How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observable covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design that are of practical importance, like sample size, the type of the outcome variable, and aspects of the selection process. We find that trimming individual observations with too much weight as well as the choice of tuning parameters is important for all estimators. The key conclusion from our simulations is that a particular radius matching estimator combined with regression performs best overall, in particular when robustness to misspecifications of the propensity score is considered an important property.propensity score matching, kernel matching, inverse probability weighting, selection on observables, empirical Monte Carlo study, finite sample properties

Does Leaving Welfare Improve Health? Evidence for Germany

Using exceptionally rich linked administrative and survey information on German welfare recipients we investigate the health effects of transitions from welfare to employment and of assignments to welfare-to-work programmes. Applying semi-parametric propensity score matching estimators we find that employment substantially increases (mental) health. The positive effects are mainly driven by males and individuals with bad initial health conditions and are largest for males with poor health. In contrast, the effects of welfare-to-work pro-grammes, including subsidized jobs, are ambiguous and statistically insignificant for most outcomes. Robustness checks that include a semi-parametric instrumental variable approach do not provide reasons for concern.Welfare programs, health effects

Does Leaving Welfare Improve Health? Evidence for Germany

Using exceptionally rich linked administrative and survey information on German welfare recipients we investigate the health effects of transitions from welfare to employment and of assignments to welfare-to-work programmes. Applying semi-parametric propensity score matching estimators we find that employment substantially increases (mental) health. The positive effects are mainly driven by males and individuals with bad initial health conditions and are largest for males with poor health. In contrast, the effects of welfare-to-work programmes, including subsidized jobs, are ambiguous and statistically insignificant for most outcomes. Robustness checks that include a semi-parametric instrumental variable approach do not provide reasons for concern.welfare programs, health effects

How to control for many covariates? Reliable estimators based on the propensity score

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observable covariates is required, like inverse pro¬bability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design that are of practical importance, like sample size, the type of the outcome variable, and aspects of the selection process. We find that trimming individual observations with too much weight as well as the choice of tuning parameters is important for all estimators. The key conclusion from our simulations is that a particular radius matching estimator combined with regression performs best overall, in particular when robustness to misspecifications of the propensity score is considered an important property.Propensity score matching, kernel matching, inverse probability weighting, selection on observables, empirical Monte Carlo study, finite sample properties

Do German Welfare-to-Work Programmes Reduce Welfare and Increase Work?

Many Western economies have reformed their welfare systems with the aim of activating welfare recipients by increasing welfare-to-work programmes and job search enforcement. We evaluate the three most important German welfare-to-work programmes implemented after a major reform in January 2005 ("Hartz IV"). Our analysis is based on a unique combination of large scale survey and administrative data that is unusually rich with respect to individual, household, agency level, and regional information. We use this richness to allow for a selection-on-observables approach when doing the econometric evaluation. We find that short-term training programmes on average increase their participants' employment perspectives and that all programmes induce further programme participation. We also show that there is considerable effect heterogeneity across different subgroups of participants that could be exploited to improve the allocation of welfare recipients to the specific programmes and thus increase overall programme effectivenessWelfare-to-work policies, propensity score matching, programme evaluation, panel data, targeting

The effect of firms' partial retirement policies on the labour market outcomes of their employees

In this paper, we assess the impact of firms introducing part-time work schemes for gradual labour market exit of elderly workers on their employees’ labour market outcomes. The analysis is based on unique linked employer-employee data that combine high-quality survey and administrative data. Our results suggest that partial or gradual retirement options offered by firms are an important tool to alleviate the negative effects of low labour market attachment of elderly workers in ageing societies

Philosophie der Tierforschung: Milieus und Akteure

Die Tierphilosophie ist eines der lebendigsten Felder der Gegenwartsphilosophie. Im Mittelpunkt stehen bislang die Frage nach dem Geist der Tiere, das Problem des Tier-Mensch-Unterschiedes und die Themenfelder der Tierethik. Die auf drei Bände angelegte »Philosophie der Tierforschung« wirft einen neuen Blick auf dieses Gebiet und ergänzt es durch eine stärkere Berücksichtigung des gesamten Kontextes der naturwissenschaftlichen Tierforschung, inklusive der philosophischen Hintergrundannahmen, der Forschungsverfahren und -orte, der Handlungslogiken, Denkstile und Sprachspiele der Forscherkollektive sowie der jeweils ausgewählten Modellorganismen. Stellten die ersten beiden, bereits erschienenen Bände der Reihe die Methoden und Programme sowie die Maximen und Konsequenzen der Tierforschung in den Mittelpunkt, widmet sich der dritte Band unter dem Leitgedanken der Forschungsumwelten den Milieus und Akteuren. Im Ausgang von der Tier-Mensch-Relationalität der Tierforschung werden dabei die verschiedenen Rollen der Forschenden und der erforschten Tiere mit dem Ziel einer Neukonfiguration des Untersuchungsfeldes herausgearbeitet

Does leaving welfare improve health? Evidence for Germany

Using exceptionally rich linked administrative and survey information on German welfare recipients we investigate the health effects of transitions from welfare to employment and of assignments to welfare-to-work programmes. Applying semi-parametric propensity score matching estimators we find that employment substantially increases (mental) health. The positive effects are mainly driven by males and individuals with bad initial health conditions and are largest for males with poor health. In contrast, the effects of welfare-to-work programmes, including subsidized jobs, are ambiguous and statistically insignificant for most outcomes. Robustness checks that include a semi-parametric instrumental variable approach do not provide reasons for concern

Permutation Coding Technique for Image Recognition Systems

A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1