Working Papers
Model averaging and double machine learning
With Christian B Hansen, Mark E Schaffer, Thomas Wiemann (Journal of Applied Econometrics, R&R)
Abstract: 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 substantially reduce the computational burden and pooled stacking enforces common stacking weights over cross-fitting folds. Using calibrated simulation studies and two applications estimating gender gaps in citations and wages, we show that DDML with stacking is more robust to partially unknown functional forms than common alternative approaches based on single pre-selected learners. We provide Stata and R software implementing our proposals.
Optimal treatment allocation using policy trees: An application to immigrant naturalization
With Alessandra Stampi-Bombelli, Dominik Hangartner, Selina Kurer (Journal of Applied Econometrics, Conditionally accepted)
Abstract: Research underscores the role of naturalization in enhancing immigrants' socio-economic integration, yet application rates remain low. We estimate a policy rule for a letter-based information campaign encouraging newly eligible immigrants in Zurich, Switzerland, to naturalize. The policy rule assigns one out of three treatment letters to each individual, based on their observed characteristics. We field the policy rule to one-half of 1,717 immigrants, while sending random treatment letters to the other half. Despite only moderate treatment effect heterogeneity, the policy tree yields a larger, albeit insignificant, increase in application rates compared to assigning the same letter to everyone.
The Labor Market Effects of Restricting Refugees’ Employment Opportunities
With Andreas Beerli, Selina Kurer, Dominik Hangartner, Michael Siegenthaler (American Economic Review, R&R)
Abstract: This paper investigates whether employment restrictions contribute to refugees having poorer labor market outcomes than citizens. Utilizing linked register data from Switzerland and within-canton policy variation between 1999-2015, we find substantial negative effects on employment and earnings when refugees are barred from working upon arrival, restricted from specific sectors or regions, or face resident prioritization. Removing 10% of refugees' outside options reduces job-to-job mobility by 7.5% and wages by 3.0%, widening the wage gap to citizens in similar jobs. The restrictions depress refugees' labor market outcomes even after they apply, but do not spur emigration nor benefit other immigrants.
Welfare Benefits and Refugee Crime
With Daniel Auer, Michaela Slotwinski, Dominik Hangartner, Selina Kurer, Stefanie Kurt, Alois Stutzer (Submitted)
Abstract: Despite intense policy debates, the relationship between social welfare and refugee crime remains understudied. Taking steps to address this gap, our study focuses on Switzerland, where mobility restrictions on exogenously assigned refugees coincide with cantons' autonomy in setting social assistance rates. Linking time-varying cantonal benefit rates between 2009 and 2016 to individual-level administrative data, we find that higher social assistance reduces criminal charges, especially for petty crimes and drug offenses. In light of limited (short-run) repercussions for refugees' labor market participation, our results suggest social assistance can be a cost-effective measure to improve refugee welfare and enhance public safety.
Ongoing
Path2Work: Targeted support of job search for refugees using an online job platform
With Mirjam Bächli, Dominik Hangartner, Rafael Lalive
Incentive or Impediment? The Short- and Long-Term Impact of Low Welfare Support on Refugee Integration
With Dominik Hangartner, Selina Kurer, Michael Siegenthaler
Peer-reviewed Publications
Ahrens, A., Casalis, M., Hangartner, D., Sánchez, R., 2024. Cash-Based Interventions Improve Multidimensional Integration Outcomes of Venezuelan Immigrants. World Development, 181 106658. ↪Link
Ahrens, A., Hansen, C.B., Schaffer, M.E., Wiemann, T., 2024. ddml: Double/debiased machine learning in Stata. Stata Journal, 24(1). ↪Link
Ahrens, A., Hansen, C.B., Schaffer, M.E., 2023. pystacked: Stacking generalization and machine learning in Stata. Stata Journal, 23(4), pp.909-931. ↪Link
Ahrens, A. and Lyons, S., 2021. Do rising rents lead to longer commutes? A gravity model of commuting flows in Ireland. Urban Studies, 58(2), pp.264-279. ↪Link
O’Toole, C., Martinez-Cillero, M. and Ahrens, A., 2021. Price regulation, inflation, and nominal rigidity in housing rents. Journal of Housing Economics, 52, p.101769. ↪Link
Ahrens, A., Hansen, C.B. and Schaffer, M.E., 2020. lassopack: Model selection and prediction with regularized regression in Stata. The Stata Journal, 20(1), pp.176-235. ↪Link ↪arXiv
Ahrens, A., FitzGerald, J. and Lyons, S., 2020. Commuting across the Irish border. Economics Letters, 190, p.109060. ↪Link
Ahrens, A. and Bhattacharjee, A., 2015. Two-step lasso estimation of the spatial weights matrix. Econometrics, 3(1), pp.128-155. ↪Link
Ahrens, A., Kovandzic, T.V. and Vieraitis, L.M., 2015. Do execution moratoriums increase homicide? Re-examining evidence from Illinois. Applied economics, 47(31), pp.3243-3257. ↪Link
For a full list, see ↪Google Scholar