Propensity score matching estimators have two advantages. One is that they overcome the curse of dimensionality of covariate matching, and the other is that they are nonparametric. However, the propensity score is usually unknown and needs to be estimated. If we estimate it nonparametrically, we...

This paper investigates the extent to which test performance is affected by shocks to non-cognitive skills. 440 students took a low stakes mathematics test. About half of them were exposed to positive affirmation while being given test instructions, whereas the other half served as controls. The...

Currently there is little practical advice on which treatment effect estimator to use when trying to adjust for observable differences. A recent suggestion is to compare the performance of estimators in simulations that somehow mimic the empirical context. Two ways to run such 'empirical Monte...

This paper investigates the extent to which test performance is affected by shocks to non-cognitive skills. 440 students took a low stakes mathematics test. About half of them were exposed to positive affirmation while being given test instructions, whereas the other half served as controls. The...

Propensity score matching estimators have two advantages. One is that they overcome the curse of dimensionality of covariate matching, and the other is that they are nonparametric. However, the propensity score is usually unknown and needs to be estimated. If we estimate it nonparametrically, we...

The paper takes advantage of exceptionally rich longitudinal data on the universe of labor force participants in Slovenia and simulates the working of an income contingent loan scheme to partly recover tuition costs. The simulations show that under the base variant (where the target cost...