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The determinants of cross-border corruption

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Abstract

We reconsider the question of what determines corruption at the cross-national level, using new methods and data: observations of occurrences of cross-national corruption. We find that economic development and a small population is associated with lower levels of corruption, as are freedom of the press, political rights, the presence of established democratic institutions, the salience of women’s role in society, and low exports of natural resources such as oil. The particular structure of the data also allows for the first time to consider the “relational aspects” of corrupt relationships, which come to the fore when parties to the corrupt transaction, the briber and the bribee, reside in different countries. Overall, we find limited evidence that the relational factors that we consider affect corruption, beyond the effects that they often have on bilateral trade.

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Notes

  1. See, for reviews, Lambsdorff (2006), Treisman (2007, 2015) and Klitgaard (2017).

  2. Victimization statistics, obtained from surveys aimed at eliciting information on bribes being paid, have been used more rarely in the relevant studies. On their shortcomings, see Treisman (2007) and Kraay and Murrell (2016).

  3. Brewster (2017) provides a convincing explanation of the main drivers of FCPA enforcement. For the determinants of the overall enforcement of the OECD’s Anti-Bribery Convention by signatories other than the United States, see Kaczmarek and Newman (2011) and Choi and Davis (2014). To date, 44 countries have signed the Convention, eight of which are non-OECD countries.

  4. We refer to the probability that a corrupt transaction is observed once it has occurred. On the other hand, the total number of corrupt transactions associated with a given foreign country obviously depend on that country’s characteristics. The “equal treatment assumption” corresponds to Assumption 1 in Escresa and Picci (2017, Appendix A), where its role is considered in guaranteeing the validity of their measure of corruption.

  5. The Poisson estimator is applied frequently in the international trade literature to datasets with structures similar to ours (following Silva and Tenreyro 2006). The known presence of convergence problems led us to use the Windmeijer and Silva (1997) version of the estimator—as implemented in the PPML routine in Stata. The occurrence of zeros in the dependent variable might have suggested the use of a zero-inflated formulation of the Poisson model. However, the determination of the presence of corrupt exchanges between two countries, vis a vis their intensity, do not seem to be two logically distinct problems, as is somehow implied when estimating such an empirical model. The results might suffer from various forms of endogeneity, which is notoriously difficult to treat because of the dubious validity—or strength-of a rather long list of candidate instruments that have been proposed. See Treisman (2007) for IV results using perception-based (and also experience-based) measures, and for some comments on the broader issue of finding suitable instruments.

  6. The determinants of the enforcement of the Convention (or of the FCPA) are not directly relevant and beyond the scope of our study. See for instance, Kaczmarek and Newman (2011) on how FCPA prosecution of non-US corporations might have pushed foreign countries to comply better with the Convention, and Brewster (2017) on how US compliance with the FCPA improved following the adoption of the Convention. Choi and Davis (2014), on the other hand, present an analysis that is conditional on FCPA enforcement, focusing on the level of sanctions.

  7. Several factors influencing the way in which corruption cases are judged, together with the criterion of presumption of innocence, likely lead to many false negatives, thus providing a further justification for considering cases regardless of their outcomes (that is, including acquittals).

  8. We cannot rule out the possibility that countries co-ordinate to carry out such industry-, or country-, “sweeps”, but we are not aware of any evidence pointing to the presence such complex form of international coordinated action. We are grateful to Matthew Stephenson for pointing out this and other possible departures from the equal treatment assumption.

  9. For several of the variables, we observe large partial correlation, which might lead to multicollinearity. In interpreting the signs of the correlations between variables, attention should be paid to how they are defined (see the Data Appendix). For example, for Democracy, higher values correspond to “more”, whereas for Freedom of the press the opposite holds.

  10. Treisman (2007) also considers a series of control variables representing historical characteristics of countries, such as their legal origin or colonial past. He finds that entering them does not influence the qualitative results for the other variable of interests. Also, the abundance of fixed effects in our model creates problems in identifying too many time-invariant variables – an issue that is familiar in the international trade literature.

  11. Our results do not suffer from the sample bias suggested in Knack and Azfar (2003), since the availability of the dependent variable is not conditional on levels of corruption.

  12. Giana Mildred, Santos Lim and Lorenzo Crippa contributed to different updates of the dataset.

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Acknowledgements

The authors are grateful for the useful comments received from  Oguzhan Dincer and Michael Johnston; Silvia Bertarelli, Antonio Musolesi, Paolo Pini and Matthew Stephenson, and participants to the 2nd Workshop on Corruption (Institute for Corruption Studies), Chicago, May 2019. L. Escresa acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 754340.

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  1. Laarni Escresa and Lucio Picci have contributed to the research study and agreed to submit the manuscript, in its present form, to Public Choice.

Authors and Affiliations

  1. Freiburg Institute for Advanced Studies, University of Freiburg, Freiburg im Breisgau, Germany

    Laarni Escresa

  2. School of Economics, University of the Philippines, Quezon City, Philippines

    Laarni Escresa

  3. Department of Economics, University of Bologna, Strada Maggiore 45, 40125, Bologna, Italy

    Lucio Picci

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  1. Laarni Escresa
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Appendices

Appendix 1

1.1 Data sources, availability and description

Corruption data Version of the dataset used: 3 May 2019. Collection of reported cases of cross-border corruption first used in Escresa and Picci (2017).Footnote 12Sources: Trace International Compendium (http://www.traceinternational.org/compendium), several US DOJ and SEC documents, OECD (various years), and other databases and publications, such as Shearman and Sterling 2013 (https://fcpa.shearman.com), Transparency International 2009 and 2013, Cheung et al. (2012), and Choi and Davis (2014). We cross-checked information also using other news sources, among them the Wall Street Journal Risk and Compliance Journal (http://www.wsj.com/news/risk-compliance-journal), and also corruption blogs such as the “FCPA Blog” (http://www.fcpablog.com). Cases reported in multiple sources were laboriously consolidated to avoid double counting. The reference period for each case is the year when the bribe was allegedly paid, but in some instances this date had to be presumed from the available data. The term public official is used in a broad sense, encompassing both bureaucrats and politicians. Cases where corruption occurs in more than one country are recorded as separate. On the other hand, if more than one bribe is allegedly paid by a firm in a single country within the same occurrence of corruption, only one case is recorded. Cases where the briber is a person (not acting on behalf of a firm) are excluded, as are all the cases pertaining to the Iraq’s “Oil for Food” affair, because of their peculiar characteristics. In the occurrences where more than one jurisdiction took action on a given case, an accurate reading of the available evidence allowed to single out the jurisdiction where the case was first enforced, that is, where it first emerged.

Colonial link Indicates whether two given countries have ever been a colony of the other in modern times. Source: Head et al. (2010).

Contiguous A dummy variable indicating the presence of a common border between pairs of countries. Source: Mayer and Zignano (2011).

Democratic since 1950 Dummy variable that indicates whether a country has been an electoral democracy since 1950 based on the classification by Beck et al. (2001). Source: Treisman (2007).

Distance The distance between the capital cities of any two given countries. Source: Mayer and Zignago (2011).

District magnitude Measure of the magnitude of an electoral district using the average number of representatives elected from each electoral district. Source: Beck et al. (2001) as cited in Treisman (2007).

Exports Exports between any two given countries. Source: United Nations COMTRADE bilateral import/export data, as organized by the Center for International Data (Available at http://cid.econ.ucdavis.edu/Html/WTF_bilateral.html, last accessed on 22 May 2019).

FH press freedom Measure of press freedom based on an evaluation of the legal environment, political and economic factors that contribute towards media independence and access to news and information. Source: Freedom House.

Fiscal decentralization Indicators of fiscal decentralization as defined in Fisman and Gatti (2002) which is the share of subnational government spending from total spending of all levels of government. Source: Government Finance Statistics, International Monetary Fund as cited in Treisman (2007).

Fuel exports Share of fuel in exports for a given country. Source: Treisman (2007).

GDP per capita Year 1999. Measured in current international dollars, PPP Source: The World Bank.

Imports % GDP Share of imports out of GDP. Source: Treisman (2007).

Language proximity Data from the Ethnologe Project (http://www.ethnologue.com/), as collected and organized by James Fearon (2003). The similarity between two languages is based on the distance between “tree branches” (“for example […] Byelorussian, Russian and Ucrainian share their first three classifications as Indo-European, Slavic, East Branch languages”; Fearon 2003). Unlike in Fearon’s work, who obtains his measure by dividing the number of branches that are in common by the maximum number of branches that any language has (which is equal to 15), we divide it by the maximum number of branches within each couple of language, so as to take into account that the granularity of the branch definition may be not the same across languages”). See also Picci (2010), from which the previous description is taken.

Newspaper circulation The number of newspapers in circulation conditional on the level of democratic liberties for a given country. Source: Adsera et al. (2003) as cited in Treisman (2007).

Open list system Indicates whether a country has an open or a closed list system. Source: Beck et al. (2001) as cited in Treisman (2007).

Political rights Extent of political rights that exist for a given country or territory. Source: Freedom House as cited in Treisman (2007).

Population Population in a given country or territory. Source: IMF-World Economic Outlook October 2018.

Presidential dem Treisman’s (2007) measure of presidentialism following Beck’s (2001) classification and where countries with FH scores below 5.5 are assigned a value of 0. Source: Treisman (2007).

Pure plurality sys Indicates whether electoral rules in a given country is based on plurality where the most number of votes win (vs. majority rules). Source: Beck et al. (2001) as cited in Treisman (2007).

Religious attitude proximity Probability that a religious person in country i encounters another religious person in country j, regardless of their religious membership and affiliation: product of shares of religious persons with respect to the whole population. Source: Maoz and Henderson (2013).

Religious proximity Probability of a person in country i meeting another person in country j who belong to the same religion: products of shares of persons with the same religion with respect to the whole population. Source: Maoz and Henderson (2013).

SD inflation Measure of variability of inflation based on the annual variance of monthly inflation. Source: Braun and di Tella (2004), as cited in Treisman (2007).

Time required to open a firm Time required to complete the regulatory process of a starting a firm. Source: Djankov et al. (2002) as cited in Treisman (2007).

Women in government Share of women in parliament (lower legislature). Source: Inter Parliamentary Union, as cited in Treisman (2007).

Years opened to trade A variable that indicates the year in which a country opened itself to trade based on Sachs et al. (1995) classification. Source: Treisman (2007).

Appendix 2

See Fig. A1 and Tables A4, A5, A4-b and A5-b.

Fig. A1
figure 2

Estimated impact of GDP per capita, recursive coefficients, base model. Cases enforced first only in third country jurisdictions. Note: Point estimates (continuous line) of the coefficient on the per capita income in the base model, together with 95% confidence interval, as additional countries are added. The left-most estimate only includes Switzerland (representing 15.9% of cases), then the Switzerland and France together (representing 29.9% of observations, etc. Thick vertical line represents data coverage (97% of total number of observed occurrences) used for the results of Tables A4 and A5

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Table A4 Pooled model. Dependent variable: all cases enforced first in 3rd country jurisdictions
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Table A5 Pooled model. Dependent variable: all cases enforced first in 3rd country jurisdictions. With relational variables
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Table A4-b Pooled model. Dependent variable: all cases enforced non in the foreign country, excluding all cases first enforced in the US
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Table A5-b Pooled model. Dependent variable: all cases enforced non in the foreign country, excluding all cases first enforced in the US. With relational variables
Full size table

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Escresa, L., Picci, L. The determinants of cross-border corruption. Public Choice 184, 351–378 (2020). https://doi.org/10.1007/s11127-019-00764-7

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