Corruption and foreign direct investment a cross country study

Chapter INTRODUCTION 1.1 Motivation and Objectives of the Study In recent years, the role of institutions in fostering economic growth and development has received increased attention by governments, policy makers, and researchers North (1990) defines institutions as “constraints human beings impose on themselves” Therefore, defined in this manner institutions act as channels, which facilitate or hinder certain courses of action (political, economic and social) that are extremely essential for the improvement of information flows, for the reduction of transaction costs, and for the enforcement and protection of property rights A very important issue that can be considered to be akin to or reflective of the quality of institutions is corruption Certain claims about corruption in developing economies are often heard: bribery and corruption can have positive effects; the costs of addressing corruption are prohibitively high; corruption is endemic everywhere; and the few resources that exist should be spent on enforcement measures The past few years have seen growing public recognition and dialogue about the problem of corruption This has included addresses by organizations like the World Bank and IMF; the increasing influence of non-governmental organizations like Transparency International; the Organization for Economic Cooperation and Development's (OECD) significant resolution to criminalize bribery abroad; and a rapidly burgeoning body of theoretical and empirical literature on the economic impact of corruption (Gray and Kaufmann 1998) The general definition of corruption and the one adopted by the World Bank is “the use of public office for private gain” However, it must be noted that corruption is not restricted only to the public sphere and can co-exist even in the private sector Indeed, often the term corruption is used synonymously with bribery, extortion and fraud The central question that is addressed in this paper is whether the phenomenon of corruption has any effect on foreign direct investment (FDI) The theoretical literature on corruption is vast1 One can divide the assumptions and conclusions of these papers into generally two strands—many papers have looked upon corruption as a phenomenon that has severe negative effects, and the construction of the models in this category reflects this assumption A relatively fewer number of papers on the other hand argue that corruption can have positive effects Given that the literature tells us there is scope for both types of effects to occur, this forms the first motivation for the thesis Secondly, even though the theoretical literature on corruption in general is extensive, its relationship with FDI in theory has been scantly studied so far Further, the empirical literature in this area does not present any consensus on the results—some papers present a negative relationship between corruption and FDI, while others posit that corruption has no significant (statistical) influence on direct foreign investment We also observe large amounts of FDI going into countries even where the perceived level of corruption is high, See Chapter on “Review of literature” for a brief insight into the theoretical literature for e.g China, Indonesia, Mexico, Argentina, and Brazil among others Therefore, it may be possible to argue that corruption is a small cost that investors are willing to bear, and that other factors such as attractive domestic returns, cheap labor, large domestic demand and so on play the major role in attracting FDI Given this background, the objectives of the study are therefore, twofold: (1) To present a simple theoretical model that will illustrate the relationship between corruption and FDI (2) To re-examine if the relationship between corruption and FDI is empirically borne out by using a broad set of panel data 1.2 Organization of the Study The organization of the thesis is as follows: Chapter reviews the literature on corruption and FDI A simple theoretical model of corruption and FDI is developed in Chapter 3; some properties of the model are examined and implications of the theoretical results derived therein are discussed The data and methodology adopted to test the empirical relationship between FDI and corruption are presented in Chapter Chapter discusses the empirical results The conclusions of this study along with some directions for future research are presented in Chapter Chapter REVIEW OF THE LITERATURE 2.1 Introduction This chapter briefly introduces the literature on corruption and goes on to review the literature pertaining to corruption and FDI in detail One of the first economic analyses of corruption was carried out by Becker (1968) Becker uses economic analysis to develop optimal public and private policies to combat illegal behavior Since then, many studies, both theoretical and empirical have examined the relationship of corruption with a variety of economic variables On the theoretical side, Rose-Ackerman (1975), Lui (1985) and Sheilfer and Vishny (1993) are some of the seminal studies in the area of corruption While most of the theoretical studies examine the negative effects of corruption, a relatively fewer number of studies state that corruption can have positive effects—for instance Lui (1985) studies the impact of bribery in the context of a queue where customers having different values of time are ranked by order of their bribe payments to the server of the queue The author shows that the bribing strategies of customers who have different time preference form a Nash equilibrium that minimizes the average value of the time costs On the empirical front, one of the earliest known studies of corruption is by Wade (1985) who studies corruption in irrigation administration in South India Since then, many studies have estimated the effect of corruption on a variety of macroeconomic variables like economic growth (Mauro 1995), government expenditure (Mauro 1998), investment (Mauro 1995; Wei 2000) and so on The study by Mauro (1995) deserves special mention here, as this was the first study to examine the phenomenon of corruption across countries and over time Using cross section data for about 67 countries for the period 1980-’83 the author finds that corruption has a statistically significant negative effect on investment and economic growth However, empirical evidence on the relationship between corruption and FDI has so far presented a mixed picture The following section reviews the empirical literature on the effects of corruption on FDI 2.2 Corruption and Foreign Direct Investment Empirical work on the effects of corruption on FDI has been of fairly recent vintage and indeed, began only in the 1990s Wheeler and Mody (1992) in a study of foreign investment of US firms, fail to find a statistically significant correlation between the size of FDI and the host country’s risk factor, a composite measure that includes corruption as one of its components The authors use data on a composite measure of risk taken from Business International (BI, now incorporated into the Economic Intelligence Unit) They focus on investments in the manufacturing sector by US multinationals in 42 countries for the period 1982-’88 The authors find that US investors give almost all the decision weight to agglomeration benefits (increasing returns that occur to economic units due to colocation) like infrastructure quality in developing economies and specialized support services in industrialized economies, and to factors such as market size which was found to eclipse labor cost as national income level rose The authors conclude that the importance of the risk factor should “be discounted, although it would not be impossible to assign it some small weight as a decision factor” (p.70) Hines (1995) uses data on US foreign investment in 35 countries for the period after 1977 and fails to find a significant relationship between total inward FDI and corruption in the host country He uses corruption data from BI and studies four indicators of US foreign investment in host countries: FDI, capital/labor ratios, joint venture activities and aircraft exports He finds no clear evidence for the fact that the anti-bribery laws enacted in the US influenced its investment in corrupt countries According to the author, “…it is noteworthy that local corruption has an insignificant effect on post-1977 growth of FDI….” (p.20) Using cross-country data for a set of 10 developing Asian economies (other than Japan and the Middle East) Lipsey (1999) analyzes the location choice of US affiliates in Asia The author defines location by the size of operation, which is measured by variables such as the stock of investment, employment, and production (as in gross product) sales The author does not exclusively address the issue of corruption but instead, uses two measures of institutional characteristics in his study: an index of overall competitiveness published in the World Competitiveness Report by the World Economic Forum, and a corruption index used in Mauro’s (1995) study and reported by BI He also examines factors such as the degree of export orientation, capital intensity, dependence on imports from the United States, and extent of research and development Using regression analysis, the author finds that variables such as market size, distance from the United States, per capita income and tax rates of US affiliates explained half the variation among the FDI recipient countries in terms of attracting US FDI He also finds that corruption has a statistically significant negative impact on US FDI in Asian countries Alternatively, countries that had the highest level of US FDI also ranked high apropos measures of institutional characteristics including low levels of perceived corruption However, when the Asian economies were ranked by their deviations from the equations, the effect of institutional variables became less clear Using data on 52 countries over the years 1991-1995, Drabek and Payne (1999) find that controlling for other factors, host countries’ attractiveness to foreign investors is closely connected to the degree of transparency The authors use a “Transparency index” from the International Country Risk Guide (ICRG), which essentially bundles several variables such as the level of corruption, law and order, and bureaucratic quality, among others They use 2SLS and simulation exercises and find a significant positive impact of transparency (higher rank indicating a higher level of transparency) on FDI However, when individual elements of the Transparency Index were used, the effect on FDI was less significant Wei (2000) studies the impact of corruption on bilateral investment from twelve source countries to forty-five host countries in 1993 He uses three separate corruption indexes from BI, ICRG and Transparency International (TI) respectively First, using the OLS method, the author regresses FDI on corruption, tax rate and a set of control variables Results indicate that a rise in the level of corruption or tax rate in the host country reduces inward FDI in a statistically significant manner Next, a modified version of the Tobit model is used to take account of zero-FDI values that were dropped in the previous estimation (a Double-Log linear model was used previously) Results are qualitatively the same as in the previous case The paper concludes that corruption reduces FDI in a way that is statistically as well as quantitatively large In addition, the author finds that despite the Foreign Corrupt Practices Act of 1977, American investors are no more averse to host country corruption than other investors In a study where the choice of entry mode of foreign investors is analyzed, Smarynzka and Wei (2000) find that corruption has a significant effect on the choice of entry mode of the foreign investor The authors use extensive firm level data on foreign investment in 73 East-European countries for the period 1988-1995 They use data on corruption from the World Bank and TI Controlling for investors’ technological sophistication, firm size, production diversification, GDP and other factors, the authors find that corruption in the host country makes the foreign investor prefer a joint venture to single ownership However, the authors conclude that if foreign investors have sophisticated technology, they are less inclined to forming joint ventures due to fear of technological leakage Habib and Zurawicki (2002) examine the effect of corruption on bilateral FDI using data on 89 countries for the period 1996-1998 They use bilateral FDI data from the International Monetary Fund (IMF 2000) and data on corruption is taken from TI Seven home countries are included in their analysis They first analyze the effect of host country corruption on FDI Then, the impact of the absolute difference between the corruption levels of the home and host countries is analyzed using the PROBIT approach Both analyses are carried out by simultaneously controlling for other factors such as population, GDP growth, and unemployment among others In the first case, corruption is found to have a statistically significant (at less than percent significance level) negative impact on FDI However, when the authors introduce variables such as political stability, the significance of the corruption variable decreases (although still negative in sign) In the second case, using the absolute difference in the corruption levels between the home and host countries, the authors find corruption to still have a negative significant impact on FDI, although results are only marginally significant (i.e at the 10 percent significance level) The literature review in this section clearly presents a mixed picture on the empirical relationship between corruption and FDI Therefore, in the next and chapters to follow, we attempt to present a theoretical as well as empirical testing of the relationship between corruption and FDI Chapter THEORETICAL FRAMEWORK 3.1 Introduction This chapter sets forth a simple model, which is used to analyze the relationship between corruption and foreign direct Investment The question that is addressed here and which indeed forms the central thrust of this chapter is whether corruption has any effect on FDI Theoretical literature on corruption has so far treated it as having either a negative or positive impact No theoretical model so far has explored the possibility that corruption can have different effects based on how the parameters of a model change (in the real world, how different economic, social and political factors change) Given this background, the construction of a theoretical model of corruption and FDI, which can throw some additional light in this area, appears important 3.2 The Model We consider a situation where a foreign firm would be a monopolist in the host market if allowed to enter The firm is faced with a choice of whether or not to invest abroad The firm’s investment decision and therefore, its actual investment I is based on the traditional cost-benefit analysis—benefits can take the form of increased returns from engaging in FDI, cheap labor, large domestic market, and so on, while high taxes, poor infrastructure and corruption (bribery) are examples of potential costs faced by the foreign firm in the 10  − R R    + γ (R − aI ) + a  + γI    ∂  β   2I  2I    2=  ∂I  I  R   (R − aI)     = R (2aI − R ) + γR I2 In the above expression (2aI − R ) can either be positive or negative If 2aI − R > , this implies I > ∂β R If this condition is satisfied, then >0 That is, a higher β would mean a 2a ∂I higher I But what we need to know is how I2 I2 changes We can see that will decrease β β with higher I and β as the rate of increase of β is faster than that of I.7 Therefore, we can conclude that I< γI decreases with higherβ and I However, if 2aI + γR − R < or Rβ R (1 − γ ) , the marginal impact of an increase in I on β is negative Therefore, with 2a γI higher β and I, will increase Rβ (a − γI ) will decrease with a higher I R  (− α ) − E (E − a ) will decrease with higherβ and I I  From equation (12), R γ R γ  + aβ  R2 RI − I = (R − aI ) In this expression, the rightI +  = ⇔ I3 + 2β β β  2β  hand-side will decrease as I increases To maintain the equality, in the left-hand-side faster rate than I β has to increase at a 45 Proof Substituting for E from equations (14) and (15), we get  R R  − a   − a γ  (− α )  + I   I  >0  R  2β  + 2γ   I  With higher β and I, we can see that the above expression will decrease Therefore, in the preceding analysis for bribery B, we see that with higher β and I, B will decrease provided I > R R (1 − γ ) , the overall effect on B is ambiguous However, if I < 2a 2a 46 Appendix 4a Sample of Countries8 Developing Countries ALGERIA ARGENTINA BANGLADESH BOLIVIA BOTSWANA BRAZIL BULGARIA BURKINA FASO CAMEROON CHILE CHINA COLOMBIA CONGO COSTA RICA COTE DIVOIRE ECUADOR EGYPT EL SALVADOR ETHIOPIA GABON GAMBIA GHANA GUATEMALA GUINEA BISSAU HAITI HONDURAS HUNGARY INDIA INDONESIA IRAN JAMAICA JORDAN KENYA KOREA MADAGASCAR MALAWI MALAYSIA MEXICO MOROCCO MOZAMBIQUE NICARAGUA NIGER NIGERIA PAKISTAN PANAMA PARAGUAY PERU PHILIPPINES SENEGAL SIERRA LEONE SRI LANKA SYRIA THAILAND TOGO TRINIDAD AND TOBAGO TUNISIA TURKEY UGANDA URUGUAY VENEZUELA ZIMBABWE Categorization of countries into developed and developing is based on the World Bank’s classification 47 Developed Countries AUSTRALIA AUSTRIA BELGIUM CANADA DENMARK FINLAND FRANCE GERMANY GREECE ICELAND IRELAND ISRAEL ITALY JAPAN NETHERLANDS NEW ZEALAND NORWAY PORTUGAL SINGAPORE SPAIN SWEDEN SWITZERLAND UNITED KINGDOM UNITED STATES 48 Appendix 4b Select descriptive statistics on all the variables used in the study are presented in the below table The descriptive statistics reported are time period specific, with the sample period of 17 years, 1984-2000 divided into averages The mean, standard deviation, minimum and maximum values are reported for all variables From the below table we can see that the mean per capita FDI in the sample has steadily increased from 0.70 million dollars in 1984-’87 to 3.05 million dollars in 1995-’00 Importantly, we can see that even though the maximum corruption level in the sample decreased from points in 1984-’87 to 6.15 points in 1995-’00, the mean level of corruption in the sample actually increased from 3.59 points to 3.64 points during the same period The mean values of GDP growth, per capita GDP, openness, export growth and consumption growth have also increased between 1984-’87 and 1995-’00 On the other hand, the mean population growth in the sample has come down from 1.86 percent to 1.59 percent during the same period Descriptive Statistics Variable Mean FDICAP Sd FDICAP Min FDICAP Max FDICAP Mean GDPGR Sd GDPGR Min GDPGR Max GDPGR 1984-‘87 0.70 1.32 -3.25 9.08 3.07 2.51 -3.19 11.88 1988-‘91 1.10 1.61 -0.34 12.6 3.21 3.26 -5.84 11.30 1991-94 1.50 1.79 -2.26 8.85 3.14 3.11 -6.79 12.79 1995-‘00 3.05 2.81 -1.77 12.80 3.42 2.02 -3.36 9.66 49 Variable Mean GDPCAP Sd GDPCAP Min GDPCAP Max GDPCAP Mean POPGR Sd POPGR Min POPGR Max POPGR Mean OPEN Sd OPEN Min OPEN Max OPEN Mean CORR Sd CORR Min CORR Max CORR Mean EXPGR Sd EXPGR Min EXPGR Max EXPGR Mean CONSGR Sd CONSGR Min CONSGR Max CONSGR 1984-‘87 5549.85 5290.42 353 18416.25 1.86 1.17 -0.43 3.75 58.36 41.08 13.88 345.75 3.59 1.55 5.10 5.33 -13.08 19.28 2.75 2.76 -7.02 10.34 1988-‘91 7082.52 6901.98 455.25 23582.75 1.87 1.18 -0.96 5.49 61.63 45.53 15.39 393.43 3.51 1.49 6.33 7.21 -35.91 25.74 2.72 3.31 -7.59 15.21 1991-94 8064.11 7821.21 494.25 26618 1.73 1.01 -0.68 4.20 64.61 41.63 16.54 346.44 3.38 1.23 6.38 6.50 -19.81 19.79 3.00 3.06 -5.39 10.59 1995-‘00 9408.06 9208.65 520.8 31268.8 1.59 0.96 -0.56 3.46 70.81 41.54 19.23 318.74 3.64 1.24 6.15 6.63 5.93 -16.66 37.4 3.30 2.45 -4.13 10.17 Notes: No of countries = 85 For sample of countries refer to Appendix 4a 50 Appendix 5a Dependent Variable: net per capita FDI inflows, annual average 19842000, Pooled OLS, All Countries Independent Variable Constant GDPGR GDPCAP -0.61 (-0.96) -0.64 (-1.01) -0.64 (-1.00) -0.61 (-1.00) 0.13* (3.94) 3.68 × 10-5 (1.61) 0.12* (3.26) 3.65 × 10-5 (1.60) -0.19 (-1.62) 0.13* (2.57) 3.66 × 10-5 (1.60) 0.13* (2.56) 3.64 × 10-5 (1.61) -0.19 (-1.60) -0.60 (-0.99) 0.07 (1.14) POPGR -0.19*** (-1.64) OPEN 0.02* (11.23) 0.06 (0.54) 0.02* (11.04) 0.05 (0.52) 0.01 (0.82) 0.02* (10.98) 0.05 (0.54) 0.01 (0.77) -0.01 (-0.24) 0.02* (2.60) 0.05 (0.41) 0.01 (0.77) -0.01 (-0.23) 0.0001 (0.06) 35.0% 34.9% 34.7% 34.5% CORR EXPGR CONSPGR -0.19*** (-1.64) CORR*OPEN CORR*GDPGR Adj R2 3.53 × 10-5 (1.55) -0.20*** (-1.70) 0.02* (2.65) 0.08 (0.71) 0.01 (0.82) -0.01 (-0.32) 0.0003 (0.14) -0.01 (-0.89) 34.4% Notes: N = 340; total number of countries = 85 (developed and developing) Figures in parentheses are t-Statistic *, ** and *** denote 1, and 10 percent level of significance respectively Standard errors are White-corrected for Heteroskedasticity 51 Appendix 5b Dependent Variable: net per capita FDI inflows, annual average 19842000, Pooled OLS, Developing Countries Independent Variable Constant 0.65 (0.90) 0.64 (0.89) 0.64 (0.89) -0.66 (-0.63) 0.09* (3.12) 8.92 × 10-5 (1.51) 0.09* (2.54) 8.84 × 10-5 (1.48) 0.09*** (1.83) 8.84 × 10-5 (1.48) 0.09** (2.00) 8.96 × 10-5 (1.52) -0.59 (-0.57) 0.04 (0.65) POPGR -0.28*** (-1.88) -0.28*** (-1.89) -0.28*** (-1.89) OPEN 0.02* (4.85) -0.13 (-1.26) 0.02* (4.87) -0.13 (-1.25) 0.003 (0.23) 0.02* (4.80) -0.13 (-1.25) 0.003 (0.23) 0.0005 (0.009) -0.28*** (-1.87) 0.001 (0.12) -0.29** (-1.98) 0.003 (0.20) 0.21 (0.91) 0.0003 (0.02) -0.004 (-0.07) -0.006 (-1.22) 21.5% 21.2% 20.9% 0.23 (1.04) 0.001 (0.07) -0.008 (-0.16) -0.005 (-1.11) -0.01 (-0.82) 20.9% GDPGR GDPCAP CORR EXPGR CONSPGR CORR*OPEN CORR*GDPGR Adj R2 21.1% 8.72 × 10-5 (1.47) Notes: N = 244; total number of developing countries = 61 Figures in parentheses are t-Statistic *, ** and *** denote 1, and 10 percent level of significance respectively Standard errors are White-corrected for Heteroskedasticity 52 Appendix 5c Dependent Variable: net per capita FDI inflows, annual average 19842000, Pooled OLS, Developed Countries Independent Variable Constant -2.93* (-2.54) -3.30* (-2.77) 0.30 (1.25) -2.67** (-2.04) 0.24 (1.03) -3.76* (-3.34) GDPGR 0.34*** (1.87) -3.38* (-2.85) 0.25 (1.60) GDPCAP 1.20 × 10-4* (2.98) -0.25 (-0.75) 1.19 × 10-4* (3.03) -0.17 (-0.52) 1.17 × 10-4* (3.00) -0.17 (-0.52) 0.0001* (2.88) -0.23 (-0.73) 0.02* (8.81) 0.04 (0.18) 0.02* (8.29) -0.01 (-0.06) 0.02* (8.30) -0.01 (-0.05) 0.04* (2.34) -0.25 (-0.76) 0.11*** (1.71) 0.10 (1.57) 0.11*** (1.67) -0.01 (-0.08) 0.002 (0.77) 0.10*** (1.67) 0.03 (0.20) -0.003 (-0.99) 54.0% 0.34* (2.81) 57.3% POPGR OPEN CORR EXPGR CONSPGR -0.05 (-0.29) CORR*OPEN CORR*GDPGR Adj R2 53.7% 54.7% 54.3% 1.91* (3.23) 0.0001* (2.89) -0.29 (-0.99) 0.01 (0.65) -0.77** (-2.16) Notes: N = 96; total number of 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GUATEMALA GUINEA BISSAU HAITI HONDURAS HUNGARY INDIA INDONESIA IRAN JAMAICA JORDAN KENYA KOREA MADAGASCAR MALAWI MALAYSIA MEXICO MOROCCO MOZAMBIQUE NICARAGUA NIGER NIGERIA PAKISTAN PANAMA PARAGUAY... World Bank’s classification 47 Developed Countries AUSTRALIA AUSTRIA BELGIUM CANADA DENMARK FINLAND FRANCE GERMANY GREECE ICELAND IRELAND ISRAEL ITALY JAPAN NETHERLANDS NEW ZEALAND NORWAY PORTUGAL... ALGERIA ARGENTINA BANGLADESH BOLIVIA BOTSWANA BRAZIL BULGARIA BURKINA FASO CAMEROON CHILE CHINA COLOMBIA CONGO COSTA RICA COTE DIVOIRE ECUADOR EGYPT EL SALVADOR ETHIOPIA GABON GAMBIA GHANA GUATEMALA