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Does corruption promote emigration? An empirical examination


This paper investigates the effects of corruption on the emigration rate of low-, medium- and high-skilled individuals at the country level. Fixed-effects, system generalized method of moments (GMM) and instrumental variable estimations are used to establish a causal relationship between emigration and corruption. The empirical results indicate that as corruption increases, the emigration rate of high-skilled migrants also increases. The emigration rate of individuals with low and medium levels of educational attainment, however, increases at low levels of corruption and then decreases beyond a threshold of 3.4–4.0, where corruption is measured on a scale of 0 (not corrupt) to 10 (totally corrupt). Splitting the sample by income inequality suggests that increased inequality reduces the ability for medium- and low-skilled migrants to emigrate. Therefore, government action should focus on controlling corruption in order to prevent a brain drain.

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  1. See de Haas (2007) for a survey of the literature.

  2. Lederman et al. (2005), Mendez and Sepulveda (2006), Aidt (2003), Dreher et al. (2008) and Meon and Weill (2010) investigate the relationship between institutions and corruption.

  3. See Massey et al. (1993) for a survey of the theories on labour migration.

  4. A similar argument can be found in Dimant et al. (2013a).

  5. Note: This is if low-skilled migrants are also low-wage migrants.

  6. See Mckenzie and Rapoport (2007) for non-linear effects between wealth and emigration.

  7. The studies of Aidt (2003) and Mendez and Sepulveda (2006), find a non-linear relationship between corruption and growth. Mendez and Sepulveda (2006) find evidence in favour of a non-linear relationship between corruption and growth in countries that are politically free as opposed to those which are not. A growth maximizing level of corruption is observed for countries that are politically free. Aidt (2003), also investigating for threshold effects between corruption and growth, observes that corruption has a significant negative impact on growth in countries with well-developed institutions and no effect on growth in countries with weak institutions. It is therefore not unreasonable to expect corruption to have non-linear effects on emigration with emigration initially increasing at low levels of corruption and then declining as corruption increases.

  8. This dataset covers 20 OECD member states on the immigrant population aged 25 years and older by gender, educational level and country of birth from 1980 to 2010 (5-year intervals). Data are available on the stock of immigrants coming from 195 countries. See Brücker et al. (2013) for greater detail.

  9. The Kaufmann et al. (1999) index updated in 2013 from the “Governance Matters” project incorporates six indicators of governance which include Voice and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law and Control of Corruption.

  10. The Kaiser (1960) rule was used to determine the number of components. Only one factor was kept as only one had eigenvalues at least equal to one.

  11. Freeman and Oostendorp (2000) use the October Inquiry Survey of wages conducted by the International Labour Organization to construct wage earnings for over 150 countries in 161 occupations.

  12. The Henley and Partners Visa Restrictions Index produced in corporation with the International Air Transport Association (IATA) is a global ranking of countries according to travel freedom that their citizens enjoy. Henley and Partners (2014) investigate visa regulations of all countries and territories across the world and rank countries according to the freedom with which its citizens can travel to other countries without obtaining a visa.

  13. The R 2 terms in the quadratic models indicated that the explanatory power of the model is increased when the quadratic term was incorporated into the models. An F test further rejected the hypothesis that the regression was linear at the 1 and 5 % significance levels against the alternative that it was quadratic.

  14. The results indicate that corruption explains between 10 to 12 % of the variance in the dependent variables.

  15. The conclusions do not change when the Polity Index is used.

  16. The results are available upon request.


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We wish to thank the editor of the journal, Klaus Zimmermann, and four anonymous referees for valuable comments; Karin Hosking for editing the paper and Remco Oostendorp for answering queries regarding the OWW database.

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Correspondence to Arusha Cooray.

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Responsible editor: Klaus F. Zimmermann



Countries of origin used in study

Afghanistan, Albania, Algeria, Argentina, Armenia, Azerbaijan, Bangladesh, Belarus, Benin, Bhutan, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cambodia, Cameroon, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo Dem Rep, Congo Rep, Costa Rica, Cote d’Ivoire, Cuba, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Fiji, Gabon, Gambia, Georgia, Ghana, Guatemala, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Iran, Iraq, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Macedonia, Madagascar, Malawi, Malaysia, Maldives, Mauritania, Mauritius, Mexico, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Russia, Rwanda, Samoa, Senegal, Serbia, Sierra Leone, Solomon Islands, Somalia, South Africa, Sri Lanka, Sudan, Suriname, Swaziland, Syria, Tajikistan, Turkey, Turkmenistan, Tuvalu, Uganda, Ukraine, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe

Table 5 Summary statistics and data sources
Table 6 IV estimation first stage regressions. Dependent variable: corruption

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Cooray, A., Schneider, F. Does corruption promote emigration? An empirical examination. J Popul Econ 29, 293–310 (2016).

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  • Corruption
  • Emigration
  • Educational attainment

JEL Classifications

  • 017
  • 05
  • D78
  • H2
  • H11
  • H26