The study used panel data from 1998 to 2014 among 48 different countries to determine the relationship between foreign direct investment and corruption. For identifying the relationship, the study employed random effect model (REM), feasible general least squares method (FGLS) and panels corrected standard errors (PCSE). The results of the three panel estimation methods reveal that the variable of corruption is statistically significant at 1%, but negative relations between corruption and FDI results were determine by using REM, FGLS and PCSE estimation methods in three different regions (South and South-East Asia, Latin America, the Caribbean and Africa). It interprets that 1% decrease in the level of corruption may leads to about 8.15, 9.25 and 11.5% increase in FDI inflows by using REM, FGLS and PCSE respectively. Other control variables like gross domestic product per capital (GDPPC), gross domestic product growth rate (GDPG), population growth rate (POPG), urban population growth rate (UPOPG), trade openness, tele-density, gross school enrolment in primary (GSEP), agglomeration, bureaucracy (BURA), law and democracy are positively statistically significant as expected and risk and inflation are negatively statistically significant.
Key words: Random effect model, feasible general least squares, panel corrected standard errors, FDI, corruption.
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