Refugees, and immigrants more generally, often do not have access to all jobs in the labor market. We argue that restrictions on employment opportunities help explain why immigrants have lower employment and wages than native citizens. To test this hypothesis, we leverage refugees' exogenous...

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...

This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. We introduce two new stacking approaches for DDML: short-stacking exploits the cross-fitting step of DDML to...

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...

We review and interpret research on the economic and political effects of receiving asylum seekers and refugees in developed countries, with a particular focus on the 2015 European refugee protection crisis and its aftermath. In the first part of the paper, we examine the consequences of...

We estimate intergenerational mobility of immigrants and their children in fifteen receiving countries. We document large income gaps for first-generation immigrants that diminish in the second generation. Around half of the second-generation gap can be explained by differences in parental...