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The Review of Economics and StatisticsVOL. LXXXII NUMBER 1FEBRUARY 2000

H OW T AXIN G IS CORR UPT ION ON IN T ER N ATION AL IN V EST O RS?

Shang-Jin Wei*

AbstractThis paper studies the effect of corruption on foreign directinvestment. The sample covers bilateral investment from twelve sourcecountries to 45 host countries. There are two central findings. First, a rise ineither the tax rate on multinational firms or the corruption level in a hostcountry reduces inward foreign direct investment (FDI). In a benchmarkestimation, an increase in the corruption level from that of Singapore tothat of Mexico would have the same negative effect on inward FDI asraising the tax rate by fifty percentage points. Second, American investorsare averse to corruption in host countries, but not necessarily more so thanaverage OECD investors, in spite of the U.S. Foreign Corrupt Practices Actof 1977.

We need to deal with the cancer of corruption. . . . We

can give advice, encouragement, and support to govern-ments that wish to fight corruptionand it is thesegovernments that, over time, will attract the larger

volume of investment. (Emphasis added).James D. Wolfensohn1

President, The World Bank

I. Introduction

This paper studies two sets of questions regarding theeffect of corruption on international direct investment.First, does corruption in host countries negatively affecttheir ability to attract foreign direct investment (FDI)? How

big is the effect relative to the host governments tax onforeign corporations? Second, is the United States a specialsource country? I will test the hypothesis that the Americaninvestors are especially sensitive to host country corruption,possibly due to the deterrent effect of the U.S. ForeignCorrupt Practices Act.

This first question is partly motivated by the observationon China. China has rampant corruption according to

various newspaper accounts as well as surveys of businessexecutives.2 Yet, for every year in the last four, China hasbeen the largest developing host of international investment.Even its FDI flow-to-GDP ratio has been among the highestamong developing countries. Indonesia is another apparentparadox. President Suharto is known as Mr. Ten Percent,as foreign corporations doing business there are naturallyexpected to pay a relatively well-defined bribe to thepresident or members of his family. Yet, Indonesia is apopular destination of FDI, particularly those from Japan.

Empirical evidence on a negative correlation betweencorruption and inward FDI has so far been elusive. In a studyof foreign investment of U.S. firms, Wheeler and Mody(1992) failed to find a significant correlation between thesize of FDI and the host countrys risk factor, a compositemeasure that includes perception of corruption as one of thecomponents. The authors concluded that the importance ofthe risk factor should be discounted, although it would notbe impossible to assign it some small weight as a decisionfactor (p. 70).

Similarly, more recently, using total inward FDI (asopposed to bilateral FDI used in this paper), Hines (1995)

failed to find a negative correlation between total inwardFDI and the corruption level in host countries. Commentingon his table A6, Hines remarked (footnote 24, p. 20), whilethe equations fit poorly, it is noteworthy that local corruptionhas an insignificant effect on post-1977 growth of FDI.3

On the other hand, popular press and policy circles seemto believe that corruption does reduce inward FDI, assuggested by the opening quote from James Wolfensohn,

Received for publication November 17, 1997. Revision accepted forpublication May 20, 1999.

* Harvard University and NBERI would like to thank Kimberly Ann Elliott, John Gallup, Daniel

Kaufman, Mary Hallward-Driemeier, James Hines, Robert Lawrence,Susan Rose-Ackerman, Ray Vernon, seminar participants at Harvard,Princeton, NYU, University of Southern California, and Federal ReserveBank of New York, and a referee and the editor (Campbell) for helpfulcomments, Greg Dorchak, Junling Wang, and especially Huamao Bai forefficient research assistance, and Mihir Desai, Paolo Mauro and XiaolunSun for supplying some of the data. I also wish to acknowledge gratefullythe financial support from The Japan-United States Friendship Commis-sion, a U.S. government agency. The views presented in the paper are mineand are not necessarily shared by any other individual or organization.

Send correspondence to Shang-Jin Wei, Kennedy School of Government,Harvard University, 79 JFK Street, Cambridge, MA 02138, USA. Fax:(617) 496-5747. Email: [email protected].

1 Transition, 7(910), p. 9, Sept/Oct., 1996.

2 According to The Wall Street Journal (Smugglers Stoke B.A.T.sCigarette Sales in China, December 18, 1996), the Chinese consume ahuge quantity of foreign-made cigarettes (one in every three cigarettes issmoked in China), but 90% of the imports do not pay duty. The BritishAmerican Tobacco (BAT) company is the largest supplier of foreign

cigarettes in China. In 1995, the company sold 400 million cigarettes thatwere duty-paid, 3 billion in duty-free shops, 4 billion in special economiczones (SEZs)many of which were transported illegally to other parts ofChinaand 38 billion to retailers who smuggled their way directly intoChina. Conversations with Hong Kong businessmen indicate that there is awell-developed fee-for-service business in Hong Kong to smuggle goodsthrough Chinese customs. There are at least four different ways tocircumvent the Chinese tariffs, most of which involve paying bribes toChinese customs officials. A business consultant who works for a majorU.S.-owned consulting firm in Hong Kong indicated that 90% of foreignwine in the Chinese market was also smuggled into the country.

3 Hines did find a significantly negative effect of corruption on U.S. FDIand interpreted it as a result of the Foreign Corrupt Practices Act. I willreturn to this later.

The Review of Economics and Statistics, February 2000, 82(1): 111

2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

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president of the World Bank. So why is the empiricalevidence so elusive? Wheeler and Mody (1992) combinedthe corruption measure with twelve other indicators to formone regressor (what the authors called RISK). Theseother indicators include attitude of opposition groupstowards FDI, government support for private business

activity, and overall living environment for expatriates,which may not be overwhelmingly correlated with govern-ment corruption, may not be precisely measured, or may notbe as important for FDI as one imagines. As a result, thenoise-to-signal ratio for the composite measure (RISK) maybe too high to show up significantly in the regressions. In thepart of the Hines paper that deals with this question, thetotal inward FDI from the IMFs IFS database may also betoo noisy.

The first objective of this paper is to reexamine thecorruption effect on a broader panel of bilateral FDI datawith a more comprehensive list of control variables. Further-more, both host country tax and corruption could have a

negative effect on inward FDI. The literature has so far notconsidered the two effects simultaneously. To reveal thebottom line, I will report evidence that corruption in ahost country does depress inward FDI in a way that isstatistically significant and quantitatively large.

The second motivation of the paper comes from the U.S.governments concern that the Foreign Corrupt Practices Act(FCPA) of 1977 may have undermined the competitivenessof American firms in the overseas markets vis-a-vis firmsfrom Europe, Japan, and elsewhere. The FCPA came as abyproduct of the Watergate hearings in the early 1970s,when many American firms were discovered paying largebribes to foreign officials in addition to contributing to

domestic political parties. As a sign of the mood of the day,the bill was passed unanimously in the Senate and the Houseand was signed into law by President Carter. At the time thelaw was enacted, it may have been hoped that other majorsource countries would follow suit. But, for a long time (upto February, 1999), the FCPA made the United States theonly source country in the world that penalized its multina-tionals or their officers with fines or jail terms for bribingforeign government officials.

On a priori ground, the American multinationals may notnecessarily dislike the law. Aside from the moral position ofthe corporate officers, the law may serve as a usefulcommitment device for them in the face of a demand forbribery by a foreign corrupt official. The law allows them tosay something to the effect, I would like to pay you. But Iam sorry I cant. If I do, I will go to jail. This commitmentdevice is not available to companies from other sourcecountries. If the American firms have the one and only kindof technology that the host country needs, the Americanfirms may very well still capture the business but with alower cost (because of no bribery). In this case, the FCPAwould not hinder the U.S. investment.4 Alternatively, if the

American firms can find a way to circumvent the law (forexample, by using a close substitute for outright briberypayment), their competitive position vis-a-vis other inves-tors would not be affected either. Hence, the effect of theFCPA on the American competitiveness becomes an empiri-cal one: Is it binding at the margin?

Using country dummies as a measure of corruption, Becket al. (1991) found a statistically significant but quantita-tively small effect of corruption on the U.S. export competi-tiveness. In the concluding chapter of J. David Richardsons1993 book, Sizing Up U.S. Export Disincentives, the authornoted under the section titled Surprisingly Small Esti-mates that across-the-board regulatory burdens, such asprocedures mandated for all businesses by the ForeignCorrupt Practices Act, seemed generally unimportant (p.131). The best and the most-recent evidence on U.S. FDI andexports was provided by James Hines (1995). Controllingfor the growth of the host country GDP, Hines foundevidence that corruption negatively affects the growth of

U.S.-controlled FDI during 19771982, their capital/laborratio, incidence of joint ventures, and aircraft exports. Heinterpreted the findings as evidence that FCPA has under-mined the competitiveness of American firms relative toother countries.

There are some reasons to think that the Hines interpreta-tion may require some additional evidence. First, corruptionmay reduce FDI from non-U.S. investors to the extent thatthey feel morally obligated to avoid bribery. Second, Ameri-can firms may be just as clever at finding covert substitutesfor bribery payments as other investors.5 Third, the degree ofcorruption in host countries tends to be highly correlatedwith many other dimensions of the government quality, such

as extent of bureaucracy and red tape, or quality of legalsystem. These features are likely to affect non-U.S. investorsas well. To attribute the U.S. FDIs negative correlation withcorruption measure to the FCPA, we need to control for theresponse of all FDI to corruption.6

The classical theoretical work on corruption includes Nye(1967), Rose-Ackerman (1975, 1978), and Shleifer andVishny (1993). In light of the literature, let me be up front

4 For a formal model of this commitment story and empirical evidence,see Kaufmann and Wei (1999).

5 Conversations with Chinese businessmen and officials suggest thatoutright financial payment is not the dominant bribery form in China(because bribe-taking officials can be prosecuted even in the Chinesecourt). Instead, sponsoring a study trip (read expense-paid tours) forofficials to a foreign country (particularly that of the home country of themultinational firm) and providing financial support for family members ofthe officials to study or work in a foreign country are popular and legalways to curry favor with the corrupt officials. Anecdotal evidence suggeststhat American firms are just as creative and active (if not more so) asinvestors from any other country.

6 Hines attempted to control for this with total inward FDI as one of theregressors. The data on total FDI are from the World Banks World Tables,and were originally reported by host countries as part of their nationalincome and product accounts. The definitions and calculation methodsdiffer considerably among the countries. Consequently, the data may havelarge measurement errors. In addition, because total FDI is affected bymany of the same factors as the U.S. FDI, it is likely to be correlated withthe error term in regressions in which the U.S. FDI is the dependentvariable. This measure of total FDI is not statistically significant in any ofhis regressions (Hines, 1995, table 2).

2 THE REVIEW OF ECONOMICS AND STATISTICS

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about the limitations of this paper. Susan Rose-Ackermanmade a distinction between bribery (including campaigncontribution) to erect or change the rules/laws to favor thepayers and bribery to deviate from an honest implementationof the exiting rules/laws. Shleifer and Vishny made adistinction between organized or efficient corruption (the

payers can get things done after a relatively well-definedbribe) and disorganized or inefficient corruption (there isstill a big residual uncertainty even after the bribe). Themeasures of corruption used in this paper cannot capture thisconceptual richness.7 I would suppose that the survey-basedcorruption measure refers mainly to the administration ofrules/laws pertinent to foreign firms and probably is weightedby efficiency level as perceived by those who were surveyed.

Corruption can have many other detrimental effects on thehost countries. In the economic sphere, corruption mayreduce growth rate, possibly as a result of reduced domesticinvestment (Mauro, 1995; Knack & Keefer, 1995; Rodrik,1996; & Kaufmann, 1996).8 In political-economy terms,

corruption often contributes to an unfair income or wealthdistribution. In political terms, corruption can breed politicalinstability. These important aspects of corruption mayinteract with its effect on inward FDI. This paper does notexplicitly study any of these effects.

The paper is organized as follows. Section II describes thedata set. Section III reports the statistical results, and sectionIV provides concluding remarks.

II. Data

The key variable to be explained is the bilateral stocks ofFDI from twelve source countries to 45 host countries.9 The

data come from table 8 of the OECD International DirectInvestment Database. The source countries include theseven largest ones in the world: the United States, Japan,Germany, the United Kingdom, France, Canada, and Italy.Many OECD member countries report both outward andinward FDI. I choose the outward FDI as it is more likely tobe consistent in definition for a given source country, and itprovides the greatest number of host countries in coverage.

The data on 1989 host countries tax rate on foreigncorporations is the minimum of the following two measures:the statutory marginal tax on foreign corporations as re-ported by Price Waterhouse (1990) and tax payment to thehost governments by the foreign subsidiaries of American

firms divided by their total income in that country. The dataon 28 of the host countries are taken from Desai & Hines(1996, appendix table 2). The rest (seventeen countries) areobtained using the Price Waterhouse source. In an appendixtable, I also provide estimates based on the statutory taxrates in 1992 as reported by Price Waterhouse and kindly

provided by Rosanne Altshuler from the data used inAltshuler, Grubert and Newlon (1998).I use three measures of corruption, all of which are based

on surveys of respondents. The first one was based onsurveys conducted and organized during 19801983 byBusiness International (BI), now a subsidiary of the Econo-mist Intelligence Unit. BI reports a number of survey-basedrankings of country risk factors, of which corruption isone. The BI corruption measure is an integer from 1 (mostcorrupt) to 10 (least corrupt) according to the degree towhich business transactions involve corruption or question-able payments. The data are kindly provided by PaoloMauro, who collected them by hand from BIs archives.

The second measure was compiled by the InternationalCountry Risk Group (ICRG). According to the Knack andKeefer paper (1995), to which the ICRG data source refersfor definitions of its variables, lower scores indicate highgovernment officials are likely to demand special paymentsand illegal payments are generally expected throughoutlower levels of government in the form of bribes connectedwith import and export licenses, exchange controls, taxassessment, police protection, or loans. The variable issupposed to be on a 06 scale. In reality, the minimum andmaximum ratings any country receives is 1 and 5, respec-tively, making it effectively a 15 scale. There is nodescription of the methodology used in deriving the countryratings. Presumably, they come from in-house expert rating,like the Business International index.

The third measure is compiled by Transparency Interna-tional (TI), an agency dedicated to fighting corruptionworldwide. The TI index is scaled from 0 (most corrupt) to 9(least corrupt). The TI index itself is an average of ten surveyresults on corruption over a number of years. The averagingprocedure used by the TI could reduce measurement error ifthe errors in different surveys are independent. On the otherhand, the ratings on different countries are derived fromdifferent surveys, potentially introducing inconsistency inthe cross-country ratings. Fortunately, the BI and TI indices

are highly correlated (with a correlation coefficient equal to0.89). In the subsequent sections, I will report estimationresults using both measures, while concentrating the discus-sion on results using the BI index.

To avoid awkwardness in interpreting the coefficients, Irecode the corruption measures in this paper so that a highnumber reflects a high level of corruption: BI index hereequals 11 minus the original BI index; ICRG here equals 7minus the original ICRG index; and the TI index here equals10 minus the original TI index.

The GDP and population data are from the InternationalMonetary Funds International Financial Statistics database.

7 A more detailed explanation is in the next section.8 Both Knack and Keefer and Rodrik employ a composite measure of

institutional quality, which is composed of rule of law, repudiation ofcontracts by governments, expropriation risk, quality of bureaucracy, andcorruption in the government. These indicators are highly correlated witheach other. Kaufmann (1996, summary, page I) found, among participantsin Harvard Universitys special mid-career programs and short-termworkshops during the summer of 1996, that a majority considercorruption about the most important challenge for economic developmentand growth for their countries, and also many regard vested financialinterest and corruption as a key reason for the lack of sufficient economicreform progress in recent times.

9 The number of host countries is constrained by the availability of dataon tax on foreign corporations and measures of corruption.

3HOW TAXING IS CORRUPTION ON INTERNATIONAL INVESTORS?

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In a few cases for which GDP data are not available, GNPdata are used instead. The wage and labor compensation dataare from the International Labor Organization, with the kindassistance of Dr. Xiaolun Sun.

The dummy on linguistic tie takes the value of 1 if thesource and host countries share a common language, and 0

otherwise. The data on distance measures the greater circledistance between the economic centers in the source-hostpair. Both data have been used in Frankel et al. and Wei(1995) and Wei (1996b).

The data on 1990 adult literacy ratio is defined as 1 minusthe adult illiteracy ratio in 1990. The adult illiteracy ratiocomes from table 1 of the World Banks World Development

Report 1995, which cites the U.N. Educational, Scientific,and Cultural Organization (UNESCO) as the original source.The report does not present illiteracy rates for high-incomecountries but does contain a footnote that reads accordingto UNESCO, illiteracy is less than 5 percent. I assign 2.5%as the illiteracy rate for these high-income countries. Accord-

ing to the World Bank Reports technical notes adultilliteracy is defined here as the proportion of the populationover the age of fifteen who cannot, with understanding, readand write a short, simple statement on their everyday life(p. 231).

The information on 1990 total secondary school enroll-ment comes from table 28 of the same World Bank report.According to the technical notes to the table (p. 241), thedata are estimates of the ratio of children of all ages enrolledin secondary school to the countrys population of secondary-school-age children. It notes that the definition of secondaryschool age differs among countries, and is most com-monly considered to be 12 to 17 years. It further notes that

late entry of more mature students as well as repetition andthe phenomenon of bunching in final grades can influencethese ratios.

Table 1 reports summary statistics on some of the keyvariables. We observe that the statutory and effective taxrates are highly correlated with each other. For the threeindicators of corruption, pairwise correlations are high,although the correlation between the BI and ICRG indices isthe lowest among all three pairs.

III. Statistical Estimation

A. Preliminary Double-Log Linear Model

I will start with a preliminary linear model (after takinglogarithms for the dependent variable, FDI, and most of theindependent variables, such as GDP and distance). Themodel will be estimated using the ordinary-least-squares(OLS) method. The dependent variable is the stock ofbilateral FDI in logarithm in 1993 from source country i tohost country j. Use taxj and corruptionj to denote host country

js tax rate on foreign corporations and its corruption level,respectively. Then, the basic regression specification is

log (FDIij) Xij 1 taxj 2 corruptionj eij

where X is a vector of control variables other than tax andcorruption that are relevant for determining the bilateral

FDI. , 1, and 2 are parameters.Many of the control variables included in the X vector

such as host country GDP and populationwill enter inlogarithmic form, as the FDI variable on the left-hand side.Hence, this specification is referred to as the double-loglinear model. The logarithmic transformation of the left-hand-side and many right-hand-side variables helps to make theerror term, e, (close to) homoskedestic, in an analogous wayto the gravity model in goods trade (e.g., Frankel et al.,1995).

I will implement a quasi-fixed-effects model. That is, theX vector in all regressions will include source countrydummies.10 The source country dummies are meant tocapture all characteristics of the source countries that may berelevant to its size of outward FDI, including its GDP andlevel of development. In addition, differences in the defini-tion of FDI across source countries can be controlled for bythe dummies under the (somewhat audacious) assumptionthat these definitions are proportional to each other exceptfor an additive error term uncorrelated with other regressorsin the regression. I do not include host country dummies asdoing so would eliminate the possibility of estimating all theinteresting coefficients including the effects of tax andcorruption.

Column 1 in table 2 presents the result of the basic OLS

regression using the Business International (BI) index as ameasure of corruption. Included as control variables are thesize of the host country by its GDP and population, both inlogarithm, the distance between the source and host coun-tries, and a dummy for whether they share a common

10 Because the twelve source countries cover a substantial fraction of theuniverse of all FDI flows in the world, a fixed-effects regression may bemore appropriate than a random-effects model (Hsiao, 1986). All regres-sions in this paper will have a constant and seven source country dummies(U.S., Japan, Germany, France, U.K., Canada, and Italy). FDI from othersource countries are relatively sparse. In order to avoid singularity ornear-singularity problems in the estimation, I merge all the remainingsource country dummies into one constant.

TABLE 1.SUMMARY STATISTICS

Mean Std Dev Minimum Maximum #obs

Tax-rate (statutory) 0.34 0.11 0.10 0.59 41Tax-rate (effective) 0.34 0.12 0.02 0.55 45Corruption (BI) 3.70 2.49 1 10 45Corruption (ICRG) 2.63 1.27 1 5 45Corruption (TI) 4.55 2.63 1 10 42

Political stability 7.93 1.17 5 10 45Hourly wage (US$) 6.82 5.22 0.18 16.15 35

Correlation Matrix(based on 41 common observations)

Tax(effective)

Corruption(BI)

Corruption(ICRG)

Corruption(TI)

Tax-ra te (sta tutory) 0.64 0.10 0.03 0.09Tax-rate (effective) 0.25 0.22 0.33Corruption (BI) 0.73 0.89Corruption (ICRG) 0.87


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language (in addition to source country fixed effects). Thecoefficient on the marginal (effective) tax rate (on foreigninvestors) is negative and statistically significant at the 5%level. An increase of one percentage point in the marginaltax rate reduces inward FDI by 4.8%. The coefficient on thecorruption measure is also negative and significant. The

numerical effect is remarkably large. A one-grade increase inthe corruption level is associated with a 26% reduction in thestock of inward FDI,11 or approximately equivalent to asix-percentage-point increase in the marginal tax rate. Inother words, under this double-log linear specification, aworsening in the host governments corruption level fromthat of Singapore (with a BI rating of 1) to that of Mexico(with a BI rating of 7.75) has the same negative effect on theinward FDI as raising the marginal tax rate by 42 percentagepoints.12

The first regression yields other interesting observations.The coefficient on the distance variable is negative andstatistically significant at the 5% level: A 1% increase in

distance is associated with a 0.6% reduction in the FDI.Thus, international investment to some extent is a neighbor-hood event. On the other hand, the coefficient on thelinguistic dummy is positive and significant at the 15%level: Sharing a common language or colonial history isassociated with a sizable increase in bilateral FDI flow.Some authors (e.g., Rauch, 1996a, 1996b) have emphasizedthe importance of networks in business transactions. While itis difficult to measure the strength of network precisely,distance and linguistic tie may capture part of it, and theevidence presented here is consistent with such a theory.

B. Modified Tobit Estimation

There is a potential problem with the double-log linearspecification in the previous subsection: Not all countriesreceive direct investment from all source countries. Thesezero-FDI observations are dropped from the sample when adouble-log specification is implemented. If it is the case thatthe desired level of FDI based on the characteristics of thehost country and host-source relation is zero or negative, wehave the classic censored-sample problem, and droppingthese observations could lead to inconsistency. Unfortu-nately, it is not feasible to apply the Tobit specification whilemaintaining the double-log structure on the two sides of theequation, as the logarithm of zero (FDI) is undefined. Hence,

I define a modified Tobit specification.

ln (FDI ij A) X uij ln(A)

if X uij ln(A)ifX uij ln(A)

in which A is a threshold parameter to be estimated and u isan i.i.d normally distributed variable with mean zero andvariance 2. In this specification, when X u exceeds a

threshold value, lnA, there will be a positive foreigninvestment, and, when X u is below the threshold value,the realized level of foreign investment is zero (and desiredlevel could be negative). Eaton and Tamura (1996) pio-neered a version of this specification. It will be estimated bythe maximum-likelihood method. The derivation of the

likelihood function is given in an appendix.The regression results with this specification are reportedin table 2, columns 2 through 6. In column 2, I have ascontrol variables the host countrys GDP and population,both in logarithm, the distance between the host and sourcecountries, and a possible linguistic/colonial connectionbetween the two. The key variables are the host countriestax rate and corruption. Both variables produce negativecoefficients that are statistically significant. Hence, whenzero observations are taken into account, we find that tax andcorruption deter foreign direct investment.

Suppose 1 and 2 are coefficient estimates for tax rateand corruption, respectively. Given the specification, a

100/1 percentage point change in tax rate and a 1/2 changein the rating of corruption would produce the same amountof change in the stock of FDI. Therefore, a one-step increasein the corruption measure is equivalent to 1002/1 percent-age points increase in the tax rate. Using the estimates incolumn 2, a one-step increase in the corruption level isequivalent to a rise in the tax rate by 7.53 percentage points,other things equal. An increase in corruption level from thatof Singapore to that of Mexico has the same negative effecton inward foreign investment as raising the tax rate by overfifty percentage points.13

The modified Tobit specification produces a larger esti-mate of the effect of corruption than does the simple linear

specification for an intuitive reason. Investors in somesource countries may find it not worthwhile to invest inhighly corrupt host countries, which may be an importantreason for why some bilateral FDI numbers are zero. Thedouble-log linear specification drops these observations (aslog of zero is undefined), which produces a downward biasin the estimated effect of corruption on FDI. This highlightsthe importance of taken into account the zero-FDI observa-tions for the question of our interest.

Let us now turn to a number of variations of the basicspecification in order to check for the robustness of the basicfinding. First, I look into the effect of differential taxtreatment of foreign source income in different countries.Many countries effectively exempt foreign-source incomefrom domestic taxation. So direct investment from thesecountries should be sensitive to foreign tax rates. In contrast,the tax codes of the United States, United Kingdom, andJapan allow their multinational firms to claim credit fortaxes paid to foreign governments (up to the limit of whatthey would have to pay to the home governments if theforeign-source income were derived domestically). Thiscould make direct investment from these source countries

11 exp (0.30) 1 0.259.12 [0.30*(7.75 1)]/(0.0483) 41.9. 13 [0.18X100X(7.751)]/(2.39) 50.8.


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insensitive to foreign tax rates (up to a limit). On the otherhand, foreign tax credit can be claimed only when profits arerepatriated. Many multinational firms from U.S., U.K., andJapan choose to reinvest a substantial fraction of theirforeign income in their facilities in the host country (Hines& Hubbard, 1990). In this case, their firms may still besensitive to the tax rates of host countries. For this reason, towhat extent FDI from these three source countries issensitive to host countries tax rate becomes an empiricalquestion.

To investigate this, I add to the regression an interactive

term, ftc*i tax-ratej, where ftc is a dummy variable taking thevalue of 1 if the source country is either the U.S., U.K., orJapan. The result is in column 3 of table 2. The coefficient ispositive but not different from zero at the 10% level. Hence,it appears that the FDIs from these three source countries are

just as sensitive to the tax rate in host countries as FDIs fromother source countries. More importantly, the estimatedeffects of tax and corruption on FDI are basically unaffectedby the inclusion of this variable.

One may speculate that political stability promotes for-eign investment and that corruption and political stability are

negatively correlated. The causality on the corruption/stability nexus can go both ways: Official corruption maybreed public discontent, which may eventually topple thegovernment, and, alternatively, instable political environ-ments may induce officials to have short horizons and tograb whatever rents available while they can. It may beuseful to investigate the independent effect of corruption onFDI after controlling for political stability. Column 4 adds ameasure of political stability in the host countries. Thecoefficient is positive and statistically significant. So hostcountries that are politically more stable attract more inward

FDI. The coefficient on corruption is slightly reduced butremains negative and significant.Column 5 adds the host countrys wage level (in loga-

rithm) to the list of regressors. This is motivated by thepopular hypothesis that many FDIs chase low-cost labor inthe host countries. This suggests a negative correlationbetween the size of inward FDI and the hosts wage level.Contrary to the expectation, the estimated coefficient for thewage variable is positive (0.35) and significant at the 5%level. Although it is not consistent with the popular labor-cost hypothesis, this finding echoes many other papers in the

TABLE 2.CORRUPTION AND FOREIGN INVESTMENT

OLS(1)

Modified Tobit USSample

(7)(2) (3) (4) (5) (6)

Tax-rate 4.83* 2.39* 2.57* 2.61 3.51* 3.66* 3.24*(0.67) (0.40) (0.60) (0.61) (0.83) (0.86) (1.31)

Corruption 0.30* 0.18* 0.18* 0.16* 0.11* 0.10* 0.16##

(0.06) (0.04) (0.04) (0.04) (0.03) (0.03) (0.11)Tax credit 0.75 0.71 0.83 0.84(0.72) (0.72) (0.78) (0.82)

Political stability 0.13* 0.20* 0.17* 0.11*(0.06) (0.08) (0.07) (0.18)

log (GDPh) 0.46* 0.39* 0.39* 0.32* 0.02 0.04 0.87*(0.11) (0.08) (0.09) (0.08) (0.15) (0.15) (0.36)

log (populationh) 0.46* 0.20* 0.20* 0.26* 0.56* 0.63* 0.22(0.12) (0.07) (0.07) (0.08) (0.19) (0.20) (0.27)

log (distance) 0.60* 0.30* 0.29* 0.29* 0.28* 0.27* 1.06*(0.07) (0.06) (0.06) (0.06) (0.06) (0.06) (0.20)

linguistic tie 0.97* 0.33* 0.33* 0.27# 0.31# 0.33* 1.51*(0.22) (0.15) (0.15) (0.15) (0.16) (0.16) (0.60)

OECD 0.50*(0.19)

log (wageh) 0.35* 0.42*(0.15) (0.16)

OECD log (wage) 0.19#(0.10) 1.06* 1.05* 1.02* 1.02* 1.02* 1.02*

(0.16) (0.15) (0.15) (0.15) (0.15) (0.26)c 1.7E4* 1.6E4* 1.6E4* 1.6E4* 1.7E4* 1.6E4*

(2.49) (2.94) (2.60) (2.92) (2.60) (13.8)A 1.6E10* 1.5E10* 1.6E10* 1.7E10* 1.7E10* 1.2E10*

(4.7E6) (2.3E7) (5.3E6) (2.3E7) (7.4E6) (1.3E8)source dummies yes yes yes yes yes yes#obs 346 563 563 563 453 453 41loglikelihood 584.5 1812.83 1820.23 1829.11 1582.00 1582.17 200.3

Notes:(1) Eicker-White standard errors based on analytic first and second derivatives are reported in the parentheses.(2) All reported coefficients and standard errors except the OLS estimates in column 1 are multiplied by 1,000. For example, the coefficient for tax rate in column 2 is 0.00239.(3) *,#,## denote significant at the 5%, 10%, and 15% levels, respectively.(4) Examples for notational convention: 1.1E6 1.1 106 1.1E6 1.1 106.(5) All regressions have source country dummies that are not reported here.


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literature.14 It is important to note that, for our purpose, thecoefficients for the tax rate and corruption measures remainnegative and statistically significant, although the size of thepoint estimates changes a bit.

There is a reason to suspect that the specification incolumn 5 may not be a fair test of the low-labor-cost

hypothesis. We know that some of the FDIs move fromdeveloped countries to developing countries (primarily aspart of vertically integrated firms), but many move fromdeveloped to developed countries (primarily in the form ofhorizontally integrated firms). Implicitly if not explicitly, thelabor-cost hypothesis is postulated only for the first type ofFDIs. To account for this, I let the labor cost assumepotentially different roles for the two types of the FDIs.Specifically, I create an OECD dummy for all host countriesthat are members of OECD up to 1993. I add an interactiveterm, OECD*log(wage), and the dummy itself, OECD, tothe list of regressors. The result is reported in column 6. Thecoefficient for log(wage) remains positive, while that for the

interactive term is negative. Hence, this data set does notsupport the hypothesis that FDI chases cheap labor indeveloping countries.

With the host countrys labor cost taken into account incolumn 5, the coefficients for tax rate and corruptionmeasures have changed a bit. As the estimated effect of taxon FDI becomes larger and that of corruption becomessmaller, a one-grade deterioration in corruption rating is nowequivalent to a 2.7 percentage point increase in tax. Anincrease in corruption from the Singapore level to theMexico level would have the same negative effect on inwardFDI as raising the corporate income tax rate by eighteenpercentage points. Because wage data are missing for anumber of host countries, it should be noted that theregression results with wage variable, such as the one incolumn 6, and those without wage data, such as the one incolumn 2, are not directly comparable.

Besides the labor-cost story, one may conjecture that ahost countrys education level or its endowment of skilledlabor may play an important role in attracting inward FDI.This is a key feature of the new FDI theory of Markusen(1984 and 1995) and Zhang (1996). As an extension, I rantwo additional regressions (not reported to save space)adding two different measures of human capital (literacyratio and enrollment of secondary schools) one at a time.

Somewhat disappointingly, neither is statistically signifi-cant. Again, the coefficients on tax rate and corruptionremain largely unchanged. In addition, I have used laborcompensation instead of wage rate in the regressions (notreported) with same qualitative answers, but the number ofobservations is substantially smaller for compensation thanfor wage data.

Because our data cover FDI from several source countriesto the same set of host countries, one may worry that theerror terms for observations involving the same host coun-tries may be positively correlated. This could bias thecoefficient estimate of the effect of corruption (and of othervariables) and bias the standard-error estimation. As a check,

we also run the same regression on the subset of all FDIsfrom one source country, namely the U.S., to the other 41host countries. This, by construction, eliminates the correla-tion in the error terms due to a common host-countrycomponent.15 The result is reported in column 7 of table 2.The coefficient on the corruption variable is now statisticallysignificant at the 15% level (which is not bad given that thereare only 41 observations and that nine parameters need to beestimated). However, the point estimate continues to benegative and is approximately of the same magnitude as thecomparable specification for the full sample (column 4 intable 2).

C. Binary Coding of the Corruption Measure

The ten-step gradation (i.e., from zero to nine) in thecorruption index may have imposed too much linearity inthe effect of corruption on FDI. To see if the negative effectof corruption is sensitive to this fine gradation, I alsoexperiment with a more coarse partition of the host coun-tries. I define a dummy that takes the value of 1 formore-corrupt host countries (BI index 6) and 0 otherwise(BI index 6). The regression result with this binarymeasure of corruption is presented in table 3. The qualitativepicture is exactly like before: More-corrupt host countriesreceive less foreign investment.

D. Alternative Indicators of Corruption

We also adopt two other measures of corruption that havebeen used in the literature. The first is the corruption ratingfrom the International Country Risk Group (ICRG). Thesecond is the Transparency International (TI) index. Theregression results are reported in table 4.

Using the average of the ICRG ratings over 19911993,the corruption coefficient still has a negative sign. It is nowsignificant at the 15% level (column 1). Coded as adichotomous variable, high-corruption (ICRG 3 on 16scale) countries are associated with a lower level of inward

FDI. The coefficient on the corruption dummy is significantat the 5% level. Using the TI indicator, the corruptionmeasure is also shown to retard the FDI much the same wayas using the BI index (albeit with somewhat smaller pointestimate). The coefficient is significant at the 5% level. If we

14 Wheeler and Mody (1992) reported a positive correlation betweenwage level and inward FDI, exactly opposite to the hypothesis of FDIchasing low labor costs.

15 Ideally, one should introduce a host-country-specific component in theerror term and run the modified Tobit regression on the full sample. Thiscould raise the efficiency of the estimation relative to the restrictedone-source-country approach adopted here. Unfortunately, it is not pos-sible to derive a closed-form expression for the likelihood function in thiscase. It will be useful to address this problem in future work.


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recode the TI index to be a dummy (high corruption if TIindex 6 on a 110 scale), the corruption coefficient is stillnegative although significant only at the 15% level.

E. Are American Investors More Sensitive to Corruption?

The Foreign Corrupt Practices Act makes the U.S. theonly source country that, up to now, provides an explicitpenalty to its firms for bribing foreign government offi-cials.16 In this section, I will examine whether or notAmerican investors are more sensitive to corruption thanthose from other source countries. To accomplish this, I willadd to the regression an interactive term, US*i Corruptionj,where USi is a dummy variable taking the value of 1 if thesource country is the United States and 0 otherwise. Thereare three plausible hypotheses.

Hypothesis 1: Corruption discourages U.S. investorsin the same way as non-U.S. investors. In this case, the

interactive term will have a zero coeffcient.

Hypothesis 2: Corruption discourages only U.S. inves-

tors. Hence, the interactive term will have a negative

coeffcient, and the generic corruption measure will no

longer be negatively correlated with FDI.

Hypothesis 3: Corruption discourages FDI from all

investors, but it depresses those from the U.S. even

more. In this case, the coeffcients on both the corrup-

tion measure and the interactive term will be negative

and significant.

The estimation results are reported in table 5. In columns1 and 2, a continuous and dichotomous measures ofcorruption (based on the BI index) are used, respectively. Incolumn 1, the coefficient estimate on the newly addedinteractive term is 0.07, which could be consistent withhypothesis 3 above. On the other hand, the coefficient is notstatistically different from zero at the 10% level, whichmeans that one cannot reject hypothesis 1 that U.S. investorsare sensitive to corruption, but no more so than an averageinvestor from other OECD countries.

In column 2, when corruption is measured by a binary

dummy, the coefficient on the interactive term(Us*Corruption) is still negative but not different from zeroin a statistical sense.

There are several plausible and not mutually exclusiveexplanations for the possibility that the American investorsare equally (but not more) averse to host country corruptionrelative to other investors. First, corruption is often anindicator for generally poor enforcement of contracts by hostgovernments and Byzantine bureaucracy that hurt everyinvestor, regardless of whether the source country govern-ment forbids bribery payment by its companies. Second, tothe extent that investors feel repulsed by corruption, theymay be deterred by it just as much as the Americans, even

without a formal law like the U.S. FCPA. Finally, whenbribery becomes a necessary part of the business deal, theAmerican firms may be just as clever as other investors atfinding covert means to pay it despite the FCPA.

Using the Transparency Internationals index for corrup-tion, these two regressions are replicated in columns 3 and 4.The results are broadly similarly as using the BI index.

IV. Concluding Remarks

This paper studies the effect of taxation and corruption oninternational direct investment from fourteen source coun-

tries to 45 host countries, with two central findings. First, anincrease in either the tax rate on multinational firms or thecorruption level in the host governments would reduceinward foreign direct investment. An increase in the corrup-tion level from that of Singapore to that of Mexico wouldhave the same negative effect on inward FDI as raising thetax rate by eighteen to fifty percentage points, depending onthe specification. Second, American investors are averse tohost country corruption but not necessarily more so thanother investors, in spite of its unique Foreign CorruptPractices Act.

16 On December 17, 1997, 28 member states of the OECD and fivenon-member states signed the Convention on Combating Bribery ofForeign Officials in International Business Transactions. This convention,in effect since February 15, 1999, having been ratified by a certain numberof national law-making bodies of the signatory countries, criminalizesbribing foreign officials by firms from these countries.

TABLE 3.CORRUPTION AS A BINARY VARIABLE

(1) (2)

Tax-rate 2.59* 3.84*(0.62) (0.89)

Corruption 0.27# 0.10#(0.14) (0.06)

Tax credit 0.72 0.88

(0.68) (0.77)Political stability 0.17* 0.26*(0.07) (0.08)

log (GDP) 0.49* 0.07(0.08) (0.17)

log (population) 0.06 0.64*(0.05) (0.21)

log (distance) 0.27* 0.24*(0.06) (0.06)

linguistic tie 0.35* 0.38*(0.15) (0.16)

OECD 0.50*(0.21)

log (wage) 0.56*(0.19)

OECD log (wage) 0.16(0.11)

1.02* 1.01*(0.14) (0.14)c 1.6E4* 1.7E4*

(3.89) (3.15)A 1.6E10* 1.7E10*

(4.3E7) (2.9E7)source dummies yes yes#obs 563 453loglikelihood 1830.52 1586.67

Note: Please see the footnotes to Table 2.


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TABLE 4.ALTERNATIVE INDICATORS OF CORRUPTION (ICRG AND TI INDICES)

ICRG Index(average of

9193 ratings)

TransparencyInternational

Index

Continuous(15 scale)

Dichotomous(ICRG 3)

Continuous(110 scale)

Dichotomous(Dummy for

TI 6)

Tax-rate 2.69* 2.78* 2.37* 2.63*(0.76) (0.77) (0.59) (0.66)

Corruption 0.12## 0.47* 0.10* 0.19##(0.08) (0.16) (0.03) (0.13)

Tax credit 0.09 0.05 0.75 0.79(0.70) (0.74) (0.73) (0.75)


log (GDP) 0.59* 0.57* 0.42* 0.54*(0.11) (0.09) (0.09) (0.09)

log (population) 0.07 0.11 0.16* 0.06(0.08) (0.08) (0.07) (0.06)

log (distance) 0.32* 0.33* 0.28* 0.30*(0.06) (0.07) (0.06) (0.07)

linguistic tie 0.47* 0.40* 0.31* 0.38*(0.17) (0.17) (0.16) (0.17)

1.12* 1.13* 1.06* 1.13*(0.16) (0.16) (0.15) (0.16)

c 1.6E4* 1.6E4* 1.7E4* 1.6E4*(4.17) (3.16) (2.91) (3.74)

A 1.4E10* 1.4E10* 1.5E10* 1.4E10*(3.6E7) (5.5E6) (4.1E6) (3.0E7)

source dummies yes yes yes yes#obs 549 549 548 548loglikelihood 1753.5 1747.6 1815.4 1795.6


TABLE 5.U.S. AS A SOURCE COUNTRY(MODIFIED TOBIT)

Corruption (BI) Corruption (TI)

Continuous(1)

Dichotomous(2)

Continuous(3)

Dichotomous(4)

Tax-rate 2.82* 2.68* 2.46* 2.45*(0.64) (0.64) (0.60) (0.60)

Corruption 0.17* 0.26# 0.09* 0.14(0.04) (0.15) (0.04) (0.13)

Corruption U.S. 0.07 0.14 0.09 0.31(0.06) (0.30) (0.07) (0.30)

Tax credit 0.87 0.75 0.97 0.83(0.72) (0.70) (0.69) (0.69)


log (GDP) 0.34* 0.51* 0.42* 0.50*(0.08) (0.09) (0.09) (0.08)

log (population) 0.28* 0.07 0.16* 0.05(0.09) (0.05) (0.07) (0.06)

log (distance) 0.31* 0.28* 0.29* 0.28*(0.06) (0.06) (0.06) (0.06)linguistic tie 0.29# 0.37* 0.31* 0.36*

(0.06) (0.16) (0.16) (0.15) 1.08* 1.05* 1.07* 1.05*

(0.15) (0.15) (0.15) (0.15)c 1.6E4* 1.7E4* 1.7E4* 1.7E4*

(4.58) (4.89) (3.03) (2.97)A 1.5E10* 1.5E10* 1.5E10* 1.6E10*

(5.4E7) (6.1E7) (7.9E6) (1.3E7)source dummies yes yes yes yes#obs 563 563 548 548loglikelihood 1810.07 1817.56 1816.8 1825.4



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Mauro, P., Corruption and Growth, Quarterly Journal of Economics 110(1995), 681712.

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APPENDIX: LIKELIHOOD FUNCTION FOR THE

MODIFIED TOBIT MODEL

Let y be the bilateral FDI flow (subscripts omitted to simplify thenotations). The hypothesized model is

ln(y A) 5X u

lnA

if X u lnA

if X u lnA

in which A is a positive threshold parameter, and u is an i.i.d. normalvariable with mean zero and variance 2. In this specification, when X u exceeds a threshold value, lnA, there will be a positive flow of foreigninvestment; and, when X u is below the threshold value, the realizedlevel of foreign investment is zero (and desired level could be negative).

Notice that

Prob (X u lnA) Prob (u lnAX) 1lnA X 2

APPENDIX TABLE 1.USING STATUTORY TAX RATE DATA

BI Index TI Index

Tax-rate 2.31* 2.19*(0.69) (0.69)

Corruption 0.14* 0.10*(0.04) (0.04)

Tax credit 0.11 0.60(0.65) (0.64)

Political stability 0.11# 0.12#(0.06) (0.06)

log (GDP) 0.46* 0.49*(0.10) (0.11)

log (population) 0.18* 0.11

(0.07) (0.07)log (distance) 0.34* 0.31*(0.06) (0.06)

linguistic tie 0.40* 0.39*(0.16) (0.16)

1.07* 1.05*(0.15) (0.15)

c 1.7E4* 1.7E4*(4.54) (3.48)

A 1.5E10* 1.6E10*(5.2E7) (2.2E7)

source dummies yes yes#obs 549 534loglikelihood 1766.11 1777.4



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where (.) is the cumulative distribution function of a standard normalvariate, and

Prob (X u lnA) 1 Prob (X u lnA) 1 1lnA X

2 .

Furthermore, the conditional density function

f(u 0X u lnA) f[u ln(y A) X 0X u lnA]

1

3

ln(y A) X

41

1

lnA X

2where (.) is the density function of a standard normal variate.

Let d be a dummy variable indicating a positive realized foreigninvestment. That is, d 1 if X u lnA, and zero otherwise. Thelikelihood function for an individual observation is

f(u 0X, y; , A, )

[ f(u 0X u lnA) Prob (X u lnA)]d

[Prob (X u lnA)]1d

51

3

ln (y A) X

46d

3 1lnA X

241d

The overall likelihood function is just the product of the individuallikelihood functions over all observations.

APPENDIX A.HOST AND SOURCE COUNTRY COVERAGE

Host Countries

BelgiumDenmarkFinlandSwitzerlandGreeceIrelandNew ZealandPortugalSpainSouthAfricaTurkey

IsraelArgentinaBrazilChileColombiaEcuadorMexicoPeruVenezuelaNigeriaEgypt

KuwaitSaudi ArabiaTaiwanHong KongIndiaSouth KoreaMalaysiaPhilippinesSingaporeThailandChina

Source and Host Countries

CanadaFranceGermanyItaly

JapanUnited KingdomUnited StatesAustria

NetherlandsNorwaySwedenAustralia


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