Many managers describe layoffs as the hardest and most painful decisions of their careers; yet,
standard economic models treat human and physical capital adjustments alike. We collect novel
data on layoffs by U.S. public firms using NLP techniques, which allows us to establish several
facts that are jointly hard to reconcile with existing models. First, firing decisions have large
adverse healthof effects on CEOs, with distress-induced layoffs estimated to reduce CEO lifespan
by 1.85 years. Second, CEOs become more reluctant to make layoffs over their tenure as they
form more connections inside the firm. After plausibly-exogenous CEO changes, instead, new
CEOs make more and shareholder value-increasing layoff decisions. Third, CEOs’ increasing
reluctance to lay off employees intensifies when layoffs are more painful for employees, such as
during recessions or the holiday season, or more painful for managers to witness, such as when
they affect socially or geographically close employees. Fourth, the documented layoff reluctance is
substantially more pronounced among CEOs with higher empathy-related traits. We also show
that long-tenured CEOs are more likely to cut R&D spending during recessions, offsetting forgone
savings from layoffs. Our results imply the need to adjust models of managerial decision-making
for a “human” component of layoff avoidance. Prosocial, or empathy-related, motives provide a
unifying explanation.
Veranstaltungsformat: Hybrid
The Second Spanish Immigration Boom
International migrants choose their country of residence to maximize their utility. As a result, their choices are informative about the relative attractiveness of countries. This paper explains why Spain became the fourth most attractive country in the world for international migrants in the period 2015-2024, what I define as the Second Spanish Immigration Boom of the century.
First, an accounting decomposition shows how, contrary to other destinations, Spanish-specific factors, correlated with economic conditions and general migration policies, have a larger weight in explaining immigration to Spain than origin-specific factors. Second, the causal relevance of bilateral visa policies is also shown, particularly in the context of Latin American immigrants, by using origins that are required a visa to enter Spain as a control for visa-free access countries in a generalized differences-in-differences setting. Finally, the effects of the Boom on immigrant selection are also analyzed, finding that the Second Boom was different from the first because educational selection improved.
Filling in the Gaps: Designing and Imputing Missing Data in Split Questionnaire Surveys
In light of challenges such as declining response rates and rising data collection costs, approaches like split questionnaire designs or planned missing data designs offer a promising strategy for survey projects to reduce respondent burden while still collecting data on a broad range of topics. They achieve this by administering only randomly selected parts of the full questionnaire to each respondent, effectively reducing questionnaire length for each respondent. This comes at the cost of large amounts of missing data, which must be imputed to make the data analyzable.
In this talk, I will present findings from my previous research, which explores how to effectively implement split questionnaire designs and impute the resulting data in the context of social surveys. Using Monte Carlo simulations grounded in real-world data from the German Internet Panel and the European Social Survey, I evaluate how different design and imputation choices affect the accuracy of estimates. The presentation will address key methodological questions, including how to construct questionnaire modules, how planned missingness interacts with traditional item nonresponse, and how general-purpose versus analysis-specific imputation strategies influence results. The goal is to provide practical insights and evidence-based recommendations for researchers considering split questionnaire designs in their own survey work.
How to control for (pre-)trends?
This paper assesses the performance of classical strategies to control for (pre-)trends in difference-in-differences designs.
We focus on three main approaches: controlling for or matching on pre-trends, extrapolating linear trends, and controlling for group-by-time fixed effects. Through Monte Carlo simulations using real labor market data, we examine incidental trends that may emerge due to correlations between treatment and unobserved characteristics.
Drawing on these simulations and supporting analytical results, we provide intuitive insights into the performance of these methods and further formalize the conditions that justify their application.
New Hires’ Informal Training and Turnover
We analyze the relationship between informal on-the-job training and turnover of new hires. To this end, we use unique survey data about different aspects of informal co-worker training and link it to both firm-level and individual-level administrative data.
We find that informal training is negatively associated with turnover of new hires six months after entering the firm. However, this relationship becomes smaller and statistically weaker after 12 months.
Further heterogeneity analysis reveals a trade-off after one year, as onboarding can, to some extent, contribute to increased retention in firms with a lower wage premium and thereby help to mitigate costly worker turnover in these firms.
Joint work with Didier Fouarge and Carolin Linckh.
Project MARS: Mentoring for Disadvantaged Adolescents at Real Scale – a project design
The shortage of skilled workers is a central challenge for the German labor market – 18% of young adults (20-34 years) do not have any occupational degree, and this proportion is up to twice as high for those from disadvantaged backgrounds.
One promising approach to tackle this challenge is through individual mentoring programs. The project team investigates how mentoring programs can be successfully scaled to transform the education system in Germany and promote social equality. Whether, and to what extent, such programs have a positive impact is crucial to the successful design of societal transformation processes. The research team is cooperating with a leading mentoring provider and conducting a randomized controlled trial with 3,000 disadvantaged young people to analyze the causal effects of mentoring. We plan to examine four key areas of scale-up: recruitment of mentors, general equilibrium effects, replacement of high-cost matching methods with machine learning, and the horizontal expansion of mentoring to vocational schools and apprentices.
The results will provide both scientific and practical insights into optimal technologies for rolling out interventions that serve societal transformation and the promotion of equal opportunities.
Monetary policy, the bank-lending channel and labor market adjustment of firms
This paper studies the real effects of monetary policy on firms' labor adjustment. Using detailed bank data together with administrative firm and worker data for Germany, we find that firms reduce employment in response to contractionary monetary policy.
We show that this employment reduction results from a relative decline in inflows rather than outflows. Inflows fall in particular for low-wage workers, whereas firms retain high-wage workers. Outflows for transitions to unemployment increase, while employment-to-employment outflows falls.
We interpret this as evidence for labor hoarding. Using variation in the bank exposure to monetary policy, we show that these results are driven by the exposure of the firm to the bank-lending channel.
Misperceptions of inequality and local contexts
We examine the relationship between local income inequality / local income levels on the one hand, and the “centre bias” on the other. The latter refers to people’s misconception of being in the middle of the national income distribution, rather than at its more extreme ends.
Local income distributions shape perceptions of inequality because co-residents are a reference group that affects the availability of opportunities for upward and downward social comparison. Theoretically, we outline four mechanisms that could link higher and lower local income inequality and income levels to residents' perceptions of their own relative income position (exposure vs. segregation, contrast vs. assimilation). Empirically, we link geo-referenced survey data to external datasets containing information on income inequality and income levels in respondents' home municipalities. Results suggest that higher local inequality is associated with a lower “centre bias” for both poor and rich respondents, supporting an “exposure” mechanism.
With respect to poorer versus richer municipalities, we find that only by tendency, either group estimates their position in the national income distribution to be somewhat higher. However, this evidence in favor of the “assimilation” mechanism is weak.
Sexual Harassment in the Workplace
We study the prevalence, perceived costs and consequences of sexual harassment (SH) in German workplaces. We first use a discrete choice experiment to estimate workers' willingness to pay (WTP) for workplaces without a history of known SH cases and preventive firm measures. Women, particularly early in their careers, display the highest WTP. Preventive measures significantly increase the attractiveness of workplaces, even when there is a history of SH.
Motivated by these results, we then document SH experiences using new data from the Linked Personnel Panel (LPP) and the IAB-OPAL online panel. SH is widespread: 20 percent of employees have either experienced SH at work personally or in their close work environment. Women are affected significantly more often than men. Women are also less likely to trust that leadership will respond appropriately to reported cases, and this lack of trust correlates with higher experienced incidence rates. Firms with active complaint procedures and preventive measures report greater employee awareness and more open discussion of SH. Taken together, our findings provide a strong economic rationale for preventive policies.
The short- and long-run effects of paying disadvantaged teenagers to go to school
We evaluate the Education Maintenance Allowance, a large conditional cash transfer that paid teenagers from lower-income backgrounds up to $3,200 per year to remain in full-time education beyond the compulsory school-leaving age.
Exploiting the program's staggered rollout in England, we find that it increased education participation and reduced crime. However, we find no improvements in test scores, no effect on qualifications beyond the lowest level, and a small negative effect on labour market outcomes up to age 30. A key channel appears to be delayed labour market entry without offsetting gains in human capital.
