Endogeneity and panel data in growth regressions: A Bayesian model averaging approach

• We develop a new Bayesian model averaging technique for panel data regression with fixed effects.
• We allow for uncertainty regarding the set of instruments, exogeneity restrictions, and the set of explanatory variables.
• We find no evidence that foreign aid has an impact on the growth rate of recipient countries.
• We find a high probability that macroeconomic policies have an impact on the GDP growth rates of developing countries.

Bayesian model averaging (BMA) has been successfully applied in the empirical growth literature as a way to overcome the sensitivity of results to different model specifications. In this paper, we develop a BMA technique to analyze panel data models with fixed effects that differ in the set of instruments, exogeneity restrictions, or the set of explanatory variables in the regression. The large model space that typically arises can be effectively analyzed using a Markov Chain Monte Carlo algorithm. We apply our technique to investigate the effect of foreign aid on per capita GDP growth. We show that BMA is an effective tool for the analysis of panel data growth regressions in cases where the number of models is large and results are sensitive to model assumptions.