This study revisits the sources of corruption using panel data for 146 countries and contributes to the literature by analyzing the relationship between remittances and corruption with a particular focus on the analysis of the distribution of the dependent variable (corruption). In cross sectional and panel settings the author fids that a one standard deviation increase in the remittances variable is associated with an increase in corruption of 0.33 points, or 25 percent of a standard deviation in the corruption index. The author also investigates whether greater remittances consistently increase corruption among the most and least corrupt countries. Our results show that among the least corrupt countries, remittances do not appear to increase corruption but signifiantly promote corruption among most corrupt countries. Our fidings are robust for diffrent sample specifiations, for regional effcts and for alternative econometrics techniques.
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This study revisits the sources of corruption using panel data for 146 countries and contributes to the literature by analyzing the relationship between remittances and corruption with a particular focus on the analysis of the distribution of the dependent variable (corruption). In cross sectional and panel settings the author fids that a one standard deviation increase in the remittances variable is associated with an increase in corruption of 0.33 points, or 25 percent of a standard deviation in the corruption index. The author also investigates whether greater remittances consistently increase corruption among the most and least corrupt countries. Our results show that among the least corrupt countries, remittances do not appear to increase corruption but signifiantly promote corruption among most corrupt countries. Our fidings are robust for diffrent sample specifiations, for regional effcts and for alternative econometrics techniques.
