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Public Choice

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01.06.2015

On the relationship between corruption and migration: empirical evidence from a gravity model of migration

verfasst von: Marie Poprawe

Erschienen in: Public Choice | Ausgabe 3-4/2015

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Abstract

This paper shows the relationship between corruption and migration. In particular, countries with much corruption are shown to encourage emigration and discourage immigration because they provide worse and unpredictable economic conditions, more insecurity, and a lower quality of life. This hypothesis is confirmed empirically with a cross-sectional dataset with bilateral migration data covering 230 countries. Well-known implications of the gravity model are confirmed here: larger populations, a common language and a common border increase migration, while distance between two countries decreases migration. Furthermore, education, GDP per capita, inflation in the destination country, as well as corruption and education in the origin country can robustly explain migration. Corruption thus appears to be a push factor of migration.

Vorheriger Artikel Does immigration impact institutions?

Nächster Artikel Does social distrust always lead to a stronger support for government intervention?

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See Tiebout (1956) and Tullock (1971) for early discussions. See also Cebula and Zafoglis (1986), Cebula et al. (2014), and Goodspeed (1998) for applications of the Tiebout–Tullock hypothesis.

Alternatively one could use a utility function containing consumption in \(t\) and \(t+1\). The conclusion drawn from this would be the same, i.e., that indirect utility is an increasing function of income.

The maximum value reached is 3,789,377, a lot further from the mean than two standard deviations. The observation responsible for this (the country-pair USA—Mexico) is clearly an outlier and is therefore removed from now on.

This type of migration occurs between wealthy countries; for example, between EU countries and the US, or within the EU.

The latter two being sub-indices of the overall economic freedom index.

It is beyond the scope of this paper to study the spatial patterns of migration and corruption and thus the paper abstracts from possible spillover effects as described by Becker et al. (2009). However, if one were to take into account the spatial aspect of migration decisions, the estimated coefficient of Corruption (i) is likely a lower bound.

For some control variables for the origin country the estimated coefficient sometimes contradicts the hypothesized sign (for example, GDP(i))—this effect can be attributed to large volumes of North-North migration in comparison to total migrant flows. In regressions excluding North–North country-pairs, the coefficients are negative.

The effect of education in the origin country is positive and statistically significant in this specification, but negative in column (4). Its overall effect is ambiguous.

Zero divides the area under the density in two. CDF(0) from now on refers to the larger of the two areas under the density function, either above or below zero.

The varying number of observations in the regressions in consequence of missing observations in some of the variables implies that using weights proportional to the integrated likelihood as a goodness-of-fit measure may not be a good indicator of the probability that a model is the true model, as explained in Sturm and de Haan (2005). The reason for using the criterion that CDF(0)\(>0.95\) is that CDF(0) will always be between 0.5 and 1; and using CDF(0)\(>0.95\) as Sala-i-Martin (1997) therefore seems too low.

With the exception of % tertiary education (i) - the sign of the coefficient can again be attributed to large North-North migration.

Its coverage in terms of countries is slightly larger as it relies heavily on the data published by Transparency International but fills any gaps with additional data.

It relies on 31 individual data sources and measures corruption in the public as well as private sector.

To facilitate comparability the indicators have been rescaled so that they range from 0 to 10, with higher scores signifying lower corruption.

Although especially for receiving countries what matters to immigrants is the perceived level of corruption, about which immigrants are likely to hear.

These data are converted to a scale between 0 and 10 and reversed so that high values imply low levels of experienced corruption. Because a worldwide index of actual corruption has become available only recently, Column (5) shows a regression for the year 2010. The World Bank bilateral migration database provides data at 10-year intervals, the next available year being 2010. All control variables were collected for the year 2010 also, and all other data sources are the same as for the year 2000.

The 38 countries are: Austria, Australia, Belgium, Bulgaria, Canada, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Mexico, New Zealand, Norway, Poland, Portugal, Romania, Slovak Republic, Slovenia, Korea, Spain, Sweden, Switzerland, The Netherlands, Turkey, United Kingdom and the United States. Years range from 1993 to 2008.

The datacode in Eurostat is tsdde230.

These differences may be because of an extension of the time period covered, and because of a different sample.

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Titel: On the relationship between corruption and migration: empirical evidence from a gravity model of migration
verfasst von: Marie Poprawe
Publikationsdatum: 01.06.2015
Verlag: Springer US
Erschienen in: Public Choice / Ausgabe 3-4/2015
Print ISSN: 0048-5829
Elektronische ISSN: 1573-7101
DOI: https://doi.org/10.1007/s11127-015-0255-x

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