This paper analyses the role of job changes in overcoming work hour mismatches (i.e., differences between actual and desired work hours). It addresses two, yet neglected, questions: (1) How do adjustments in desired work hours, additionally to adjustments in actual work hours, contribute to the...

This paper analyses the role of job changes in overcoming work hour constraints and the work hour mismatches resulting from these constraints (i.e., differences between actual and desired work hours). Building on previous findings that job change increases the flexibility of actual work hours,...

This paper analyses the role of job changes in overcoming work hour constraints and the work hour mismatches resulting from these constraints (i.e., differences between actual and desired work hours). Building on previous findings that job change increases the flexibility of actual work hours,...

This study investigates the dose-response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study proposes solutions to two highly practically...

This paper consolidates recent methodological developments based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction methods to control for confounding in the estimation of (i) standard average effects, (ii)...

We systematically investigate the effect heterogeneity of job search programmes for unemployed workers. To investigate possibly heterogeneous employment effects, we combine non-experimental causal empirical models with Lasso-type estimators. The empirical analyses are based on rich...

We systematically investigate the effect heterogeneity of job search programmes for unemployed workers. To investigate possibly heterogeneous employment effects, we combine non-experimental causal empirical models with Lasso-type estimators. The empirical analyses are based on rich...

This study investigates the dose-response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study proposes solutions to two highly practically...

We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data. We consider 24...