Corruption and Growth Under Weak Identification

The goal of this paper is to revisit the influential work of Mauro [1995] focusing on the strength of his results under weak identification. He finds a negative impact of corruption on investment and economic growth that appears to be robust to endogeneity when using two-stage least squares (2SLS). Since the inception of Mauro [1995], much literature has focused on 2SLS methods revealing the dangers of estimation and thus inference under weak identification. We reproduce the original results of Mauro [1995] with a high level of confidence and show that the instrument used in the original work is in fact 'weak' as defined by Staiger and Stock [1997]. Thus we update the analysis using a test statistic robust to weak instruments. Our results suggest that under Mauro's original model there is a high probability that the parameters of interest are locally almost unidentified in multivariate specifications. To address this problem, we also investigate other instruments commonly used in the corruption literature and obtain similar results.

Technology Creation, Diffusion, and Growth Cycles

Standard macroeconomic models that assume an exogenous stochastic process for multifactor productivity offer the interpretation that recessions are the result of ''bad news'' (technological regress) and expansions are the result of ''good news'' (technological advancement). The view taken here is that both expansions and recessions are the result of ''good news'' in the sense that in both cases, aggregate production possibilities have increased. Recessions can be thought of as the transition from one technological frontier to the next.