Publikationen

In this paper we provide a general two-step framework for linear projection estimators of impulse responses in structural vector autoregressions (SVARs). This framework is particularly useful for situations when structural shocks are identified from information outside the VAR (e.g. narrative shocks). We provide asymptotic results for statistical inference and discuss situations when standard inference is valid without adjustment for generated regressors, autocorrelated errors or non-stationary variables. We illustrate how various popular SVAR models fit into our framework. Furthermore, we provide a local projection framework for invertible SVAR models that are estimated by instrumental variables (IV). This class of models results in a set of quadratic moment conditions used to obtain the asymptotic distribution of the estimator. Moreover, we analyse generalized least squares (GLS) versions of the projections to improve the efficiency of the projection estimators. We also compare the finite sample properties of various estimators in simulations. Two highlights of the Monte Carlo results are (i) for invertible VARs our two-step IV projection estimator is more efficient compared to existing projection estimators and (ii) using the GLS projection variant with residual augmentation leads to substantial efficiency gains relative to standard OLS/IV projection estimators.

The vocational identity of trainees is one component of their professional competence and is considered to be a central goal of vocational education and training (VET) programs. From the numerous identity constructs and conceptualizations, this study focuses on the organizational identification of trainees, that is, the extent to which trainees internalize the values and goals of their training company and perceive themselves as part of this company. We are specifically interested in the development, predictors, and effects of trainees’ organizational identification, as well as the interrelations between organizational identification and social integration. We use longitudinal data of n  = 250 trainees in dual VET programs in Germany at the very beginning of their VET program (t1), after 3 months (t2), and after 9 months (t3). A structural equation model was used to analyze the development, predictors, and effects of organizational identification for the first 9 months of training and the cross-lagged effects between organizational identification and social integration. The results showed a high stability of trainees’ organizational identification over the first 9 months. Regarding the predictors, the results indicated positive direct and indirect effects of the formal socialization tactics implemented by the training company, as well as of support by the trainer at the beginning of the training. However, collegial support at the beginning of the training did not seem to play a significant role in organizational identification. Moreover, organizational identification positively affected trainees’ emotional engagement and self-perceived competence while negatively predicting dropout intentions after 9 months of training. Finally, the cross-lagged effects between organizational identification and social integration were not significant, and only at t3 were these constructs positively correlated. However, regarding the development, predictors, and effects, very similar results were found for organizational identification and social integration. The results underline the positive significance of organizational identification for the individual, the company, and society, even at this early stage of training. The results are discussed regarding both their scientific and practical implications.

Using new data from a three-wave panel survey administered in Germany between May 2020 and May 2021, this paper studies the impact of a negative shock affecting all strata of the population, such as the development of COVID-19, on preferences for redistribution. Exploiting the plausibly exogenous change in the severity of the infection rate at the county level, we show that, contrary to some theoretical expectations, the worse the crisis, the less our respondents expressed support for redistribution. We provide further evidence that this is not driven by a decrease in inequality aversion but might be driven by the individuals’ level of trust.

Organizational decisions are often made by groups rather than individuals. Depending on the group composition, each member's characteristics—like gender and motivated beliefs—can influence the final group investment decision. To capture this, we design two types of investment situations in a randomized controlled laboratory experiment—one with fixed chances of success and one with performance-dependent chances of success. This novel design entails the perceived ability to “beat the odds” of the investment and thus models real-life investment situations more accurately than standard lottery choice. Our results demonstrate the benefits of mixed group composition in terms of both gender and overconfidence: Groups with all men and/or all overconfident group members consistently overinvest when a possibility to “beat the odds” is present, but not in standard situations. We explore several channels for our results and find that (i) individual probability perception, (ii) leader responsibility allocation and (iii) spillover effects from priming show significant effects.

Employee referral programs (ERPs) are randomly introduced in a grocery chain. On direct effects, larger referral bonuses increase referral quantity but decrease quality, though the increase in referrals from ERPs is modest. However, the overall effect of having an ERP is substantial, reducing attrition by 15% and significantly decreasing labor costs. This occurs, partly, because referrals stay longer than nonreferrals, but, mainly, from indirect effects: nonreferrals stay longer in treated than in control stores. The most supported mechanism for these indirect effects is workers value being involved in hiring. Attrition impacts are larger in higher performing stores and better local labor markets.

Do principals' distributive preferences affect the allocation of incentives within firms? We run a Principal-Agent lab experiment, framed as a firm setting. In the experiment, subjects are randomized in the principal or worker position. Principals must choose piece rate wage contracts for two workers that differ in terms of ability. Workers have to choose an effort level that is non-contractible. Principals are either paid in proportion to the output produced (Stakeholder treatment) or paid a fixed wage (Spectator treatment). We study how principals make trade-offs between incentive concerns (motivating workers to maximize output) and their own normative distributive preferences. We find that, despite the firm-frame and the moral hazard situation, principals do hold egalitarian concerns, as principals are on average willing to trade off their firm's performance (and so their own income) for more wage equality among their workers. The willingness to reduce inequality among workers is sensitive to both extensive and intensive margin incentives, which shows that principals' choices are shaped by incentives that they face themselves.

Past research has shown that CEOs become overconfident through biased self-attribution in previous M&A transactions, which negatively affects subsequent M&A deals. However, M&A decisions are de jure and de facto not only influenced by a single person, the CEO, but by a group of individuals, namely the board of directors. In this study, we investigate whether and to what extent the overconfidence of board directors affects key acquisition outcomes. Building on the overconfidence literature, we argue that board directors become overconfident through biased self-attribution in recent M&A deals and hypothesize that their overconfidence leads to poorer subsequent M&A outcomes. By studying investor reactions and premiums paid for a broad set of public acquisitions carried out by large U.S. companies, we find strong support for our predictions.

Gender segregation of occupations and entire industries is widespread. The segregation could be the result of perceived job-specific productivity differences between men and women. It could also result from the belief that homogeneous teams perform better or from in-group bias of male or female recruiters. We investigate these explanations in two samples: students and personnel managers. The subjects bet on the productivity of teams that are homogeneous with respect to gender at the outset and then either remain homogeneous or become diverse. The teams work on tasks that differ with respect to gender stereotypes. We obtain similar results in both samples. Women are picked more often for the stereotypically female task, and men are picked more often for the stereotypically male task. Subjects believe that gender-diverse teams perform better, especially in the task with complementarities, and they display an own-gender bias. Elicited expectations about the bets of others reveal that subjects expect the gender stereotypes of tasks but underestimate others’ bets on diversity.

Recently, there has been a growing concern with the (non-)replicability of scientific findings. Many reasons for the ‘replication crisis’ have been brought forward, including uncorrected multiple-hypotheses testing, ex-post theorising, p-hacking, publication bias, and under-powered studies (see, for example, the excellent discussion in Renkewitz & Keiner, 2019). Four years ago, Experimental Economics called for proposals to be reviewed before collecting data. The aim was to gain experience with this new form of organising the research process that has been gaining popularity in many other disciplines over the past decade.

Policy reforms are often multifaceted. In the rent-seeking literature policies are usually taken as one-dimensional. This paper models policy formation using a political contest with endogenous policy proposals containing two dimensions, e.g. access and quality of education. The two dimensions provide an opportunity to trade off one policy over another to make the lobbying opposition less aggressive. In a first stage, the government proposes a reform over the two policies, and in a second stage engages in a contest with an interest group over the enactment of the proposed reform. As a result, the government makes a compromise, under-proposing in the policy the interest group opposes and over-proposing in the policy the interest group desires. Effectively, there will be strategic bundling of desired policies with undesired ones in an attempt to increase enactment probability and overall utility. We study this prediction empirically using a newly complied dataset on education legislation in the states of California, Illinois and Texas. Results suggest that stronger opposition is associated with less quality reforms. Moreover, as predicted by the model, when bundling access reforms together with quality, the negative effect is counteracted.

We collect information about more than 5000 Prussian politicians, digitize administrative data on the provision of health-promoting public goods, and gather local-level information on workers’ movements to study why elites in industrializing countries implement policies that improve the health of the poor. Using county-level variation in elite structure, we present OLS and IV estimates, suggesting that elites improve access to health services due to pressure exerted by workers’ movements. By contrast, policies that prevent disease outbreaks are implemented without such pressure. Analyses of roll- call votes substantiate the findings of the county-level analysis.

This paper exploits the idea of combining pretesting and bagging to choose between competing portfolio strategies. We propose an estimator for the portfolio weight vector, which optimally trades off Type I against Type II errors when choosing the best investment strategy. Furthermore, we accommodate the idea of bagging in the portfolio testing problem, which helps to avoid sharp thresholding and reduces turnover costs substantially. Our Bagged Pretested Portfolio Selection (BPPS) approach borrows from both the shrinkage and the forecast combination literature. The portfolio weights of our strategy are weighted averages of the portfolio weights from a set of stand-alone strategies. More specifically, the weights are generated from pseudo-out-of-sample portfolio pretesting, such that they reflect the probability that a given strategy will be overall best performing. The resulting strategy allows for a flexible and smooth switch between the underlying strategies and outperforms the corresponding stand-alone strategies. Besides yielding high point estimates of the portfolio performance measures, the BPPS approach performs exceptionally well in terms of precision and is robust against outliers resulting from the choice of the asset space.