We study time preferences by means of a longitudinal lab experiment involving both monetary and non-monetary rewards (leisure). Our novel design allows to measure whether participants prefer to anticipate or delay gratification, without imposing any structural assumption on the instantaneous...

The canonical model of life-cycle labor supply predicts a positive response of labor supplied to transitory wage changes. We tested this prediction by conducting a randomized field experiment with bicycle messengers. In contrast to previous studies we can observe in which way working hours as...

Not enough is known about the responsiveness of individuals, in particular those who tend to work under different incentives, to changes in marginal tax rates. We ask whether changes in marginal tax rates are less distortionary for workers engaged in a contest. To examine this potential...

We adapt the design of five experimental studies on retirement decision-making and conduct reproductions with a larger sample from the broader population. We reproduce most of the main effects of the original studies. In particular, we find that consumption decisions are less efficient when...

Inspired by Fröhlich and Oppenheimer (1990), an experimental survey in the lab was designed to find out if preferences for three different redistribution schemes differ under a veil of ignorance. The three schemes are a stylized version of the status quo German welfare state (A), a control...

In the past years, work time in many industries has become increasingly flexible opening up a new channel for intertemporal substitution. To study this, we set up a two-period model with wage uncertainty. This extends the standard savings model by allowing a worker to allocate a fixed time...

I investigate causal machine learning (CML) methods to estimate effect heterogeneity by means of conditional average treatment effects (CATEs). In particular, I study whether the estimated effect heterogeneity can provide evidence for the theoretical labour supply predictions of Connecticut's...

Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment effects (CATEs). In this study, I investigate whether CML methods add value compared to conventional CATE estimators by re-evaluating Connecticut's Jobs First welfare experiment. This...