Hybrid Archive - Page 4 of 14 - IAB - Institut für Arbeitsmarkt- und Berufsforschung

Refugee Labor Market Integration at Scale: Evidence from Germany’s Fast-Track Employment Program

This study examines the Job-Turbo, a nationwide initiative launched by the German government in 2023 to accelerate refugee employment.

Governments continue to face challenges integrating refugees into local labor markets, and many past interventions have shown limited impact.

This study examines the Job-Turbo, a nationwide initiative launched by the German government in 2023 to accelerate refugee employment - primarily among individuals from Ukraine and eight other major countries of origin. Using monthly administrative panel data from Germany’s network of public employment service offices and a difference-in-differences design, we find sizable increases in both caseworker - refugee contact and job placements over a 23-month follow-up. Among Ukrainian refugees, the exit-to-job rate nearly doubled. Effects were broad-based - spanning demographic subgroups, unemployment durations, skill levels, regions, and local labor-market conditions - and concentrated in regular, unsubsidized employment. The program also raised both the rate and the share of sustained placements, consistent with improved match quality. Other refugee groups saw meaningful gains as well, though increases in job placements were concentrated among men and in low-skill jobs, with comparatively modest effects for women. We detect no negative spillovers for German or other immigrant job seekers, finding no evidence of either resource reallocation or displacement.

These results suggest that intensified job-search assistance - embedded early in the integration process and implemented at scale through public employment infrastructure - can meaningfully improve refugees’ labor-market outcomes even amid substantial arrivals.

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

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.

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.

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.