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Archives: IAB-Veranstaltungen

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 16th 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:

  • Labor supply, labor demand, and unemployment
  • Evaluation of labor market institutions and policies
  • Education, qualification, and job tasks
  • Wage determination and life-cycle earnings
  • Gender, family, and labor market discrimination
  • Inequality, poverty, and intergenerational mobility
  • Migration and international labor markets
  • Regional labor markets and spatial disparities
  • Impact of technological change, digitalization, and climate change on the labor market

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

This study sheds light on the impact of different types of job retention programs such as short-time work.

This study sheds light on the impact of different types of job retention programs such as short-time work (STW).

We analyze the causal effect of an episode of STW on labor market outcomes up to five years later and compare this to the effects of sudden unemployment episodes. Using data from German Socio-Economic Panel (1992–2022), we employ an event-study approach to analyze the effect of unemployment and STW on career trajectories and unpaid care work.

Results show that workers with periods of short-time work have higher employment and wage stability than workers with periods of unemployment. There are no gender differences in the effects of STW on employment and hours worked.

This paper estimates experimental impacts of a supported work program on employment, earnings, benefit receipt, and other outcomes over a four-year follow-up period.

This paper estimates experimental impacts of a supported work program on employment, earnings, benefit receipt, and other outcomes over a four-year follow-up period.

Case managers addressed employment barriers and provided targeted financial assistance while participants were eligible for 30 weeks of fully subsidized employment. Program access increased employment rates by 21 percent and earnings by 16 percent while participants were receiving services. Though gains attenuated after services stopped, treatment group members experienced lasting improvements in employment stability, job quality, and well-being, and we estimate the program's marginal value of public funds to be in line with other adult workforce programs. Post-program impacts are concentrated among participants who were hired by their host-site employer post-program, suggesting that encouraging employer learning about potential match quality is a key mechanism underlying the program’s impact, and additional descriptive evidence supports this interpretation.

Machine learning methods provide no evidence of treatment effect heterogeneity in a broad sample of job seekers using a rich set of baseline characteristics from a detailed application survey. We conclude that subsidized employment programs with a focus on creating permanent job matches can be beneficial to a wide variety of unemployed workers in the low-wage labor market.

This study documents how the application of causal machine learning methods can successfully increase sales revenue.

If treatment effects vary systematically, customizing treatment status at the individual level may help increasing the overall effectiveness of an intervention. We document how the application of causal machine learning methods can successfully increase sales revenue generated within a loss framing treatment in an online field experiment. We combine this data with a behavioral experiment measuring sensitivity to loss aversion at the individual level. Our results show that treatment status as assigned by causal machine learning is consistent with treatment assignment based on economic theory.

Joint: Kevin Bauer, Andreas Grunewald, Florian Hett, Johanna Jagow, Maximilian Speicher

"New work" - we develop an algorithm that identifies new job-titles in the US economy based on their vector distance from the closest existing job title in the previous census.

"New work", namely the introduction of types of jobs that did not exist earlier, is an essential part of innovation and employment growth for advanced economies. Using text analysis, we develop an algorithm that identifies new job-titles in the US economy based on their vector distance from the closest existing job title in the previous census. We use this method to generate a measure of "new work" from 1980 to 2010 in each of 354 occupations and we construct its distribution across 766 commuting zones. We first show how this measure of "new work" is associated to task and skill characteristics of workers in the occupations and to employment growth, skill bias and innovation in the commuting zones. Then we analyze whether local population density, human capital and manufacturing intensity in the 1980, and/or local exposure to structural "shocks" in the 1980-2010, relating to trade competition, technological change, immigration and age changes predict the creation of new work.

Our main findings are that the share of college educated and the density of population in 1980 are the strongest predictors of New Work creation in the 1980-2010 period. The aging of population and exposure to computer adoption were also associated to New Work creation, while robot adoption was negatively associated to it. The exposure to immigration and trade had a more nuanced and differentiated correlation to new work.

This paper studies the allocation of time among workers across jobs that vary in their remote intensity.

The proportion of employees who work remotely has surged from under 5% to over 60% between January to March 2020, converging to roughly 28% of days working from home versus in the office as of 2023. Motivated by these large structural shifts in the nature of work, this paper studies the allocation of time among workers across jobs that vary in their remote intensity. Drawing on the American Time Use Survey between 2019 and 2022, I document three main results. First, time allocated to leisure increased and to work decreased among more remote jobs with no significant change in home production. Second, these changes were concentrated among males, singles, and those without children. Third, these declines in labor supply cannot explain the recent decline in productivity; in contrast, sectors with greater remote work intensity exhibited greater productivity growth. In addition, I will also present results from a complementary paper that draws on employee engagement and labor market data from over 70,000 workers. While there is a positive association between always WFH and satisfaction, it vanishes after controlling for employee compensation, occupation, demographics, and workplace environment characteristics (e.g., feeling appreciated at work). Employees who always WFH also have a higher intention to leave their job than employees who never work from home. In contrast, less frequent WFH arrangements relate to higher satisfaction but no difference in intention to leave, and their impact is limited relative to workplace environment characteristics.

This paper proposes novel natural language methods to measure worker rights from collective bargaining agreements (CBAs) for use in empirical economic analysis.

This paper proposes novel natural language methods to measure worker rights from collective bargaining agreements (CBAs) for use in empirical economic analysis. Applying unsupervised text-as-data algorithms to a new collection of 30,000 CBAs from Canada in the period 1986-2015, we parse legal obligations (e.g. “the employer shall provide...”) and legal rights (e.g. “workers shall receive...”) from the contract text. We validate that contract clauses provide worker rights, which include both amenities and control over the work environment. Worker-rights clauses increase with firm size, and companies that provide more worker rights score highly on a survey indicating pro-worker management practices. Using time-varying province-level variation in labor income tax rates, we find that higher taxes increase the share of worker-rights clauses while reducing pre-tax wages in unionized firms, consistent with a substitution effect away from taxed compensation (wages) toward untaxed amenities (worker rights). Further, an exogenous increase in the value of outside options (from a Bartik instrument for labor demand) increases the share of worker rights clauses in CBAs. Combining the regression estimates, we infer that a one-standard-deviation increase in worker rights is valued at about 5.4% of wages.

How does labor demand and the generosity of welfare benefits affect the labor market integration of refugees? We analyze the effect of both factors in a common framework.

How does labor demand and the generosity of welfare benefits affect the labor market integration of refugees? We analyze the effect of both factors in a common framework. For identification, we exploit the exogenous placement of refugees in Austria, business cycle fluctuations, and large variation in benefit levels between federal states and type of protection. Higher labor demand at the time of receiving protection status increases employment rates and decreases the likelihood to receive welfare benefits. But the effects are short-lived. Higher benefit levels reduce employment rates initially but the effect is economically small and also temporary. Higher benefit levels also reduce marginal effects of variation in labor demand. Both shocks do not affect the likelihood of remaining in Austria but do affect internal migration.

This paper studies the long-term consequences on firms and workers of the credit crunch triggered by the 2007-2008 global financial crisis.

This paper studies the long-term consequences on firms and workers of the credit crunch triggered by the 2007-2008 global financial crisis. Relying on a unique matched bank-employer-employee administrative dataset, we construct a firm-specific credit supply shock and examine firms’ and workers’ outcomes for 11 years after the crisis. We find that highly-exposed firms shrink permanently and invest less; these effects are larger for high capital-intensive firms. The impact on workers’ earnings is also long-lasting, especially for high skilled workers, who are more complementary to capital. Displaced workers reallocate mostly to low capital-intensive firms, experiencing persistent wage losses.

This paper investigates whether task overlap can equalize the effects of automation for unemployed job seekers displaced from routine jobs.

This paper investigates whether task overlap can equalize the effects of automation for unemployed job seekers displaced from routine jobs. Using a language model, we establish a novel job-to-job task similarity measure. Exploiting the resulting job network to define job markets flexibly, we find that only the most similar jobs affect job finding. Since automation-exposed jobs overlap with other highly exposed jobs, task-based reallocation provides little relief for affected job seekers. We show that this is not true for more recent software exposure, for which task overlap mitigates the distributional consequences.

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