Martin Huber

This paper presents the outcomes of an online coin-tossing experiment evaluating cheating behaviour among Ukrainian students. Over 1,500 participants were asked to make ten coin tosses and were randomly assigned to one of three treatment groups tossing coins (1) online, (2) manually, or (3) having the choice between tossing manually or online. The study outcomes suggest that students are more inclined to cheat when they perceive the coin toss to be more ‘private’. Moreover, the students’ attitudes towards corruption appear to matter for the extent of their cheating, while socio-demographic characteristics were less important.

This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity, effect heterogeneity, share of treated, and sample size. The results suggest that in general, theoretically justified bootstrap procedures (i.e., wild bootstrapping for pair matching and standard bootstrapping for “smoother” treatment effect estimators) dominate the asymptotic approximations in terms of coverage rates for both matching and weighting estimators. Most findings are robust across simulation designs and estimators.

This paper proposes a nonparametric method for evaluating treatment effects in the presence of both treatment endogeneity and attrition/non‐response bias, based on two instrumental variables. Using a discrete instrument for the treatment and an instrument with rich (in general continuous) support for non‐response/attrition, we identify the average treatment effect on compliers as well as the total population under the assumption of additive separability of observed and unobserved variables affecting the outcome. We suggest non‐ and semiparametric estimators and apply the latter to assess the treatment effect of gym training, which is instrumented by a randomized cash incentive paid out conditional on visiting the gym, on self‐assessed health among students at a Swiss university. The measurement of health is prone to non‐response, which is instrumented by a cash lottery for participating in the follow‐up survey.

This paper presents the outcomes of an anti-corruption educational intervention among Ukrainian students based on an online experiment. More than 3,000 survey participants were randomly assigned to one of three different videos on corruption and its consequences (treatment groups) or a video on higher education (control group). The data suggest a high level of academic dishonesty and misconduct among young people, but also a negative attitude toward corruption in general, highlighting the ambivalence of corruption in the country. We find that one video, which presented a thrilling story abut a victim of corruption related to common bribery in an accessible way, was effective in promoting awareness of the negative consequences of corruption. In contrast, the other two treatment videos, which more closely followed the style of TV news or documentaries on corruption, did not generally promote negative attitudes toward corruption. Presenting corruption issues in a catchy way therefore appears to matter for the effectiveness of such interventions.

This paper evaluates the effects of awarding vouchers for vocational training on the employment outcomes of unemployed voucher recipients in Germany, as well as the potential mechanism through which they operate. This study assesses the direct effects of voucher assignment net of ac¬tual redemption, which may be driven by preference shaping and learning about possible human capital investments or simply by the costs of information gathering. Using a formal mediation analy-sis framework based on sequential conditional independence assumptions and semiparametric match¬ing estimators, our results suggest that the negative short-term and positive long-term em-ployment ef¬fects of receiving a voucher are mainly driven by actual training participation. However, the direct ef¬fect of just obtaining a voucher is negative over the short-run as well. This result points to potential losses in the effectiveness of such training provision systems if individuals decide not to redeem vouchers, as employment chances are lower than under non-award over the short-run and under redemption over the long-run, which makes non-re¬demption the least attractive option.

In the presence of an endogenous binary treatment and a valid binary instrument, causal effects are point identified only for the subpopulation of compliers, given that the treatment is monotone in the instrument. With the exception of the entire population, causal inference for further subpopulations has been widely ignored in econometrics. We invoke treatment monotonicity and/or dominance assumptions to derive sharp bounds on the average treatment effects on the treated, as well as on other groups. Furthermore, we use our methods to assess the educational impact of a school voucher program in Colombia and discuss testable implications of our assumptions.

Previous research found that less accommodating caseworkers are more successful in placing unemployed workers into employment. This paper explores the causal mechanisms behind this result using semi-parametric mediation analysis. Analyzing rich linked job seeker-caseworker data for Switzerland, we find that the positive employment effects of less accommodating caseworkers are not driven by a particularly effective mix of labor market programs but, rather, by other dimensions of the counseling process, possibly including threats of sanctions and pressure to accept jobs.

Using a comprehensive simulation study based on empirical data, this paper investigates the finite sample properties of different classes of parametric and semi-parametric estimators of (natural or pure) direct and indirect causal effects used in mediation analysis under sequential conditional independence assumptions. The estimators are based on regression, inverse probability weighting, and combinations thereof. Our simulation design uses a large population of Swiss jobseekers and considers variations of several features of the data generating process and the implementation of the estimators that are of practical relevance. We find that no estimator performs uniformly best (in terms of root mean squared error) in all simulations. Overall, so-called 'g-computation' dominates. However, differences between estimators are often (but not always) minor in the various setups and the relative performance of the methods often (but not always) varies with the features of the data generating process.