development of the fisheries (freshwater) industry is related

to the construction of artificial lakes for the hydroelectric power stations. Artificial lakes are

used by freshwater fish farms; indeed, fishery is an emerging industry in Guizhou.

55

Table 3.6 Substitution effects and fabrication effects from 2002 to 2007

Sector R

(substitution effect)

S

(fabrication effect)

1 Crop 0.93 0.98

2 Forestry 0.97 1.03

3 Livestock 0.73 1.04

4 Fisheries (freshwater) 1.00 0.99

5 Other agric. 1.12 1.19

6 Mining 0.99 1.00

7 Food, beverage, tobacco 1.02 1.00

8 Leather, knitwear 0.98 0.98

9 Timber, paper printing 1.04 0.97

10 Chemical, medical, nonmetal 1.05 0.99

11 Metal 1.01 0.97

12 Industrial machinery 0.92 1.02

13 Electricity, energy 1.09 1.04

14 Construction 0.94 0.99

15 Transport 0.99 1.04

16 Postal & information 0.93 0.97

17 Retail & wholesale 1.07 0.97

18 Lodging & catering 1.06 0.99

19 Finance & real estate 1.00 0.96

20 Social services 0.94 1.00

21 Education & public administration 1.01 1.01


Figure 3.6 Indices of substitution and fabrication effects

56

3.6 Summary

This chapter explores the role of the crop sector in the transitional economy of Guizhou

from 2002 to 2007. During this period, the most important policies related to the grain sector

and regional development programs of the last 20 years have been implemented in Guizhou.

Through IO analysis, the impact of these policies and programs on the regional economy and

the crop sector has been discussed. Further, RAS analysis has provided essential information

about the developing trend in the crop sector. A summary of the implications is presented as

follows.

1) From 2002 to 2007, China’s western development policies made the agriculture-

based regional economy in Guizhou more dependent on primary resource exportation and

government expenditure. The provincial government has emphasized ecological

/environmental issues rather than an economic perspective that considers improvements in rural

welfare.

2) The crop sector is by far the most important sector for domestic consumption

according to the production inducement coefficients of domestic consumption demand. The

largest employment influence coefficient in the crop sector indicates that crop productivity in

Guizhou province is extremely low. Even worse, the employment influence coefficient became

larger in 2007 compared with 2002. Low productivity indicates that the surplus rural labor

force has not successfully transferred from the agricultural sector to the nonagricultural sector.

During this period, the grain production support policy was implemented in Guizhou and the

employment effect in the nonagricultural sector in Guizhou was weak; thus, the surplus rural

labor force was retained in the crop sector. These results imply inevitable rural poverty in

Guizhou. The employment sensitivity coefficient of the crop sector was the highest in Guizhou;

moreover, the employment sensitivity coefficient increased from 2002 to 2007. When the

surplus rural labor force was retained in Guizhou during this period, employment in the crop

sector became more sensitive to fluctuations in the Guizhou economy.

57

3) From 2002 to 2007, although crop production in Guizhou maintained a high level of

self-sufficiency, production in the crop sector induced by government expenditure increased

significantly. These results are connected to the grain production subsidy policy that was

implemented in Guizhou. Grain production dependence on government support became much

stronger in 2007. However, export-induced production in the crop sector decreased. The

produced goods of the crop sector were not traded in the national market, which indicates a

lower degree of market competitiveness for grain production in Guizhou. Grain production in

Guizhou was indirectly influenced by the specific regional grain production support policies in

China. Since Guizhou was defined as a sales market for staple grains, grain support policies

were not implemented in Guizhou. Staple grain production in Guizhou became less competitive

than production in policy-supported areas.

4) The net backward linkage of the crop sector is less than 1, which indicates low

contribution of crop sector to the regional economic growth. From 2002 to 2007, although

production in the crop sector was still crucial for local consumption, demand from the whole

economy became smaller. Meanwhile, the net forward linkage of the crop sector indicates that

the demand for primary inputs from the overall economy in the crop sector became stronger

from 2002 to 2007. Production in the crop sector became costlier. The sustainability of crop

production in the Guizhou economy has thus been questioned.

5) From 2002 to 2007, the results of the RAS analysis indicate that the livestock sector

is a potentially shrinking sector. Further, the fisheries sector is a potentially growing sector. The

livestock sector needs a supply of feed grain in its production process. The potential in the

livestock sector may have influenced feed grain production in Guizhou. In contrast, the

fisheries sector needs a supply of rapeseed meal for fish feed. The growing potential of the

fisheries sector may be related to the promotion of rapeseed production in Guizhou. Although

production in the crop sector became less sustainable from 2002 to 2007, the results of the RAS

analysis imply that the production situation in the crop sector may have varied in accordance

with the types of crop. Demand from a potentially growing sector can promote specialized

grain production. However, further research is necessary to confirm this implication.

58

3.8 References

Deming, W.E. and Stephan, F.F., 1940. On a least squares adjustment of a sampled frequency

table when the expected marginal totals are known. The Annals of Mathematical

Statistics, 11(4), pp.427-444.

De Mesnard, L., 2002. Note about the concept of net multipliers. Journal of Regional Science,

42, pp. 545-548.

Dietzenbacher, E., 2005. ‘More on multipliers. Journal of Regional Science, 45, pp. 421-426.

Ghosh, A., 1958. Input-output approach to an allocative system. Economica, 25, pp. 58-64.

Ichimura, S. and Wang, H.J. eds., 2003. Interregional input-output analysis of the Chinese

economy (Vol. 2). World Scientific.

Kagatsume, M. ,2005. An Economic analysis on Interrelations among Rural Industries,

Structure, Agricultural Productivities and Climate Change, The Progress Report of ICCAP, pp.

122-131. The Research project on Impact of Climate Change on Agricultural Production

System in Arid Areas, Research Institute of Human & Nature (RIHN).

Kagatsume, M., 2006. Impacts of climate change and the EU accession on Turkish rural

industries by the input-output model and Markov-transition matrix, the advanced report of

ICCAP, pp. 119-127. The Research Project on Impact of Climate Change on Agricultural

Production System in Arid Areas, Research Institute of Human & Nature (RIHN).

Leontief, W. W., 1936. Quantitative input-output relations in the economic system of the United

States. Review of Economics and Statistics, 18, pp. 105-125.

Leontief, W. W., 1941. The Structure of American Economy, 1919-1929: An Empirical

Application of Equilibrium Analysis. Cambridge: Cambridge University Press.

Miller, R. and Blair, P.D., 2009. Input-Output Analysis: Foundations and Extensions, 2nd

Edition. New York: Cambridge University Press.

Oosterhaven, J. and Stelder, D., 2002. Net multipliers avoid exaggerating impacts: with a bi-

regional illustration for the Dutch transportation sector. Journal of Regional Science, 42, pp.

533-543.

Oosterhaven, J., 2004. On the definition of key sectors and the stability of net versus gross

multipliers. Research Report 04C01, SOM Research School, University of Groningen.

Oosterhaven, J., 2007. The net multiplier is a new key sector indicator: reply to De Mesnard’s

comment. Annals of Regional Science, 41, pp. 273-283.

Stone, R., 1961. Input-Output and National Accounts, Paris: Organization for European

Economic Cooperation.

Temurshoev, U. and Oosterhaven, J., 2014. Analytical and empirical comparison of policy-

relevant key sector measures. Spatial Economic Analysis, 9(3), pp. 284-308.

59

Chapter 4

Grain Industry and Regional Market Integration in

Guizhou

4.1 Introduction

Chapter 3 indicates that from 2002 to 2007, grain in Guizhou was not traded in China’s

domestic grain market and was less competitive. Grain was mainly supplied for the intra-

provincial markets in Guizhou. However, the efficiency of the intra-provincial grain market in

Guizhou is unclear. Grains have diverse functions in the economy and are collected and

distributed through different enterprises and institutions. The attention that China’s central and

regional governments pay to the grain market varies depending on the types of grain. It is

necessary to investigate the efficiency of the intra-provincial market by distinguishing between

the different grains.

In this chapter, we investigate the integration structure of the intra-provincial market in

Guizhou with regard to rice, soybeans, wheat flour, and rapeseed oil from 2008 to 2016. Rice

and soybeans are primary grain products. Rice is a staple grain and the most commonly

produced in Guizhou. Soybeans have various functions in the food processing industry in

Guizhou apart from the oil-crushing industry and feed industry. Wheat flour and rapeseed oil

are processed grain products. Rapeseed oil is the most consumed edible oil in Guizhou and a

major cash crop. Rapeseed meal is used in the local fishery industry. Wheat is not a staple grain

in Guizhou. The key wheat production regions are far from this province, but there is a stable

demand for wheat flour. In this study, the most important concerns are (1) the integration

structure of the regional market and the distribution efficiency regarding each grain product,

(2) the influence of government intervention on grain market integration, and (3) the difference

between the market integration of primary grain products and processed products.

60

The first concern is directly related to the regional demand–supply situation and the

profits of local grain farmers. Regional grain demand can be met by local grain production and

grain imports from other provinces. Effective market integration between a surplus market and

a deficit market indicates the successful establishment of a connection between local producers

and consumers. Separate or less efficient integration among markets indicates that local grain

production may encounter difficulties in attaining profitable prices. Without effective local

supply, grain deficit markets must depend on grain imports, resulting in a fiscal burden for a

regional economy. The efficiency of market integration between a surplus market and a deficit

market needs to be estimated.

The second concern of this study notes the different government influences on grain

market integration. Strong government intervention in the grain market still exists in China. In

Guizhou, the rice market is monitored by the government. Moreover, SOEs control rice

procurement and distribution. The government intervenes in the rapeseed market occasionally,

but rural brokers, private enterprises, and small-scale millers are active in the rapeseed market

and the processed products’ market. Guizhou is not a key producing area for wheat and

soybeans, and the government seldom intervenes in the market for these two grains. It is

necessary to compare the market integration of grains that are subject to diverse government

intervention and distributed by different entities.

The market integration of primary grain products and processed grain products is a field

of further exploration regarding regional rural industrial development in Guizhou. Market

integration with a surplus market for processed products implies the development of a local

grain processing industry. Rural industrial development extends the value chain of regional

grain production. Rural industrial development is crucial for rural income generation. Further,

a comparison of market integration for primary and processed grain products is important.

This chapter conducts market integration analysis by utilizing a price time series of four

grain products in three regional wholesale markets. Regional spatial market integration is

represented as long-run price equilibrium between markets with transaction costs. Co-

integration test methods are generally applied in price time-series analysis. The VAR model

61

and VECM are applied for a co-integration test. Long-run co-integration, short-run divergence

adjustment, and the causality relationship between prices from different regional markets are

discussed.

This chapter is organized as follows. In section two, characteristics of each grain

product in Guizhou and the regional market’s properties are explained. Section three interprets

the statistical method and data source. Section four presents the test results and the

interpretation of the results. A summary section completes this chapter.

4.2 Grain markets in Guizhou

Figure 4.1 shows the location of Guizhou and its neighboring provinces. Hunan

province is a key rice-producing region and the only market with a rice surplus near Guizhou.

Guizhou and three other neighboring provinces (Sichuan, Chongqing, and Yunnan) have

markets with rice deficits. Guangxi province barely manages self-sufficiency for rice. Wheat

is not the staple food for the southern Chinese. Wheat production in Guizhou and the

neighboring provinces is low. The key wheat producing regions are far from Guizhou. However,

there is a small amount of production in Yunnan province. Sichuan has been the largest

rapeseed-producing area in recent years. Chongqing is the most industrialized and urbanized

region in southwestern China.

Figure 4.1 Map of Guizhou and its three main cities

(1) The characteristics of grains in the Guizhou market

62

In 2014, the sown area of grains accounted for 67% of the regional cropped area in

Guizhou (NBS, 2014). Figure 4.2 shows the sown area for five major grains in Guizhou in the

20 years from 1995 to 2014. Before 2005, rice is the most planted crop in Guizhou; however,

the rice area has continued to decline since 2005. The maize area has increased in Guizhou and

exceeded the rice area in 2005 to become the most planted crop in the province. In the 1990s,

wheat was the third-largest planted crop in Guizhou; however, the total wheat area sharply

declined by nearly 50% in the 2000s. In contrast, rapeseed is the most planted cash crop in

Guizhou. In 2003, the rapeseed area has exceeded the wheat area to become the third-largest

grown grain in Guizhou; however, soon afterward, in 2005, the rapeseed area declined. The

government then launched a production support policy for rapeseed in Guizhou. Consequently,

the rapeseed area has continued to increase since 2007. The soybean area in Guizhou is smaller

than the areas of the other four grains and is relatively stable.

Figure 4.2 The major grain areas in Guizhou from 1995 to 2014


Figure 4.2 shows the grain outputs from 1995 to 2014. Total grain output in Guizhou

was the highest level in history at 11 million tons in the 2000s. Rice is by far still the most

produced grain in Guizhou. A severe drought in 2010 setback grain production in Guizhou.

Production did not recover to the prior level until 2016.

Rice is a staple grain in Guizhou. Its production accounts for more than 50% of the total

grain output of the province. Most local rice is the Indica variety. According to the business

0

100

200

300

400

500

600

700

800

900

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Unit:1000 ha

Rice Wheat Maize Soybean Rapeseed

63

report published by China’s futures market for grains, only 17.2% of the early rice output in

Guizhou is distributed in markets. This figure is the lowest in China (Zhengzhou commodity

exchange 2009). China’s central government has asked each province to take care of its own

food security; thus, the provincial government pays significant attention to rice production.

SINOGRAIN and its subsidiary corporations control rice procurement, processing, and

distribution in Guizhou.

Rapeseed oil is the most consumed edible oil in Guizhou province. A temporary

procurement and national stockpiling policy has been implemented in Guizhou to support

rapeseed production. However, compared with rice, government intervention in the rapeseed

market is relatively weak. Procurement, processing, and distribution for rapeseed and its

processed products is mainly promoted by rural brokers, private enterprises, and small-scale

millers. Rapeseed meal is used in the feed industry for fish farming.

Wheat is the most commercialized agricultural product in China. However, the key

wheat producing area is in northern China and far from Guizhou. Since wheat is not a staple

grain in Guizhou, the provincial government does not intervene in the wheat market. Wheat

used to be collected and distributed by SOEs. The reform of the grain reserve system in

Guizhou enables private entities access to the wheat market. Consequently, SOEs and private

entities are now active in the wheat market.

There is little government intervention in the soybean market. Soybeans are consumed

in Guizhou and are also a primary material in the food manufacturing and seasoning industry.

Rural brokers and private enterprises promote soybean collection and distribution.

64

Figure 4.3 Major grain output in Guizhou from 2002 to 2014


(2) Market conditions in three regional markets in Guizhou

Three agricultural wholesale markets account for 60% of grain distribution in Guizhou.

Their locations are identified in Figure 4.1 with symbols. For illustrative purposes, these

regional markets are named by using the names of the cities where they are located.

Guiyang is in the center of Guizhou. It is the capital and a transport hub. Zunyi is in

northern Guizhou, which is the area where most of the grains are produced. Liupanshui is in

western Guizhou. The altitude in western Guizhou is higher than in the east. Liupanshui suffers

from rocky desertification. Table 4.1 indicates the population densities and grain production

situations of the three areas where the wholesale markets are located.

Table 4.1 Basic features of three cities in Guizhou

City

Populatio

n density

(people

per km2)

Rice

area

(thousan

d ha)

Wheat

area

(thousan

d ha)

Soybean

area

(thousan

d ha)

Rapesee

d area

(thousan

d ha)

Cultivatio

n rate

(%)

Farmland

in

degradatio

n

(%)

Guiyang* 396.3 33.37 3.95 6.75 38.97 35. 92 19.87

Zunyi 221 159.9 26.37 39.13 132.71 29. 51 14.3

Liupansh

ui 270.2 15.74 29.16 6.59 8.11 37. 85 31.05

Note: *Guiyang is the capital city of Guizhou province and the transportation hub.

Statistical yearbook of Guizhou (2015)

0

200

400

600

800

1000

1200

1995 1996 1997 1998 1999 2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14

Rice Wheat Maize Soybeans Rapeseed

65

In Guizhou, Zunyi is the key grain production area because of the relatively good

cultivation conditions in the north (flat and fertile soils, and ample rainfall). Apart from wheat,

all outputs in Zunyi are the highest in the province. In particular, Zunyi provides one-third of

the rice in the intra-provincial markets. Guiyang is the most populous and urbanized area in

Guizhou. Further, Guiyang market is a market with a grain deficit. However, Guiyang is the

transport hub; thus, the grain imports from other provinces are first transported to Guiyang and

then distributed to other markets. In Liupanshui, the cultivation conditions are generally

unsuitable for grain production. Wheat production needs less water and is thus preferred in

Liupanshui. Liupanshui produces more wheat than the other two regions.

4.3 Methods and data sources

4.3.1 The spatial market integration model and testing methods

(1) The conceptual model of spatial market integration

A large and established body of literature has considered spatial market integration for

agricultural products. Agricultural production is typically distributed over a wide spatial area.

Moreover, agricultural commodities are costly to transport. Spatial linkages for agricultural

commodities are reflected in their prices. Economists analyze spatial agricultural market

integration by analyzing equilibrium behavior in price series and price transmission in markets.

Most market integration research is based on the fundamental concept of competitive

pricing behavior. Enke (1951), Samuelson (1952), and Takayama and Judge (1971) proposed

theoretical models for spatial market integration within the context of the law of one price

(LOP). According to LOP, trade between spatially separated markets is profitable only when

the price difference between deficit and surplus markets exceeds transaction costs

(transportation costs, etc.). This theory states that when spatial markets are integrated, any price

difference in these separated markets is equal to transaction costs.

In reality, price behavior is observed not to follow LOP. Market equilibrium is imperfect

because of the price distortions and variations in transaction costs (Fackler & Goodwin 2002).

66

Several researchers (including Dercon 1995, McNew 1996, Awokuse & Bernard 2007, and

Ghosh 2010) have improved and extended the Enke-Samuelson-Takayama-Judge model. They

have discussed different conditions for arbitrage in long-run and short-run market integration.

Based on the findings from these studies, we formulate a regression equation for long-run price

equilibrium in spatially separated markets as follows:

𝑌𝑡 = 𝑎 + 𝑏2𝑇𝑡 + 𝑏1𝑋𝑡 + 𝑢𝑡 (4.1)

Here, 𝑋𝑡 denotes the price of the commodity in exporting market A at time t. 𝑌t denotes

the price of the same commodity in importing market B. 𝑇𝑡 denotes transaction costs. LOP

holds on condition of the value of 𝑎 and b. When 𝑎 = 0, 𝑏1 = 1, and 𝑏2 = 0, strict LOP holds

and prices in one market are transmitted to another market on a one-for-one basis. Weak LOP

holds when 𝑎 ≠ 0, 𝑏2 ≠ 0, and 𝑏1 is not equal to 1. Proportional and constant transaction costs

are involved in equilibrium between markets.

Some other approaches, such as the parity bound model and the gravity model, have

also been used for testing market integration (Baulch 1997, Poncet 2005). Here, we argue the

advantage of using LOP, despite criticisms of non-stationary transfer costs in trade. In

comparison with other methods, such as the parity boundary model, the data requirement for

analysis based on LOP is small. This is very useful for testing market integration in developing

countries. Market data are difficult to obtain and price time series are often the only data

available for analysis in developing countries. Other methods, such as the parity bound model,

need further data (for example, transaction costs, tariffs, etc.), which in some situations are

impossible to obtain or very time consuming. In addition, aggregate methods such as the gravity

model cannot describe regional specific characteristics that affect trade (Baldwin & Gu 2003).

The LOP model can describe regional characteristics in agricultural markets. If an appropriate

testing framework is employed and the results are interpreted correctly, the LOP model

provides useful insights into the issue of spatial relations of arbitrage in a regional market

situation.

Although the price equilibrium relationship between spatially separated markets is

explained above, ordinary regression procedures are not appropriate to test for price

67

equilibrium. The nature of non-stationarity in time series leads to spurious regression;

consequently, a researcher may draw incorrect conclusions about market integration. However,

co-integration and error correction models can be adapted to solve the spurious regression

problem.

(2) Non-stationarity and the co-integration test

Price data are time series and have a stochastic process. The assumptions of the classical

linear regression model (CLRM) and ordinary least squares (OLS) estimates require both 𝑌𝑡

and 𝑋𝑡 to be stationary (the stationary condition for time series is E(Y𝑡) = 𝜇, 𝑉𝑎𝑟(Y𝑡) =

𝐸(Y𝑡 − 𝜇) = 𝜎2, 𝐶𝑜𝑣(Y𝑡, Y𝑡+𝑘) = 𝛾𝑘 = 𝐸[∗ (Y𝑡 − 𝜇)(Y𝑡−𝑘 − 𝜇)] , the same as 𝑋𝑡 ). If time

series are non-stationary, the results obtained from regression are totally spurious or

meaningless, even with high R2s and very low values of Durbin–Watson (DW) statistics. These

regressions are called spurious regressions (Granger and Newbold 1974).

For this reason, we need to check the stationarity of price data prior to any regression

estimation. According to Stock and Watson (2007), time series variables can fail to be

stationary in various ways. However, two types of non-stationary time series are especially

relevant for the regression analysis of economic time series data. These are (1) random walk

and (2) time breaks. Since there are no time breaks in this research, we consider the random

walk process. Thus, we present Y𝑡 into the random walk model form as follows:

Y𝑡 = 𝜇Y𝑡−1 + e𝑡 (4.2)

Here, e𝑡 is assumed to be the independently and identically distributed random variable

with E[e𝑡] = 0 and 𝑉𝑎𝑟(Y𝑡) = 𝜎2. Rewrite (4.2) as Y𝑡 − 𝜌Y𝑡−1 = e𝑡 by using the lag operator

L so that 𝐿Y𝑡 = Y𝑡−1, 𝐿2Y𝑡 = 𝐿Y𝑡−2 and so on. We can then write (4.2) as

(1 − 𝜇𝐿)Y𝑡 = e𝑡 (4.3)

By using algebra, we have

Y𝑡 = e𝑡 + 𝜇e𝑡−1 + 𝜇2e𝑡−2 + 𝜇3e𝑡−3 + ⋯ (4.4)

This equation is the Markov first-order autoregressive model AR[(1)] process.

Equation (4.3) is presented as a moving average process of infinite order. If Y0 = 0 , the

following results are confirmed:

68

𝐸[Y𝑡] = 0; 𝑉𝑎𝑟(Y𝑡) =𝜎2

1−𝜇2; 𝑐𝑜𝑣(Y𝑡, Y𝑡−𝜏) =

𝜏2𝜎2

1−𝜇2, 𝜏 = 1,2 ; 𝑐𝑜𝑟𝑟(Y𝑡, Y𝑡−𝜏) = 𝜇𝜏, 𝜏 =

1,2 …

Because 𝐸[Y𝑡] , 𝑉𝑎𝑟(Y𝑡) and 𝑐𝑜𝑣(Y𝑡, Y𝑡−𝜏) do not depend on t, AR[(1)] is stationary

when |𝜇| ≤ 1 . When μ = 1 , AR[(1)] is a random walk process. When μ > 1 , AR[(1)]

explodes. However, if we consider the difference part of equation (4.2), when μ = 1,

𝑌𝑡 − 𝑌𝑡−1 = ∆𝑌𝑡 = e𝑡 (4.5)

It is easy to show that while 𝑌𝑡 is non-stationary, its first difference is stationary. This is

widely known as the difference stationary process. In this regard, a stationary series can be

obtained from a non-stationary series after a d times differencing transformation. A time series

is said to be integrated in order (d) or denoted as I(𝑑). The assumption of the disturbance term

e𝑡 as an independent and identically distributed (IID) process needs to hold, unless the critical

Dickey–Fuller values cannot be applied (Gujarati 2003, 2011).

Because time series are always non-stationary, OLS is not suitable for time series

regression. However, time series are proved to be stationary after making 𝑑 time of difference

and I(𝑑) . Moreover, if price 𝑌𝑡 and 𝑋𝑡 are really related, then they are expected to move

together and a combination of them can be found that eliminates the non-stationarity. This is

called co-integration relation and was first introduced for time-series regression by Granger

(1981) and elaborated further by Engle and Granger (1987). Engle and Yoo (1987), Phillips

and Ouliaris (1990), Stock and Watson (1988), Phillips (1987), Phillips and Perron (1988), and

Johansen (1988, 1991, 1995) contributed to this concept.

Reconsider equation (4.1) by assuming that Y𝑡 and X𝑡 are integrated in the same order,

d. Because the transaction cost of grain is generally due to the transportation process and

transport infrastructure, a construction program has been implemented in Guizhou province.

This study accounts for the changing trend of transaction costs in Guizhou; thus, we rearrange

the transaction cost term 𝑏2𝑇𝑡 in (4.1) as a linear time trend and obtain the following:

𝑢𝑡 = 𝑌𝑡 − 𝑏1𝑋𝑡 − 𝑐 − 𝑏′2𝑡 (4.6)

By taking the residuals, we have:

�̂�𝑡 = 𝑌𝑡 − �̂�1𝑋𝑡 − (�̂� + 𝑏′̂2𝑡) (4.7)

69

If �̂�𝑡~𝐼(0), then Y𝑡 and X𝑡 are said to be integrated.

In this research, in order to understand the market integration structure, we need to

know the dynamic causal relationship between 𝑌𝑡 and 𝑋𝑡, which is the long-run co-integration

relationship between two markets. The way in which a market reacts to the change in another

market, which is a short-run relationship, also needs to be discussed. Engle and Granger (1987)

provided a two-step approach for testing the existence of a long-run co-integration relationship

between variables. In this regard, the short-run dynamic can be captured by utilizing an error

correction model.

However, Engle and Granger’s approach has received several criticisms regarding

various different points such as the predetermination of the integration order, bias from the first

step transmitting to poor second-step estimates, and non-normal distribution of long-run

parameters.

Johansen (1988, 1991) developed a full information maximum likelihood method that

provides the distribution of two test statistics for the null of no co-integration, referred to as the

trace and eigenvalue tests. Johansen’s approach enables a multivariable system of co-

integration. Pesaran and Shin (1998) and Pesaran et al. (2001) have suggested the need to obtain

the long-run parameters from a general linear autoregressive distributed lag (ARDL) model.

This so-called bounds test for co-integration can be applied regardless of whether the 𝑌𝑡 and 𝑋𝑡

are purely I(0), purely I(1), or mutually co-integrated.

The following procedure for a co-integration test is chosen because it depends on

stationarity in data and a regression relationship in level data. Consider a simple ARDL model

as follows:

Yt = c0 + c1𝑡 + ∑ βiY𝑡−i𝑝𝑖=1 + ∑ α𝑖X𝑡−i

𝑞𝑖=1 + α0Xt + u𝑡 (4.8)

Since all markets in Guizhou are at the same level, there is simultaneity among prices.

All prices should be treated in the same way and each equation has the same repressors. This

assumption leads to the VAR models. VAR is defined as a system of ARDL equations with the

lag 𝑝 = 𝑞. This reduced form of a VAR model is given by

𝑌𝑡 = 𝑎1,0 + 𝑎1,1𝑡 + ∑ 𝑐1,𝑖𝑌𝑡−i𝑝𝑖=1 + ∑ 𝑑1,i𝑋𝑡−i

𝑝𝑖=1 + 𝑢1,𝑡 (4.9)

70

𝑋𝑡 = 𝑎2,0 + 𝑎2,1𝑡 + ∑ 𝑐2,i𝑋𝑡−p𝑝𝑖=1 + ∑ 𝑑2,i𝑦𝑡−i

𝑝𝑖=1 + 𝑢2,𝑡 (4.10)

In order to extend the definition to a multivariable form by using the lag operator L, the

VAR model is obtained as

ϕ(𝐿)(Z𝑡 − 𝜇 − 𝜌𝑡) = 𝑢𝑡 , Z𝑡 ∈ ℝ𝑘 and 𝑢𝑡~𝑖,𝑖,𝑑(0, Ω) (4.11)

Here, we can rewrite the VAR model in VECM form as

∆𝑍𝑡 = 𝑎0 + 𝑎1𝑡 + ∏𝑍𝑡−1 + ∑ Γ𝑗∆𝑍𝑡−𝑖 + 𝜀𝑡𝑝−1𝑗=1 , 𝑡 = 1,2, … (4.12)

Equation (4.12) describes the multiple co-integration relationship in time series. The

long-run multiplier matrix is defined by ∏ ≡ −(I𝑚 − ∑ Φ𝑖𝑝𝑖=1 ) and the short-run response

matrix lag polynomial by Γ(𝐿) ≡ 𝐼𝑚 − ∑ Γ𝑖𝑝𝑖=1 𝐿𝑖, Γ𝑖 = − ∑ Φ𝑗, 𝑖 = 1, … , 𝑝 − 1𝑝

𝑗=𝑖+1 . Consider

that there are 𝑟 co-integration relationships in (4.12). In order to express 𝛼𝛽′ = Π, both 𝛼 and

𝛽 are (𝑘×r) matrices of rank r.

Before all estimations, we first employ an augmented Dickey–Fuller (ADF) statistic to

test the stationarity of the data in both levels and the first-differences. We then compare the

results with those of the other stationary test to check for consistency. If all the price series are

not I(1), this indicates that the prices series are integrated in different orders; thus, a bounds

test needs to be considered for an estimation of market integration. However, in general, time

series are I(1); thus the Johansen approach can be applied for the co-integration test. This

research has found that all price data are I(1) (the results are presented in the next section).

Thus, the Johansen approach is applied.

(3) Johansen procedure for co-integration estimation

In order to estimate co-integration between 𝑋𝑡 and 𝑋𝑡 , this study follows the Toda–

Yamamoto (T–Y) (Toda & Phillips 1994, Toda & Yamamoto 1995) procedure. The optimal lag

lengths in the VAR model and VECM need to be selected by equations (4.9) and (4.10). Lag

length selection can affect the correct inference about co-integrating vectors and rank. The

setting of the order of lag 𝑝 affects the short-run behavior of the integration model because

omitted variables instantly become part of the error term. It is necessary to inspect the data and

the functional relationship very carefully before proceeding with estimation (Braun & Mittnik

71

1993, Lutkepohl 1993). One of the 𝑝 selection methods is to estimate 𝑝 by minimizing an

“information criterion.” Akaike information criterion (AIC) and Schwarz information criterion

(SIC) are standard criteria used to confirm optimal 𝑝. The results are supplemented by Hannan–

Quinn information criterion (HQC) and Bayesian information criterion (BIC). SIC is

SIC(p) = ln𝑅𝑆𝑆(𝑝)

𝑇+ (𝑝 + 1)

ln 𝑇

𝑇 (4.13)

where 𝑅𝑆𝑆(𝑝) is the sum of the squared residuals of the estimated AR[(p)].

AIC is another information criterion that presents as

AIC(p) = ln𝑅𝑆𝑆(𝑝)

𝑇+ (𝑝 + 1)

2

𝑇 (4.14)

HQC is a criterion for model selection. It is an alternative to AIC and BIC.

HQC(p) = ln𝑅𝑆𝑆(𝑝)

𝑇+ (𝑝 + 1)

2ln(ln 𝑇)

𝑇 (4.15)

BIC is presented as

BIC(p) = −2 ∙ ln(𝐿) + (𝑝 + 1)ln 𝑇

𝑇 (4.16)

Under normal distribution, BIC becomes SIC; thus, they become the same. However,

depending on the assumption of distribution, under non-normal distribution they are different.

In this research, BIC is used to check for consistency.

A pretest of non-causality in the VAR models, (4.9) and (4.10), is constructed before

the Johansen co-integration test. Consider that 𝑝 lagged values of 𝑋 are zero in (4.9) as the null

hypothesis for non-causality. The presence of co-integration in the VAR model suggests trace

and the need for a maximum eigenvalue test for the VECM to establish a co-integration

estimation. A VECM is preferred to the VAR model because a VECM models the data instead

of testing for Granger non-causality, unless the sample size is extremely small (Zapata &

Rambaldi 1997). However, in order to avoid type I and type II errors, a non-causality test of

the VAR model as a pretest procedure is first constructed. The results are then used for checking

consistency (Toda & Yamamoto 1995).

By using a standard Wald test, the test statistics are asymptotically chi-square

distributed, with 𝑝 degrees of freedom, under the null. Rejection of the null implies a rejection

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of Granger non-causality and shows that the price data are co-integrated. A similar procedure

is applied to test non-causality in (4.10).

The mean of the co-integrating relationship and the mean of the differenced series bring

deterministic trends in the VECM. In order to recall the spatial market integration relation in

equation (4.6), transaction cost is assumed to be trend stationary. In the VAR model, time trend

is eliminated, but trend is presented as an error correction term and enables the co-integration

equation to be trend stationary. The VECM for spatial market integration is

∆𝑃𝑡 = 𝛼(𝛽′𝑃𝑡−1 + 𝜇 + 𝜌𝑡) + ∑ 𝐷𝑗∆𝑃𝑡−𝑗 + 𝛾 + 𝜀𝑡𝑝−1𝑗=1 (4.17)

Equation (4.17) describes the multiple co-integration relationship in price data. There,

𝑃𝑡 = (𝑋′𝑡, 𝑌′𝑡) . Term 𝛼𝛽′𝑃𝑡−1 is used for estimating the long-run effects. If co-integration

exists in the market pair, r = 1. β′𝑃𝑡−1 is the error correction term and matrix α measures the

strength of co-integration in the VECM. γ = (𝛾𝑦, 𝛾′𝑥) is partitioned so as to be conformable

with 𝑃𝑡 = (𝑌𝑡, 𝑋𝑡′)′. The error term is presented as 𝜀𝑡.

As aforementioned, the long-run multiplier matrixΠ = 𝛼𝛽′ indicates the co-integration

vector as r. The value of r equals the number of nonzero eigenvalues in matrix Π. In other

words, r is the number of linearly independent co-integrating vectors (Engle & Granger 1987).

Denote 𝜆𝑖 as estimated eigenvalues from the matrix Π. Thus, the value of r equals 0 or

k when no co-integration exists in the VECM. When r equals 0, 𝜆𝑖 equals 0. When r equals

n, 𝜆𝑖 is not equal to 0. Co-integration exists in the VECM when 0 ≤ 𝑟 < 𝑘. The eigenvalue

from 𝜆1 to 𝜆𝑟 does not equal 0, and the eigenvalue from 𝜆𝑟+1to 𝜆𝑛 equals 0. Accordingly, we

test the null hypothesis of rank 𝑟 = 0 . If the null hypothesis is rejected, subsequent null

hypotheses (H0: 𝑟 = 1, H0: 𝑟 = 2, etc.) are tested until a null hypothesis is no longer rejected.

The likelihood ratio statistic for the trace test is written as the following equation:

(λ-trace) = −𝑇 ∑ ln(1 − 𝜆𝑖)𝑛𝑖=𝑟+1 (4.18)

The right-hand side of equation (4.18) is the sum of the tested 𝜆𝑖 and T is the number

of observations. For the maximum eigenvalue test, the null hypothesis of the r co-integration

vector against the alternative of 𝑟 + 1 is tested by

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(λ-max) = −𝑇ln(1 − 𝜆𝑖+1) (4.19)

The critical values of these two maximum likelihood tests are shown by Osterwald-

Lenum (1992) and Maddala and Kim (1998). Maximum eigenvalue results are preferred to a

trace test.

The Johansen co-integration test does not provide information about the direction of

causation between the variables; thus, causality tests are necessary. Causality in price data

indicates the ability of prices in one market to predict (and therefore cause) the prices in another

market. We use a Granger-causality statistic to interpret the price signal transmission direction

in market pairs. Granger (1969) developed a simple causality test method. In this current

research, suppose variable 𝑋𝑡 is said to be Granger-cause 𝑌𝑡. Thus, if 𝑌𝑡 can be predicted with

greater accuracy by using past values of the 𝑋𝑡 variable rather than not using such past values,

all other terms remain unchanged.

Then, in order to test for the stationary variables ∆𝑋𝑡 and ∆𝑌𝑡, the procedure proposed

by Toda and Yamamoto (1995) is followed. In this regard, the VAR model for testing non-

causality is written as

∆𝑌𝑡 = ∑ 𝜃1i∆𝑋𝑡−i𝑝𝑖=1 + ∑ 𝜃2i∆𝑌𝑡−i

𝑝𝑖=1 + 𝜃1 + 𝜆1(𝑌𝑡−1 − 𝑏1𝑋𝑡−1) + 𝑣1𝑡 (4.20)

∆𝑋𝑡 = ∑ 𝜃3i∆𝑌𝑡−i𝑖=𝑝𝑖=1 + ∑ 𝜃4i∆𝑋𝑡−i

𝑖=𝑝𝑖=1 + 𝜃2 + 𝜆2(𝑌𝑡−1 − 𝑏1𝑋𝑡−1) + 𝑣2𝑡 (4.21)

For equation (4.20), the null causality hypothesis is

𝐻0: 𝜃21 = ⋯ = 𝜃2i = 𝜆1 = 0 (4.22)

Rejection of this null hypothesis implies co-integration causality from X to Y. A similar

test can be derived for Granger-causality from X to Y in equation(4.21).

4.3.2 Data sources and processing

The time series of grain prices were collected from three regional agricultural wholesale

markets in Guizhou province from January 2008 to March 2016 (Figure 4.4). Four grains are

divided into two groups. Rice and soybeans are primary grain products. Wheat flour and

rapeseed oil are the processed grain products group. Raw data were recorded twice or three

74

times per week by the Grain Bureau of Guizhou. Row price data are converted into monthly

series by taking the simple arithmetic mean in each month.

Figure 4.4 Prices series of rice and soybeans in three regional Markets in Guizhou (1/2008–

3/2016)

4.4 Results and discussion

We label the three regional markets with acronyms: GY (Guiyang), ZY (Zunyi), and

LPS (Liupanshui). The three market pairs are labeled the nearest, medium, and farthest in order

to explore the distance impacts on spatial market integration. The GZ–ZY pair is the nearest:

They are 150 km apart. The medium market pair is GY–LPS, which are 250 km apart. LPS is

higher in elevation by 1000 meters than GY. The market pair ZY and LPS are 400 km apart

and are 1000 meters different in terms of elevation. ZY is at the same altitude as GY.

4.4.1 Unit root test for stationarity

The stationarity test results are presented in Table 4.2. The ADF test fails to reject the

null hypothesis of the presence of unit root in each of the price series in the level test statistics.

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After taking the first difference of each price series, the ADF test rejects this null hypothesis.

Thus, we conclude that all price series are integrated as order one, or I(1).

Table 4.2 ADF test for the order of integration

Markets Level test statistics First difference test statistics

Rice

GY 1 -7.372 **

ZY 1.522 -7.976**

LPS 1.12 -7.416

Wheat (flour)

GY -1.22 -7.739 **

ZY -0.737 -8.111**

LPS -1.536 -6.916 **

Rapeseed (oil)

GY -1.406 -8.209**

ZY -2.115 -8.084**

LPS -2.04 -7.604**

Soybeans

GY 1.439 -9.908**

ZY 0.635 -8.905**

LPS 1.299 -9.395**

Note: ** indicate rejection of the null hypothesis of the presence of unit root at the 5%

and 1% levels respectively.


4.4.2 The VAR model and VECM test with regard to long-run co-integration

Table 4.3 presents the statistics from the Granger non-causality test of the level data

VAR model. The appropriate maximum lag length for the VAR model selected by AIC and BIC

are presented in parentheses.

The causality Wald test for price date shows that for primary grain products, all the

three market pairs rejected non-causality in price data. The causality test of the VAR model

indicates that co-integration exists in the prices of all three markets. However, for processed

grain products, there are two market pairs that could not reject non-causality in price data.

These are the GY–LPS market pair for wheat flour distribution and the ZY–LPS pair for

rapeseed oil distribution.

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Table 4.3 Granger causality Wald test and selected lag length in the VAR model

Independent market Dependent market GY ZY LPS

Rice

GY / 0.008** (4) 0.000** (2)

ZY 0.037* (4) / 0.000** (4)

LPS 0.047*(2) 0.277(4) /

Soybeans

GY / 0.000** (2) 0.004** (2)

ZY 0.250(2) / 0.014*(4)

LPS 0.090(2) 0.000*(4) /

Wheat (flour)

GY / 0.007**(3) 0.253(2)

ZY 0.859(3) / 0.432(2)

LPS 0.428(2) 0.000**(2) /

Rapeseed (oil)

GY / 0.000** (4) 0.726(4)

ZY 0.284(4) / 0.458(4)

LPS 0.000** (4) 0.072(4) /

Note: * and ** indicate rejection of the null hypothesis of the presence of unit root at

the 5% and 1% levels respectively.


The causality test for the level data VAR model provides a cross-check on the validity

of the co-integration test results. The presence of co-integration in the VAR model suggests the

use of the VECM. Lag length, which is selected in the VAR model, is applied in the VECM.

Table 4.4 shows the rank statistics of the Johansson maximum likelihood test. In the

primary grain products group, for rice prices the results from the VECM approach indicate that

a co-integration relation exists in all three market pairs. The results show three regional markets

integrated for rice distribution. Market integration with the local grain-producing area market

(ZY–GY and ZY–LPS) needs four time lags for co-integration. There are two deficit markets

(GY and LPS) integrated with two time lags. For soybeans, a co-integration relation only exists

in the nearest market pair (GY–ZY). The time lag length for co-integration in this pair is two.

In the processed grain products group, for wheat flour a co-integration relation exists

in the farthest market pair (ZY–LPS). The time lag length for co-integration in this pair is two.

Wheat output in LPS is the highest in the three markets. Wheat production in ZY is lower than

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that in LPS but nearly the same. GY is a wheat-deficit market with little wheat production.

However, GY does not integrate with the other two markets for wheat flour distribution. For

rapeseed oil, a co-integration relation exists in the nearest (GY–ZY) and medium market pairs

(GY–LPS). The time lag lengths for co-integration in the two market pairs are both two.

Rapeseed production in ZY is the highest in the three regional markets. Rapeseed production

in the other two markets, GY and LPS, is much smaller than in ZY. The producing-area market

ZY is not integrated to the two deficit markets. Instead, GY, as the transport hub, integrates

with the other two markets for rapeseed oil distribution.

Table 4.4 Rank test for long-run co-integration

Market

pairs Distance p

Max.

eigen test

Critical

values

Trace

test

Critical

values

Ra

nk

Co-

integratio

n

Rice

GY–ZY Nearest 4 1.84 3.84 1.84 3.84 1 Yes

GY–LPS Medium 2 0.31 3.84 0.31 3.84 1 Yes

ZY–LPS Farthest 4 0.02 3.84 0.02 3.84 1 Yes

Soybeans

GY–ZY Nearest 2 7.46 12.5 7.46 12.3 1 Yes

GY–LPS Medium 2 9.94 11.4 9.94 12.5 0 No

ZY–LPS Farthest 4 7.77 11.4 7.78 12.5 0 No

Wheat（flour）

GY–ZY Nearest 3 11.9 19 15.3 25.3 0 No

GY–LPS Medium 2 8.85 14.1 11.2 15.4 0 No

ZY–LPS Farthest 2 1.76 3.76 1.76 3.76 1 Yes

Rapeseed (oil)

GY–ZY Nearest 4 7.91 12.5 7.91 12.3 1 Yes

GY–LPS Medium 4 5.78 12.5 5.78 12.3 1 Yes

ZY–LPS Farthest 4 6.43 11.4 6.53 12.5 0 No


4.4.3 Granger causality test results and market integration structure

Co-integration itself cannot be used to make inferences about the direction of causation

between the variables. Granger causality provides additional evidence of price transmission

direction. The Granger causality test statistics are presented in Table 4.5. The results of price

transmission direction with the time lags for grains are illustrated in Figures 4.5 and 4.6. Figure

78

4.5 shows the market integration and price transmission direction with time lags for primary

grain products. Figure 4.6 shows the market integration and price transmission direction with

time lags for processed grain products in markets.

As shown in Table 4.5, in the primary grain products group, the Granger causality tests

suggest that Granger causes between GY–ZY and GY–LPS for rice distribution are

bidirectional. Rice price transmission in the ZY–LPS market pair is unidirectional. The ZY

market’s Granger causes influence prices in the LPS market. The Granger causal significance

level between the GY and ZY markets is 3%. The Granger causal significance level from GY

to LPS is 6% and the opposite is 10%. Rice price transmission in the ZY–LPS market pair is

unidirectional. The significance level of ZY Granger cause LPS is 1%. Soybeans price

transmission in the GY–ZY market is unidirectional. The significance level of the GY market

Granger cause ZY market is 1%.

In the processed grain products group, all the causality relations between the market

pairs are unidirectional. The Granger causality test suggests that wheat flour price transmission

in the ZY–LPS pair is ZY Granger cause LPS with a 1% significance level. Rapeseed oil price

transmission in the GY–ZY market pair is GY Granger cause ZY with a 1% significance level.

The GY–LPS market pair is LPS Granger cause GY with a 3% significance level.

Table 4.5 Granger causality test for market pairs

Primary grain products Processed grain products

Independent market Dependent market Independent market Dependent market GY ZY LPS GY ZY LPS

Rice Wheat (flour)

GY / 3% 6% GY / -- --

ZY 3% / 1% ZY -- / *

LPS 10% * / LPS -- 1% /

Soybeans Rapeseed (oil)

GY / 1% -- GY / 1% *

ZY * / -- ZY * / --

LPS - -- / LPS 3% -- /

Note: * indicates no signification of causality and -- denotes no co-integration.


79

Figure 4.5 describes market integration for the primary grain products group. The price

transmission direction with time lag is indicated among the markets. Bidirectional causal

relation is only found in the rice price data. The GY market, as the transport hub, is integrated

with the other two markets. Their Granger causal relationships are bidirectional. The time lag

lengths for integration in these market pairs differ. The time lag length for co-integration in the

GY–LPS market pair is shorter than those in the GY–ZY and ZY–LP pairs. The ZY market, as

the grain-producing area, is integrated with the other two markets in a longer time lag. The

different time lag lengths indicate the dissimilarity in market integration structure. Integration

in two deficit markets needs a shorter time lag than market integration between the producing

area market and the consuming area market.

The market integration for soybean distribution indicates that price transmission is from

GY to ZY. Consider the production situation in these two markets. Even though ZY produces

the highest soybean output within Guizhou province, prices in the transport hub market GY

Granger cause prices in ZY, which indicates the soybean deficiency in the ZY market. The time

lag length for integration between these markets is two, which is similar to the case of rice

distribution in the GY–LPS pair.

Figure 4.5 Primary grain products price transmission direction with time lags in markets

Figure 4.6 describes market integration for the processed grain products group. The GY

market has two pairs of integration relation for rapeseed oil distribution (GY–ZY and GY–

LPS). Although the ZY market is a rapeseed-producing area market, the ZY market is not

integrated with the most rapeseed-deficient market, LPS. As indicated in Figure 4.4, grain

80

product prices continue to rise from 2008 to 2016 except for rapeseed oil. Rapeseed oil prices

declined during this period. Such price decline implies an over-supply of rapeseed oil products

in the regional markets. The Granger causality test results indicate that rapeseed oil prices in

the consumption market (LPS) Granger cause prices in the transport hub market (GY). At the

same time, prices in the GY market Granger cause prices in the producing-area market ZY.

Both market pairs need four months of time lag for co-integration.

Causality test results assume that wheat flour prices in the LPS market Granger cause

prices in the ZY market. The time lag length of the ZY–LPS market pair is two. Wheat

production in these two markets is similar. Although wheat production in the GY market is the

lowest, GY does not integrate with the other two regional markets for wheat flour distribution.

As indicated in Figure 4.3, the price of wheat flour continues to rise in the last eight years.

Moreover, the total wheat production in Guizhou is also increasing, which implies a strong

consumption demand for wheat. However, the key wheat-producing area is far from Guizhou

province; thus, the three regional markets are wheat flour deficit markets. The market

integration structure indicates that the transport hub market GY does not distribute wheat flour

imports to other markets. This result implies that provincial trade protection for wheat may

exist in Guizhou province.

Figure 4.6 Processed grain product price transmission direction with time lags in markets

4.4.4 Long-run market equilibrium and short-run divergence adjustment

81

Apart from the test for co-integration in price data, the VECM provides detailed

information of long-run market equilibrium and short-run divergence adjustment in integrated

regional markets. The estimation statistics of the VECM test are presented in Table 4.6.

The GY market is deficient in all grains; however, this market is also the transport hub

for imported grains. As shown in Table 4.6, the co-integrating parameter β that characterizes

the long-run equilibrium relationship is near to 1 for integration with GY. The error correction

coefficient α, which assess the speed at which the market returns to its equilibrium, suggests

that the other two markets’ adjustments to the long-run relationship with GY are relatively fast

(larger than the absolute value 0.1). In particular, the fastest adjustment in the primary grain

products group is rice prices in the LPS market (0.32). The fastest adjustment in the processed

grain product group is rapeseed oil prices in the ZY market (-0.39).

The ZY area produces the highest grain output within Guizhou apart from wheat. For

the ZY market, in the case of primary grain product distribution, the co-integrating parameter

β is near to 1 for integration with ZY. In the case of processed grain product distribution, the

co-integration parameter β is less than 1 for integration with ZY. The co-integration parameter

β of the market pair GY–ZY for rapeseed oil distribution is -0.48. The co-integration parameter

β of the market pair LPS–ZY for wheat flour distribution is -0.53. The estimation of the error

correction coefficient α indicates that the GY market’s adjustments to the long-run relationship

with ZY for primary grain product rice and processed grain product rapeseed oil distribution

are relatively fast (0.17 for rice prices and -0.21 for rapeseed oil prices). However, the

adjustment of LPS to the long-run relationship with ZY is relatively slow (less than the absolute

value 0.1).

As a remote and grain-deficit market, LPS is integrated with fewer market pairs than

the other two regional markets. The estimation of the error correction coefficient α indicates

that only the GY market’s adjustment to the long-run relationship with LPS for rapeseed oil is

relatively fast (-0.23). The adjustments of other markets to the long-run relationship with LPS

for rice and wheat flour are relatively slow (less than the absolute value 0.1).

82

Table 4.6 VECM estimates regarding four grain products

GY Primary grain products

Processed grain products

Parameter

estimates

ZY

(Rice)

LPS

(Rice)

ZY

(Soybeans

)

ZY

(Rapeseed

oil)

LPS

(Rapeseed

oil)

Long-run

equilibrium

relationship (β)

-0.935* -0.945* 1 1 1

The speed

adjustment (α) 0.141* 0.319* -0.185 -0.389* -0.217**

ZY

Parameter

estimates

GY

(Rice)

LPS

(Rice)

GY

(Soybean)

GY

(Rapeseed

oil)

LPS

(Wheat

flour)

Long-run

equilibrium

relationship (β)

1 -0.971* -1.049* -0.481* -0.537

The speed

adjustment (α) 0.174** 0.086** -0.046 -0.209* 0.0432*

LPS

Parameter

estimates

GY

(Rice)

ZY

(Rice)

ZY

(Wheat

flour)

GY

(Rapeseed

oil)

Long-run

equilibrium

relationship (β)

1 1 1 -0.432

The speed

adjustment (α) 0.096* 0.067** 0.043* -0.227*

Note: * and ** indicate rejection of the null hypothesis of the presence of unit root at

the 5% and 1% levels respectively.


4.5 Summary

Using price time series, this chapter examined the intra-provincial market integration

structure for the distribution of four major grain products in Guizhou province in the last eight

years. A spatial market integration model has been described in-depth. Moreover, we follow

the Johansson approach to test co-integration in the VECM. The presence of co-integration in

price data indicates long-run spatial market integration for grain distribution. The market

integration structure and distribution efficiency are discussed via the explanation of the co-

integration estimation results such as long-run equilibrium, short-run adjustment, co-

83

integration lag length, and causal direction. The three focuses of the chapter were mentioned

at the beginning. The following is a summary of the results.

Primarily, this study investigates local demand–supply efficiency. The results indicate

that the producing-area market (ZY) is not dominant in grain distribution in Guizhou province.

In contrast, the transport hub market (GY) is much more active in terms of integration with the

other two regional markets. The remote and grain-deficient market LPS mostly integrates with

the GY market as opposed to the ZY market for grain distribution. Further, the adjustments of

the ZY and LPS markets to the divergence of integration with GY is relatively quick, whereas

the adjustments of the GY and LPS markets to the divergence of integration with ZY are relative

slow. The causality test results also indicate the importance of GY in regional market

integration. Apart from rice, prices in GY Granger cause all prices in other markets. Active

market integration with the transport hub market GY and the ineffectiveness of market

integration with the producing-area market ZY imply that local demand is met via imported

products. This result also provides implications regarding the difficulties for local famers to

sell their products in markets because of reasons such as insufficient market infrastructures and

limited marketing channels. Local grain production becomes less competitive even in intra-

provincial markets.

The influence of biased regional grain politics on intra-provincial grain market

integration has been observed in Guizhou. Although estimations are given for market

integration regarding different grains, the results indicate that government intervention and the

participation of SOEs in the marketing channel improve market integration for grain

distribution. Rice is the government’s most important grain product in Guizhou province. Rice

procurement and distribution are mainly promoted by SOEs in the grain reserve system. Market

integration for rice exists in all three regional markets. The producing-area market ZY

integrates with the remote and deficit market LPS. Moreover, ZY Granger causes prices in LPS.

In particular, the bidirectional causal relation of price transmission is only found in market

integration for rice. Bidirectional price transmission relations within market pairs are irrelevant

to the demand–supply relationship in markets. Such bidirectional price transmission may be

84

caused by the government’s monitoring system for rice prices in markets. There are two market

pairs that were found to be integrated for rapeseed oil distribution, while soybeans and wheat

flour are integrated in only one market pair. The results may relate to regional government

support for rapeseed production; however, no such policy exists for wheat and soybean

production. Further, the separation of the soybean and wheat markets may be because

provincial retrenchment has restricted wheat and soybean distribution to Guizhou.

This study’s further comparison of market integration for primary grain products and

processed grain products indicates the underdevelopment of the grain processing industry in

Guizhou. As a regional cash crop, rapeseed serves an important function in the regional

economy. Rapeseed oil is the most consumed edible oil in Guizhou province. Rapeseed meal

can be applied in the feed industry for feed processing. Rapeseed production is supported by

the regional government, but the decline of the rapeseed oil price implies that rapeseed oil

supply exceeds regional demand. The market integration for rapeseed oil distribution indicates

that the local rapeseed producing-area market ZY has a weak influence on rapeseed oil

distribution in Guizhou. ZY does not integrate with the remote market LPS for rapeseed oil

distribution. Moreover, the price in ZY is influenced by the transport hub market GY. The

adjustment of ZY to integration with GY is quicker than the adjustment of GY to ZY. The

transport hub GY integrates the ZY and LPS markets. These results imply that rapeseed oil

distribution in the Guizhou markets may relate mainly to imported products. In Guizhou, local

rapeseed collection, processing, and distribution are promoted by small-scale private entities.

The underdevelopment of the processing industry and the weak market participation impede

the integration of the regional rapeseed oil market.

However, market integration for wheat flour is slightly different. Before the 2000s,

wheat flour marketing was controlled by regional SOEs within the grain reserve system. In the

early 2000s, the provincial government loosened control on wheat because wheat was not a

policy-protected grain in Guizhou. The recent trend of wheat flour price increases and the

continuance in the increase of local wheat production indicate a strong consumption demand

for wheat flour in Guizhou province. Market integration for wheat flour distribution is between

85

two markets with similar producing situations (ZY–LPS). Market integration in the two

regional markets indicates that the regional grain reserve system is still functioning for wheat

flour distribution. However, the short-run adjustment for integration equilibrium is slow, which

implies an insufficiency of market integration for wheat flour distribution. Regional wheat

production has still to be maintained in Guizhou because of provincial retrenchment and less

effective inter-provincial trade for wheat flour. However, if inter-provincial trade can be

improved, imports may influence regional wheat production.

86

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Chapter 5

Conclusions and Policy Implications

As described in Chapter 1, Guizhou has been one of the poorest provinces in China.

The situation in this province differs from neighboring provinces such as Yunnan Province,

which has enjoyed a richer life with abundant natural food resources although its income level

is similarly poor. In recent decades, Guizhou has faced difficulties in improving regional

economic development and increasing rural income. In these circumstances, in the western

inland provinces of China, grain sector development is crucial for solving the issues of regional

economic backwardness and severe rural poverty.

The objective of this study is to analyze the role and performance of the grain sector in

Guizhou province, which is located in the southwestern inland area of China. This study

provides a quantitative understanding of changes in Guizhou’s grain sector in the context of

economic transition. In particular, this study focuses on policy impacts on the grain sector in

Guizhou and government intervention. The Chinese government has gradually deregulated its

grain market. However, the government and SOEs still significantly intervene in the grain

market because of concerns about food security and grain self-sufficiency. The econometric

methodology of IO analysis and time series (VECM) are applied for this study. First, through

IO analysis of the economic structural change of the Guizhou economy from 2002 to 2007, this

study explores the role of the grain sector in a transitional economy. An intra-provincial market

integration estimation then provides informative detail of regional grain market performance.

In the preceding chapters, a consistent picture of the grain sector in this province has

been explained. This chapter now summarizes the major results of the foregoing methodologies.

Some implications and conclusions from these analyses are deduced. This study’s limitations

and issues for future study are described in the last part of the chapter.

89

5.1 General summary

Several findings have already been highlighted and discussed in-depth in the preceding

chapters. Some of the major results are summarized as follows.

(1) The production inducement effect based on IO analysis indicates that production in

the crop sector is mainly used for domestic consumption. The share of crop sector production

in the Guizhou economy declined from 2002 to 2007; however, the crop sector has still been

the most important sector for domestic consumption. The production inducement effect derived

from government expenditure increased in the crop sector. Nonetheless, in other agricultural

sectors, the production inducement effect derived from government expenditure decreased. The

inducement effects derived from exports declined in all agricultural sectors significantly during

this period. These results show that the crop sector has become more dependent on government

expenditure and less dependent on inter-provincial trade.

(2) The skyline analysis based on the IO table provides information about the self-

sufficiency situation and the structure of interregional trade in the crop sector. The crop sector

maintained a self-sufficiency rate of more than 100% from 2002 to 2007. However, a decrease

of the interregional export (outflow) and import (inflow) ratios implies that the crop sector has

become more self-supply oriented in the provincial market.

(3) The employment effects derived from IO analysis explore the employment situation

and labor productivity in the crop sector. This study found that employment influence and the

sensitivity coefficient in the crop sector were considerably higher than in any other sectors.

Moreover, the two coefficients became larger in 2007. A high employment influence coefficient

indicates that more labor will work for a crop production increase and that labor productivity

reduces. A high employment sensitivity in the crop sector indicates that more labor will enter

the crop sector for overall economic expansion. This extra labor tends to be the surplus rural

labor force.

(4) Inter-sectorial linkage derived from IO analysis explains the relation between the

crop sector and the RoE. Net backward linkage in the crop sector was less than 1, which implies

that production in the crop sector is needed less by all economic sectors than the production it

90

generates. The net backward linkage in the crop sector became weak from 2002 to 2007, which

indicates that the demand of the crop sector from overall economic development decreased.

Net forward linkage in the crop sector was larger than 1 and relatively larger than most other

sectors. This result indicates that all sectors need more primary input from the crop sector

compared with the primary input that the crop sector needs from the whole economy. The net

forward linkage in the crop sector also weakened from 2002 to 2007. From this result, an

increase of primary input from other sectors to the crop sector in 2007 can be deduced.

(5) RAS analysis indicates the growth potential of each sector. Because of the mix-up

effects, the crop sector as a whole cannot be classified into a potential growth or shrinking

group. However, the livestock sector, which is supported by feed grain production from the

crop sector, was clarified as a potentially shrinking industry in Guizhou. The fishery sector

(freshwater), which has a relation with rapeseed grain production in the crop sector, was

clarified as a potential growth sector. These results imply that production in the crop sector

derives complicated growth potential in various related sectors.

(6) The estimated result of market integration analysis indicates that intra-provincial

market integration was not favorable for local grain production distribution in Guizhou from

2008 to 2016. Grain production in one regional market is much higher than the other two

markets. However, this producing-area market is not dominant in regional grain distribution.

Prices in this market do not influence other markets generally and other markets are relatively

slow to adjust to price divergence within this market. The market is not integrated with the

remote and deficient market for the distribution of two grains (soybeans and rapeseed). In

contrast, the transport hub market is significantly influential in regional grain distribution.

According to price transmission direction and the reaction of other markets to price divergence

within this market, the remote deficient market has less integration with the other markets;

however, it is mostly integrated with the transport hub market. Inefficient intra-provincial

market integration for grain distribution was found in this research.

(7) The estimated result of market integration analysis indicates that market integration

relations differ depending on the grain. The most efficient market integration was observed for

91

rice distribution because rice production and distribution is strongly supported and controlled

by the Guizhou government. Three regional markets were integrated for rice distribution.

Bidirectional price transmission in markets was only found for rice distribution. However,

market integration for soybean distribution, where the government does not intervene and

where distribution is handled by private entities, was the most inefficient. Only the nearest

market pair was integrated. Moreover, this integration does not reflect the local production

situation.

(8) With regard to rapeseed, the result of market integration analysis implies that

rapeseed might be oversupplied and that its processing industry is less developed in Guizhou.

The provincial government promotes rapeseed production; however, the rapeseed oil price has

declined in recent years. Market integration for rapeseed oil is not observed between the

rapeseed-producing area market and the remote deficient market. The rapeseed oil price in the

rapeseed-producing area market is influenced by the transport hub market. With regard to wheat

flour, the result of market integration analysis offers a different situation. Wheat production is

not supported by the government; however, SOEs still have a strong influence in the wheat-

marketing channel. Weak market integration exists between two regional markets with similar

wheat-production situations. The transport hub market is not integrated with the other two

regional markets. Inter-provincial trade barriers and local production protection may be

suggested through this market integration structure.

5.2 Concluding remarks and policy implications

In Guizhou province, several regional development programs were launched in the

early 2000s. Because Guizhou is an inland backward province, regional economic development

in the last 15 years has been mainly promoted by government investment. Capital-intensive

primary resources and the energy sector became the leading industries. At the same time,

because the central government had placed higher priority on the “grain for green” project

(reducing crops in eroding, sloped arable land and promoting the recovery of forests) and the

“ecological emigration” scheme in the last two decades, the provincial government has

92

emphasized ecological/environmental issues rather than the economic perspective that

considers the improvement of rural welfare.

By employing the method of the IO analysis framework, this study indicates the

evidence of strong government support in the crop sector in Guizhou. The significant policy

concerns about food security and grain self-sufficiency, with the crop sector including grain as

a major product, are reflected by the production inducements of significantly increased

government expenditure and investment in recent years. The skyline analysis based on the IO

table shows that the crop sector has maintained a high level of self-sufficiency.

Measurement based on IO analysis also reveals that production in the crop sector

became less important for regional development during economic transition. Production

inducement through domestic consumption in the crop sector ranks first in Guizhou province;

however, production inducement through exports has decreased. Besides, net backward linkage

in the crop sector is extremely weak and was weakened further during economic transition. The

production inducement effect indicates that the crop sector became oriented to the domestic

market and has thus led to a shrinkage of interregional trade. Weak net backward linkage

implies that production in the crop sector is needed less by demand from the whole economy

Judging from the production inducement effect and backward linkage, although the crop sector

is still important for domestic consumption, its contribution to overall economic growth

became weaker during the economic structural change in this region.

At the same time, poor production performance in the crop sector was clarified by the

measurement based on IO analysis. Employment influence and sensitivity coefficients in the

crop sector are much larger than in any other sectors in the Guizhou economy. A larger

employment influence coefficient in the crop sector indicates a declining tendency in labor

productivity. A larger employment sensitivity coefficient implies an increasing trend for a

surplus labor force in the crop sector. This employment is increasingly very unstable and

sensitive to economic fluctuation. Weakening of net forward linkage shows that the demand of

primary input from the whole economy to the crop sector has increased. Through these results,

93

this study suggests that production in the crop sector became less efficient during economic

transition.

The estimated results of grain market integration explained the detailed information

about the inefficient distribution infrastructure of the grain sector. In this relation, grain

distribution efficiency in Guizhou was shown to depend greatly on government intervention

and SOE participation. Monitored by provincial government and controlled by SOEs, market

integration for rice is the most efficient in this province. In addition, government interest has

focused more on market stabilization and food security rather than improvements to the

margins of market participants. However, since private entities are small-scale and have limited

resources, they are less capable of product collection in villages. Thus, local production cannot

be efficiently distributed in markets. Further, oversupply and a less developed situation in the

processing industry are factors that have been deduced from market integration for rapeseed

oil distribution, which is the major cash grain supported by the provincial government.

Because of the lower contribution to economic development, and the poor productivity

and inefficient distribution in the crop sector, this study suggests that crop production in

Guizhou is unprofitable and that government support has become a heavy finical burden.

However, production in the crop sector is still important for local consumption. A larger

employment inducement coefficient was observed on both sides of response and influence in

its relationship with the whole economy. This indicates that the relation between the crop sector

and the whole economy in this province is becoming progressively stronger in employment

effects. Further, results from the RAS analysis indicate that the crop sector is not classified as

a potentially shrinking sector and that many industrial sectors that are supported by production

in the crop sector are potentially growing. Thus, it was clarified that the policy to promote this

sector is especially crucial for solving the poverty and depressed situation of Guizhou province

by absorbing the unemployed laborers.

However, the Chinese government has put a higher priority on the “grain for green”

project (reducing cropping in the sloped areas and promoting recovery of forests) and the

“ecological emigration” scheme in the last 15 years. Having focused on major grain food

94

security and market stability, these government policies have been biased toward

ecological/environmental issues rather than economic development issues. These policy biases

have contributed to Guizhou province being left behind in terms of national rapid economic

growth and have so far impeded economic development in this province. Local farmers cannot

get out of poverty. Judging from the major results clarified in this research, it can be strongly

pointed out that these biased policies that work against the situation in this province should be

revised.

Following the foregoing conclusions, several policy implications are now provided.

1. Policymakers need to balance the promotion of each grain’s production and develop

a flexible policy for improving grain sector development. Foremost, the results from the RAS

analysis indicate that production in the grain sector should be adjusted to meet different

demands from other sectors. Governmental support policy biased towards staple grain

production is inappropriate for satisfying the various demands from economic sectors.

2. Improvement of the technological level in crop production is vital for grains’

sustainable production. Production in Guizhou’s crop sector is highly reliant on labor force

input, a situation that results in poverty for farmers and less competitive products. Meanwhile,

when employment in the crop sector becomes unstable and sensitive to economic fluctuation,

a macro change in the economy seriously influences the stability of production in the crop

sector. Laborsaving production methods are essential for sustainability in the crop sector during

economic structural change in the regional economy.

3. Local agroindustry development will benefit the long-run development of the crop

sector and an increase in rural welfare. This study suggests that the agroindustry in Guizhou is

also underdeveloped. Most crop production is induced by final demand. Moreover, net

backward linkage in the crop sector is extremely weak. The development of the local

agroindustry should improve the net backward linkage of the crop sector and thus enhance the

contribution of the crop sector to economic growth. Besides, through local agroindustry

development, the surplus rural labor force can be absorbed. The labor force can work in grain

95

production and generate nonagricultural income at the same time. Thus, production in the crop

sector can be maintained.

4. Market efficiency for local grain distribution should be improved. Reducing

transaction costs, improving the market infrastructure, improving facilities and services, and

supporting private participants in grain processing and distribution are necessary for smooth

local grain product distribution. Market integration between the producing market and the

consumption market is crucial for grain sector development

5.3 Limitations of the study

Some limitations in this dissertation are noted here. First, the economic analysis in this

dissertation is measured by using traditional orthodox methods at a highly-aggregated level.

The aim of this study is focused on the grain sector. However, because of the limitation of

available data, we measured the crop sector as an aggregate of all crop (including non-grain

crop) production instead of dealing with each grain sector separately in terms of economic

structural change in Guizhou. Second, the Guizhou economy has experienced further structural

change (such as marketing channel change) even after 2007, a period that we have not

examined in this study. In this regard, although China’s economy has slowed down because of

the global economic crisis, economic growth in Guizhou was temporarily been higher than

growth at the national level. However, since 2010, a sharp decline in grain production and its

deficiency have been found in Guizhou. We need to investigate further the changes in the crop

sector in relation to Guizhou’s economy, an economy that can be expected to develop

continuously and change in the future.

Third, with regard to market integration measurement, some other factors such as the

trust relation between market participants, consumption customs, the quality of products from

different regions, and the transport channel for market efficiency were not analyzed in this

study. Further research is needed by collecting this information to explore the reality in relation

to Guizhou’s agricultural market. Fourth, we compared market integration relations for only

96

four major grain products in Guizhou; maize, the second most important grain crop, has not

been analyzed because of data deficiency.

Finally, the IO tables can only be processed in terms of general value base and not volume

base. In addition, the market integration analyses adopted traditional orthodox methodologies

based predominantly on price information, which is the only available data for market activities

in this province. In this sense, the IO analysis and the market integration analysis were carried

out based on price information, which may be affected by temporary monetary fluctuations.

97

Acknowledgements

First and foremost, I would like to express my sincere gratitude and appreciation to my

supervisor, Professor Dr. Masaru Kagatsume for his kind help, his suggestions and revisions of

my dissertation, and for guiding me during my three years at Kyoto University.

I would like to express my thanks to Professor Dr. Chieko Umetsu, and Associate

Professor Dr. Jinhu Shen for teaching and supporting me on many occasions. I am also grateful

to Professor Dr. Junichi Ito and Professor Dr. Seiichi Fukui in the Division of Natural Resource

Economics for their guidance and assistance.

It is my pleasure to thank all members of the Laboratory of Regional Environmental

Economics, Graduate School of Agriculture, Kyoto University, who supported me and with

whom I shared an unforgettable memory here in Japan.

Finally, I wish to thank my parents for their support and encouragement throughout my

study.

98

Appendix

Appendix 1a Guizhou input–output table for 2002

Units：million Chinese Yuan

Sector 1 2 3 4 5 6 7 8 9 10 11 12 13 14

15 16 17 18 19 20 21 Total

intermediate

use

Household

consumption

Government

consumption

Gross

capital

formation

Exports Final

Demand Imports

Gross

output

1 Crop 2509 0 3610 9 222 0 5751 29 7 1813 4 0 0 0

15 0 0 263 0 0 0 14234 12781 0 274 2799 15855 3544 26545

2 Forestry 0 300 0 0 13 230 0 1 229 688 8 248 1 44

0 0 0 7 4 0 0 1775 85 0 -6 474 553 486 1842

3 Livestock 0 0 53 0 0 0 1723 1 0 18 0 0 0 0

0 0 0 1360 0 0 0 3157 7876 0 1876 3728 13480 3757 12880

4 Fisheries 0 0 0 85 3 0 21 0 0 0 0 0 0 0

0 0 0 144 0 0 0 252 335 0 0 27 362 61 554

5 Other agric. 484 32 24 8 15 0 0 15 0 0 0 0 0 0

0 0 0 0 0 0 0 578 0 1172 91 163 1427 0 2005

6 Mining 368 0 252 0 11 919 132 2 19 2522 4622 77 2780 1056

107 0 0 30 0 52 7 12954 355 0 64 3064 3482 6542 9894

7 Food, beverage, tobacco 0 0 0 48 0 0 1811 0 1 83 12 2 0 0

14 1 22 2250 1 0 0 4247 11953 0 -1828 16859 26985 6803 24429

8 Leather, knitwear 0 0 0 0 0 25 39 507 13 392 64 31 14 150

11 130 49 33 2 10 3 1473 2877 0 -60 742 3559 3220 1812

9 Timber, paper printing 7 2 0 1 3 43 756 13 516 302 56 96 22 566

70 96 107 47 264 148 519 3636 622 0 197 880 1699 2698 2637

10 Chemical, medical, nonmetal 3508 40 451 0 108 488 817 237 243 4644 1589 1043 145 7240

1505 32 101 239 275 131 863 23700 2783 0 -114 12056 14726 11596 26830

11 Metal 215 28 0 0 7 287 105 14 58 655 5997 2565 38 9523

47 7 17 20 7 13 45 19648 925 0 1614 20120 22659 14751 27556

12 Industrial machinery 79 95 142 0 4 654 284 50 64 801 640 3714 422 6779

2003 1300 515 271 118 394 1059 19387 2468 0 13820 9415 25703 29544 15546

13 Electricity, energy 335 0 65 1 14 1088 306 112 185 2246 4853 552 1513 226

526 65 73 330 114 68 401 13076 1527 0 0 3104 4631 2766 14941

14 Construction 0 0 0 0 0 10 3 3 1 9 11 4 3 0

57 15 240 164 143 19 91 773 0 0 45598 926 46524 2660 44637

15 Transport 580 45 180 20 32 918 863 50 167 1895 2403 487 891 2324

285 99 181 374 214 308 433 12750 2477 0 647 2347 5471 4646 13576

16 Postal & information 25 1 2 1 2 35 57 27 14 133 45 74 30 124

226 278 149 72 452 125 244 2118 1784 0 0 1298 3082 0 5200

17 Retail & wholesale 705 14 48 7 22 153 864 167 193 982 831 586 42 3081

186 301 70 551 85 86 260 9237 2979 0 1057 0 4037 0 13515

18 Lodging & catering 16 3 0 1 6 85 124 39 45 267 61 131 41 219

201 80 200 45 318 170 586 2640 2051 0 0 4892 6943 0 9583

19 Finance & real estate 55 1 2 2 2 224 622 81 88 739 281 353 1151 513

344 597 3459 444 607 243 77 9885 2890 0 1248 745 4883 4286 10482

20 Social services 126 23 56 10 19 95 858 40 22 922 177 371 314 2294

304 63 757 171 280 429 2444 9777 2363 996 455 820 4634 9037 5374


administration 54 0 42 2 2 62 46 13 11 130 37 89 30 78

23 40 119 29 128 355 716 2005 4797 22934 0 2025 29756 9701 22059

Total intermediate inputs 9066 585 4929 196 485 5316 15183 1404 1879 19240 21691 10425 7436 34219

5925 3104 6060 6845 3011 2554 7748 167301 63929 25102 64933 86486 240450 116096 291897

Compensation of employees 12467 898 5671 255 1183 2780 1775 562 522 3593 2727 3104 2769 7386

4660 820 2830 1102 2559 1972 13612 73246

Depreciation of fixed capital 442 31 201 10 38 535 968 62 191 1301 1220 673 3139 864

1430 1386 533 58 3215 139 567 17002

Net tax subsidies on production 810 59 369 16 53 1051 5920 148 179 2145 1514 1079 1101 1835

818 186 1968 549 728 282 42 20852

Operating surplus 3759 270 1710 77 246 212 583 -364 -134 552 404 265 496 334

743 -297 2124 1029 969 428 91 13496

Total value added 17478 1258 7951 358 1520 4578 9246 408 758 7590 5865 5121 7505 10418

7650 2095 7455 2738 7471 2820 14311 124596

Total inputs 26545 1842 12880 554 2005 9894 24429 1812 2637 26830 27556 15546 14941 44637

13576 5200 13515 9583 10482 5374 22059 291897

Source: Statistics bureau of Guizhou province, China

99

Appendix 1b Guizhou input–output table for 2007

Units：million Chinese yuan

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Total

intermediate

use

Household

consumption

Government

consumption

Gross

capital

formation

Exports Final

Demand Imports

Gross

output

1 Crop 3554 0 3689 0 844 0 5559 108 31 1833 0 3 0 0 554 0 1 1086 161 63 1 17486 17271 0 1627 4136 23034 1300 39220

2 Forestry 93 566 858 0 47 232 195 1 403 1123 8 1 0 31 0 0 0 46 0 0 0 3602 1 0 11 714 726 1552 2777

3 Livestock 0 0 147 0 0 0 542 5 4 31 0 0 0 0 0 0 0 245 0 0 0 975 15550 0 880 6705 23134 946 23163

4 Fisheries 0 0 0 0 6 0 6 0 0 0 0 0 0 0 0 0 0 392 0 0 0 404 573 0 -1 45 616 116 904

5 Other agric. 424 16 251 8 0 0 523 18 0 205 12 1 0 0 0 0 0 0 0 0 0 1459 0 2177 4 0 2182 0 3641

6 Mining 564 1 480 0 49 7131 287 5 32 6952 8070 148 9907 3084 6 0 13 39 0 0 13 36781 2841 0 697 13980 17518 9152 45147

7 Food, beverage, tobacco 208 0 1650 226 0 0 2067 2 11 185 0 5 0 0 17 0 7 2987 0 0 55 7422 14379 0 10 25211 39600 10491 36531

8 Leather, knitwear 0 0 0 0 0 213 49 328 15 353 44 116 45 233 48 22 134 74 76 309 106 2166 6459 0 -6 456 6909 7962 1114

9 Timber, paper printing 6 2 0 3 11 452 2285 29 1494 844 79 250 77 826 49 169 141 86 378 377 1114 8671 3160 0 1346 1450 5956 10285 4342

10 Chemical, medical, nonmetal 5963 134 151 1 532 1814 1875 103 218 18581 4858 2080 398 11060 3149 64 86 386 357 1863 2969 56644 17370 0 1581 29114 48065 39917 64791

11 Metal 389 60 0 0 29 1124 179 6 64 1191 12164 7573 205 11304 36 19 31 45 38 752 90 35296 841 0 11704 44473 57018 31404 60910

12 Industrial machinery 0 0 0 0 56 3277 297 17 81 1339 1763 6340 1023 4840 1325 2053 413 182 872 347 3641 27865 10843 0 34296 18557 63696 60921 30640

13 Electricity, energy 237 1 125 3 65 2308 356 42 265 5156 11544 885 26954 2208 843 597 622 916 235 429 1514 55307 5846 0 7 15995 21848 161 76994

14 Construction 0 0 0 0 0 62 26 0 1 24 15 22 17 0 150 208 140 182 134 51 198 1231 1535 0 83725 1189 86449 30394 57286

15 Transport 571 37 295 32 109 6025 916 31 113 4012 3967 978 2274 2849 1484 194 2675 343 571 735 1342 29551 5520 925 1748 6014 14208 8973 34786

16 Postal & information 40 0 26 1 9 68 42 1 7 192 26 61 60 101 115 1110 140 54 454 165 916 3588 7210 0 463 3336 11009 51 14547

17 Retail & wholesale 786 103 679 16 139 1212 1986 138 268 2639 3939 1887 1091 2829 703 916 140 1582 273 469 1002 22794 10373 0 2582 0 12955 8082 27667

18 Lodging & catering 32 5 14 3 25 252 158 3 21 447 99 197 199 369 283 208 1435 239 1030 661 2956 8636 9181 0 0 6503 15685 8129 16192

19 Finance & real estate 60 0 13 2 9 555 382 8 54 2078 435 417 5070 626 8503 367 1624 565 596 1342 1575 24279 12740 0 1139 2003 15883 11971 28191

20 Social services 195 24 201 17 80 463 1127 6 30 1234 317 828 1092 2777 1262 640 2142 272 1228 494 1516 15944 3889 4775 291 2726 11682 9762 17864


administration 70 0 15 2 7 869 100 8 15 325 123 470 838 96 137 61 497 54 293 181 1411 5572 15293 44686 0 5153 65131 14562 56141

Total intermediate inputs 13190 950 8592 315 2017 26056 18956 858 3128 48743 47464 22263 49248 43234 18665 6629 10241 9773 6696 8238 20419 365674 160875 52564 142101 187761 543301 266129 642846

Compensation of employees 19223 1349 10758 435 1199 8488 2642 114 322 4944 2037 3170 4302 8009 4538 1683 3574 1662 4649 4412 30979 118488

Depreciation of fixed capital 0 0 0 0 0 3168 8715 42 131 2780 3118 1610 3811 1935 2186 491 4051 670 3017 569 457 36751

Net tax subsidies on production 1210 85 677 27 76 1643 793 15 128 3021 1425 838 5716 885 2150 3734 1618 697 8825 1835 2409 37811

Operating surplus 5597 393 3132 127 349 5792 5424 85 633 5302 6867 2759 13916 3223 7248 2010 8183 3391 5004 2810 1878 84123

Total value added 26030 1827 14568 589 1624 19091 17575 255 1215 16048 13447 8377 27745 14052 16122 7918 17426 6420 21495 9626 35722 277172

Total inputs 39220 2777 23160 904 3641 45147 36531 1114 4342 64791 60910 30640 76994 57286 34786 14547 27667 16192 28191 17864 56141 642846

Source: Statistics bureau of Guizhou province, China

title economic analysis on grain market integration …...economic analysis on grain market...

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