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Did pre-WTO agreements curb corruption?
Economic Analysis and Policy 47 (2015) 1–10
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Did pre-WTO agreements curb corruption? Deepraj Mukherjee ∗ Department of Economics, Kent State University, OH, USA
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Article history: Received 25 November 2014 Received in revised form 26 June 2015 Accepted 26 June 2015 Available online 29 June 2015 JEL classification: D73 F13 C23
abstract Using country-level panel data, this study tests whether a country’s decision to join a preferential trade agreement (PTA) or existing participation in one affects its corruption levels. The sample for this study pertains mostly to first-generation PTAs that arose prior to the World Trade Organization (WTO). The results suggest that member nations that entered into pre-WTO PTAs exhibited higher levels of corruption. This claim is general and is not indicative of the undesirability of trade agreements. Nevertheless, the findings reveal interesting factors related to trade agreements in the pre-WTO period. © 2015 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.
Keywords: Corruption International trade Preferential trade agreement Panel data
1. Introduction The past two decades have witnessed remarkable globalization, marked by substantial growth in international trade and enhanced diffusion of new products, services, and technologies. A key conduit of such global economic integration is preferential trade agreements (PTAs), which lower trade barriers among member countries by reducing the tariffs associated with certain products. As PTAs have proliferated, intra-PTA trade as a share of world trade has increased to an estimated 35% in 2008, compared with a meager 18% in 1990 (World Trade Report, 2011). Nor does this proliferation show any signs of slowing. World Trade Organization (WTO) members are parties to 13 PTAs each, on average, and PTA activity has transcended both regional boundaries and levels of economic development, such that many PTAs encompass developing countries. On average, African nations belong to four different agreements, and Latin American countries belong to seven (Cardamone and Scoppola, 2012). In addition to these quantitative averages, anecdotal evidence affirms the importance of PTAs. For example, the Economic Community of West African States (ECOWAS), set up in 1979 (pre-WTO), aimed to create a free-market space among member countries, eliminate redundant immigration procedures, harmonize customs duties, and unify monetary zones. The free movement of persons and goods also has been a long-standing objective of ECOWAS (1979), though this primary objective remains elusive. The West African region continues to be one of the most challenging places in the world for establishing economic and individual freedom (Economic Freedom of the World 2014 Annual Report, 2014). Despite 5.5% gross domestic product (GDP) growth on average in 2014, West African countries continue to rank among the poorest in the world. Economic transparency is virtually nonexistent; trade is paralyzed by high tariffs, bureaucracies, and stiff regulatory barriers. Among ECOWAS countries, intra-regional trade constitutes accounts for only 9.3% of total exports. The Mano River Union (2013),
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http://dx.doi.org/10.1016/j.eap.2015.06.001 0313-5926/© 2015 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.
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D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10
established in 1973, includes Liberia, Sierra Leone, Guinea, and Ivory Coast as member countries. After 42 years, its initial goal to establish an economic union remains elusive; the power struggle for mineral-rich soil still sparks conflicts among the member nations. The Inter-Governmental Authority on Development (IGAD) was established among Djibouti, Ethiopia, Somalia, Eritrea, Sudan, South Sudan, Kenya, and Uganda. Regional instability continues to hamper growth and prosperity in the area though. All three of these pre-WTO agreements indicate serious issues and raise pertinent questions about how PTAs might affect the institutional mechanisms of their member countries. Using comprehensive, cross-country panel data, this study investigates in particular whether pre-WTO agreements help to reduce corruption. The results, contrary to what might be expected, show that entering into or participating in a PTA agreement boosts the level of corruption shown by members. Rather than improving their institutional quality, countries seemingly use these agreements to pursue private interests (lobbying and bribery). The current analysis involves an extensive panel of 140 countries, and it does not differentiate the other members of the trade agreement. Thus, these results should not be taken to indicate that all PTAs increase corruption; instead, they indicate that not all PTAs enhance development. This conclusion may reflect the makeup of many pre-WTO PTAs, which include developing countries where the agreements had no binding constraints to reduce corruption specifically. If such pacts included developed countries or countries with already low corruption levels, the member countries might have an incentive to reduce their corruption, possibly due to a binding constraint set by the developed or less corrupt country. Instead, the dominance of PTAs among nations with relatively high corruption levels in the study sample resulted in higher corruption once the countries entered into agreements. Following a review of studies of national corruption, this article therefore elucidates a central hypothesis, and then explains the data and methodology adopted. The final two sections illustrate the empirical findings and key conclusions. 2. Literature review Corruption is among the greatest obstacles to the socio-economic development of a country. High levels of corruption disrupt poverty reduction efforts, distort the rule of law, and weaken countries’ institutional foundations. In 2008, the World Bank allocated 18.8% of its total lending budget to improving public sector governance in various countries. Yet its own indicators reveal that corruption still prevails as the most compelling factor plaguing developmental initiatives in developing and poor nations. Because corruption reduces economic growth (Mauro, 1995), distorts government expenditures (Mauro, 1998), retards foreign investment (Wei, 2000), and reduces the effectiveness of foreign aid (Princeton Survey Research Associates, 2003), the need to combat corruption has increased with growing globalization. In a global economy, corruption is transnational in nature, demanding new and strengthened international responses. Seldadyo and Haas (2006) find that welfare levels are important determinants of corruption levels, as are the quality of the government, military, and institutions (e.g., political freedom, judiciary, and information). Both theoretical research and anecdotal evidence suggest that a set of determinants influences corruption levels. For example, substantial research notes that the degree of trade openness, income and income distribution, and size of the public sector are key economic factors that affect national corruption levels. Krueger (1974) shows that quantitative trade restrictions shift resources from directly productive activities to unproductive, rent-seeking ones. Bhagwati and Srinivasan (1980) expand this analysis to consider revenue seeking, where economic agents try to obtain revenues obtained from protectionist tariffs. In a literature review, Bhagwati (1982) summarizes the effects of trade restrictions on such unproductive, profit-seeking activities. Despite the proliferation of conceptual research, only a handful of studies examine the impact of trade openness on corruption. Ades and Di Tella (1997) find that industrial policies favoring certain industries increase corruption, and Gurgur and Shah (2005), Brunetti and Weder (2003), and Knack and Azfar (2003) affirm this finding, establishing the negative relationship between trade openness and corruption. Among socio-demographic factors, schooling and population are closely associated with corruption. Economies with high human capital (proxied by years of schooling) exhibit lower levels of corruption (Brunetti and Weder, 2003; Van Rijckeghem and Weder, 1997), though using panel data models with fixed effects, Frechette (2001) finds that schooling aggravates corruption levels. Conflicting evidence also emerges regarding a country’s population: Knack and Azfar (2003) show that corruption increases as population rises, whereas Tavares (2003) reports that population negatively affects corruption. A general consensus instead affirms that democracy reduces corruption, because it makes political parties more accountable to the electorate and enables the press to operate more freely (Brunetti and Weder, 2003; Kunicova and RoseAckerman, 2005). However, some aspects of democratic elections may create opportunities for corruption (Kunicova and Rose-Ackerman, 2005; Persson and Tabellini, 2003). Political instability is widely believed to increase corruption (Leite and Weidmann, 1999; Park, 2003). Furthermore, both bureaucracy and the regulatory framework can serve key functions in controlling corruption (Becker, 1968). The role of the legal system and the rule of law feature prominently in many studies of governance quality and its consequences for development (Easterly and Levine, 1997; North, 1990). Strong legal foundations and efficient legal systems with well-specified entry deterrents protect property rights and provide a stable framework for economic activity. But legal systems also differ in the degree to which they protect property rights and the quality of government they provide. Extant empirical literature suggests that a common law system, mostly found in the former colonies of Britain, offers better protection of property rights than the civil law system typically associated with the former colonies of continental Europe (La Porta et al., 1998).
D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10
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Qualitative attributes of a society, such as its major religion and cultural origin, also might explain corruption levels. Authors including Bonaglia et al. (2001), Treisman (2000), and La Porta et al. (1998) observe that countries with many Protestants tend to exhibit lower corruption levels, and Paldam (2001) argues that countries dominated by two religions – reform Christianity (e.g., Protestant and Anglican) and tribal – experience lower levels of corruption than countries in which other religions dominate. Among the cultural variables, a colonial heritage, or the ‘‘command and control habits and institutions and the divisive nature of the society left behind by colonial masters’’ (Gurgur and Shah, 2005, p. 18), is associated with corruption, though evidence about the significance of this variable is mixed. Countries that are former colonies tend to suffer from more corruption (Gurgur and Shah, 2005; Tavares, 2003). Persson and Tabellini (2003) measure the influence of a colonial history by dividing all former colonies into three groups: British, Spanish–Portuguese, and other. They find that former British colonies tend to have a lower propensity for corruption. This observation received support from Herzfeld and Weiss (2003), who also find that former British colonies exhibit lower levels of corruption. Moving beyond these variables, this study investigates the potential effect of a particular form of trade openness, namely, participation in a preferential trade agreement (PTA). These trade agreements represent a type of binding constraint, imposing some standardized obligations that must be followed by their members. If a country enters into a PTA, it might be required to follow a specified code of conduct, which should have a bearing on its institutional quality in general and the level of corruption in particular. 3. Conceptual foundation and central hypothesis A preferential trade agreement (PTA) implies an agreement among two or more countries to grant and receive more favorable trade conditions among themselves than to external countries (Srinivasan and Canonero, 1995). The term PTA often is used interchangeably with regional trade agreement in pre-WTO trade literature. In the years following the establishment of the WTO (1995–2010), more than 300 PTAs notified the WTO of their existence (World Trade Report, 2011), compared with only 124 PTAs during the 47 years (1948–1995) in which the General Agreement on Tariffs and Trade was in place. The first wave of PTAs under this prior agreement mainly involved agreements among advanced economies (north–north) or developing countries (south–south); among the post-WTO agreements, only one-quarter involve north–north agreements, whereas close to 40% are north–south or south–south. These PTAs also issue binding constraints for the member countries that extend beyond removing trade protections, to include development-oriented actions, such as integrating factor markets, making institutional changes, harmonizing standards, and cooperating intensively on trade facilitation (e.g., reducing red tape for border crossing, removing behind-theborder restrictions). However, virtually no literature analyzes the impact of pre-WTO PTAs on the institutions of a country. To start to bridge this gap, the current study analyzes the effect of PTAs on the corruption levels of member countries. As noted previously, pre-WTO PTAs such as ECOWAS often were specific to the north or south, and the number of agreements during the first wave of PTAs was higher among countries traditionally classified as part of the south. Yet most of these PTAs excluded the seemingly essential element of free movement. For example, in an interview with a trader, this local expert explained that ‘‘The impact of bribery at the borders is quite overbearing for me, my business as well as my client/partners, as you could observe. Bribes cost almost 35% annual cost of my business’’. As such statements signal, the substantial number of PTAs among developing countries in the pre-WTO period may have increased the level of corruption among the member countries. Formally, Hypothesis 1. Entering into preferential trade agreements (PTA) in the pre-WTO period enhances a member country’s level of corruption. 4. Data and major variables The cross-country panel data pertain to 138 countries (see Appendix A for a list) over 1984–2003. These data came from several sources. The main dependent variable, corruption, reflects information gathered from the International Country Risk Guide (ICRG, 2012) database. Previous literature has used this database extensively (Braun and Di Tella, 2004; Knack and Azfar, 2003). It is maintained by Political Risk Services (PRS, 2001), which offers multiple indexes to assess the political, economic, or social risk of a country. Corruption falls under the category of political risk. In general, to assess the political stability of a country, this service assigns risk points to pre-selected political risk factors. Each index consists of several such components, each of which spans between a minimum (0) and a maximum number, which depends on the fixed weight the component receives in the overall risk assessment. Lower scores on the risk indicator thus imply greater risk. In addition, this corruption indicator measures corruption within the political system. The ICRG database includes various types of corruption, such as financial corruption, which is mostly faced by businesspeople, in the form of bribes required to acquire import and export licenses, tax assessments, police protection, or loans. Of even greater threat to foreign business are other prominent forms of corruption, such as excessive patronage, nepotism, job reservations, favors-for-favors, secret party funding, and suspiciously close ties between politics and business (PRS, 2001). This particular index varies from 0 to 6, with 0 representing a high risk situation and 6 representing the minimum risk. The independent variable is a PTA dummy. This study considers only multilateral trade agreements among countries, not bilateral ones. Most countries enter into several multilateral agreements. This variable takes a value of 1 if the country
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D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10 Table 1 Pre-WTO PTA agreements. Year
Countries with no PTA
Countries with at least one PTA
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
63 57 57 57 48 48 48 45 41 36 15
73 79 79 79 88 88 88 91 95 100 121
enters into at least one PTA between 1984 and 2003. That is, after entering its first PTA, the country maintains the value of 1 for this variable. For periods in which it is not a member of any PTA agreement, it takes a value of 0. Countries that entered a PTA before the starting year of the sample period earn scores of 1 for the entire analysis. The data and information about the different PTA agreements came from a database provided by Medvedev (2006). Most of the countries in this sample had entered into a PTA prior to the formation of the WTO (see Appendix B). As summarized in Table 1, from a list of 138 countries, data were available for about 136 countries that were a part of at least one PTA, and 121 of these had joined some PTA before the emergence of the WTO in 1994. Regional dummy variables provide the controls for the specification, to address any potential regional biases. The regional classification of countries mimics the World Bank’s classification. Thus, the dummy variables are Sub-Saharan Africa (SSA), Latin America and the Caribbean (LAC), Middle East and North Africa (MENA), East Asia and Pacific (EAP), Europe and Central Asia (EAC), and South Asia (SA). Some countries in SSA may have higher corruption levels, and the regional dummy should take this tendency into account. Other controls included GDP per capita, GDP growth, number of telephone lines per 1000 individuals, and urban population. The GDP provides a measure of economic well-being; using GDP per capita and growth simultaneously supports tests for economic growth convergence. The number of telephone lines offers a proxy for the available infrastructure, which is important because better infrastructural facilities may help reduce corruption. The urban population control variable, equal to the pertinent proportion of the total population, reflects the prediction that an educated, urban populace may affect the level of corruption. Legal rules protecting investors’ rights in countries are crucial determinants of economic growth. For example, La Porta et al. (1998) recognize that laws in different countries often are not written down from scratch but usually are implanted. Therefore, this study also includes dummies to represent foundations in English common law, French civil law, Scandinavian law, and German law. These legal origin dummies provide proxies for legal institutions; the polity from the Polity IV project also provides an indicator of political institutions or the extent of democracy/autocracy of a nation. This variable equals the difference between the autocracy score and the democracy score, such that it ranges from −10 (perfect autocracy) to +10 (perfect democracy). The autocracy (AUTOC) and democracy (DEMOC) variables incorporate factors such as the extents to which citizens are allowed to express preferences about the political system, constraints on the powers of the chief executive, and civil liberties enjoyed by the populace. This popular indicator of the quality of the formal institutions of a country has been used extensively (e.g., Dromel et al., 2009 and Honohan, 2004). According to Acemoglu et al. (2001), the current institutions of ex-colonies and their economic performance has been shaped greatly by the early institutions designed by the colonizers. Therefore, this study includes a dummy for ex-colony countries (data taken from Acemoglu et al., 2001). As suggested by Stulz and Williamson (2003), religion affiliation variables provide additional controls, adopted from Shleifer et al. (2004). 5. Results To test whether a country entering into a multilateral trade agreement changes its level of corruption, the empirical specification is as follows: Corruptionit = β0 + β1 PTADummyit + β2 Xit + ηit .
(1)
Starting with ordinary least squares specifications, the independent variable of interest is PTADummyit . If this dummy for country i takes a value of 1 in period t, then the country entered into its first PTA agreement during the sample period. If it entered into an agreement before 1985, that country takes a value of 1 throughout. If the coefficient of this dummy variable is positive, participating in or joining a PTA during 1984–2003 helped reduce corruption in that country. If being in or entering a PTA instead aggravated corruption for the country, the coefficient would be negative. The results in Table 2 show that the coefficients are negative and significant for all specifications. Countries enter into PTA agreements to suit their own private interests and get involved in bribery and other corrupt practices. The level of corruption worsens after countries enter the agreements. The control variables all retain their sign and significance.
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Table 2 Pooled ordinary least square regressions: Impact of PTA membership on corruption. Independent variables
(1)
(2)
(3)
(4)
PTA dummy
−0.354***
−0.282***
−0.277***
−0.208***
(0.0681) Yes Yes 8.26e−05*** (4.51e−06) 0.0108** (0.00511)
(0.0663) Yes Yes 2.42e−05*** (5.97e−06) 0.0116** (0.00525) 0.00364*** (0.000280)
(0.0665) Yes Yes 2.58e−05*** (6.14e−06) 0.0117** (0.00522) 0.00368*** (0.000282) −0.00151 (0.00140)
Intercept
3.240*** (0.158)
2.442*** (0.167)
2.490*** (0.174)
(0.0713) Yes Yes 4.14e−06 (9.32e−06) 0.0238*** (0.00629) 0.00218*** (0.000386) 0.00725*** (0.00158) 0.0774*** (0.0187) 2.422*** (0.203)
Observations R-squared
2264 0.520
2262 0.551
2262 0.551
1709 0.625
Legal (UK) Legal (France) GDP per capita GDP growth Telephone Urban population Years of schooling
Notes: Robust standard errors are in parentheses. All specifications included unreported year dummies. ∗ p < 0.1. ** p < 0.05. *** p < 0.01.
Table 3 contains the results of the fixed effect regressions. The coefficient of the PTA dummy is consistently negative and significant. The results in Table 4 reflect the effects of membership in a PTA on other political risk measures (i.e., government stability, internal conflict, law and order, ethnic tension, and democratic accountability1 ), conventionally included in related studies, because corruption is also a measure of political risk. For example, democratic accountability might reduce corruption by making political parties more accountable to the electorate and enabling the press to operate freely (Brunetti and Weder, 2003; Kunicova and Rose-Ackerman, 2005). Conversely, political instability measures such as internal conflict, inefficient law and order, and ethnic tensions provide proxies for corruption (Leite and Weidmann, 1999; Park, 2003). The data for these variables came from the ICRG database. Yet the results show that other than for democratic accountability, the PTA dummy is negative and significant for the specifications. That is, entering into a PTA agreement not only enhances corruption but also affects other political risk measures. The results with a subsample of only developing countries remained unaffected, as Table 5 shows. This confirmation substantiates the support for the hypothesis: Developing countries tend to have higher levels of corruption, and PTAs among them result in the inefficient outcome of increased corruption. Several robustness checks also provided additional confirmation. The main independent variable was a dummy variable by construction, so a system generalized method of moments (GMM) cannot be applied, because the dummy variable would be differenced away in the GMM estimation. To overcome this limitation, trade openness provided a proxy for the PTA dummy. Membership in a PTA increases the international exposure of its member countries, which seemingly should lead to constraints that help curb corruption. Trade openness, measured as the sum of imports and exports divided by GDP (World Bank, 2006), provides a good indicator of this level of internationalization. The GMM also provides several other benefits. In particular, a common but critical challenge in panel data models is the endogeneity of the variables. Despite the focus in this study on the impact of durable political systems and education on corruption levels, it is reasonable to anticipate that corruption might affect those variables too. A highly corrupt nation might result in the overthrow of the ruling party,2 and greater corruption might leave the populace less interested in education, because their honest efforts are unlikely to be rewarded. To control for this joint determination with corruption, two-stage least squares is a possible option, with instrumental variable (IV) estimation. The challenge is finding truly exogenous instruments that are uncorrelated with the error term. Even when IV estimates are consistent, they suffer from inherent biases and problematic finite sample properties
1 Government stability refers to a government’s ability to stay in office and meet its commitments, measured by government unity, legislative strength, and popular support. The score ranges from 0 to 12, where 0 indicates ‘‘very high risk’’ and 12 is ‘‘very low risk’’. Internal conflict refers to the political stability in a country, based on the extent of civil opposition to the government, whether the government engages in violence (direct or indirect) against its own people, and the occurrence of civil wars. This indicator varies from 0 to 4, where 4 indicates ‘‘very low risk’’ and 0 points to ‘‘very high risk’’. For law and order, the law component assesses the strength and impartiality of the legal system, and the order component determines popular observance of the law. It varies from 0 to 6 points. Ethnic tensions indicate the degree of tensions in a country based on ethnic, language, or regional partitions. If such classifications exist but the groups dwell peacefully together, these countries still earn higher ratings. Lower ratings only refer to settings marked by mostly consistent attitudes. Finally, democratic accountability ‘‘is a measure of how responsive government is to its people, on the basis that the less responsive it is, the more likely it is that the government will fall, peacefully in a democratic society, but possibly violently in a non-democratic one’’ ICRG Database (2012). 2 In a similar context, Blanco and Grier (2009) show that measures of mass resentment toward the party in power are critical sources of political instability of a nation.
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D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10
Table 3 Fixed effect regressions: Impact of PTA membership on corruption. Independent variables
(1)
−0.115
PTA dummy
(2) *
−0.124
(0.0599) −5.20e−05*** (9.18e−06) 0.00215 (0.00313)
GDP per capita GDP growth
(3) **
−0.119
(0.0601) −6.14e−05*** (1.07e−05) 0.00228 (0.00314) 0.000849* (0.000480)
Telephone
(4) **
−0.100
(0.0603) −6.04e−05*** (1.08e−05) 0.00237 (0.00314) 0.000903* (0.000483) 0.00781 (0.00696)
Urban population
(0.0685)
−8.04e−05*** (1.40e−05) 0.0114*** (0.00401) −0.000154 (0.000568) −0.00316 (0.00789) −0.0866** (0.0431) 4.489*** (0.560)
Years of schooling Intercept
3.250*** (0.118)
3.163*** (0.125)
2.676*** (0.452)
Observations R-squared
2264 0.204
2262 0.207
2262 0.207
1709 0.219
Notes: Standard errors are in parentheses. All specifications included unreported year dummies. * p < 0.1. ** p < 0.05. *** p < 0.01.
Table 4 Pooled OLS regressions: Impact of PTA membership on political risk. Independent variables/ PTA dummy Legal (UK) Legal (France) Legal (Scandinavian) Legal (German) SSA EAP ECA LAC MENA SA GDP per capita GDP growth Telephone Urban population Years of schooling Intercept Observations R-squared
Government stability **
Internal conflict ***
−0.307
−0.739
(0.127) −0.427*** (0.164) −0.527*** (0.170) −0.540 (0.368) 0.316 (0.252) −0.835*** (0.196) −0.467** (0.187) −0.601** (0.264) −1.147*** (0.164) −0.0825 (0.195) −1.306*** (0.264) 5.96e−06 (1.42e−05) 0.0733*** (0.00841) −0.00112* (0.000588) 0.00958*** (0.00247) 0.0526* (0.0304) 9.118*** (0.364)
(0.147) −0.967*** (0.166) −1.364*** (0.180) 2.071*** (0.442) 0.186 (0.181) −0.934*** (0.269) −0.134 (0.220) −1.660*** (0.371) −1.005*** (0.234) −1.271*** (0.321) −2.661*** (0.376) 1.79e−05 (1.71e−05) 0.0742*** (0.0140) 0.00117 (0.000767) 0.0140*** (0.00334) 0.105** (0.0436) 9.540*** (0.440)
1709 0.625
1709 0.519
Law and order ***
Ethnic tension **
−0.255
−0.197
(0.0696) −0.298*** (0.0793) −0.599*** (0.0888) 0.205 (0.200) −0.199** (0.0956) −0.447*** (0.128) −0.172* (0.104) 0.0379 (0.144) −0.640*** (0.113) −0.321** (0.139) −0.957*** (0.158) 7.49e−05*** (8.99e−06) 0.0338*** (0.00559) 0.000453 (0.000358) 0.00373** (0.00160) 0.0377** (0.0190) 2.976*** (0.213)
(0.0863) −1.367*** (0.114) −0.883*** (0.121) 2.034*** (0.249) −0.166 (0.137) −0.387** (0.166) −0.0559 (0.149) −1.968*** (0.185) 0.441*** (0.145) −0.130 (0.207) −0.882*** (0.222) −5.74e−07 (1.26e−05) 0.0217*** (0.00812) 0.00149*** (0.000523) 0.0117*** (0.00215) −0.0185 (0.0252) 4.134*** (0.294)
1709 0.700
Notes: Robust standard errors are in parentheses. All specifications included unreported year dummies. * p < 0.1. ** p < 0.05. *** p < 0.01.
1709 0.410
Democratic accountability
−0.132 (0.0998)
−0.511*** (0.0955)
−0.190* (0.0971)
−0.468 (0.324)
−0.333*** (0.0996)
−0.148 (0.173) 0.191 (0.171) 0.702** (0.303) 0.222 (0.140) −0.358** (0.159) 0.621*** (0.216) 2.40e−05** (9.73e−06) 0.0162** (0.00761) 0.00228*** (0.000444) −0.00849*** (0.00227) 0.193*** (0.0238) 3.036*** (0.282) 1709 0.525
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Table 5 Developing nations, impact of membership in PTA on corruption. Independent variables
(1)
(2)
(3)
(4)
PTA dummy
−0.376***
−0.297***
−0.304***
−0.157
(0.0999) Yes 0.593*** (0.148) 0.449*** (0.147) 0.502*** (0.156) 0.597*** (0.125) 0.594*** (0.131) 0.169 (0.183) 7.05e−05*** (7.47e−06) 0.0150*** (0.00540)
(0.0985) Yes 0.495*** (0.147) 0.326** (0.144) 0.149 (0.179) 0.433*** (0.131) 0.434*** (0.135) 0.0944 (0.181) 2.50e−05** (1.20e−05) 0.0152*** (0.00560) 0.00264*** (0.000524)
(0.0982) Yes 0.516*** (0.148) 0.341** (0.144) 0.142 (0.179) 0.429*** (0.130) 0.430*** (0.134) 0.119 (0.182) 2.09e−05* (1.27e−05) 0.0153*** (0.00564) 0.00254*** (0.000528) 0.00196 (0.00179)
Intercept
1.543*** (0.333)
1.066*** (0.326)
1.000*** (0.333)
(0.112) Yes 0.220 (0.154) 0.00864 (0.149) −0.0598 (0.219) 0.110 (0.139) 0.275** (0.135) −0.203 (0.186) 1.37e−05 (1.68e−05) 0.0268*** (0.00648) 0.00126 (0.000777) 0.0124*** (0.00204) 0.0256 (0.0224) 0.926*** (0.219)
Observations R-squared
1680 0.188
1678 0.201
1678 0.201
1226 0.300
Legal dummies SSA EAP ECA LAC MENA SA GDP per capita GDP growth Telephone Urban population Years of schooling
Notes: Robust standard errors are in parentheses. All specifications included unreported year dummies. * p < 0.1. ** p < 0.05. *** p < 0.01.
(Baum, 2008; Murray, 2006). Thus, in the presence of weak instruments, IV instruments may not be an improvement over ordinary least squares (Baum, 2008). Difference GMM estimators transform the model into first differences, then apply sequential moment conditions. The lagged levels of the variables serve as instruments for the endogeneity and parameter estimates for the GMM (Arellano and Bond, 1991). It is also important to capture persistence in the dependent variable (corruption); the dynamic panel estimators are designed to handle this persistence. Many corruption determinants, including colonial origins, legal origins, and regional groups, also are time invariant. The dynamic panel estimators can handle these fixed effects too while avoiding dynamic panel bias (Nickell, 1981). Finally, the dynamic panel estimators address heteroskedasticity and autocorrelation across panels. The p values of the Sargan and Basmann tests (Sargan, 1983) show that overidentification restrictions cannot be rejected at conventional levels. Trade openness exerts a negative but insignificant effect. These findings are robust to alternative specifications with strong cluster standard deviations and GMM specifications. With trade openness as a proxy for PTA, the fixed effect specifications reveals findings that are unchanged, as Table 6 shows. 6. Conclusions and implications This study investigates the impact of international trade on the quality of institutions in a country and its corruption, by controlling for whether a country is a part of any trade agreement with any other country. Entering into pre-WTO trade agreements in general did not help countries traditionally assigned to the south in reducing their corruption; instead, incumbent regimes apparently used membership in PTAs to maximize their private interests. It would be interesting to observe the effect of post-WTO PTAs on corruption. Intuitively, the monitoring and regulations established by a world body such as the WTO demand that member countries implement certain transparency measures, which may reduce corruption. Therefore, a meaningful extension of the current analysis could incorporate PTAs formed in the post-WTO era. These results also bear testimony that countries can enter trade agreements merely to further their agendas, especially if those PTA span multiple countries with weak or poor institutions. Agreements between these countries perpetuate their bad institutions and increase their corruption. This result has important implications for the international community and global economy: Trade openness is necessary across the board, and trade agreements should be monitored. Governments that are still part of pre-WTO agreements should reconsider and reevaluate their goals and objectives, in an effort to achieve the desired economic prosperity of their local regions.
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D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10 Table 6 GMM and fixed effect regressions: Impact of trade openness on corruption. Independent variables
GMM with cluster
Fixed effect
Trade openness
−0.000927
−0.257
(0.00411) Yes Yes 9.11e−06 (2.83e−05) 0.0229** (0.0102) 0.00224** (0.00108) 0.00720 (0.00517) 0.0686 (0.0622) 2.099*** (0.644)
(0.540) – – −0.000143 (0.000148) −0.114 (0.267) −0.00717 (0.0141) −0.0710 (0.141) −0.138 (0.278) −8.179 (27.13)
1698 0.617
1698
Legal dummies Regional dummies GDP per capita GDP growth Telephone Urban population Years of schooling Intercept Observations R-squared Wald Chi-square Hansen J test
1707.85 p = 0.61
Notes: Robust standard errors are in parentheses. All specifications included unreported year dummies. ∗ p < 0.1. ** p < 0.05. *** p < 0.01.
Appendix A. List of countries
Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Belarus Belgium Bolivia Botswana Brazil Brunei Bulgaria Burkina Faso Canada Cameroon Chi Chile Colombia Congo Congo, DR Costa Rica Cote d’Ivoire Croatia Cuba Cyprus
Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, DPR Kuwait Latvia Lebanon Liberia Libya Lithuania Luxembourg Madagascar Malawi
Norway Oman Pakistan Panama Papua New Guinea Peru Philippines Poland Portugal Qatar Romania Russia Saudi Arabia Senegal Serbia & Montenegro Sierra Leone Singapore Slovakia Slovenia Somalia South Africa South Korea Spain Sri Lanka Sudan Suriname Sweden Switzerland Syria Tanzania Thailand (continued on next page)
D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10
Czech Republic Denmark Dominican Republic Ecuador Egypt El Salvador Estonia Ethiopia Finland France Gabon Gambia Germany Ghana Greece
Malaysia Mali Malta Mexico Moldova Morocco Mozambique Myanmar Namibia Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria
9
Togo Trinidad & Tobago Tunisia Turkey UAE Uganda Ukraine United Kingdom United States Uruguay Venezuela Vietnam Yemen Zambia Zimbabwe
Appendix B. Multilateral preferential trade agreements, Pre-WTO (1994) Agreement AFTA ACC AMU Bangkok Agreement CACM CACM—Venezuela CACM—Colombia CAN CARICOM CARICOM—Venezuela CEFTA CER ECO CEPGL ECOWAS EFTA—Bulgaria EFTA—Czech Republic EFTA—Hungary EFTA—Israel EFTA—Poland EFTA—Romania EFTA—Slovak Republic EFTA—Turkey EFTA EU—Algeria EU—Andorra EU—Bulgaria EU—Cyprus EU—Czech Republic EU—Egypt EU—Hungary EU—Iceland EU—Malta EU—Norway EU—OCTs EU—Poland EU—Republic of San Marino EU—Romania EU—Slovak Republic
Year 1992 1989 1989 1976 1961 1993 1985 1988 1973 1993 1993 1983 1992 1977 1975 1993 1992 1993 1993 1993 1993 1992 1992 1960 1976 1991 1993 1973 1992 1977 1992 1973 1971 1973 1971 1992 1992 1993 1992
Notified to the WTO Yes No No Yes Yes No No Yes Yes No Yes Yes Yes No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes (continued on next page)
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D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10
Agreement EU—Switzerland and Lichtenstein EU—Syria EU GCC GSTP IGAD LAIA MRU MERCOSUR MSG PATCRA PTAES PTN SACU SPARTECA TRIPARTITE
Year 1973 1977 1958 1982 1989 1986 1981 1973 1991 1993 1977 1981 1973 1969 1981 1968
Notified to the WTO Yes Yes Yes Yes Yes No Yes No Yes Yes Yes No Yes No Yes Yes
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Full length article
Did pre-WTO agreements curb corruption? Deepraj Mukherjee ∗ Department of Economics, Kent State University, OH, USA
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Article history: Received 25 November 2014 Received in revised form 26 June 2015 Accepted 26 June 2015 Available online 29 June 2015 JEL classification: D73 F13 C23
abstract Using country-level panel data, this study tests whether a country’s decision to join a preferential trade agreement (PTA) or existing participation in one affects its corruption levels. The sample for this study pertains mostly to first-generation PTAs that arose prior to the World Trade Organization (WTO). The results suggest that member nations that entered into pre-WTO PTAs exhibited higher levels of corruption. This claim is general and is not indicative of the undesirability of trade agreements. Nevertheless, the findings reveal interesting factors related to trade agreements in the pre-WTO period. © 2015 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.
Keywords: Corruption International trade Preferential trade agreement Panel data
1. Introduction The past two decades have witnessed remarkable globalization, marked by substantial growth in international trade and enhanced diffusion of new products, services, and technologies. A key conduit of such global economic integration is preferential trade agreements (PTAs), which lower trade barriers among member countries by reducing the tariffs associated with certain products. As PTAs have proliferated, intra-PTA trade as a share of world trade has increased to an estimated 35% in 2008, compared with a meager 18% in 1990 (World Trade Report, 2011). Nor does this proliferation show any signs of slowing. World Trade Organization (WTO) members are parties to 13 PTAs each, on average, and PTA activity has transcended both regional boundaries and levels of economic development, such that many PTAs encompass developing countries. On average, African nations belong to four different agreements, and Latin American countries belong to seven (Cardamone and Scoppola, 2012). In addition to these quantitative averages, anecdotal evidence affirms the importance of PTAs. For example, the Economic Community of West African States (ECOWAS), set up in 1979 (pre-WTO), aimed to create a free-market space among member countries, eliminate redundant immigration procedures, harmonize customs duties, and unify monetary zones. The free movement of persons and goods also has been a long-standing objective of ECOWAS (1979), though this primary objective remains elusive. The West African region continues to be one of the most challenging places in the world for establishing economic and individual freedom (Economic Freedom of the World 2014 Annual Report, 2014). Despite 5.5% gross domestic product (GDP) growth on average in 2014, West African countries continue to rank among the poorest in the world. Economic transparency is virtually nonexistent; trade is paralyzed by high tariffs, bureaucracies, and stiff regulatory barriers. Among ECOWAS countries, intra-regional trade constitutes accounts for only 9.3% of total exports. The Mano River Union (2013),
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D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10
established in 1973, includes Liberia, Sierra Leone, Guinea, and Ivory Coast as member countries. After 42 years, its initial goal to establish an economic union remains elusive; the power struggle for mineral-rich soil still sparks conflicts among the member nations. The Inter-Governmental Authority on Development (IGAD) was established among Djibouti, Ethiopia, Somalia, Eritrea, Sudan, South Sudan, Kenya, and Uganda. Regional instability continues to hamper growth and prosperity in the area though. All three of these pre-WTO agreements indicate serious issues and raise pertinent questions about how PTAs might affect the institutional mechanisms of their member countries. Using comprehensive, cross-country panel data, this study investigates in particular whether pre-WTO agreements help to reduce corruption. The results, contrary to what might be expected, show that entering into or participating in a PTA agreement boosts the level of corruption shown by members. Rather than improving their institutional quality, countries seemingly use these agreements to pursue private interests (lobbying and bribery). The current analysis involves an extensive panel of 140 countries, and it does not differentiate the other members of the trade agreement. Thus, these results should not be taken to indicate that all PTAs increase corruption; instead, they indicate that not all PTAs enhance development. This conclusion may reflect the makeup of many pre-WTO PTAs, which include developing countries where the agreements had no binding constraints to reduce corruption specifically. If such pacts included developed countries or countries with already low corruption levels, the member countries might have an incentive to reduce their corruption, possibly due to a binding constraint set by the developed or less corrupt country. Instead, the dominance of PTAs among nations with relatively high corruption levels in the study sample resulted in higher corruption once the countries entered into agreements. Following a review of studies of national corruption, this article therefore elucidates a central hypothesis, and then explains the data and methodology adopted. The final two sections illustrate the empirical findings and key conclusions. 2. Literature review Corruption is among the greatest obstacles to the socio-economic development of a country. High levels of corruption disrupt poverty reduction efforts, distort the rule of law, and weaken countries’ institutional foundations. In 2008, the World Bank allocated 18.8% of its total lending budget to improving public sector governance in various countries. Yet its own indicators reveal that corruption still prevails as the most compelling factor plaguing developmental initiatives in developing and poor nations. Because corruption reduces economic growth (Mauro, 1995), distorts government expenditures (Mauro, 1998), retards foreign investment (Wei, 2000), and reduces the effectiveness of foreign aid (Princeton Survey Research Associates, 2003), the need to combat corruption has increased with growing globalization. In a global economy, corruption is transnational in nature, demanding new and strengthened international responses. Seldadyo and Haas (2006) find that welfare levels are important determinants of corruption levels, as are the quality of the government, military, and institutions (e.g., political freedom, judiciary, and information). Both theoretical research and anecdotal evidence suggest that a set of determinants influences corruption levels. For example, substantial research notes that the degree of trade openness, income and income distribution, and size of the public sector are key economic factors that affect national corruption levels. Krueger (1974) shows that quantitative trade restrictions shift resources from directly productive activities to unproductive, rent-seeking ones. Bhagwati and Srinivasan (1980) expand this analysis to consider revenue seeking, where economic agents try to obtain revenues obtained from protectionist tariffs. In a literature review, Bhagwati (1982) summarizes the effects of trade restrictions on such unproductive, profit-seeking activities. Despite the proliferation of conceptual research, only a handful of studies examine the impact of trade openness on corruption. Ades and Di Tella (1997) find that industrial policies favoring certain industries increase corruption, and Gurgur and Shah (2005), Brunetti and Weder (2003), and Knack and Azfar (2003) affirm this finding, establishing the negative relationship between trade openness and corruption. Among socio-demographic factors, schooling and population are closely associated with corruption. Economies with high human capital (proxied by years of schooling) exhibit lower levels of corruption (Brunetti and Weder, 2003; Van Rijckeghem and Weder, 1997), though using panel data models with fixed effects, Frechette (2001) finds that schooling aggravates corruption levels. Conflicting evidence also emerges regarding a country’s population: Knack and Azfar (2003) show that corruption increases as population rises, whereas Tavares (2003) reports that population negatively affects corruption. A general consensus instead affirms that democracy reduces corruption, because it makes political parties more accountable to the electorate and enables the press to operate more freely (Brunetti and Weder, 2003; Kunicova and RoseAckerman, 2005). However, some aspects of democratic elections may create opportunities for corruption (Kunicova and Rose-Ackerman, 2005; Persson and Tabellini, 2003). Political instability is widely believed to increase corruption (Leite and Weidmann, 1999; Park, 2003). Furthermore, both bureaucracy and the regulatory framework can serve key functions in controlling corruption (Becker, 1968). The role of the legal system and the rule of law feature prominently in many studies of governance quality and its consequences for development (Easterly and Levine, 1997; North, 1990). Strong legal foundations and efficient legal systems with well-specified entry deterrents protect property rights and provide a stable framework for economic activity. But legal systems also differ in the degree to which they protect property rights and the quality of government they provide. Extant empirical literature suggests that a common law system, mostly found in the former colonies of Britain, offers better protection of property rights than the civil law system typically associated with the former colonies of continental Europe (La Porta et al., 1998).
D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10
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Qualitative attributes of a society, such as its major religion and cultural origin, also might explain corruption levels. Authors including Bonaglia et al. (2001), Treisman (2000), and La Porta et al. (1998) observe that countries with many Protestants tend to exhibit lower corruption levels, and Paldam (2001) argues that countries dominated by two religions – reform Christianity (e.g., Protestant and Anglican) and tribal – experience lower levels of corruption than countries in which other religions dominate. Among the cultural variables, a colonial heritage, or the ‘‘command and control habits and institutions and the divisive nature of the society left behind by colonial masters’’ (Gurgur and Shah, 2005, p. 18), is associated with corruption, though evidence about the significance of this variable is mixed. Countries that are former colonies tend to suffer from more corruption (Gurgur and Shah, 2005; Tavares, 2003). Persson and Tabellini (2003) measure the influence of a colonial history by dividing all former colonies into three groups: British, Spanish–Portuguese, and other. They find that former British colonies tend to have a lower propensity for corruption. This observation received support from Herzfeld and Weiss (2003), who also find that former British colonies exhibit lower levels of corruption. Moving beyond these variables, this study investigates the potential effect of a particular form of trade openness, namely, participation in a preferential trade agreement (PTA). These trade agreements represent a type of binding constraint, imposing some standardized obligations that must be followed by their members. If a country enters into a PTA, it might be required to follow a specified code of conduct, which should have a bearing on its institutional quality in general and the level of corruption in particular. 3. Conceptual foundation and central hypothesis A preferential trade agreement (PTA) implies an agreement among two or more countries to grant and receive more favorable trade conditions among themselves than to external countries (Srinivasan and Canonero, 1995). The term PTA often is used interchangeably with regional trade agreement in pre-WTO trade literature. In the years following the establishment of the WTO (1995–2010), more than 300 PTAs notified the WTO of their existence (World Trade Report, 2011), compared with only 124 PTAs during the 47 years (1948–1995) in which the General Agreement on Tariffs and Trade was in place. The first wave of PTAs under this prior agreement mainly involved agreements among advanced economies (north–north) or developing countries (south–south); among the post-WTO agreements, only one-quarter involve north–north agreements, whereas close to 40% are north–south or south–south. These PTAs also issue binding constraints for the member countries that extend beyond removing trade protections, to include development-oriented actions, such as integrating factor markets, making institutional changes, harmonizing standards, and cooperating intensively on trade facilitation (e.g., reducing red tape for border crossing, removing behind-theborder restrictions). However, virtually no literature analyzes the impact of pre-WTO PTAs on the institutions of a country. To start to bridge this gap, the current study analyzes the effect of PTAs on the corruption levels of member countries. As noted previously, pre-WTO PTAs such as ECOWAS often were specific to the north or south, and the number of agreements during the first wave of PTAs was higher among countries traditionally classified as part of the south. Yet most of these PTAs excluded the seemingly essential element of free movement. For example, in an interview with a trader, this local expert explained that ‘‘The impact of bribery at the borders is quite overbearing for me, my business as well as my client/partners, as you could observe. Bribes cost almost 35% annual cost of my business’’. As such statements signal, the substantial number of PTAs among developing countries in the pre-WTO period may have increased the level of corruption among the member countries. Formally, Hypothesis 1. Entering into preferential trade agreements (PTA) in the pre-WTO period enhances a member country’s level of corruption. 4. Data and major variables The cross-country panel data pertain to 138 countries (see Appendix A for a list) over 1984–2003. These data came from several sources. The main dependent variable, corruption, reflects information gathered from the International Country Risk Guide (ICRG, 2012) database. Previous literature has used this database extensively (Braun and Di Tella, 2004; Knack and Azfar, 2003). It is maintained by Political Risk Services (PRS, 2001), which offers multiple indexes to assess the political, economic, or social risk of a country. Corruption falls under the category of political risk. In general, to assess the political stability of a country, this service assigns risk points to pre-selected political risk factors. Each index consists of several such components, each of which spans between a minimum (0) and a maximum number, which depends on the fixed weight the component receives in the overall risk assessment. Lower scores on the risk indicator thus imply greater risk. In addition, this corruption indicator measures corruption within the political system. The ICRG database includes various types of corruption, such as financial corruption, which is mostly faced by businesspeople, in the form of bribes required to acquire import and export licenses, tax assessments, police protection, or loans. Of even greater threat to foreign business are other prominent forms of corruption, such as excessive patronage, nepotism, job reservations, favors-for-favors, secret party funding, and suspiciously close ties between politics and business (PRS, 2001). This particular index varies from 0 to 6, with 0 representing a high risk situation and 6 representing the minimum risk. The independent variable is a PTA dummy. This study considers only multilateral trade agreements among countries, not bilateral ones. Most countries enter into several multilateral agreements. This variable takes a value of 1 if the country
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D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10 Table 1 Pre-WTO PTA agreements. Year
Countries with no PTA
Countries with at least one PTA
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
63 57 57 57 48 48 48 45 41 36 15
73 79 79 79 88 88 88 91 95 100 121
enters into at least one PTA between 1984 and 2003. That is, after entering its first PTA, the country maintains the value of 1 for this variable. For periods in which it is not a member of any PTA agreement, it takes a value of 0. Countries that entered a PTA before the starting year of the sample period earn scores of 1 for the entire analysis. The data and information about the different PTA agreements came from a database provided by Medvedev (2006). Most of the countries in this sample had entered into a PTA prior to the formation of the WTO (see Appendix B). As summarized in Table 1, from a list of 138 countries, data were available for about 136 countries that were a part of at least one PTA, and 121 of these had joined some PTA before the emergence of the WTO in 1994. Regional dummy variables provide the controls for the specification, to address any potential regional biases. The regional classification of countries mimics the World Bank’s classification. Thus, the dummy variables are Sub-Saharan Africa (SSA), Latin America and the Caribbean (LAC), Middle East and North Africa (MENA), East Asia and Pacific (EAP), Europe and Central Asia (EAC), and South Asia (SA). Some countries in SSA may have higher corruption levels, and the regional dummy should take this tendency into account. Other controls included GDP per capita, GDP growth, number of telephone lines per 1000 individuals, and urban population. The GDP provides a measure of economic well-being; using GDP per capita and growth simultaneously supports tests for economic growth convergence. The number of telephone lines offers a proxy for the available infrastructure, which is important because better infrastructural facilities may help reduce corruption. The urban population control variable, equal to the pertinent proportion of the total population, reflects the prediction that an educated, urban populace may affect the level of corruption. Legal rules protecting investors’ rights in countries are crucial determinants of economic growth. For example, La Porta et al. (1998) recognize that laws in different countries often are not written down from scratch but usually are implanted. Therefore, this study also includes dummies to represent foundations in English common law, French civil law, Scandinavian law, and German law. These legal origin dummies provide proxies for legal institutions; the polity from the Polity IV project also provides an indicator of political institutions or the extent of democracy/autocracy of a nation. This variable equals the difference between the autocracy score and the democracy score, such that it ranges from −10 (perfect autocracy) to +10 (perfect democracy). The autocracy (AUTOC) and democracy (DEMOC) variables incorporate factors such as the extents to which citizens are allowed to express preferences about the political system, constraints on the powers of the chief executive, and civil liberties enjoyed by the populace. This popular indicator of the quality of the formal institutions of a country has been used extensively (e.g., Dromel et al., 2009 and Honohan, 2004). According to Acemoglu et al. (2001), the current institutions of ex-colonies and their economic performance has been shaped greatly by the early institutions designed by the colonizers. Therefore, this study includes a dummy for ex-colony countries (data taken from Acemoglu et al., 2001). As suggested by Stulz and Williamson (2003), religion affiliation variables provide additional controls, adopted from Shleifer et al. (2004). 5. Results To test whether a country entering into a multilateral trade agreement changes its level of corruption, the empirical specification is as follows: Corruptionit = β0 + β1 PTADummyit + β2 Xit + ηit .
(1)
Starting with ordinary least squares specifications, the independent variable of interest is PTADummyit . If this dummy for country i takes a value of 1 in period t, then the country entered into its first PTA agreement during the sample period. If it entered into an agreement before 1985, that country takes a value of 1 throughout. If the coefficient of this dummy variable is positive, participating in or joining a PTA during 1984–2003 helped reduce corruption in that country. If being in or entering a PTA instead aggravated corruption for the country, the coefficient would be negative. The results in Table 2 show that the coefficients are negative and significant for all specifications. Countries enter into PTA agreements to suit their own private interests and get involved in bribery and other corrupt practices. The level of corruption worsens after countries enter the agreements. The control variables all retain their sign and significance.
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5
Table 2 Pooled ordinary least square regressions: Impact of PTA membership on corruption. Independent variables
(1)
(2)
(3)
(4)
PTA dummy
−0.354***
−0.282***
−0.277***
−0.208***
(0.0681) Yes Yes 8.26e−05*** (4.51e−06) 0.0108** (0.00511)
(0.0663) Yes Yes 2.42e−05*** (5.97e−06) 0.0116** (0.00525) 0.00364*** (0.000280)
(0.0665) Yes Yes 2.58e−05*** (6.14e−06) 0.0117** (0.00522) 0.00368*** (0.000282) −0.00151 (0.00140)
Intercept
3.240*** (0.158)
2.442*** (0.167)
2.490*** (0.174)
(0.0713) Yes Yes 4.14e−06 (9.32e−06) 0.0238*** (0.00629) 0.00218*** (0.000386) 0.00725*** (0.00158) 0.0774*** (0.0187) 2.422*** (0.203)
Observations R-squared
2264 0.520
2262 0.551
2262 0.551
1709 0.625
Legal (UK) Legal (France) GDP per capita GDP growth Telephone Urban population Years of schooling
Notes: Robust standard errors are in parentheses. All specifications included unreported year dummies. ∗ p < 0.1. ** p < 0.05. *** p < 0.01.
Table 3 contains the results of the fixed effect regressions. The coefficient of the PTA dummy is consistently negative and significant. The results in Table 4 reflect the effects of membership in a PTA on other political risk measures (i.e., government stability, internal conflict, law and order, ethnic tension, and democratic accountability1 ), conventionally included in related studies, because corruption is also a measure of political risk. For example, democratic accountability might reduce corruption by making political parties more accountable to the electorate and enabling the press to operate freely (Brunetti and Weder, 2003; Kunicova and Rose-Ackerman, 2005). Conversely, political instability measures such as internal conflict, inefficient law and order, and ethnic tensions provide proxies for corruption (Leite and Weidmann, 1999; Park, 2003). The data for these variables came from the ICRG database. Yet the results show that other than for democratic accountability, the PTA dummy is negative and significant for the specifications. That is, entering into a PTA agreement not only enhances corruption but also affects other political risk measures. The results with a subsample of only developing countries remained unaffected, as Table 5 shows. This confirmation substantiates the support for the hypothesis: Developing countries tend to have higher levels of corruption, and PTAs among them result in the inefficient outcome of increased corruption. Several robustness checks also provided additional confirmation. The main independent variable was a dummy variable by construction, so a system generalized method of moments (GMM) cannot be applied, because the dummy variable would be differenced away in the GMM estimation. To overcome this limitation, trade openness provided a proxy for the PTA dummy. Membership in a PTA increases the international exposure of its member countries, which seemingly should lead to constraints that help curb corruption. Trade openness, measured as the sum of imports and exports divided by GDP (World Bank, 2006), provides a good indicator of this level of internationalization. The GMM also provides several other benefits. In particular, a common but critical challenge in panel data models is the endogeneity of the variables. Despite the focus in this study on the impact of durable political systems and education on corruption levels, it is reasonable to anticipate that corruption might affect those variables too. A highly corrupt nation might result in the overthrow of the ruling party,2 and greater corruption might leave the populace less interested in education, because their honest efforts are unlikely to be rewarded. To control for this joint determination with corruption, two-stage least squares is a possible option, with instrumental variable (IV) estimation. The challenge is finding truly exogenous instruments that are uncorrelated with the error term. Even when IV estimates are consistent, they suffer from inherent biases and problematic finite sample properties
1 Government stability refers to a government’s ability to stay in office and meet its commitments, measured by government unity, legislative strength, and popular support. The score ranges from 0 to 12, where 0 indicates ‘‘very high risk’’ and 12 is ‘‘very low risk’’. Internal conflict refers to the political stability in a country, based on the extent of civil opposition to the government, whether the government engages in violence (direct or indirect) against its own people, and the occurrence of civil wars. This indicator varies from 0 to 4, where 4 indicates ‘‘very low risk’’ and 0 points to ‘‘very high risk’’. For law and order, the law component assesses the strength and impartiality of the legal system, and the order component determines popular observance of the law. It varies from 0 to 6 points. Ethnic tensions indicate the degree of tensions in a country based on ethnic, language, or regional partitions. If such classifications exist but the groups dwell peacefully together, these countries still earn higher ratings. Lower ratings only refer to settings marked by mostly consistent attitudes. Finally, democratic accountability ‘‘is a measure of how responsive government is to its people, on the basis that the less responsive it is, the more likely it is that the government will fall, peacefully in a democratic society, but possibly violently in a non-democratic one’’ ICRG Database (2012). 2 In a similar context, Blanco and Grier (2009) show that measures of mass resentment toward the party in power are critical sources of political instability of a nation.
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D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10
Table 3 Fixed effect regressions: Impact of PTA membership on corruption. Independent variables
(1)
−0.115
PTA dummy
(2) *
−0.124
(0.0599) −5.20e−05*** (9.18e−06) 0.00215 (0.00313)
GDP per capita GDP growth
(3) **
−0.119
(0.0601) −6.14e−05*** (1.07e−05) 0.00228 (0.00314) 0.000849* (0.000480)
Telephone
(4) **
−0.100
(0.0603) −6.04e−05*** (1.08e−05) 0.00237 (0.00314) 0.000903* (0.000483) 0.00781 (0.00696)
Urban population
(0.0685)
−8.04e−05*** (1.40e−05) 0.0114*** (0.00401) −0.000154 (0.000568) −0.00316 (0.00789) −0.0866** (0.0431) 4.489*** (0.560)
Years of schooling Intercept
3.250*** (0.118)
3.163*** (0.125)
2.676*** (0.452)
Observations R-squared
2264 0.204
2262 0.207
2262 0.207
1709 0.219
Notes: Standard errors are in parentheses. All specifications included unreported year dummies. * p < 0.1. ** p < 0.05. *** p < 0.01.
Table 4 Pooled OLS regressions: Impact of PTA membership on political risk. Independent variables/ PTA dummy Legal (UK) Legal (France) Legal (Scandinavian) Legal (German) SSA EAP ECA LAC MENA SA GDP per capita GDP growth Telephone Urban population Years of schooling Intercept Observations R-squared
Government stability **
Internal conflict ***
−0.307
−0.739
(0.127) −0.427*** (0.164) −0.527*** (0.170) −0.540 (0.368) 0.316 (0.252) −0.835*** (0.196) −0.467** (0.187) −0.601** (0.264) −1.147*** (0.164) −0.0825 (0.195) −1.306*** (0.264) 5.96e−06 (1.42e−05) 0.0733*** (0.00841) −0.00112* (0.000588) 0.00958*** (0.00247) 0.0526* (0.0304) 9.118*** (0.364)
(0.147) −0.967*** (0.166) −1.364*** (0.180) 2.071*** (0.442) 0.186 (0.181) −0.934*** (0.269) −0.134 (0.220) −1.660*** (0.371) −1.005*** (0.234) −1.271*** (0.321) −2.661*** (0.376) 1.79e−05 (1.71e−05) 0.0742*** (0.0140) 0.00117 (0.000767) 0.0140*** (0.00334) 0.105** (0.0436) 9.540*** (0.440)
1709 0.625
1709 0.519
Law and order ***
Ethnic tension **
−0.255
−0.197
(0.0696) −0.298*** (0.0793) −0.599*** (0.0888) 0.205 (0.200) −0.199** (0.0956) −0.447*** (0.128) −0.172* (0.104) 0.0379 (0.144) −0.640*** (0.113) −0.321** (0.139) −0.957*** (0.158) 7.49e−05*** (8.99e−06) 0.0338*** (0.00559) 0.000453 (0.000358) 0.00373** (0.00160) 0.0377** (0.0190) 2.976*** (0.213)
(0.0863) −1.367*** (0.114) −0.883*** (0.121) 2.034*** (0.249) −0.166 (0.137) −0.387** (0.166) −0.0559 (0.149) −1.968*** (0.185) 0.441*** (0.145) −0.130 (0.207) −0.882*** (0.222) −5.74e−07 (1.26e−05) 0.0217*** (0.00812) 0.00149*** (0.000523) 0.0117*** (0.00215) −0.0185 (0.0252) 4.134*** (0.294)
1709 0.700
Notes: Robust standard errors are in parentheses. All specifications included unreported year dummies. * p < 0.1. ** p < 0.05. *** p < 0.01.
1709 0.410
Democratic accountability
−0.132 (0.0998)
−0.511*** (0.0955)
−0.190* (0.0971)
−0.468 (0.324)
−0.333*** (0.0996)
−0.148 (0.173) 0.191 (0.171) 0.702** (0.303) 0.222 (0.140) −0.358** (0.159) 0.621*** (0.216) 2.40e−05** (9.73e−06) 0.0162** (0.00761) 0.00228*** (0.000444) −0.00849*** (0.00227) 0.193*** (0.0238) 3.036*** (0.282) 1709 0.525
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Table 5 Developing nations, impact of membership in PTA on corruption. Independent variables
(1)
(2)
(3)
(4)
PTA dummy
−0.376***
−0.297***
−0.304***
−0.157
(0.0999) Yes 0.593*** (0.148) 0.449*** (0.147) 0.502*** (0.156) 0.597*** (0.125) 0.594*** (0.131) 0.169 (0.183) 7.05e−05*** (7.47e−06) 0.0150*** (0.00540)
(0.0985) Yes 0.495*** (0.147) 0.326** (0.144) 0.149 (0.179) 0.433*** (0.131) 0.434*** (0.135) 0.0944 (0.181) 2.50e−05** (1.20e−05) 0.0152*** (0.00560) 0.00264*** (0.000524)
(0.0982) Yes 0.516*** (0.148) 0.341** (0.144) 0.142 (0.179) 0.429*** (0.130) 0.430*** (0.134) 0.119 (0.182) 2.09e−05* (1.27e−05) 0.0153*** (0.00564) 0.00254*** (0.000528) 0.00196 (0.00179)
Intercept
1.543*** (0.333)
1.066*** (0.326)
1.000*** (0.333)
(0.112) Yes 0.220 (0.154) 0.00864 (0.149) −0.0598 (0.219) 0.110 (0.139) 0.275** (0.135) −0.203 (0.186) 1.37e−05 (1.68e−05) 0.0268*** (0.00648) 0.00126 (0.000777) 0.0124*** (0.00204) 0.0256 (0.0224) 0.926*** (0.219)
Observations R-squared
1680 0.188
1678 0.201
1678 0.201
1226 0.300
Legal dummies SSA EAP ECA LAC MENA SA GDP per capita GDP growth Telephone Urban population Years of schooling
Notes: Robust standard errors are in parentheses. All specifications included unreported year dummies. * p < 0.1. ** p < 0.05. *** p < 0.01.
(Baum, 2008; Murray, 2006). Thus, in the presence of weak instruments, IV instruments may not be an improvement over ordinary least squares (Baum, 2008). Difference GMM estimators transform the model into first differences, then apply sequential moment conditions. The lagged levels of the variables serve as instruments for the endogeneity and parameter estimates for the GMM (Arellano and Bond, 1991). It is also important to capture persistence in the dependent variable (corruption); the dynamic panel estimators are designed to handle this persistence. Many corruption determinants, including colonial origins, legal origins, and regional groups, also are time invariant. The dynamic panel estimators can handle these fixed effects too while avoiding dynamic panel bias (Nickell, 1981). Finally, the dynamic panel estimators address heteroskedasticity and autocorrelation across panels. The p values of the Sargan and Basmann tests (Sargan, 1983) show that overidentification restrictions cannot be rejected at conventional levels. Trade openness exerts a negative but insignificant effect. These findings are robust to alternative specifications with strong cluster standard deviations and GMM specifications. With trade openness as a proxy for PTA, the fixed effect specifications reveals findings that are unchanged, as Table 6 shows. 6. Conclusions and implications This study investigates the impact of international trade on the quality of institutions in a country and its corruption, by controlling for whether a country is a part of any trade agreement with any other country. Entering into pre-WTO trade agreements in general did not help countries traditionally assigned to the south in reducing their corruption; instead, incumbent regimes apparently used membership in PTAs to maximize their private interests. It would be interesting to observe the effect of post-WTO PTAs on corruption. Intuitively, the monitoring and regulations established by a world body such as the WTO demand that member countries implement certain transparency measures, which may reduce corruption. Therefore, a meaningful extension of the current analysis could incorporate PTAs formed in the post-WTO era. These results also bear testimony that countries can enter trade agreements merely to further their agendas, especially if those PTA span multiple countries with weak or poor institutions. Agreements between these countries perpetuate their bad institutions and increase their corruption. This result has important implications for the international community and global economy: Trade openness is necessary across the board, and trade agreements should be monitored. Governments that are still part of pre-WTO agreements should reconsider and reevaluate their goals and objectives, in an effort to achieve the desired economic prosperity of their local regions.
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D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10 Table 6 GMM and fixed effect regressions: Impact of trade openness on corruption. Independent variables
GMM with cluster
Fixed effect
Trade openness
−0.000927
−0.257
(0.00411) Yes Yes 9.11e−06 (2.83e−05) 0.0229** (0.0102) 0.00224** (0.00108) 0.00720 (0.00517) 0.0686 (0.0622) 2.099*** (0.644)
(0.540) – – −0.000143 (0.000148) −0.114 (0.267) −0.00717 (0.0141) −0.0710 (0.141) −0.138 (0.278) −8.179 (27.13)
1698 0.617
1698
Legal dummies Regional dummies GDP per capita GDP growth Telephone Urban population Years of schooling Intercept Observations R-squared Wald Chi-square Hansen J test
1707.85 p = 0.61
Notes: Robust standard errors are in parentheses. All specifications included unreported year dummies. ∗ p < 0.1. ** p < 0.05. *** p < 0.01.
Appendix A. List of countries
Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Belarus Belgium Bolivia Botswana Brazil Brunei Bulgaria Burkina Faso Canada Cameroon Chi Chile Colombia Congo Congo, DR Costa Rica Cote d’Ivoire Croatia Cuba Cyprus
Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, DPR Kuwait Latvia Lebanon Liberia Libya Lithuania Luxembourg Madagascar Malawi
Norway Oman Pakistan Panama Papua New Guinea Peru Philippines Poland Portugal Qatar Romania Russia Saudi Arabia Senegal Serbia & Montenegro Sierra Leone Singapore Slovakia Slovenia Somalia South Africa South Korea Spain Sri Lanka Sudan Suriname Sweden Switzerland Syria Tanzania Thailand (continued on next page)
D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10
Czech Republic Denmark Dominican Republic Ecuador Egypt El Salvador Estonia Ethiopia Finland France Gabon Gambia Germany Ghana Greece
Malaysia Mali Malta Mexico Moldova Morocco Mozambique Myanmar Namibia Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria
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Togo Trinidad & Tobago Tunisia Turkey UAE Uganda Ukraine United Kingdom United States Uruguay Venezuela Vietnam Yemen Zambia Zimbabwe
Appendix B. Multilateral preferential trade agreements, Pre-WTO (1994) Agreement AFTA ACC AMU Bangkok Agreement CACM CACM—Venezuela CACM—Colombia CAN CARICOM CARICOM—Venezuela CEFTA CER ECO CEPGL ECOWAS EFTA—Bulgaria EFTA—Czech Republic EFTA—Hungary EFTA—Israel EFTA—Poland EFTA—Romania EFTA—Slovak Republic EFTA—Turkey EFTA EU—Algeria EU—Andorra EU—Bulgaria EU—Cyprus EU—Czech Republic EU—Egypt EU—Hungary EU—Iceland EU—Malta EU—Norway EU—OCTs EU—Poland EU—Republic of San Marino EU—Romania EU—Slovak Republic
Year 1992 1989 1989 1976 1961 1993 1985 1988 1973 1993 1993 1983 1992 1977 1975 1993 1992 1993 1993 1993 1993 1992 1992 1960 1976 1991 1993 1973 1992 1977 1992 1973 1971 1973 1971 1992 1992 1993 1992
Notified to the WTO Yes No No Yes Yes No No Yes Yes No Yes Yes Yes No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes (continued on next page)
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D. Mukherjee / Economic Analysis and Policy 47 (2015) 1–10
Agreement EU—Switzerland and Lichtenstein EU—Syria EU GCC GSTP IGAD LAIA MRU MERCOSUR MSG PATCRA PTAES PTN SACU SPARTECA TRIPARTITE
Year 1973 1977 1958 1982 1989 1986 1981 1973 1991 1993 1977 1981 1973 1969 1981 1968
Notified to the WTO Yes Yes Yes Yes Yes No Yes No Yes Yes Yes No Yes No Yes Yes
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