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 geographic assignment in Switzerland, within-canton variation in labor market restrictions, and linked register data 1999-2016. We document large negative employment and earnings effects of banning refugees from working in the first months after arrival, from working in certain sectors and regions, and from prioritizing residents over refugees. Consistent with an effect of outside options on wages, removing 10% of jobs reduces refugees' hourly wages by 2.8% and increases the wage gap to similar host-country citizens in similar jobs by 2.2%. Furthermore, we show that restrictions reduce refugees' earnings even after they cease applying. Restrictions do not spur refugee emigration nor improve earnings of non-refugee immigrants.

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 many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using DDML in combination with stacking estimation which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation.

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 predictors p may be large and possibly greater than the number of observations, n. We offer three different approaches for selecting the penalization ('tuning') parameters: information criteria (implemented in lasso2), K-fold cross-validation and h-step ahead rolling cross-validation for cross-section, panel and time-series data (cvlasso), and theory-driven ('rigorous') penalization for the lasso and square-root lasso for cross-section and panel data (rlasso). We discuss the theoretical framework and practical considerations for each approach. We also present Monte Carlo results to compare the performance of the penalization approaches.

In this paper, we explore the impact of a housing rent inflation cap of 4 per cent on price changes for tenancy contracts. We assess the implications of the regulations on the share of the market experiencing: 1) a price decline; 2) unchanged rents (nominal rigidity); 3) a positive growth rate below the cap; 4) the maximum allowable growth; and 5) growth above the cap. Our identification strategy uses a multinomial logit difference-in-difference approach applied to a novel micro panel dataset at the property level in Ireland. We find the overall inflation rate fell by 3 percentage points following the regulations, driven by a reduction in the share of individual contracts pricing above the regulatory maximum. We find an increase in the likelihood of nominal rigidity at the expense of high-growth rates. However, we also find a higher probability of small increases, at or below the regulatory level, relative to nominal rigidity after the regulations which is consistent with landlords trying to maintain real returns as price resets are not allowed between tenancies. Heterogeneous effects by landlord type and starting rent levels are evident.

This paper examines the manner in which owner-occupiers housing costs are incorporated in the official inflation index. In particular, the focus is on the net acquisitions and the payments approach, which are currently used by the Central Statistics Office (CSO). The paper provides a detailed overview of the two approaches, along with some suggestion for further refinement.