about three and a half times larger than the push factor.<sup>14</sup> Table A2 estimates a specification using the bilateral difference (between _o_ and _d_ ) in corruption levels. The coefficients show a positive relationship between the gap in corruption and the bilateral flow that occurs from _o_ to _d_ .

As omitted variable bias may be a cause for concern, we include additional factors that might have a simultaneous effect on both corruption levels and migration flows. These include: GDP per capita, inflation rates, the importance of agriculture and services in the economy, and also whether citizens of country _o_ have the right to work in country _d_ . Reassuringly, the sign and the significance of the estimated coefficients on the four corruption indicators remain unchanged. However, as shown in Table 4, both push and pull factors have smaller magnitudes following the inclusion of these additional controls: the former is estimated to be between 3% and 24%, while the latter is between 8% and 13%.<sup>15</sup> Averaging the results across the four columns: a 1-unit increase in the corruption level in the origin country is associated with an 11% increase in out-migration. The same 1-unit increase in the destination country is associated with a 10% decline in in-migration.<sup>16</sup> Lastly, to control for differences in legal frameworks and regulations between countries, we include indicators on the employment legislation, product market regulation, and social expenditure. Results are presented in Table 5. All the estimates on the corruption indicators remain broadly unchanged: an increase in the corruption level in the origin country _o_ (resp., the destination country _d_ ) is associated with a rise (resp., fall) in migration flows of between 18% and 33% (resp., 27% and 53%).

# **4 Conclusions**

Policies to shape migration flows have become more important in a growing number of countries, especially those facing a brain drain. As such, policy makers have been looking at ways to encourage native citizens to stay, while attracting foreign workers. By looking

> interactions between the gravity control variables and the corruption indicators, in Table A3 we estimate a specification that explicitly includes the interaction between each control variable and the bilateral difference in the corruption indicator.

> 14Table A1 further includes country fixed effects in the specifications of Eq. 1 and Eq. 2. Results remain broadly unchanged.

> 15Legal barriers to mobility also matter substantially in the migration decision: removing all work restrictions between two countries is associated with a 172% increase in bilateral migration flows. As the dependent variables are log-transformed, to obtain the exact impact on the right to work variable, the coefficient _β_ must also be transformed according to the formula: _e_<sup>_β_</sup> _−_ 1. Taking the average value (0.28) across columns (1) to (4), the coefficient is 1.72.

> 16These results are in line with the existing literature. For example, Poprawe (2015) finds that a 1-unit increase in the corruption level is associated with a 22% increase in out-migration and a 14% decline in in-migration.

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