The effect of corruption on firm investment in the presence of missing data

There is a relatively large empirical literature on the effects of corruption on firms. However, while the firm data on corruption suffers from a considerable amount of nonresponse, there has been little attention to this aspect of the data and its consequences. This paper provides first-hand evidence on this issue. The first contribution of this study is to analyze the association between the likelihood of data being missing and observed data in the sample. It is found that nonresponse is strongly correlated with firms’ characteristics, which implies that removing missing data from the sample is not justifiable and leads to biased estimates. Then, the paper explores the effect of corruption on firm investment, using multiple imputation approach to account for missing data. The results suggest that there is a striking difference between the estimates of a model with and without missing data. While a naive model finds a statistically significant adverse relation between corruption and firms' investment, the estimates of the multiple imputation approach are both insignificant and smaller in magnitude.