We use data from over 20,000 firms in 60 countries to identify constraints on the growth offirms. We interpret managers´ answers to survey questions on the extent to which variousaspects of their external environment inhibit the performance of their firm as measuring theshadow cost of...

We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or...

We use data from over 20,000 firms in 60 countries to identify constraints on the growth of firms. We interpret managers' answers to survey questions on the extent to which various aspects of their external environment inhibit the performance of their firm as measuring the shadow cost of...

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of...

This paper uses the BEEPS firm-level data to study the process of convergence of transition countries with developed market economies. The primary focus of the study is on competition and market structure, finance and the structure of lending to firms, and how firms respond to the economic...

This paper uses a "local average treatment effect" (LATE) framework in an attempt to disentangle the separate effects of criminal and noncriminal gun prevalence on violence rates. We first show that a number of previous studies have failed to properly address the problems of endogeneity, proxy...