Across many disciplines, the fixed effects estimator of linear panel data models is the default method to estimate causal effects with nonexperimental data that are not confounded by time-invariant, unit-specific heterogeneity. One feature of the fixed effects estimator, however, is often...

This paper proposes a new strategy to identify causal effects. Instead of finding a conventional instrumental variable correlated with the treatment but not with the confounding effects, we propose an approach which employs an instrument correlated with the confounders, but which itself is not...

We place young professionals into established firms to shadow middle managers. Using random assignment into program participation, we find positive average effects on wage employment, but no average effect on the likelihood of self-employment. We match individuals to firms using a...

We estimate the causal effect of parents' unemployment on unemployment among their children in their own adulthood. We use administrative data for Austrian children born between 1974 and 1984 and apply an instrumental variables (IV) identification strategy using parents' job loss during a mass...

We study the long-term effects of a randomized intervention targeting children's socio-emotional skills. The classroom-based intervention for primary school children has positive impacts that persist for over a decade. Treated children become more likely to complete academic high school and...

We analyze data from top-tier professional athletes and find that female and male athletes differ in the timing and in the extent of their reactions to a change of the rules which increased the risk of failure. Male athletes increased risk-taking in the more risky environment immediately after...

This chapter describes the main impact evaluation methods, both experimental and quasi-experimental, and the statistical model underlying them. Some of the most important methodological advances to have recently been put forward in this field of research are presented. We focus not only on the...

This paper presents a life-cycle model with human capital investment during working life through training and provides a novel empirical test of human capital theory. We exploit a sizable pension reform across adjacent cohorts in a regression discontinuity setting and find that an increase in...

We investigate heterogenous employment effects of Flemish training programmes. Based on administrative individual data, we analyse programme effects at various aggregation levels using Modified Causal Forests (MCF), a causal machine learning estimator for multiple programmes. While all...

After a decade of correlational research, this study is the first to measure the causal impact of (general) smartphone use on educational performance. To this end, we merge survey data on general smartphone use, exogenous predictors of this use, and other drivers of academic success with the...