2019/09/18 by Konstantin Görgen, Görgen, Konstantin, Melanie Schienle +1
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Applications (stat.AP) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (stat.ML) #Migration and Labor Dynamics #Spatial and Panel Data Analysis #econ.EM #stat.AP #stat.ML
paper · pdf · doi:10.48550/arxiv.1909.08299
openalex publication_date 2019/09/18 · arxiv created 2021/01/04 · arxiv updated 2021/01/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We use official data for all 16 federal German states to study the causal effect of a flat 1000 Euro state-dependent university tuition fee on the enrollment behavior of students during the years 2006-2014. In particular, we show how the variation in the introduction scheme across states and times can be exploited to identify the federal average causal effect of tuition fees by controlling for a large amount of potentially influencing attributes for state heterogeneity. We suggest a stability post-double selection methodology to robustly determine the causal effect across types in the transparently modeled unknown response components. The proposed stability resampling scheme in the two LASSO selection steps efficiently mitigates the risk of model underspecification and thus biased effects when the tuition fee policy decision also depends on relevant variables for the state enrollment rates. Correct inference for the full cross-section state population in the sample requires adequate design -- rather than sampling-based standard errors. With the data-driven model selection and explicit control for spatial cross-effects we detect that tuition fees induce substantial migration effects where the mobility occurs both from fee but also from non-fee states suggesting also a general movement for quality. Overall, we find a significant negative impact of up to 4.5 percentage points of fees on student enrollment. This is in contrast to plain one-step LASSO or previous empirical studies with full fixed effects linear panel regressions which generally underestimate the size and get an only insignificant effect.