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Geographic Clustering of Corruption in the United States

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Abstract

We test the hypothesis that US corporations headquartered in states with greater public corruption are also prone to more unethical behavior when operating abroad. We exploit passage of Foreign Corrupt Practices Act (FCPA) that curtailed bribery of foreign officials and find firms in corrupt states, especially those exporting to more corrupt countries, suffer greater performance decline following FCPA, suggesting larger loss from anticipated bribery restrictions. Controlling for industry, firms in corrupt states are more likely to be targets of FCPA enforcement actions. They are also more likely to have paid foreign bribes, as disclosed during pre-FCPA investigations.

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Notes

  1. http://cpi.transparency.org/cpi2012/results/.

  2. Several recent papers have utilized this epidemiological approach to study unethical behavior, ranging from illegal parking by U.N. diplomats (Fisman and Miguel 2007) to corporate tax evasion (DeBacker et al. 2015) and accounting fraud (Liu 2016). In a similar vein, several studies have used measures of local geographic culture, such as religiosity, around a firm’s headquarters to explain corporate behavior (e.g., Hilary and Hui 2009; Grullon et al. 2010; McGuire et al. 2012; and Parsons et al. 2018).

  3. The evidence suggests that some US corporations suffered economic costs due to FCPA, though the legal actions and penalties appear to have been scarce (Salbu 2000). It is possible that compliance with the FCPA was driven by corporate concern about loss in firm reputation if there was a bribery charge, rather than the perceived likelihood of a significant fine. In addition to restrictions on bribery, the FCPA imposes costs by requiring corporations to adopt a system of internal accounting controls and maintaining accurate records of foreign transactions.

  4. Our results are consistent with those reported by Zeume (2017), where the passage of the UK Bribery Act in 2010 adversely affected the value for UK firms operating in high-corruption regions of the world. An important difference is that while Zeume (2017) studies the effect of foreign corruption, we examine whether local public corruption is symptomatic of a local corrupt social norm that induces corrupt behavior abroad by private individuals, such as firms’ managers.

  5. See, e.g., Stulz and Williamson (2003), Chui et al. (2010), Li et al. (2011, 2013), Eun et al. (2015), Pevzner et al. (2015), Griffin et al. (2017), among others. See Aggarwal et al. (2016) and Karolyi (2016) for surveys on the literature.

  6. https://www.justice.gov/sites/default/files/criminal-fraud/legacy/2010/04/11/houseprt-95-640.pdf.

  7. https://www.gpo.gov/fdsys/pkg/STATUTE-91/pdf/STATUTE-91-Pg1494.pdf.

  8. Although we may misspecify the headquarter location of some firms in 1977, we note that firm relocation is a rare event. Pirinsky and Wang (2006) find only 118 incidences of headquarter relocation in a sample of more than 5000 firms over 15 years.

  9. See Svensson (2005) for a description of various measures of corruption that have been used in the literature.

  10. We use the corruption data up to 2011 to capture the long-term persistent component of state corruption. Our empirical results are robust if we measure average state corruption based on only the first two years (1976 to 1977) or five years (1976 to 1980) of conviction data.

  11. In the Internet Appendix we also show that states with top-tercile level of public corruption are located in eight out of nine Census divisions in the USA. Thus, state public corruption does not appear geographically concentrated.

  12. For example, Bloom et al. (2012) compare US-based multinational corporations and domestic firms in Europe, and show that the former type of firms exhibits higher productivity due to better management practices originating in the USA. Dougal et al. (2018) also argue that value creation are attributable to upper management by showing that headquarter city fixed effects can explain a large proportion of variation in firm values.

  13. In unreported tests, we find that our main empirical results are robust to excluding New York and California from the sample.

  14. The results are robust if we measure average state corruption based on only the first 2 years (1976 to 1977) or five years (1976 to 1980) of conviction data.

  15. As Pirinsky and Wang (2006) show, relocation of corporate headquarter is a rare event. Any omitted relocation during the sample period would induce noise in our estimate and create bias against our proposed hypotheses.

  16. For example, see Yermack (1996), Morck et al. (1988), McConnell and Servaes (1990), Lang and Stulz (1994), Gompers et al. (2003), Cremers et al. (2017).

  17. One possible confounding event that might have driven the observed changes in firm value is the presidential election in 1976. States that voted for (against) Jimmy Carter might have been economically better (worse) off due to the election outcome. In an unreported test, we check the robustness of our result to the inclusion of an interaction term between Post FCPA and a binary variable indicating states that had voted for Jimmy Carter. Our result shows that the added interaction term is insignificant, while the diff-in-diff estimator for FCPA and state corruption remains significantly negative and similar in magnitude as in the baseline model.

  18. However, we do not argue that only firms that export are affected by FCPA. For example, firms that do not export products or services can still be constrained by FCPA if they have foreign subsidiaries where they directly produce and sell products in the foreign markets. Our underlying assumption is that, ceteris paribus, firms that export to corrupt countries are more subject to the legal ban on foreign bribery activities by FCPA.

  19. Data are available at http://faculty.som.yale.edu/peterschott/sub_international.htm. The underlying data are collected from the US Census Bureau. We thank Peter Schott for making the data available.

  20. We note that all the surveys were conducted after the passage of FCPA. Hence, our test relies on an assumption that the relative ranking of country-level corruption is stable over time. To ensure the validity of the assumption we perform the test using CPI index in two different years. We choose the surveys in 1996 and 2017 because the 1996 survey is closer to the event period, while the 2017 survey covers a wider range of countries (180). Since lower corruption score in LLSV and CPI indicates higher corruption, we take the negative value of the scores so that a higher score indicates higher corruption.

  21. The dummy variables for year 1981–83 and 1984–86 and constant across firms, and thus they are absorbed by year fixed effects and cannot be estimated explicitly.

  22. In an unreported test, we also show that the export to corrupt destination countries was significantly greater from states with higher levels of corruption. This is consistent with existing evidence that cultural proximity affects international trades (e.g., Guiso et al. 2009; Melitz 2008; Felbermayr and Toubal 2010).

  23. In Question 6 of the survey, the authors asked: “Suppose you were to rank all states in terms of level of corruption of their government employees (including elected officials, political appointees, and civil servants). Where would your state rank?”. 293 reporters from 47 states responded to this question. We use the ranking based on the responses to Question 6 to measure the level of corruption. There was no response for Massachusetts, New Hampshire, and New Jersey, and thus these three states are not included in Boylan and Long’s measure. The Spearman correlation between the convictions-based measure and the Boylon–Long measure is 0.44 and statistically significant.

  24. The survey results are available at: http://ethics.harvard.edu/blog/measuring-illegal-and-legal-corruption-american-states-some-results-safra.

  25. The most legally corrupt states include Kentucky, Illinois, Nevada, Mississippi, New Jersey, Alabama, New Mexico, New York, Georgia, Pennsylvania, and Wisconsin; The most illegally corrupt states include Arizona, California, Kentucky, Alabama, Illinois, New Jersey, Georgia, New Mexico, Pennsylvania, Florida, Indiana, Rhode Island, and Texas. Among these states, 11 out 17 have above-median level of corruption based on the number of convictions. Among states that are considered both legally and illegally corrupt, all are ranked above median in terms of the number of convictions.

  26. Note that these surveys were conducted after the sample period of our empirical test and hence they may not properly reflect the relative level of corruption during the period around FCPA. However, any measurement error should bias against finding results consistent with our hypothesis.

  27. The industry groups are Aerospace/Defense, Agriculture, Basic Materials, Communication Services, Conglomerates, Construction, Consumer Goods, Engineering, Financial, General Trading, Healthcare, Industrial Goods, Medical Equipment, Oil & Gas, Real Estate, Services, Technology, Telecommunications, Transportation, Utilities, and Others.

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Acknowledgements

We thank Ray Fisman, Mark Humphery-Jenner, Ivalina Kalcheva, Jayant Kale, Diana Knyazeva, Hamed Mahmudi, William Megginson, Frank Yu, seminar participants at Georgia Institute of Technology and University of Oklahoma, and conference participants at 2015 SFS Finance Cavalcade, 2015 Oxford IFABS Conference, 2015 Australasian Finance & Banking Conference, 2016 Midwest Finance Association Annual Meeting, and 2017 China International Conference in Finance. All remaining errors are our own.

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  1. Scheller College of Business, Georgia Institute of Technology, 800 West Peachtree Street NW, Atlanta, GA, 30308, USA

    Nishant Dass

  2. Naveen Jindal School of Management, University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080, USA

    Vikram Nanda & Steven Chong Xiao

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Dass, N., Nanda, V. & Xiao, S.C. Geographic Clustering of Corruption in the United States. J Bus Ethics 173, 577–597 (2021). https://doi.org/10.1007/s10551-020-04513-5

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