IAB-Veranstaltungen Archiv - Page 5 of 33 - IAB - Institut für Arbeitsmarkt- und Berufsforschung

The Effects of Reducing Mandatory Job Search in Unemployment Insurance

This paper studies the effects of a market-level reduction in job search provided by unemployment benefit recipients.

This paper studies the effects of a market-level reduction in job search provided by unemployment benefit recipients. We exploit a market-level policy change in Switzerland, where a subset of Public Employment Services reduced the number of required applications by 25% and abolished mandatory vacancy referrals.

Using detailed administrative data and difference-in-differences designs, we find that the policy change increased the average duration of unemployment spells by about 6%, while increasing average reemployment earnings by about 2%. At the firm side, vacancy filling and posting reduced substantially.

Local conditions and integration when randomly placed refugees can move away

This paper demonstrates that random dispersal policies (RDP) are not sufficient for causal identification for two reasons.

A large body of migration literature uses random dispersal policies (RDP) to estimate the importance of local factors for integration.

This paper demonstrates that RDP is not sufficient for causal identification for two reasons. First, while RDP ensures that local conditions are exogenous to immigrant characteristics, they still correlate with other observed and unobserved local factors. Second, onward mobility requires careful consideration, as it can be endogenous to factors at the initial location. We theoretically show that estimates from continuousinstruments based on RDP contain three components: the causal effect of interest, ”multiple-treatment bias” (MTB), and ”mobility bias” (MB). The extent of these biases depends on the interrelations of local factors and onward mobility, which can be partly observed. We empirically investigate these biases using novel administrative data from Germany that cover the universe of all refugees between 2013 and 2018 and feature random dispersal.

The central empirical finding is that estimates that ignore MB and MTB cannot be compared and can even change signs.

Joint: Marco Schmandt, Constantin Tielkes, Felix Weinhardt

Family Policy, Careers and Workplace Technology

This workshop aims to advance research on labor market outcomes, family policy, and career development, drawing on rich register data from Germany and Norway.

This workshop aims to advance research on labor market outcomes, family policy, and career development, drawing on rich register data from Germany and Norway. Key themes include how workplace structures, public policy, and firm behavior influence career trajectories, economic mobility, and workforce well-being. Contributions may address, but are not limited to, the following topics:

Careers, promotions, wages and compensation, human capital
Family, social, and tax policies (e.g., childcare, parental leave, affirmative action, mentor programs)
Innovations and technology in the workplace
Firm organization, management practices, and corporate outcomes
Flexible work arrangements and work environment
Gender inequality and intersectional perspectives within labor markets

In cooperation with Norwegian School of Economics and Statistics Norway.

Skill-Replacing Technology and Bottom-Half Inequality

In this model, technology substitutes the usage of skill in routine tasks in contrast to standard RBTC models.

I propose a model of a skill-replacing routine-biased technological change (SR-RBTC). In this model, technology substitutes the usage of skill in routine tasks in contrast to standard RBTC models, which assume technology replaces the workers themselves.

The SR-RBTC model explains three key trends that are inconsistent with standard RBTC models: 1) why specifically middle wages declined even though workers in routine occupations are dispersed across the entire bottom half of the wage distribution, 2) why middle wages stopped declining while the technological change continued, and 3) why there is no substantial decline in the average wage of workers inroutine occupations. I derive two new testable predictions from the model: a decreasein return to skill and a decrease in skill level in routine occupations. I use an interactive fixed-effects model to confirm both predictions.

Since SR-RBTC violates the ignorability assumption required by standard decomposition methods, I introduce a “skewness decomposition” to show that SR-RBTC is the main driver of bottom-half inequality trends.

Prosociality and Layoffs

This study is about layoffs by U.S. public firms using NLP techniques, which allows to establish several facts that are jointly hard to reconcile with existing models.

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.

17th interdisciplinary PhD-Workshop – Perspectives On (Un-)Employment

The workshop provides an opportunity for graduate students to present their ongoing work in the field of theoretical and empirical labor market research.

The IAB’s Graduate School (GradAB) and the FAU invites young researchers to its 17th interdisciplinary Ph.D. workshop “Perspectives on (Un-)Employment”. The workshop provides an opportunity for graduate students to present their ongoing work in the field of theoretical and empirical labor market research and receive feedback from leading scholars in the discipline. The workshop will focus on but not be limited to empirical research in the following fields:

Inequality, poverty, and intergenerational mobility
Labor supply, labor demand, and unemployment
Gender, family, and discrimination
Evaluation of labor market institutions and policies
Health, labor market integration, and job security
Globalization, international trade and labor markets
Education, qualification, and job tasks
Wage determination and life-cycle earnings
Migration and international labor markets
Establishments and the workplace
Regional labor markets and spatial disparities
Technological change and digitalization
The impact of climate change on the labor market

We welcome papers that apply quantitative, qualitative or mixed methods.

The Second Spanish Immigration Boom

This paper explains why Spain became the fourth most attractive country in the world for international migrants in the period 2015-2024.

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

This talk will present findings from previous research, which explores how to effectively implement split questionnaire designs and impute the resulting data.

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.