the oecd anti-bribery convention: changing the currents of trade

Journal of Development Economics 97 (2012) 73–87

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Journal of Development Economics

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The OECD Anti-Bribery Convention: Changing the currents of trade

Anna D'Souza ⁎Economic Research Service, USDA, 1800 M St. NW, Washington, DC, United States

⁎ Tel.: +1 202 694 5170.E-mail address: [email protected].

0304-3878/$ – see front matter. Published by Elsevierdoi:10.1016/j.jdeveco.2011.01.002

a b s t r a c t
a r t i c l e i n f o
Article history:Received 6 January 2010Received in revised form 23 December 2010Accepted 5 January 2011

JEL classification:F10F23F53

Keywords:Gravity modelInternational tradeCorruptionOECD Anti-Bribery Convention

This paper examines how criminalizing the act of bribing a foreign public official affects international tradeflows using a watershed global anti-corruption initiative — the 1997 OECD Anti-Bribery Convention. I exploitvariation in the timing of implementation by exporting countries and in the level of corruption of importingcountries to quantify the Convention's effects on bilateral exports. I use a large panel of country pairs tocontrol for confounding global and national trends and shocks. I find that, on average, the Convention caused areduction in exports from signatory countries to high corruption importers relative to low corruptionimporters. In particular, we observe a 5.7% relative decline in bilateral exports to importers that lie onestandard deviation lower on the Worldwide Governance Indicators corruption index. This suggests that bycreating large penalties for foreign bribery, the Convention indirectly increased transaction costs betweensignatory countries and high corruption importers. The Convention may have induced OECD firms to diverttheir exports to less corrupt countries; while non-OECD firms not bound by the Convention may haveincreased their exports to corrupt countries. I also find evidence that the Convention's effects differed acrossproduct categories.

B.V.

Published by Elsevier B.V.

1. Introduction

Until the end of the 1990s, it was commonplace for largemultinational firms to bribe foreign public officials. In many countries,these payments were even tax-deductible. Media reports andanecdotal evidence indicate that firms offered bribes to negotiatepreferential customs duties, create barriers to entry, and obtaingovernment contracts. In response to growing concern over themagnitude of transnational bribery and the potential economicdistortions it creates, the Organization of Economic Cooperation andDevelopment (OECD) created the 1997 Convention on CombatingBribery of Foreign Public Officials in International Business Transac-tions. This multilateral agreement criminalized the act of bribing aforeign public official, which previously had been illegal only for USfirms. It was heralded as the first anti-corruption initiative with globalreach, covering almost all industrialized nations, which jointlyrepresent over 75% of world exports. Although the OECD's intentionwas to create a level playing field in international business wherecontracts are awarded on merit rather than bribes paid, there mayhave been unintended consequences for trade. Despite the Conven-tion's importance, there has been no systematic study of its impact oninternational trade; this paper seeks to fill this gap.

This paper quantifies the effects of the OECD Anti-BriberyConvention on bilateral exports. In creating large criminal and civil

penalties, the Convention increased the cost of bribery, e.g., costs toavoid detection and punishment (Shleifer and Vishny, 1993). If, as aresult, OECD firms supplied fewer bribes, their chances of obtainingexport contracts in environments where bribery is common practicewill have declined. I examine this hypothesis by testing whethercountries that implemented the Convention export relatively less toimporters with higher levels of corruption.

I find that, on average, the Convention caused a reduction in totalbilateral exports fromcountries that criminalized foreign bribery to highcorruption importers, relative to low corruption importers. In particular,we observe a 5.7% average decline in bilateral exports to more corruptimporters relative to less corrupt importers that lie one standarddeviation lower on the Worldwide Governance Indicators corruptionindex. For example, there is a 5.7% decline in exports to countries withcorruption levels similar to those of Croatia (median level of corruptionin the sample) relative to countries with corruption levels similar tothose of Italy (one standard deviation less corrupt on the index). Thissuggests that by increasing the cost of bribery, the Convention indirectlyincreased transaction costs between high corruption importers andthose countries that criminalized foreign bribery, inducing some firmsto reduce exports and others to exit the markets. These firms may havediverted their exports to less corrupt countries; while non-OECD firmsnot bound by the Convention may have increased their exports tocorrupt countries. I also find that the Convention differentially affectedexporter behavior, with a larger effect observed for exporters withhigher levels of corruption. The findings suggest that for somemultinational firms, standards of governance at home may influencetheir behavior abroad.

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74 A. D'Souza / Journal of Development Economics 97 (2012) 73–87

The empirical analysis further demonstrates that the effects of theConvention vary by product category, suggesting that the mecha-nisms of bribery may differ across industries. In particular, the relativedecline in bilateral exports to more corrupt countries is much largerfor homogeneous products (−13.3%) than for differentiated ones(−3.3%). This may partly reflect the ease with which public officialscan find substitutes from firms that are willing and able to bribe afterthe Convention. The results could also reflect the fact that bribes areeasier to hide in transactions involving specialized or differentiatedgoods, e.g., in infrastructure or defense projects (Rose-Ackerman,1999).

This paper extends previous research in several dimensions. First,literature on the effects of anti-corruption legislation on trade hasfocused exclusively on the 1977 US Foreign Corrupt Practices Act(FCPA) — the unilateral precursor to the OECD Convention thatcriminalized foreign bribery for US firms.1,2 In contrast, I focus on themultilateral OECD Convention, which was more widely implementedand includes a systematic monitoring process.3,4 Second, theseprevious studies examine a single exporter (US) and a limited set ofimporters, while this paper focuses on a richer set of countries. Third,these studies generally rely on few controls for normal bilateral tradeflows between countries. They often fail to control for global trends orshocks, e.g., 1970s oil crisis, and use ad-hoc or one-dimensionalmeasures of corruption (e.g., news articles on bribery; expert opinionpolls).

Relative to this earlier research, the current paper exploits extensiveproduct-level trade data on a large panel of 143 exporters and 155importers, along with more comprehensive measures of corruption, inorder to better identify the effect of criminalizing foreign bribery.5 Themethodology extends the standard versionof the gravitymodel of trade,which relates bilateral trade flows to importer and exporter character-istics that may promote or hinder trade. The model controls for normaltrade flows between nations and for global and national trends andshocks that could confound the results. I use a pair fixed effectsestimator that exploits the time-series variation in bilateral exports andsweeps out cross-sectional variation, allowing for heterogeneoustrading relationships.6 I exploit variation in the timing of implementa-tion for exporting countries that adopt the Convention and variation inthe level of corruption in importing countries. This source ofidentification (i.e., exporter–importer–time variation) allows me toeffectively control for important unobservable time-varying countrycharacteristics that could be correlated with the endogenous exporter

1 Studies on the impact of the US FCPA find large declines in US business activity incountries with high levels of corruption (Hines, 1995), in particular in countries wherethe US did not have a regional advantage (Beck et al., 1991). There are few studies onthe direct impact of corruption on international trade; one exception is Lambsdorff(1998), who finds inconclusive evidence that an importer's level of corruptioninfluences exporter behavior.

2 Two papers (Cuervo-Cazurra 2006, 2008) examine the effects of the OECDConvention on foreign direct investment. Results suggest that countries that adopt theConvention invest relatively less in high corruption countries and are more sensitive tohost country corruption than countries that do not adopt the Convention.

3 All thirty members of the OECD and eight non-members have adopted theConvention. All OECD members were required to adopt the Convention due to itsstatus as a binding legal agreement. All signatories are required to participate in theOECD Working Group on Bribery, which monitors and evaluates national implementa-tion of the Convention.

4 We might expect the effect of criminalizing foreign bribery to be different ifimplemented on a unilateral versus multilateral scale. In a theoretical work, Beck andMaher (1989) show that discriminatory regulation that only holds for certain firms isless effective in reducing levels of bribes than uniform regulation that holds for allfirms.

5 It would be ideal to look at actual bribes paid, but such data do not exist.Merchandise export data are the most comprehensive and broadly available.Commercial service export data are only available for a subset of countries beginningin 1999.

6 This estimator accounts for country pairs that traditionally trade more or less oftenwith each other.

adoption and implementation of the Convention or with the importer'slevel of corruption.

To the best of my knowledge, this paper is the first systematicassessment of the impact of the OECD Anti-Bribery Convention onbilateral exports. While there is much research in political economyand economics on the impact of international organizations andtreaties (e.g., WTO, and NAFTA), there is no reason to believe, ex-ante,that this particular international agreement would have an effect ontrade patterns. In particular, for the Convention to have had an effect,(i) firms would have to believe that the Convention is being enforced(and countries would have to enforce to a certain degree) and (ii) theexpected penalties from being caught would have to be large enoughto enter a firm's decision-making calculus. The results suggest that infact the Convention did have an impact on exports and, more broadly,international agreements can have measurable effects on domesticeconomic outcomes. Furthermore the results provide new evidence ofcorruption in international trade by documenting changes in bilateralexports due to the criminalization of bribery; that is, if firms were notengaging in bribery, we would see no effect.7

This paper broadly contributes to an emerging literature ininternational trade that highlights the importance of a country'sinstitutional quality and regulatory framework in promoting orhindering trade.8 Anderson and Marcouiller (2002, 2005) and Nunn(2007) show that imperfect contract enforcement, corruption, andinsecurity increase transaction costs, which in turn significantlyreduce the volume of trade between countries.9 By making briberymore costly, the OECD Anti-Bribery Conventionmay have exacerbatedsuch patterns.

The next section provides details on the Convention. Section 3discusses a conceptual framework and describes themain hypotheses.Section 4 describes the estimation strategy and the data. Section 5presents the main empirical results for bilateral exports and Section 6presents the results by product category. Section 7 providessensitivity analysis and Section 8 concludes.

2. The OECD initiative

From a business perspective, the adoption of the OECD Conven-tion on Combating Bribery of Foreign Public Officials in 1997remains a major milestone in the fight against corruption ininternational trade (International Chamber of Commerce, 2007).10

The OECD Convention was the first global initiative that targetedthe “supply-side” of bribery, i.e., bribe-paying multinationals.11 TheConvention requires signatories to “implement a comprehensive set oflegal, regulatory and policy measures to prevent, detect, investigate,prosecute and sanction bribery of foreign public officials”.12 A publicofficial includes anyone who exercises a public function at any level ofgovernment, including in public agencies and enterprises, and ininternational organizations. Bribery is broadly defined to includeaiding, abetting or authorizing bribery; it pertains to payments that

7 In a similar spirit of forensic economics, Fisman and Wei (2004) use detailedindustry data from China and Hong Kong to investigate tax evasion and corruption.They find frequent underreporting of value and misclassification of goods from high-tax to low-tax categories — most likely facilitated by bribes to customs agents.

8 See Belloc (2006) for a survey.9 This evidence provides a potential explanation for the disproportionate amount of

trade observed between high-income countries.10 ICC Note (2007), p.1.11 In 1996 the Organization of American States (OAS) adopted the first convention tocriminalize foreign bribery — The Inter-American Convention Against Corruption.However, this agreement did not require signatories to criminalize foreign bribery andunlike the OECD Convention, the OAS convention has no system of monitoring orevaluation.12 The OECD Fights Foreign Bribery (2007) p. 1.

Table 1Important years in the implementation of the OECD Convention, by signatory country.Source: OECD working group on bribery annual report (2006).

Signatory Ratification Entry into force Entry into force ofimplementing legislation

Argentina⁎ 2001 2001 1999Australia 1999 1999 1999Austria 1999 1999 1998Belgium 1999 1999 1999Brazil⁎ 2000 2000 2002Bulgaria⁎ 1998 1999 1999Canada 1998 1999 1999Chile⁎ 2001 2001 2002Czech Republic 2000 2000 1999Denmark 2000 2000 2000Estonia⁎ 2004 2005 2004Finland 1998 1999 1999France 2000 2000 2000Germany 1998 1999 1999Greece 1999 1999 1998Hungary 1998 1999 1999Iceland 1998 1999 1998Ireland 2003 2003 2001Italy 2000 2001 2000Japan 1998 1999 1999South Korea 1999 1999 1999Luxembourg 2001 2001 2001Mexico 1999 1999 1999Netherlands 2001 2001 2001New Zealand 2001 2001 2001Norway 1998 1999 1999Poland 2000 2000 2001Portugal 2000 2001 2001Slovak Republic 1999 1999 1999Slovenia⁎ 2001 2001 1999Spain 2000 2000 2000Sweden 1999 1999 1999Switzerland 2000 2000 2000Turkey 2000 2000 2003United Kingdom 1998 1999 2002United States⁎⁎ 1998 1999 1977

⁎ Non-OECD member nations.⁎⁎ US ratified the Convention but the legislation was in place from 1977, after thepassage of the FCPA.

75A. D'Souza / Journal of Development Economics 97 (2012) 73–87

afford firms an unfair or unwarranted advantage in, for example,securing a government contract, acquiring an import permit, orstarting a business.13 The Convention also requires participation in asystematic program of monitoring and evaluation conducted by theOECD Working Group on Bribery. The group is made up ofrepresentatives from each signatory country.14,15

Although in all countries it had been illegal to bribe domesticpublic officials, until the Convention, the United States was the onlycountry to outlaw the bribery of foreign public officials.16 Since OECDconventions are binding and therefore must be adopted unanimously,conventions usually require a lengthy negotiation process. Duringnegotiations a number of countries, including Germany, France, Japanand the UK, voiced concerns that early ratification would translateinto trade disadvantages. Their firms claimed that bribing publicofficials was a necessary part of the business transactions in certaincountries. In fact, in several countries bribes were considered tax-deductible business expenses (e.g., Belgium, Denmark, Iceland,Norway, France, Germany, and Sweden, among others).17 As acompromise, it was agreed that the Convention would only enterinto force after ratification by five of the ten largest OECD exporters,representing at least 60% of total OECD exports. (In most countries,including China, Russia and India, foreign bribery is still not illegal.)

Negotiations took place in three rounds in 1997, ratification beganin 1998, and the OECD Convention entered into force on February 15,1999. (See Table 1 for a list of dates of national legislationimplementation.) Scholars have described the rapid negotiations ofthe OECD Convention as the result of a few key developments: (i)mounting US pressure on OECD members due to shifts in preferencesof domestic interest groups (i.e., large multinationals gave up hopethat the FCPAwould be repealed and instead began to lobby for globalcriminalization of foreign bribery); (ii) increased public awareness ofcorruption due to a multitude of domestic bribery scandals in Europe,as well as the work of non-governmental organizations likeTransparency International; and (iii) growing complaints fromdeveloping countries over supply-driven bribery by powerful multi-national firms.18

Each signatory country is required to adopt national legislationthat criminalized foreign bribery in accordance with its own set ofnational laws and sanctions for similar economic crimes.19 Penaltiesvary by country but typically include large fines for firms, and finesand incarceration for individuals.20 In France and Australia, punish-ment includes 10 years in prison and/or a fine. In many European

13 Similar to the US Foreign Corrupt Practices Act, the Convention does not cover“facilitation” or “grease” payments used to induce public officials to perform regularlyrequired services.14 To date the group has completed a first phase evaluation for all countries; it iscurrently completing a second phase.15 Appendix I (available online) provides more information on the Convention andits related documents.16 The US criminalized foreign bribery through the Foreign Corrupt Practices Act of1977. Sweden also passed legislation in 1977 that criminalized bribery of any public orprivate employee. By default, foreign public officials were included, however, specificcases could only be prosecuted and pursued if the host country in which the briberytook place asked the Government of Sweden to prosecute. These restrictions werejudged to have severely hampered the effectiveness of the law (Nichols, 1997).Moreover, until 1999 Sweden used to allow tax deductions for “commissions”, asbribes are often called in developing countries. In the empirical work, I do not considerSweden's adoption of the 1977 law; rather, I use Sweden's 1999 implementation of theOECD Convention in the empirical analysis. The results are robust to Sweden'sexclusion from the sample.17 Information on the tax-deductibility of bribes was gathered by the author fromcountry reports available here: http://www.oecd.org/document/24/0,3343,en_2649_34859_1933144_1_1_1_1,00.html.18 Tarullo (2004).19 This feature, known as “functional equivalence”, requires each country to changeits laws to incorporate the tenets of the Convention, e.g., in some countries bribery ispart of the penal code, in others it is part of the criminal code.20 In this paper, I do not use data on penalties as they are not available for allsignatory countries.

countries, the prison sentence is up to 5 years and there are separatefines for natural and legal persons. Japan's penalties include amaximum imprisonment of 3 years and a maximum fine of 3 millionJapanese Yen. In contrast, the US has stricter penalties — up to $2million for a criminal violation and up to 15 years in prison.21

Canada, Germany, South Korea, Sweden, the UK and the US havehad convictions of foreign bribery. Over 60 companies and individualshave been penalized, e.g., fines of millions of dollars and disqualifi-cation from participating in public procurement markets.22 Mostinvestigations stem from allegations of fraud made by competitors,tax accountants in revenue collection agencies, importing countryparties, foreign diplomatic missions, and international financialbusinesses. Recently Transparency International, a prominent inter-national anti-corruption NGO, gathered data on OECD Convention-related prosecutions and investigations in select countries; the datashow substantial increases in both. In 2005 there were 50 reportedprosecutions and 51 reported investigations. In 2006 there were99 prosecutions and 176 investigations. In 2007 there were over 250prosecutions and 263 investigations. And in 2008 there were 351prosecutions and 336 investigations (Progress Report on Enforcement ofthe OECD Convention, 2009, 2008, 2007, 2006, 2005). These estimatesmost likely provide a lower bound for several reasons. First, governments

21 See implementing legislation by country, available at https://www.oecd.org/document/30/0,2340,en_2649_34859_2027102_1_1_1_1,00.html.22 OECD Working Group on Bribery Annual Report (2006), p.164.

http://www.oecd.org/document/24/0,3343,en_2649_34859_1933144_1_1_1_1,00.html


https://www.oecd.org/document/30/0,2340,en_2649_34859_2027102_1_1_1_1,00.html

https://www.oecd.org/document/30/0,2340,en_2649_34859_2027102_1_1_1_1,00.html


do not always disclose ongoing investigations. Second, some countriesonly keep such information at the court level, i.e., if prosecutions do notreach court, there is no data on them. In some cases, prosecutors andfirms reach plea agreements, e.g., cases brought by the US Department ofJustice, but these data are not easily accessible.23 Third, in some countriesvarious government agencies deal with bribery investigations, making itdifficult to obtain consistent, complete information.24

Convictions and investigations across signatory countries suggestthat nations are serious about prosecuting infringements of the OECDAnti-Bribery Convention and that the Convention is being enforced.According to the OECD Working Group on Bribery, however,enforcement continues to vary across countries; comprehensivedata on enforcement is not currently available.25

3. Conceptual framework

In this section I present a simple conceptual framework to describewhat we should expect and to aid in interpreting the results. By creatinglarge criminal penalties for being caught bribing a foreign public official,the OECD Anti-Bribery Convention increased the cost of such bribery forOECD firms. (For ease of exposition, I denote firms from countries thatadopt and do not adopt the Convention as OECD and non-OECD firms,respectively.) The additional cost stems from being caught (with certainprobability) and the penalties if caught, i.e., legal fees, fines, andimprisonment. If bribery is illegal, firms that bribe must exploit moreexpensive channels to avoid detection and punishment, e.g., setting upoffshore accounts or hiring third-party intermediaries (Shleifer andVishny, 1993). As the cost of supplying a bribe increases, the supply ofbribes by OECD firms will decrease. This, in turn, will reduce theirlikelihood of obtaining a government contract in high corruptioncountries, assuming that in such environments the probability of winninga contract is an increasing function of bribes paid.26 As a result, we shouldexpect a decline in OECD exports to high corruption countries after theimplementation of the OECD Anti-Bribery Convention. This is the centralhypothesis that is tested in the empirical analysis.27 (I assume that thereis a credible threat of enforcement and that the importing countryenvironment remains fixed, i.e., corrupt public officials continue todemand bribes after the Convention; below I discuss the case wherethese assumptions may not hold.)

We may also be interested in identifying effects for specific groupsof products. I examine differential effects based on the degree ofdifferentiation within an industry since the theoretical literature oncorruption suggests that levels of corruption may vary acrossindustries based on product characteristics. I also examine differentialeffects for industries in which the government (relative to privateactors) is likely to import a disproportionate amount of goods sincethe Convention criminalized bribes to foreign public officials.

23 Recent Trends and Patterns in FCPA Enforcement (2006), p. 7.24 See Mid-term Study of Phase 2 Reports (2006), p. 71–72.25 See Mid-term Study of Phase 2 Reports, (2006), for recommendations of nationalenforcement procedures.26 In its report, Bribery in Public Procurement (2007), the OECD states, “Expressed aspercentage of a contract, bribes in transnational business may range from 5 to 25% oreven more (p.47).” The theoretical and empirical corruption literature indicates thatbribery may undermine competitive bidding markets and alter the composition ofgovernment expenditure and/or international trade. Shleifer and Vishny (1993)describe a typical case, “To maximize the value of their personal revenues, bureaucratsprohibit imports of goods on which bribes cannot be collected without detection, andencourage imports of goods on which they can collect bribes. As a consequence, themenu of both consumer and producer goods available in the country is determined bycorruption opportunities rather than tastes or technological needs”. Mauro (1998)finds that government expenditure may be skewed towards sectors in which it iseasier to hide graft, e.g., defense, and away from sectors where it is difficult to hidegraft, e.g., education.27 Since measures of corruption are on a relative scale, it is impossible to identifyabsolute reductions in exports to high corruption countries; therefore in the empiricalwork, I estimate changes in exports to countries that are relatively more corrupt (thanother importers) using the Worldwide Governance index on corruption.

We might expect a larger effect for differentiated, specializedproducts due to the greater scope for corruption in markets with lesscompetition and higher rents.28 Also, since contracts in differentiatedproducts are more relationship-intensive, pre-Convention contractingmay have involved more bribery.29 At the same time, Rose-Ackerman(1999) argues that it is easier to inflate prices to provide kickbackswithout getting caught when dealing with complex or capital-intensive goods (i.e., differentiated products) since cost estimationis more complicated. In this case, we would expect a smaller effect fordifferentiated products relative to products sold on exchanges (wherethere is a known world price).

If we view export contracts and bribes as the outcomes of abargaining process, we would also expect larger effects of theConvention on homogenous products. If there are few availablesubstitutes and the public project must be completed, the publicofficial will have less bargaining power vis-à-vis the OECD firm;therefore the firmmay retain its contracts evenwhen paying a smallerbribe. Furthermore, if public officials can easily find substitutes sold bynon-OECD firms that are willing and able to bribe, then they mayswitch to such contractors. I test these alternative predictions in theempirical work below using the Rauch (1999) product classification.

The predictions above assume that the Convention is beingenforced and that the level of corruption in the importing countryremains constant after the Convention. If OECD countries do notenforce the Convention at all, we should not see any effects; however,evidence from prosecutions and convictions indicate that theConvention is being enforced in many countries. If the importingcountry environment changes as a result of the Convention, ourexpectations may also change. If all OECD firms stop bribing andpublic officials do not have any outside options to procure necessaryproducts, then the officials will no longer be able to demand bribesand procurement corruption will decline.30 If such bribes had beenadding to the overall transaction costs between OECD countries andcorrupt countries, we might observe an increase in OECD exports tocorrupt countries after the Convention.31 This no-corruption equilib-rium, however, is not supported by recent survey data or currentprosecutions which indicate that OECD firms still engage in briberywhen doing business abroad.32 Moreover, if there are competing non-OECD firms (foreign or domestic) that are willing to bribe or if thepublic official has full discretionary power in pursuing a project, i.e., ifhe can cancel the project if he is unsatisfied with the bribes offered,then OECD firms will not possess sufficient bargaining power to stopbribing while maintaining the same level of business. As a result, wewould expect a relative decline in exports to corrupt countries.

Finally, we might expect the Convention to have had the mostinfluence on trade in industries in which the government is relativelymore likely to be an importer. As a rough approximation (since suchdata do not exist across countries), I use the US input–output tables toidentify such industries, for example, military hardware.

4. Methodology and data

This section describes the empirical strategy and data used toidentify changes in export behavior caused by the OECD Anti-Bribery

28 See Krueger (1974) and Rose-Ackerman (1975, 1999).29 Rauch (1999) discusses the importance of network search mechanisms intransactions involving differentiated products. He classifies goods into homogeneousand differentiated products; I use this classification in the empirical work. Nunn(2007) also argues that differentiated goods involve relationship-specific contracting;he too uses the Rauch classification in his empirical analysis.30 At the total export level of aggregation, there are many non-OECD firms that arenot bound by the Convention; however, for certain product groups OECD firms mayonly compete among themselves.31 Eliminating the need to bribe could also have an impact on OECD and non-OECDfirms that were not previously in the market because they were unable to pay a bribe.32 See Hellman et al. (2000, 2002); Batra et al. (2004); Transparency InternationalProgress Report (2009).

37 I use the standard deviation of the previous year's monthly nominal exchangerates as a measure of exchange rate volatility, as in Rose (2000).


Convention. The analysis focuses on the intensive margin of trade andasks whether, conditional on trading, the Convention affectedbilateral exports. (Issues of selection and the extensive margin oftrade are discussed in Section 5.2.)

I utilize the gravity model of international trade to control fornormal trade flows between countries. The model describes bilateraltrade flows as a function of trade barriers and frictions, as well asfactors that promote trade.33 In recent years, it has been used toestimate the impact of regional trade agreements (Frankel, 1997;Krueger, 1999), the WTO (Rose, 2004), and currency unions (Rose,2000; Baldwin, 2006).

I present three equations, each of which addresses specific pitfallsin gravity estimation that have been acknowledged in the literature.The models are estimated using ordinary least squares.34 All modelsuse a pair fixed effects estimator that allows for unobservedheterogeneity in bilateral trading relationships. This methodologycontrols for time-invariant differences between exporters that mayhave led to the adoption of the Convention or the timing of itsimplementation (neither of which was random).

I first ask whether the Convention affected bilateral exports to alltrading partners regardless of their level of corruption (Eq. (1)).35 Ithen turn to the main hypothesis: whether the Convention had aheterogeneous impact on OECD exports to high corruption countriesversus low corruption countries (Eqs. (2) and (3)).

4.1. Empirical specifications

The first specification examines the effect of the Convention ontotal bilateral exports to all trading partners:

ln Exportsijt = δ1Convit + Xijtβ + αij + αt + εijt ð1Þ

where i indexes the exporter, j indexes the importer, and t indexestime. The dependent variable is the log of the value of bilateral exportsfrom exporter i to importer j in year t in current US dollars. Thisspecification, with pair fixed effects (αij) and time dummies (αt), isthe most commonly used specification in the gravity literature.

Convit is an indicator variable that turns on after exporter iimplements legislation criminalizing foreign bribery, i.e., the legisla-tion enters into force; throughout the sample period it equals zero fornon-signatory countries and equals one for the US.36 The δ1 coefficientprovides an estimate of the effect of the Convention on overallbilateral exports for countries that implemented the Convention. Anegative coefficient would imply that, on average, exports fromsignatory countries declined relative to exports from non-signatorycountries. Though the former group was directly affected by theConvention, the latter group may have been indirectly affected, i.e.,some trade may have been diverted to non-signatory countries.Without firm-level data, it is not possible to separately identify theeffects for signatories and non-signatories.

Xijt denotes factors that vary over time by exporter, importer orcountry pair. These factors include indicators for a common currency

33 The model dates back to Tinbergen (1962). Although previously criticized for weaktheoretical underpinnings, the model has been shown to be broadly consistent withthe major theories of international trade (Deardoff, 1998; Anderson and van Wincoop,2003, 2004).34 In Section 5.2, I present results using a Poisson pseudo maximum-likelihoodestimation technique that was introduced recently into the gravity model literature;the main results are qualitatively the same.35 The setup can be viewed in a difference-in-differences (DD) framework, wherecountries that adopted the Convention make up the treatment group and countriesthat did not adopt make up the control group. However in contrast to a typical DDsetting, the Convention indirectly affects the control group, whose relative profitabilitydepends on the actions of the treatment group.36 For years in which the implementing legislation enters into force during the fourthquarter, the indicator turns on in the following year. The results are robust to the useof the actual calendar year of the legislation.

and membership in the same regional free trade agreement or WTO/GATT; a measure of exchange rate volatility37; the logs of GDP percapita for exporter i and importer j; logs of total population forexporter i and importer j; and an interaction between a linear timetrend and the log of the product of GDP per capita.38 (GDP is inthousands of US dollars; population is in thousands.)

The interaction between the time trend and the per capita GDPscaptures trends in trading relationships between countries based ontheir level of development. This control is particularly importantbecause the coefficient of interest could otherwise pick up generaltrends in trade between rich and poor countries since signatorycountries are mostly rich and the more corrupt countries (where weexpect the Convention to have had an impact) are largely poor. Amore flexible specification would estimate separately the logs of GDPand the logs of population for exporter i and importer j, however it isnecessary to include the main effects of each country's GDP per capitain order to correctly estimate and interpret the interaction term. (As atest of robustness, I include measures of GDP and populationseparately; the results are qualitatively the same.)

αij are asymmetric pairfixedeffects (αij≠αji).39 Thepairfixedeffectsaddress the omitted variable bias due to omitted multilateral resistanceterms, as described in Anderson and van Wincoop (2003). In atheoretical derivation of the gravity model, the authors show thatbilateral trade between two countries depends on the barriers betweenthe two countries relative to the barriers between each country and therest of the world. The multilateral resistance terms, which are typicallyexcluded from empirical gravity models, depend on all bilateral tradebarriers and vary over time. Exporter and importer fixed effects (or inthis case, pair fixed effects, which subsume exporter and importer fixedeffects) completely control for the bias due tomultilateral resistance in across-section setting. 40 However, in a panel setting, it is necessary toaccount for the time-series variation in the bias as well by includingexporter- and importer-specific time dummies.41 In Eq. (1), the variableof interest (Convit) varies by exporter over time and cannot be identifiedif exporter-specific time dummies are included; thus this equation onlyaccounts for the cross-sectional bias due to the multilateral resistanceterms.

Furthermore, the pair fixed effects estimator exploits the time-series variation in exports around country-pair averages andeliminates the cross-sectional variation between bilateral pairs, aswell as between importers and between exporters. This methodologycontrols for time-invariant differences between exporters that mayhave led to the adoption of the Convention or the timing of itsimplementation.

The time dummies (αt) control for macroeconomic shocks ortrends that could confound the results, e.g., global changes intransport costs due to oil shocks or technology, and deflation factorsfor export and GDP data (Baldwin and Taglioni, 2006, p.7). Finallyrobust standard errors (εijt) are clustered by pair, in order to allow forcorrelation in errors between observations from the same pair.42

38 Note that the linear time trend does not enter the equation separately because it issubsumed by time dummies.39 Note that the time-invariant pair variables typically included in gravity regres-sions, such as distance, indicators for common language, common colonizer, colonialties, and being landlocked, cannot be estimated due to the pair fixed effects.40 This method is common practice in the literature; see Feenstra (2002) andAnderson and van Wincoop (2003).41 See Baldwin and Taglioni (2006), p.12.42 This method allows for cross-sectional correlation, not time-series correlation, inthe standard errors. The results are robust to clustering at the exporter or importerlevel; however I present results clustered by pair as is convention in the literature.Ideally one would want to cluster by OECD membership since OECD members, whomake up the majority of signatory countries, may experience common shocks thatcould result in a correlation of errors, but it is not possible to estimate a model withonly two clusters. Therefore the current method of estimation may understate thestandard errors.

45 Of the 234 countries and territories available in Comtrade for the sample period,ten were excluded due to limited or sporadic trade data and 41 were excluded due tolimited or no corruption data (e.g., Antarctica) — a significant proportion of the latterare territories or protectorates of other countries (e.g., Netherlands Antilles). Belgiumand Luxembourg were excluded because they jointly report trade data until 1999;Macao was excluded because it is a protectorate of China. Eighteen countries lack GDPor population data and seven countries lack exchange rate data. Notably, Taiwan isexcluded from the sample due to missing control variable data.46 Although this proportion is much higher than what is typically found in theliterature, the difference is explained by the selected sample and the careful treatment


The next two models include an interaction between theConvention dummy and a measure of the level of corruption inimporting countries to test whether bilateral trade was differentiallyaffected based on the importer's level of corruption. Eq. (2) is thebenchmark specification:

ln Exportsijt = δ1Convit + δ2 Convit4Corrj� �

ijt+ Xijtβ + αij + αt + εijt

ð2Þ

Corrj is an index that measures the pre-Convention level ofcorruption in importer j. I use the 1998 Control of Corruption measurefrom theWorldwide Governance Indicators by Kaufmann et al. (2007)due to its widespread country coverage.43 I reverse the scale so highervalues correspond to higher levels of corruption. The index is aweighted average of variables related to perceptions of andexperience with corruption from household, firm and expert surveyscollected by multiple organizations. (The results are robust to the useof alternative measures of corruption; see Section 7.)

The main variable of interest in the paper, (Convit *Corrj)ijt,captures the heterogeneous effects of the Convention. A negativecoefficient for δ2 would indicate that the Convention decreasedexports from countries that criminalized foreign bribery to importerswith high levels of corruption, relative to importers with lower levelsof corruption.

Note that the pair fixed effects (that account for exporter fixedeffects) capture permanent differences between exporters that adoptthe Convention and those that do not. They also account forpermanent differences in the trading relationships between signato-ries and high (or low) corruption importers. For example, signatorycountries – who on average have higher standards of governance –

may always prefer to export to importers with lower levels ofcorruption. Moreover, the pair fixed effects control for time-invariantfactors that may be correlated with the variable of interest but aredifficult to observe or to measure, e.g., political, ethnic, and culturallinkages.44 For example, if countries traditionally traded with eachother before the Convention due to unobservable ties betweencorrupt cronies, the estimator controls for this relationship byallowing a unique intercept for each bilateral pair.

The preferred specification is as follows:

ln Exportsijt = δ3 Convit � Corrj� �

ijt+ Xijtβ + αij + αit + αjt + εijt

ð3Þ

where αit and αjt are exporter- and importer-specific time dummies,respectively. The exporter-time dummies subsume the Convit variable.This preferred specification fully controls for the multilateral resistanceterms described above. In a comprehensive review of the empiricalgravity literature, Baldwin and Taglioni (2006) argue that when possiblethe optimal specification should include exporter- and importer-specifictime dummies and pairfixed effects. (Such a specification is rarely used inthe literature because many variables of interest vary by exporter orimporter over time. See Subramanian andWei (2007) andMitchener andWeidenmier (2008) for examples. See Ruiz and Vilarrubia (2007) andNovy (2008) for illustrations of the time-series bias caused by omittingtime-varying multilateral resistance terms.)

The exporter- and importer-specific time dummies also control forpotentially important country-specific time-varying factors that couldconfound the results and for which I am missing data, e.g., on-goingtrade liberalization, average tariff levels. Therefore the preferredspecification (Eq. (3)) identifies the effect of the OECD Conventionafter controlling for all exporter and importer trends and shocks.

43 The 1998 index is the first with broad country coverage and uses data from 1997,before ratification of the Convention.44 See Cheng and Wall (2004) and Baldwin and Taglioni (2006).

Moreover these time-varying dummies help to address time-varyingdifferences between exporters that may have led to the non-randomimplementation of the Convention.

4.2. Sample

The sample of exporters and importers was selected based on dataavailability for the fifteen-year span: 1992–2006. Data on controlvariables are available for 155 countries; export data are available for143 of these155 countries in theUNComtradedatabase.45 (SeeTable 2.)(Appendix I, available online, provides detailed descriptions of the datasources.) There are 22,022 pairs (143⁎154) in the sample, for a total of330,330 possible exporter–importer–time observations; some of theseobservations (22.41%) are missing export data. The regression analysisuses only positive trade flows in order to perform a logarithmictransformation, as in the vast majority of empirical gravity papers;however, countries may choose to trade in a non-random way. Table 3displays characteristics of pairs with positive trade flows and pairs withzero trade flows. The full sample consists of 256,318 observations withnon-missing export data, with approximately 69% positive tradeflows.46 Pairs with positive trade tend to be larger, in terms of GDPand population. They are also more likely to share a common border, acommon language, and a common currency, and are more likely toparticipate in a regional trade agreement. Potential selection bias due tothese non-random factors is discussed in Section 5.2.

The descriptive statistics also underscore stark differences be-tween signatory and non-signatory countries. There are 94.3% positiveexport flows for signatory countries, but only 58.5% positive exportflows for non-signatory countries. Conditional on exporting, averageexports is $774,959,000 for signatories and $107,654,600 for controls.(Average exports for signatories not including the US are$670,251,200.) Also, signatory countries have smaller populationsand are richer relative to non-signatory countries. These differencessuggest that it is important to control for both permanent and time-varying differences between these two groups, as is done in Eq. (3).

It is also important to rule out large divergences in pre-Conventionexport trends for signatory and non-signatory countries since the choiceto ratify the Conventionwas not random. Fig. 1 plots average exports overtime for “Treatment” and “Control.” Since we expect a decline in exportsfrom signatory countries to high corruption importers, I denote as“Treatment” those bilateral country pairs consisting of signatory countryexporters and high corruption (above themedian) importers. All bilateralcountry pairs involving non-signatory exporters serve as the “Control”.We can evaluate pre- and post-trends relative to 1999–2000, whenseveral signatory countries implemented the Convention. In the pre-treatment period, we see no clear divergence in trends; however in thepost-treatment period,we see amarked change in trends. Specifically, weobserve a decline in exports of the treatment group relative to the controlgroup. The econometric results below confirm these patterns.

To investigate the Convention's effect on specific types of exports, Iuse the Rauch product classification: (i) homogeneous — sold on anorganized exchange; (ii) reference priced — prices listed in tradepublications; and (iii) differentiated— all other commodities.47 At the

of missing and zero trade flows, as described in Appendix I, available online.47 Rauch (1999) includes both a liberal and a conservative classification of products.In this paper I use the liberal classification, however the results are robust to the use ofthe conservative classification.

Table 2Countries in the final sample.Source: UN COMTRADE.

Albania Dominican Rep. Latvia SenegalAlgeria Ecuador Lebanon SeychellesAngola⁎ Egypt, Arab Rep. Liberia⁎ Sierra LeoneArgentina El Salvador Libya⁎ SingaporeArmenia Equatorial

Guinea⁎Lithuania Slovak Republic

Australia Estonia Macedonia, FYR SloveniaAustria Ethiopia Madagascar Solomon Islands⁎

Bahrain Fiji Malawi South AfricaBangladesh Finland Malaysia SpainBarbados France Maldives Sri LankaBelarus Gabon Mali St. Kitts and NevisBelize The Gambia Malta St. LuciaBenin Germany Mauritania St. Vincent, the GrenadinesBhutan⁎ Ghana Mauritius SudanBolivia Greece Mexico SurinameBrazil Grenada Moldova SwedenBrunei Guatemala Morocco SwitzerlandBulgaria Guinea Mozambique Syrian Arab RepublicBurkina Faso Guinea-Bissau⁎ Nepal TajikistanBurundi Guyana Netherlands TanzaniaCambodia Haiti New Zealand ThailandCameroon Honduras Nicaragua TogoCanada Hong Kong,

ChinaNiger Tonga

Cape Verde Hungary Nigeria Trinidad and TobagoCentral African Rep. Iceland Norway TunisiaChad⁎ India Pakistan TurkeyChile Indonesia Panama UgandaChina Ireland Papua New

GuineaUkraine

Colombia Israel Paraguay United Arab EmiratesComoros Italy Peru United KingdomCongo, Dem. Rep.⁎ Jamaica Philippines United StatesCongo, Rep. Japan Poland UruguayCosta Rica Jordan Portugal VanuatuCote d'Ivoire Kazakhstan Qatar VenezuelaCroatia Kenya Romania VietnamCzech Republic Kiribati⁎ Russian

FederationYemen

Denmark Korea, Rep. Rwanda ZambiaDjibouti⁎ Kyrgyz Republic Samoa ZimbabweDominica Lao PDR⁎ Saudi Arabia

⁎ Export data not available.

Fig. 1. Trends in average exports in thousands $US for treatment and control groups. Note:(1) treatment group includes bilateral pairs of signatory country exporters and highcorruption country importers, where high corruption is defined as corruption levels abovethe median; (2) control group includes all bilateral pairs in which the exporter is a non-signatory country.

48 Average annual bilateral exports from signatory countries are $821,903,100.49 While the estimated standard errors take into account the correlation in errorterms within country pairs (i.e., using a cluster structure for the error term), it ispossible that all OECD countries experience common shocks and therefore havecorrelated error terms. Empirically, it is impossible to cluster the error terms at theOECD/non-OECD level; therefore the estimated standard errors may be under-estimated, so the results must be taken with caution.50 This concern is discussed in Baldwin and Taglioni (2006), p. 18.


3-digit SITC Revision 3 product level (257 product lines from 001–899), 22.2% of products are classified as homogeneous; 26.1% asreference-priced and 51.8% as differentiated. About half (49.8%) of thenon-missing trade flows of homogeneous products are positive, incontrast to 54.4% for reference-priced products and 63.9% fordifferentiated products. (Online Table 1 summarizes the positiveexport product samples by signatory and non-signatory countries.) Inthe product regressions, the dependent variable is the total value ofexports for each type of good for a given country pair in a given year.The magnitudes of average real exports of homogeneous andreference-priced goods are similar and are substantially smallerthan that of differentiated products. Similar to the total aggregateexports, there are large differences in characteristics betweensignatory and non-signatory countries.

5. Bilateral export results

The empirical analysis shows that, on average, the OECD Anti-Bribery Convention reduced bilateral exports from OECD countries toimporters with high levels of corruption relative to importers withlower levels of corruption (Table 4). (For ease of exposition, I useOECD to refer to countries that implemented the Convention,although this includes 5 non-OECD nations.)

There is no evidence that the Convention had an effect on averageOECD bilateral exports to all importing countries. When the

Convention dummy is included in Eq. (1) without its interactionwith the importer's level of corruption, its coefficient is notstatistically different from zero (though it is negative). The impactof the Convention is detected once we allow the effect to vary acrossimporters based on their level of corruption (Eqs. (2) and (3)).

In the preferred specification (Eq. (3)), we observe a statisticallysignificant 5.66% decline in OECD exports to more corrupt countriesrelative to less corrupt countries that lie approximately one standarddeviation lower on the corruption index. For example, OECD exports tocountrieswith corruption levels similar to that of Croatia (median level ofcorruption) decline 5.66% (or $46.5 million) relative to OECD exports tocountries with lower corruption levels similar to that of Italy (onestandard deviation below Croatia).48 At the tails of the corruptiondistribution,we observe a large (20%) decline in bilateral OECD exports tocountries with corruption levels similar to that of Liberia (most corrupt insample) relative to countries with corruption levels similar to that ofSweden (least corrupt in sample). The difference in the coefficient ofinterest between columns 2 and 3 suggests that not controlling for time-varying multilateral resistance terms (with country-specific timedummies in Eq. (3)) or other time-varying country characteristics leadsto a biased result in the benchmark specification (column 2).49

The coefficients on the traditional gravity variables mostly have theexpected signs. Countries that are larger tend to trade more with eachother. Participating in a regional trade agreement significantly increasestrade between country pairs, as does a higher level of development (asmeasured by GDP per capita). In column 1, being part of a currencyunion is shown to significantly increase trade, as shown in Rose (2000),although in the second and third columns, this effect is imprecise andchanges sign. The coefficient on WTO membership is also sensitive tothe specification. Since there is little variation in these twomembershipvariables during the sample period, it may not be possible to separatelyidentify their effects from the time-invariant pair effects.50 The effects ofexchange rate volatility are not precisely estimated, though theevidence in the literature has been mixed as well.

To examine the effects of other traditional gravity modelvariables – common language, common colonizer, common border,colonial ties, distance, island nations, and landlocked nations – I run aspecification with exporter and importer fixed effects and timedummies so that these pair-specific coefficients can be identified.While this specification does not address every pitfall described

Table 3Sample statistics.Sources: Export data are from UN COMTRADE; GDP and population data are from the World Bank World Development Indicators (2008); exchange rate date are from the IMFInternational Financial Statistics (2008); corruption data are from Kaufmann et al. (2007); distance data are from the Centre d'Etudes Prospectives et d'Informations Internationals;the remaining variables come from Rose (2000, 2004) for 1992–2000, with the author's updates from 2001–2006 using IMF, World Trade Organization, and CIA sources.

Positive flows only Positive and zero flows

Full sample Signatories Non-signatories Full sample Signatories Non-signatories

Log exports 8.21 9.52 7.33 8.21 9.52 7.33Exports (in '000 US$) 440,761 821,903 183,991 304,382 774,959 107,655Export dummy 1 1 1 0.69 0.94 0.59

Control variables Mean Mean

Exporter GDP (in $US million) 400,900 870,300 84,186 223,800 800,400 43,534Importer GDP (in $US million) 302,100 219,600 357,700 206,700 203,000 207,900Exporter population (in '000) 61,615 41,674 75,049 39,134 39,527 39,012Importer population (in '000) 47,318 39,078 52,869 36,662 36,659 36,663Exporter GDP Per Capita (in $US '000) 10,409 20,054 3901 6892 19,196 3044Importer GDP Per Capita (in $US '000) 8110 6759 9021 6453 6373 6478Exchange Rate Volatility 0.07 0.07 0.07 0.08 0.07 0.08Log distance (in '000 km) 8.61 8.63 8.60 8.75 8.66 8.78Corruption importer 1998 −0.10 0.01 −0.18 0.03 0.04 0.03

Percent Percent

Strict currency union 1.06 1.02 1.08 0.79 0.96 0.71Regional trade agreement 1.78 1.12 2.23 1.36 1.06 1.48One in GATT/WTO 25.38 21.92 27.70 29.60 23.26 32.25Both in GATT/WTO 72.41 77.82 68.76 67.13 76.30 63.30None in GATT/WTO 2.21 0.25 3.54 3.26 0.44 4.45Border 2.78 2.10 3.24 1.99 2.00 1.98Common language 16.23 9.56 20.71 15.98 9.09 18.86Common colonizer 8.70 0.15 14.46 10.22 0.14 14.44Colonial ties 2.15 3.35 1.34 1.50 3.17 0.80Landlocked country pairs

Neither 73.86 71.36 75.54 71.18 70.85 71.32One 24.15 26.18 22.78 26.53 26.71 26.46Both 1.99 2.46 1.68 2.29 2.44 2.23

Island country pairsNeither 63.91 64.33 63.64 59.74 62.98 58.38One 31 32 31 35 33 36Both 4.67 3.89 5.19 5.31 4.08 5.83

Observations 177,009 71,249 105,760 256,318 75,565 180,753

Note: Author's calculations.

51 Addressing the Challenges of International Bribery and Fair Competition (2003), p.37–38.52 Miguel (2008), p.1.


above, it does account for time-invariant country-specific factors andglobal trends. The coefficients on these pair-specific characteristicsare strongly statistically significant and are in line with the literature.(Results are available from the author upon request.)

5.1. Discussion and additional results

Thefindings are consistentwith themainpredictionsdescribed above.By increasing the costs of foreignbribery, theConventionhadadifferentialimpact on exports to countries with higher levels of corruption relative tocountries where bribery is less common. Several firm-level stories areconsistent with these findings. OECD firms may have reduced theirexports to high corruption countries; may have completely exited suchmarkets; ormaynothave solicitednewcontracts in suchmarkets. Someofthe existing or potential trade contracts may have been diverted to firmsfrom countries not bound by the Convention, e.g., China; as a result, theConvention may have led to trade creation between non-OECD countriesand high corruption countries.

Anecdotal evidence from the international business community, aswell as firm reports and news investigations, lend support to some ofthese potential mechanisms. For example, in its 2003 annual report oninternational bribery – Addressing the Challenges of International Briberyand Fair Competition – the US Department of Commerce writes:

We estimate that between May 1, 2002 and April 30, 2003, thecompetition for 40 contracts worth $23 billion may have beenaffected by bribery by foreign firms of foreign officials. This is a

sharp drop from the previous five years, which averaged veryclose to 60 contracts each year. The decline in alleged incidents offoreign bribery is based almost entirely on the actions of firmsfrom two prominent OECD member states. There was no changein the number of contracts sought by non-OECD member statefirms, raising their share of this activity to 40% during the past12 months.51

Moreover, some non-OECD firms may have a comparativeadvantage in working in more corrupt environments. In a recentarticle on economic growth in Africa, Ted Miguel (2008) writes:

[The] Chinese … have a major advantage over their Westerncounterparts in that they know how to make money in adeveloping-country business environment where the rule of lawis optional, corruption and bribery are the norm, and infrastruc-ture is patchy. Their experiences at home give them a big leg upon the competition.52

I examine next the effects of the Convention for groups ofexporters, based on their own level of corruption since we mightexpect different effects for exporters with low levels of corruption –

whomay not be accustomed to bribing – and those with high levels ofcorruption—whomay bemore likely to offer and pay bribes based on

Table 4The impact of the OECD convention on bilateral exports.Sources: see Table 3.

(1) (2) (3)

Convention −0.004[0.019]

0.00438[0.019]

Convention×corruption importer −0.0744⁎⁎⁎

[0.012]−0.0566⁎⁎⁎

[0.0142]Regional trade agreement 0.111⁎⁎⁎

[0.0364]0.0967⁎⁎⁎

[0.036]0.100⁎⁎

[0.0411]Currency union 0.100⁎⁎

[0.0490]0.0573[0.050]

−0.023[0.0576]

Exchange rate volatility 0.0111[0.0354]

0.00466[0.035]

−0.0767[0.144]

One country in GATT/WTO 0.128⁎

[0.0708]0.122⁎

[0.071]−1.214⁎⁎⁎

[0.460]Both countries in GATT/WTO 0.329⁎⁎⁎

[0.0747]0.329⁎⁎⁎

[0.075]−2.198⁎⁎

[0.914]Log exporter GDP per capita 0.397⁎⁎⁎

[0.0289]0.402⁎⁎⁎

[0.029]Log importer GDP Per Capita 0.649⁎⁎⁎

[0.0267]0.659⁎⁎⁎

[0.027]Log exporter population 1.416⁎⁎⁎

[0.174]1.378⁎⁎⁎

[0.176]Log importer population 0.406⁎⁎⁎

[0.124]0.454⁎⁎⁎

[0.125]Interaction: time trend×logproduct GDP per capita

−0.00179⁎⁎

[0.000700]−0.00271⁎⁎⁎

[0.001]0.0139⁎⁎⁎

[0.00389]

N 174,464 174,464 174,464R2 0.91 0.911 0.919

Time dummies Yes Yes N/aPair fixed effects Yes Yes YesCountry-specific time dummies No No Yes

(1) OLS estimation with robust standard errors clustered by pair, in brackets. Eachcolumn represents a separate regression. Columns 1–3 run Eqs. (1)–(3), respectively.(2) The dependent variable is the log of exports for a country-pair in a given year. Forsignatory countries, mean of log exports is 9.52 and mean of exports is $821,903,100.(3) Variable of interest is Convention×corruption importer; Convention equals 1 forsignatories in years when legislation criminalizing foreign bribery is in force, and zerootherwise. Corruption importer has a mean of−0.103 and a standard deviation of 1.05.(4) Sample: positive export flows from 1992–2006. (5) Exports and GDP are measuredin US dollars. (6) Population is in thousands; distance is in thousand km.

⁎⁎⁎ Denotes significance at 1%.⁎⁎ Denotes significance at 5%.⁎ Denotes significance at 10%.

Table 5Coefficients on interest (Convention×corruption importer) for select exporters,grouped by level of corruption.Sources: see Table 3.

Terciles Group 1 Group 2 Group 3

Benchmark −0.0323⁎

[0.0176]−0.0802⁎⁎⁎

[0.0189]−0.102⁎⁎⁎

[0.0226]Preferred −0.0193

[0.0190]−0.0633⁎⁎

[0.0196]−0.0920⁎⁎

[0.0227]

Quartiles Group 1 Group 2 Group 3 Group 4

Benchmark −0.03[0.0203]

−0.0642⁎⁎⁎

[0.0196]−0.0965⁎⁎⁎

[0.0234]−0.0982⁎⁎⁎

[0.0254]Preferred −0.0102

[0.0210]−0.0523⁎

[0.0211]−0.0811⁎⁎

[0.0234]−0.0896⁎⁎

[0.0252]

Quintiles Group 1 Group 2 Group 3 Group 4 Group 5

Benchmark 0.00437[0.0225]

−0.0505⁎⁎

[0.0217]−0.0688⁎⁎⁎

[0.0213]−0.164⁎⁎⁎

[0.0253]−0.0616⁎

[0.0329]Preferred 0.0126

[0.0232]−0.0399[0.0228]

−0.0571⁎⁎

[0.0220]−0.147⁎⁎

[0.0256]−0.0591[0.0316]

Leastcorrupt

Mostcorrupt

(1) Each coefficient comes from a separate regression using OLS estimation, with robuststandard errors clustered by pair, in brackets. (2) The dependent variable is the log ofexports for a country-pair in a given year. (3) The top, middle and bottom panels divideexporters into three, four and five groups, respectively, based on their level ofcorruption. (4) Regressions include the positive export flows from 1992–2006 for theselect exporters and all non-signatory countries and the US. (5) The benchmarkspecification uses Eq. (2); the preferred specification uses Eq. (3).



the culture of bribery at home.53 Table 5 displays the coefficient ofinterest for groups of exporters, grouped into terciles, quartiles, andquintiles, using the benchmark (Eq. (2)) and preferred (Eq. (3))specifications. (I rank exporters employing the same corruption indexused for importers.)

In general, we observe stronger effects for exporters with higherlevels of corruption. For exporters with the lowest levels ofcorruption, we observe no statistically significant effect. Thesefindings are not surprising; on average we would expect fewerfirms from these countries to have been engaged in bribery before theintroduction of the Convention. These low corruption countries arealso, on average, more developed and may have firms that are moreproductive or have higher quality products, giving them a competitiveadvantage vis-à-vis their counterparts. As a result, such firms may notneed to rely on bribes to get on a list of bidders for public contracts.Finally, some firms might exploit the political clout of their homecountries to win contracts, e.g., French aviation and nuclear firmssigned contracts worth over $30 billion with Chinese partners duringPresident Sarkozy's 2008 visit to China.54

53 Lambsdorff (1998) provides some evidence that exporters exhibit differenttendencies to offer bribes. See, for example, Kaufmann et al. (2007) for a discussionof country differences and trends in governance.54 Clark and Lague (2007).

Before the Convention, twelve countries allowed bribes to bededucted from business expenses for tax purposes.55 The 1996 OECDRecommendation of the Council on the Tax Deductibility of Bribes toForeign Corrupt Officials encouraged members to disallow suchdeductions; most countries adopted such laws just before adoptingthe Convention or at the same time. I test whether changes in the tax-deductible status of bribes had an impact on bilateral exports, eitheralone or coupled with the OECD Convention. I run a series ofregressions that include a dummy which equals one if bribes are nottax deductible in a signatory country; the regressions use thebenchmark specification since the non-deductible dummy, whichvaries by exporter over time, is not identified in the preferredspecification. I also include an interaction between the non-deductibledummy and the importer's level of corruption (analogous to the OECDConvention variables). (Table 6).

Disallowing tax deductions for bribes paid does not have asignificant effect on bilateral exports from signatory countries to allbilateral partners (column 1), nor does it have an effect once theimporter's level of corruption is taken into account (column 2). (Thep-value of the non-deductible coefficient is approximately 0.14; whilenot significant at conventional levels, the negative coefficient mayreflect increased accounting and bookkeeping costs that occur atheadquarters and affect a firm's overall costs.) The main coefficient ofinterest is robust to the inclusion of the non-deductible dummy(columns 3 and 4).

Finally, I explore whether the effects of the Convention are isolatedto the date of implementation, occurred with a lag, or werepermanent. Wemight expect the Convention to have affected exportsaround the time of implementation, but not years later once exportersadjust to the changes in transaction costs. On the other hand, we may

55 Australia, Austria, Denmark, France, Germany, Iceland, Netherlands, New Zealand,Norway, Sweden, Switzerland and Portugal. Information on the tax-deductibility ofbribes was gathered by the author from country reports available here: http://www.oecd.org/document/24/0,3343,en_2649_34859_1933144_1_1_1_1,00.html.

Unlabelled image



Table 6Results including the non-deductible status of bribery.Sources: see Table 3.

(1) (2) (3) (4)

Non-deductible bribes −0.032[0.022]

−0.031[0.021]

−0.0263[0.022]

−0.0263[0.0215]

Non-deductiblebribes×corruption importer

−0.0191[0.0166]

Convention×corruptionimporter

−0.0735⁎⁎⁎

[0.0122]−0.0735⁎⁎⁎

[0.0122]Convention 0.0109

[0.0190]0.0109[0.0190]

Regional trade agreement 0.110⁎⁎⁎

[0.0364]0.110⁎⁎⁎

[0.0364]0.0959⁎⁎⁎

[0.0359]0.0959⁎⁎⁎

[0.0359]Currency union 0.102⁎⁎

[0.0490]0.0959⁎

[0.0494]0.0592[0.0499]

0.0592[0.0499]

Exchange rate volatility 0.0108[0.0354]

0.00994[0.0354]

0.00451[0.0354]

0.00451[0.0354]


[0.0705]0.126⁎

[0.0705]0.122⁎

[0.0709]0.122⁎


[0.0744]0.327⁎⁎⁎

[0.0744]0.329⁎⁎⁎

[0.0748]0.329⁎⁎⁎


[0.0292]0.393⁎⁎⁎

[0.0292]0.398⁎⁎⁎

[0.0292]0.398⁎⁎⁎

[0.0292]Log importer GDP per capita 0.648⁎⁎⁎

[0.027]0.649⁎⁎⁎

[0.027]0.659⁎⁎⁎

[0.027]0.659⁎⁎⁎


[0.167]1.396⁎⁎⁎

[0.168]1.372⁎⁎⁎

[0.176]1.372⁎⁎⁎


[0.125]0.422⁎⁎⁎

[0.125]0.461⁎⁎⁎

[0.125]0.461⁎⁎⁎


−0.002⁎⁎

[0.001]−0.002⁎⁎

[0.001]−0.003⁎⁎

[0.001]−0.003⁎⁎

[0.001]

N 174,464 174,464 174,464 174,464R2 0.91 0.91 0.91 0.91

Time dummies Yes Yes Yes YesPair fixed effects Yes Yes Yes Yes

(1) OLS estimation, with robust standard errors clustered by pair, in brackets. Eachcolumn represents a separate regression. The regressions use the benchmarkspecification (Eq. (2)). (2) The dependent variable is the log of exports for a country-pair in a given year. (3) Variable of interest is Non-deductible bribes, which equals onein years when bribes are not tax-deductible, and zero otherwise. There are 12 signatorycountries where bribery was tax deductible prior to the advent of the OECD Convention.(4) Sample: positive export flows from 1992–2006. (5) Exports and GDP are measuredin US dollars. (6) Population is in thousands; distance is in thousand km.


56 The procedure is similar to a Heckman correction. The first stage entails a Probitselection equation that uses costs of entry (and alternatively, common religion) as anexclusion restriction; the second stage incorporates the selection correction, as well asa proxy for firm heterogeneity. Data on costs of entry are not available over time andcommon religion does not change over time, therefore I am unable to use theseexclusion restrictions in a panel setting.


observe persistent effects if the permanent increase in transactioncosts due to the new expected penalties continued to affect bilateralrelationships for signatory countries. It is also possible that the effectsof the Convention occurred with a lag. First, trading relationships taketime to be established and are usually long-term; therefore we mightnot see an immediate adjustment to increases in transaction costs.Second, the effect of the Convention is driven by the behavior of firmsin signatory countries relative to the behavior of firms in controlcountries; the latter may have been slower to react to changes in theinternational arena, e.g., the time necessary to establish newexporting relationships in corrupt countries. Therefore, the exacttemporal pattern of the effects is an empirical question.

I incorporate the element of timing by including interactions of theimporter's level of corruption (Corrj) with dummies for distance in timefrom the year of implementation (either in one-year or two-yearintervals) in lieu of the variable of interest, (Convit*Corrj)ijt. (Results areavailable upon request.) The coefficients on the interaction termssuggest that the effect of the Convention was permanent; they arestatistically significant and of similar magnitude. Further, the coeffi-cients are not statistically different from each other (i.e., the p-value ofthe F-statistic of the equality of coefficients is over 0.45). Rather thancausing a temporary or lagged effect, the criminalization of foreign

bribery seems to have augmented overall transaction costs betweenbilateral trading partners, causing a lasting effect.

5.2. Selection

Selection bias – due to the exclusion of zero trade flows – leads to acorrelation between the independent variables and unobservablefactors contained in the standard errors. For example, countries thathave high bilateral trade barriers are likely to have low unobservablebarriers that make it worthwhile to trade. This correlation will resultin a downward bias on the estimates of the observable trade barriers.This is a concern in the gravity literature where it is common practiceto exclude country pairs with zero trade flows.

I use two strategies to address this concern. The first strategyexploits a Poisson estimator for count data to incorporate zero tradeflows; the second strategy limits the analysis to countries that weretrading together before the Convention. (In a recent paper, Helpmanet al. (2008) propose a two-stage estimation procedure to addressselection; however I am unable to implement this procedure due todata limitations.56)

Santos Silva and Tenreyro (2006) propose the use of a pseudomaximum-likelihood (PML) Poisson estimation method to correct forthe bias created by log linearizing a model under heteroscedasticity (acommon method in the traditional gravity model). An additionalbenefit of this method is that it allows for the inclusion of zero tradeflows. The dependent variable is the value of exports from exporter ito importer j in year t. Due to the large number of fixed effects in thepreferred specification, I use the benchmark specification (Eq. (2)).

The main result of the paper is robust to using this estimationprocedure (Table 7). The first column includes all non-missing tradeflows (zeros and positives); the second column includes only positivetrade flows (as in the OLS estimation above). As in the empiricalillustration in Santos Silva and Tenreyro (2006), there is little differencein the coefficients for the whole sample versus the positive-tradesample, suggesting that the bias caused by log-linearization is drivenmainly by heteroscedasticity rather than truncation. The magnitude ofthe coefficient of interest is smaller when using the Poisson PMLprocedure than the log-linearized OLS model, which is also consistentwith Santos Silva and Tenreyro's findings (p.650–651).

As an additional test, I limit the sample to country pairs that tradedtogether in the beginning of the sample period. This method testschanges in the intensive margin of trade, conditional on trading in anearlier period. I run the preferred specification on the sample of countrypairs that tradedwith each other at the beginning of the sample period,1992, as well as 1993, 1994 and 1995. The results are qualitatively thesame. (Results are available from the author upon request.)

6. Results by product category

In this section I discuss first the results relating to the level ofproduct differentiation, then the results relating to industries inwhichthe government has a disproportionate presence.

I use the Rauch classification of goods to investigate whether theConvention differentially affected bilateral exports of specific catego-ries of products. Table 8 presents the results for homogeneous,reference-priced and differentiated products using the benchmark(left panel) and preferred (right panel) specifications.

The coefficient of interest is negative and statistically significantacross products and specifications, consistent with the story of

Table 7Results using Poisson estimation.Sources: see Table 3.

(1) (2)

Zero and positive trade flows Positive trade flows

Convention×corruptionimporter

−0.0381⁎⁎

[0.0194]−0.0368⁎⁎

[0.0185]Convention 0.0246

[0.0292]0.0261[0.0305]

Regional trade agreement 0.0681⁎⁎

[0.0321]0.0686⁎⁎

[0.0296]Currency union 0.0352

[0.0357]0.0344[0.0346]

Exchange rate volatility −0.00418[0.0467]

0.00809[0.0443]


[0.0974]0.183⁎


[0.0981]0.508⁎⁎⁎


[0.0565]0.606⁎⁎⁎


[0.0712]0.588⁎⁎⁎

[0.0620]Log exporter population 0.718⁎⁎

[0.289]0.701⁎⁎⁎

[0.253]Log importer population −0.408⁎⁎

[0.168]−0.419⁎⁎


−0.0103⁎⁎⁎

[0.00118]−0.0102⁎⁎⁎

[0.00107]

N 221,345 172,913Log likelihood −1.97E+09 −1.92E+09

(1) Pseudo maximum-likelihood estimation with bootstrapped standard errors inbrackets. Each column represents a separate regression. Both columns use thebenchmark specification (Eq. (2)). (2) The dependent variable is the value of exportsfor a country-pair in a given year. (3) Variable of interest is Convention×corruptionimporter; Convention equals 1 for signatories in years when legislation criminalizingforeign bribery is in force, and zero otherwise. Corruption importer has a mean of−0.103 and standard deviation of 1.05. (4) Column 1 includes zero and positive tradeflows from 1992–2006; column 2 includes only positive trade flows from 1992–2006.(5) Exports and GDP are measured in US dollars. (6) Population is in thousands;distance is in thousand km.


58 Furthermore, there is a special Transitional Safeguard Mechanism in place during a12-year period starting from the date of accession in cases where imports of productsof Chinese origin cause or threaten to cause market disruption to the domestic


increased transaction costs betweenOECD countries andmore corruptimporting countries. However, the differences in magnitude suggestthat the underlyingmechanisms of briberymay differ across products.After the Convention, OECD countries exported 13.3% fewer homoge-neous products, on average, to more corrupt countries relative to lesscorrupt countries (one standard deviation lower on the corruptionindex). In contrast, we observe a 3.3% relative reduction in bilateralexports of differentiated products. (Results from the preferredspecification are most plausible since homogenous and reference-priced goods, which are very similar, exhibit similar magnitudes.)

These findings are consistent with several explanations describedin the conceptual framework section. Firms selling specialized goodsmay havemore relative bargaining power since foreign public officialshave fewer outside options when negotiating with them; therefore,even if these firms reduced their bribes or increased their prices tocover the additional costs imposed by the Convention, they may havebeen able to retain business. Whereas, in more competitiveindustries – where substitutes are readily available – officials mayhave shifted their contracts to firms that were willing and able tobribe after the Convention. (US firms have lodged complaints to theUS Department of Commerce that firms from countries where foreignbribery is not illegal have won public contracts by bribing foreignpublic officials.57) Finally, the smaller effects that we observe for

57 Addressing the Challenges of International Bribery and Fair Competition (2003), p.37–38.

differentiated products may be due to the ease with which briberymay be concealed in such markets.

The disaggregated industry-level analysis (examining selectindustries in which the government plays an important role)generated inconclusive findings. (Results not reported.) Despitemuch variation in the coefficient of interest, no systematic patternsemerged. The inability to find consistent patterns is a significantlimitation of this study and raises some concern regarding theinterpretation of the main results of this paper. It is possible that theinconsistencies at the disaggregated industry-level reflect a morecomplex relationship between the OECD Anti-Bribery Convention,corruption, and international trade than is evident in the main resultsof the paper; therefore the results above should be interpreted withsome caution.

7. Robustness

To test the robustness of the results, I pursue a number ofstrategies, including using alternative measures of corruption, check-ing for outliers, and exploring alternative explanations for the results.(All results are available from the author upon request.)

I first investigate an alternative explanation for changes in exportpatterns that we observe, namely, China's entry into the World TradeOrganization (WTO). If China – a large exporter who is yet to adoptthe Convention – systematically increases its exports to highcorruption countries, relative to low corruption countries, we wouldobserve patterns similar to the main results above. The timing of thisevent, however, is not consistent with the observed results — Chinaonly entered the WTO in December 2001, after 27 of 33 countries hadimplemented legislation criminalizing foreign bribery and most tariffand trade restrictions were slowly phased out between 2002 and2005.58 Furthermore, to a certain extent the empirical specificationcontrols for this possibility; the interaction between a linear timetrend and the log of the product of per capita GDPs controls forchanges over time in the trading relationships between countriesbased on their level of development. As an additional test, I replacethis interaction with an interaction between a linear time trend, adummy for China as the exporting country and the log of per capitaGDP for the importing country. The results are robust. I also re-estimate the regressions for total exports using data from 1992–2001,before China fully entered the WTO; the results remain robust.

Next, I consider potential omitted factors that could confound theresults. In order to bias the coefficient of interest in the preferredspecification, an omitted variable must vary by country pair over timeand be correlatedwith either the exporter's year of implementation orthe importer's level of corruption. One possibility is a bilateral tradeagreement or sanction. Since it is difficult to obtain such data for allbilateral pairs in my sample, I collected data on importing countriesthat were under UN sanctions during the sample period.59 There islittle change in the coefficient of interest when these importers areexcluded from the sample.

Another factor that could bias the results involves unobservabletrends between rich exporters and poor importers. The empiricalmodel includes an interaction term (between a time trend and the logproduct of per capita GDP) to control for this potential confounder. Asa robustness check, I replace this interaction term with more flexibleinteractions that more broadly capture potential omitted trends. I test

producers of other WTO members. From http://www.wto.org/english/thewto_e/countries_e/china_e.htm.59 These countries include Angola, Ethiopia, Haiti, Liberia, Libya, Rwanda, Serbia/Montenegro, Sierra Leone, Somalia, and South Africa.

http://www.wto.org/english/thewto_e/countries_e/china_e.htm

http://www.wto.org/english/thewto_e/countries_e/china_e.htm

Table 8The impact of the OECD Convention on total bilateral exports and bilateral exports by product category.Sources: see Table 3.

(1) (2) (3) (4) (5) (6) (7) (8)

Full sample Homogeneous Reference-priced Differentiated Full sample Homogeneous Reference-priced Differentiated


[0.012]−0.159⁎⁎⁎

[0.0190]−0.101⁎⁎⁎

[0.0148]−0.0836⁎⁎⁎

[0.0128]−0.0566⁎⁎⁎

[0.0142]−0.133⁎⁎⁎

[0.0220]−0.104⁎⁎⁎

[0.0175]−0.0329⁎⁎

[0.0145]Convention 0.00438

[0.019]0.012[0.0291]

−0.0209[0.0234]

0.0615⁎⁎⁎

[0.0190]Regional trade agreement 0.0967⁎⁎⁎

[0.036]0.271⁎⁎⁎

[0.0598]0.233⁎⁎⁎

[0.0484]0.0208[0.0352]

0.100⁎⁎

[0.0411]0.491⁎⁎⁎

[0.0675]0.228⁎⁎⁎

[0.0532]−0.0155[0.0381]

Currency union 0.0573[0.050]

0.270⁎⁎⁎

[0.0762]0.0932⁎

[0.0559]0.0402[0.0553]

−0.023[0.0576]

0.302⁎⁎⁎

[0.0821]−0.0378[0.0626]

−0.0747[0.0565]

Exchange rate volatility 0[0.035]

−0.01[0.0540]

0.06[0.0428]

0.0716⁎

[0.0373]−0.0767[0.144]

0.182[0.247]

−0.155[0.181]

−0.115[0.148]


[0.071]0.303⁎⁎⁎

[0.109]0.07[0.0811]

0[0.0678]

−1.214⁎⁎⁎

[0.460]−2.133⁎⁎⁎

[0.739]−1.222⁎⁎

[0.553]−1.007⁎⁎


[0.075]0.518⁎⁎⁎

[0.116]0.285⁎⁎⁎

[0.0865]0.187⁎⁎⁎

[0.0722]−2.198⁎⁎

[0.914]−4.110⁎⁎⁎

[1.471]−2.108⁎

[1.102]−1.764⁎⁎


[0.029]0.395⁎⁎⁎

[0.0426]0.323⁎⁎⁎

[0.0364]0.463⁎⁎⁎


[0.027]0.568⁎⁎⁎

[0.0399]0.743⁎⁎⁎

[0.0322]0.695⁎⁎⁎


[0.176]0.782⁎⁎⁎

[0.245]2.498⁎⁎⁎

[0.212]1.305⁎⁎⁎


[0.125]0.781⁎⁎⁎

[0.181]−0.459⁎⁎⁎

[0.142]0.205⁎

[0.121]Interaction: time trend×log productGDP per capita

−0.00271⁎⁎⁎

[0.001]−0.00689⁎⁎⁎

[0.00110]−0.00611⁎⁎⁎

[0.000940]−0.00298⁎⁎⁎

[0.000744]0.0139⁎⁎⁎

[0.00389]0.0214⁎⁎⁎

[0.00604]0.0121⁎⁎

[0.00483]0.00998⁎⁎⁎

[0.00379]

N 174,464 125,843 137,508 161,345 174,464 125,843 137,508 161,345R2 0.911 0.845 0.89 0.918 0.919 0.861 0.902 0.927

Time dummies Yes Yes Yes Yes N/a N/a N/a N/aPair fixed effects Yes Yes Yes Yes Yes Yes Yes YesCountry-specific time dummies No No No No Yes Yes Yes Yes

(1) OLS estimation, with robust standard errors clustered by pair, in brackets. (2) The dependent variable is the log of exports for a country-pair in a given year. For comparison,columns 1 and 5 reproduce the results for the full sample from Table 4. The other columns use aggregate samples for each product group, listed at the top of the column. Columns 1–4use the benchmark specification (Eq. (2)); columns 5–8 represent the preferred specification (Eq. (3)). (3) Variable of interest is Convention×corruption importer; Conventionequals 1 for signatories in years when legislation criminalizing foreign bribery is in force, and zero otherwise. (4) Sample: positive export flows from 1992–2006. (5) Exports andGDP are measured in US dollars. (6) Population is in thousands; distance is in thousand km.


60 I use the World Bank regional classification: Africa, Asia (East Asia developing andNIC and South Asia), Latin America, Middle East and North Africa, Eastern Europe andthe former USSR, and Island nations.


three terms that interact a time trendwith a dummy for rich exportersand a dummy for poor importers, using different definitions of richand poor based on average per capita GDP; specifically, (i) above andbelow the mean; (ii) above the top third and below the bottom third;and (iii) above the top quarter and below the bottom quarter(Table 9). The results are qualitatively similar, though the magnitudeof the coefficient of interest declines.

I test alternative measures of corruption and poor governance. Iuse the 1998 Corruption Perception Index by Transparency Interna-tional and three alternative 1998measures of governance provided byKaufmann et al. (2007): (i) rule of law; (ii) regulatory quality; and(iii) government effectiveness. I reverse the governance indices sothat higher values indicate worse governance. I substitute the indices,one at a time, for the corruption index used above. There is littlequalitative effect on the results, which is not surprising given the highdegree of correlation among the indicators. This exercise suggests thatthe effects of the Convention may be determined by a broader set ofinstitutional and governance factors.

To test for non-linearities in the effects of the corruption index, Ireplace the index with dummy variables for different parts of thedistribution. I divide the index in thirds and in fourths and then Iinteract the dummies with the Convention dummy. In each case thelowest (least corrupt) group is excluded from the regressions. Thecoefficients of interest are displayed in Table 10. The findings reinforcethe main results; importing countries at various points of thedistribution experience a decline in OECD exports from countries

that criminalize bribery relative to the least corrupt group ofimporting countries.

I also examine whether individual countries or groups of countriesare driving the results. I systematically exclude exporters that adoptthe Convention one at a time; the results are not sensitive to thisstrategy. Nor are the results sensitive to the exclusion of large non-signatory exporters (i.e., China, India, and Russia) or oil-producingnations (i.e., Algeria, Ecuador, Indonesia, Nigeria, Qatar, Saudi Arabia,UAE, and Venezuela). The results are also robust to the systematicexclusion of geographical groups of importing countries.60

8. Conclusion

As academics and policymakers struggle to understand andpromote economic growth in today's developing countries, manyhave come to believe that corruption has a profound influence oneconomic performance and long-run development. This paperinvestigates one particular form of corruption— the bribery of foreignpublic officials. It provides empirical evidence that criminalizingforeign bribery has affected firm behavior and that bribery indeed

Table 9Robustness check.Sources: see Table 3.

(1) (2) (3) (4) (5) (6)


[0.0116]−0.0432⁎⁎⁎

[0.0115]−0.0353⁎⁎⁎

[0.0116]−0.0286⁎⁎

[0.0146]−0.0328⁎⁎

[0.0144]−0.0273⁎

[0.0145]Convention 0.00697

[0.0187]−0.0259[0.0187]

−0.0297[0.0187]

Interaction (i) −0.0107⁎⁎⁎

[0.00209]−0.0199⁎⁎⁎

[0.00308]Interaction (ii) 0.00509⁎⁎⁎

[0.00189]0.0175⁎⁎⁎

[0.00291]Interaction (iii) 0.00998⁎⁎⁎

[0.00217]0.0208⁎⁎⁎

[0.00301]Regional trade agreement 0.059

[0.0366]0.0790⁎⁎

[0.0360]0.0474[0.0369]

0.0414[0.0418]

0.0835⁎⁎

[0.0411]0.0402[0.0417]

Currency union 0.0328[0.0496]

0.0395[0.0500]

0.0223[0.0502]

−0.0475[0.0577]

−0.0316[0.0578]

−0.0477[0.0577]


−0.0153[0.0353]

−0.0228[0.0354]

−0.113[0.144]

−0.0939[0.144]

−0.109[0.144]

One country in GATT/WTO 0.142⁎⁎

[0.0702]0.149⁎⁎

[0.0701]0.150⁎⁎

[0.0701]−1.063⁎⁎

[0.417]−1.065⁎⁎

[0.416]−1.066⁎⁎


[0.0739]0.359⁎⁎⁎

[0.0738]0.361⁎⁎⁎

[0.0738]−1.899⁎⁎

[0.828]−1.906⁎⁎

[0.826]−1.905⁎⁎

[0.827]Log GDP per capita exporter 0.397⁎⁎⁎

[0.0290]0.375⁎⁎⁎

[0.0294]0.372⁎⁎⁎

[0.0294]0.963[0.679]

1.006[0.680]

0.972[0.679]

Log GDP per capita importer 0.624⁎⁎⁎

[0.0257]0.628⁎⁎⁎

[0.0259]0.623⁎⁎⁎

[0.0259]0.571[0.451]

0.567[0.451]

0.57[0.451]

Log population exporter 1.389⁎⁎⁎

[0.172]1.499⁎⁎⁎

[0.168]1.503⁎⁎⁎

[0.167]Log population importer 0.661⁎⁎⁎

[0.118]0.650⁎⁎⁎

[0.119]0.651⁎⁎⁎

[0.119]

N 174,464 174,464 174,464 174,464 174,464 174,464R2 0.911 0.91 0.911 0.92 0.92 0.92

Time dummies Yes Yes Yes N/a N/a N/aPair fixed effects Yes Yes Yes Yes Yes YesCountry-specific time dummies No No No Yes Yes Yes

(1) OLS estimation with robust standard errors clustered by pair, in brackets. (2) The dependent variable is the log of exports for a country-pair in a given year. (3) Interaction terms i,ii, and iii are of the following form: time trend×dummy for rich exporter×dummy for poor importer. Rich and poor indicate the relationship to average GDP per capita; lying (i)above or below the mean; (ii) above the top third or below the bottom third; (iii) above the top quarter or below the bottom quarter. (4) Sample: positive export flows from 1992–2006. (5) Exports and GDP are measured in US dollars. (6) Population is in thousands; distance is in thousand km.


61 Hines (1995) shows a 4% average decline in US shares of aircraft imports in corruptcountries relative to US shares of aircraft imports in non-corrupt countries.


occurs to a measurable extent in international business transactions. Iexamine the international trade impact of the 1997 OECDAnti-BriberyConvention— the first global anti-corruption initiative targeted at thesupply-side of bribery. The Convention created criminal and civilpenalties for firms and managers caught bribing foreign publicofficials. I identify the effects of criminalizing foreign bribery usingvariation in the timing of implementation along with variation in thelevel of corruption in importing countries. I use product-level paneldata on 143 exporters and 155 importers from 1992–2006 to explorethe effects on total bilateral exports and bilateral exports by productcategory using the Rauch classification.

Controlling for a variety of confounding factors, I find that, onaverage, countries that implemented the Convention reduced bilat-eral exports by 5.7% to more corrupt countries relative to less corruptcountries one standard deviation lower on the corruption index; thistranslates to an approximate $46 million relative decline in bilateralexports. While we have no reason ex-ante to expect the Convention tohave deterred firms from bribing (for example, if there is noenforcement, if penalties are too low, or if it is very easy to hidebribery), the findings are consistent with economic theory, whichsuggests that by creating penalties for foreign bribery, the Conventionincreased the costs of doing business in corrupt countries wherewinning a contract is a function of bribes paid. As a result, some firmsfrom signatory countries may have decreased exports to more corruptcountries, while others may have completely exited these markets.

The main effect also captures other possible changes at the firmlevel. OECD firms may have redirected some of their exports to lesscorrupt countries. And non-OECD firms may have increased theirexports to corrupt countries, possibly picking up former OECDbusiness. These findings support previous work on the US, whichshowed an economically and statistically significant decline in USbusiness activity in corrupt countries following the adoption of the USForeign Corrupt Practices Act.61

The second key empirical finding is that the relative decline inexports to high corruption countries is observed across productcategories, with larger relative declines for homogenous goods(−13.3%) and smaller relative declines for differentiated ones(−3.3%). These findings are consistent with several explanations.Sellers of differentiated or specialized products may have morebargaining power vis-à-vis public officials than sellers of homogeneousproducts; therefore the former may be able to obtain export contractseven if they bribe less or increase their prices to cover the costs ofevading detection. An alternative explanation follows a typology ofpublic procurement bribery proposed by Rose-Ackerman (1999). Sheargues that graft is more easily concealed in contracts involvingspecialized products. It follows that the Convention may not have had

Table 10Results with corruption index as a categorical variable.Sources: see Table 3.

(1) (2) (3) (4) (5) (6) (7) (8)

Full sample Homogeneous Reference-priced Differentiated Full sample Homogeneous Reference-priced Differentiated

Convention×bribeprone2 −0.137⁎⁎⁎

[0.0365]−0.161⁎⁎⁎

[0.0576]−0.208⁎⁎⁎

[0.0446]−0.0624⁎

[0.0370]−0.141⁎⁎⁎

[0.0408]−0.231⁎⁎⁎

[0.0627]−0.131⁎⁎⁎

[0.0484]−0.113⁎⁎⁎

[0.0413]Convention×bribeprone3 −0.102⁎⁎⁎

[0.0383]−0.330⁎⁎⁎

[0.0629]−0.183⁎⁎⁎

[0.0484]−0.0319[0.0390]

−0.146⁎⁎⁎

[0.0420]−0.160⁎⁎

[0.0699]−0.322⁎⁎⁎

[0.0530]−0.0624[0.0427]

Convention×bribeprone4 −0.118⁎⁎⁎

[0.0435]−0.389⁎⁎⁎

[0.0704]−0.219⁎⁎⁎

[0.0553]−0.07[0.0444]

Regional trade agreement 0.0971⁎⁎

[0.0412]0.491⁎⁎⁎

[0.0677]0.226⁎⁎⁎

[0.0534]−0.0143[0.0382]

0.0996⁎⁎

[0.0411]0.494⁎⁎⁎

[0.0675]0.225⁎⁎⁎

[0.0534]−0.015[0.0381]

Currency union −0.0208[0.0575]

0.319⁎⁎⁎

[0.0825]−0.0277[0.0627]

−0.0688[0.0563]

−0.0291[0.0577]

0.306⁎⁎⁎

[0.0827]−0.0445[0.0628]

−0.0787[0.0566]


0.173[0.247]

−0.157[0.181]

−0.116[0.148]

−0.0745[0.144]

0.179[0.247]

−0.157[0.181]

−0.114[0.148]

One country in GATT/WTO −1.223⁎⁎⁎

[0.460]−2.104⁎⁎⁎

[0.738]−1.238⁎⁎

[0.552]−1.009⁎⁎

[0.430]−1.229⁎⁎⁎

[0.460]−2.108⁎⁎⁎

[0.738]−1.241⁎⁎

[0.552]−1.012⁎⁎

[0.430]Both countries in GATT/WTO −2.221⁎⁎

[0.914]−4.063⁎⁎⁎

[1.471]−2.151⁎

[1.098]−1.773⁎⁎

[0.853]−2.227⁎⁎

[0.914]−4.060⁎⁎⁎

[1.469]−2.147⁎

[1.098]−1.773⁎⁎

[0.853]Interaction: time trend×log productGDP per capita

0.0138⁎⁎⁎

[0.00389]0.0220⁎⁎⁎

[0.00583]0.0116⁎⁎

[0.00464]0.00953⁎⁎⁎

[0.00364]0.0138⁎⁎⁎

[0.00389]0.0221⁎⁎⁎

[0.00583]0.0116⁎⁎

[0.00464]0.00956⁎⁎⁎

[0.00364]

N 174,464 125,843 137,508 161,345 174,464 125,843 137,508 161,345R2 0.919 0.861 0.902 0.927 0.919 0.861 0.902 0.927

Time dummies N/a N/a N/a N/a N/a N/a N/a N/aPair fixed effects Yes Yes Yes Yes Yes Yes Yes YesCountry-specific time dummies Yes Yes Yes Yes Yes Yes Yes Yes

(1) OLS estimation, with robust standard errors clustered by pair, in brackets. (2) The dependent variable is the log of exports for a country-pair in a given year. All regressions use thepreferred specification in Eq. (3). (3) Variables of interest are Convention×bribeprone2, Convention×bribeprone3, and Convention×bribeprone4; Convention equals 1 forsignatories in years when legislation criminalizing foreign bribery is in force, zero otherwise. Bribeprone categorical variables are dummies for various parts of the distribution. Incolumns (1)–(4), the corruption index is broken into three groups; the bottom third (least corrupt) is excluded. In columns (5)–(8), the corruption index is broken into four groups;the bottom quartile (least corrupt) is excluded. (4) Sample: positive export flows from 1992–2006.

⁎⁎⁎ Denotes significance at 1%.⁎ Denotes significance at 10%.

⁎⁎ Denotes significance at 5%.


as much bite in transactions involving differentiated products whereprices are more fungible and bribes are hidden more easily.

Finally, I find evidence that the effects of the OECD Anti-BriberyConvention differ by the level of corruption in the exporting country.Specifically, we observe more pronounced effects for exportingcountries with high levels of corruption than for those with lowerlevels of corruption, suggesting that the behavior of multinational firmsmay be shaped by the institutional culture in their home countries.

What remains to be answered is whether the OECD Anti-BriberyConvention attained its objective of reducing foreign bribery. Even ifOECD firms were deterred from bribing, competitors not bound by theConvention may have increased their bribes paid. In effect, theConvention may have altered the composition of bribe payers, as wellas the composition of firms doing business in more corrupt countries.Such changes may have had distributional and efficiency implicationsthat cannot be explored with aggregate data, underscoring the need forfirm-level analysis. While this current paper has examined theaggregate effects of one specific anti-corruption initiative, it drawsattention topotentialfirm-level responses and,morebroadly, highlightsthe roles of domestic and international institutions in fosteringeconomic transactions.

Acknowledgments

I am grateful to two anonymous referees for their detailed andconstructive comments and to the editor for an exceptionally thoughtfulreview of an earlier draft. I would like to thank Sandra Black, Dora Costa,Daniel Dias, Frederico Finan, Daniel Kaufmann, Kris Mitchener, andRomainWacziarg; the seminar participants at UCLA; and the participantsat the 2009 International Society for New Institutional Economics annualconference and the 2009 Pacific Conference for Development Economics.

Remaining errors aremy own. The views expressed here are those of theauthor and may not be attributed to the Economic Research Service,USDA.

Supplementary data and documentation

Supplementary data to this article can be found online atdoi:10.1016/j.jdeveco.2011.01.002.

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