We propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data. Specifically, we extend the correlated random coefficients representation of linear quantile regression (e.g., Koenker, 2005; Section 2.6). We show that panel data allows the...

We evaluate the potential of wage subsidy programs for reducing non-employment of the disabled by exploiting a reform of the Danish Flexjob scheme targeted towards employing the long-term (partially) disabled. Firms received a salary reimbursement for all employees granted a Flexjob. We examine...

We estimate the causal impact of school smoking bans in Germany on the propensity and intensity of smoking. Using representative longitudinal data, we use variation in state, year, age cohort, school track, and survey time for implementation of such smoking bans to identify the effects of...

We analyse how the financial support for long-term elderly care affects the household’s propensity to save. Using the difference-in-differences estimator, we investigate the 2002 Scottish reform, which introduced free formal personal care for all the Scottish elderly aged 65 and above. We find...

This paper suggests a causal framework for disentangling individual level treatment effects and interference effects, i.e., general equilibrium, spillover, or interaction effects related to treatment distribution. Thus, the framework allows for a relaxation of the Stable Unit Treatment Value...

This paper suggests a causal framework for disentangling individual level treatment effects and interference effects, i.e., general equilibrium, spillover, or interaction effects related to treatment distribution. Thus, the framework allows for a relaxation of the Stable Unit Treatment Value...

We estimate the earnings losses of a cohort of workers displaced during the Great Recession and decompose those long-term losses into components attributable to fewer work hours and to reduced hourly wage rates. We also examine the extent to which the reduced earnings, work hours, and wages of...

Inference for estimates of treatment effects with clustered data requires great care when treatment is assigned at the group level. This is true for both pure treatment models and difference-in-differences regressions. Even when the number of clusters is quite large, cluster-robust standard...

When there are few treated clusters in a pure treatment or difference-in-differences setting, t tests based on a cluster-robust variance estimator (CRVE) can severely over-reject. Although procedures based on the wild cluster bootstrap often work well when the number of treated clusters is not...

I demonstrate that Ai and Norton's (2003) point about cross differences is not relevant for the estimation of the treatment effect in nonlinear difference-in-differences models such as probit, logit or tobit, because the cross difference is not equal to the treatment effect, which is the...