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Archives: IAB-Veranstaltungen

Urban labor markets provide agglomeration advantages to workers and firms. However, the distributional consequences are not fully understood.

Urban labor markets provide agglomeration advantages to workers and firms. However, the distributional consequences are not fully understood. Agglomeration benefits are unevenly shared among low- and high-skilled workers. At the same time, many large urban labor markets around the world have experienced strongly rising housing costs in recent decades, especially for renters and young first-time homebuyers, putting these groups at risk of being priced out of the local labor market. The workshop aims to bring together junior and senior researchers working on these and related issues and welcomes both empirical and theoretical contributions. The list of topic includes, but is not limited to

  • Distributional consequences of agglomeration benefits
  • Labor market outcomes and housing affordability
  • Highly-local income inequality
  • Spatial extent of local labor markets and commuting patterns
  • Neighborhood effects and segregation
  • Interactions between local housing and labor markets

This workshop invites empirical contributions using either the IAB Establishment Panel, one of its derivatives (LPP/LIAB), or other matched employer-employee data.

Celebrating the 30th anniversary of the IAB Establishment Panel Survey, this workshop invites empirical contributions using either the IAB Establishment Panel, one of its derivatives (LPP/LIAB), or other matched employer-employee data. Research projects from all areas of labour market research are welcome, including personnel economics, sociology and economics of vocational education and training, industrial relations, or industrial economics. Papers may address research questions in any of these areas as well as methodological questions.

Numerous governments provide income-contingent childcare subsidies.

Numerous governments provide income-contingent childcare subsidies. In this paper, we estimate the dynamic marginal efficiency cost of redistribution (MECR) associated with a large-scale program of this kind in Germany, and compare them with the MECR associated with the benchmark redistributive tool, the income tax. To do so, we integrate methods from public finance theory into a dynamic structural heterogeneous-household model of childcare demand and maternal labor supply. We also incorporate social mobility concerns into the MECR and find the MECR of the childcare subsidies to be significantly lower at the margin, suggesting that childcare subsidies are the more efficient redistributive tool.

This lecture is about how artificial intelligence can be used to reduce friction in markets.

We explore how Artificial Intelligence can be leveraged to help frictional markets to clear. We design a collaborative-filtering machine-learning job recommender system that uses job seekers' click history to generate relevant personalised job recommendations. We deploy it at scale on the largest online job board in Sweden, and design a clustered two-sided randomised experiment to evaluate its impact on job search and labour-market outcomes. Combining platform data with unemployment and employment registers, we find that treated job seekers are more likely to click and apply to recommended jobs, and have 0.7 percent higher employment within the 6 months following first exposure to recommendations. At the job-worker pair level, we document that recommending a vacancy to a job seeker increases the probability to work at this workplace by 10 percent. We propose a decomposition exercise of the net employment effects into three channels. The most important channel corresponds to the increase in the number of applications due to recommendations (first channel), partly offset by the lower conversion into employment of marginal applications (second channel). Congestion effects (third channel) are not a significant contributor to the overall effect. We also find larger employment effects when recommended vacancies are less popular, and for recommendations that broaden search further away in geographical and occupational distance.

We study how online job search advice affects the job search strategies and labor market outcomes of unemployed workers.

We study how online job search advice affects the job search strategies and labor market outcomes of unemployed workers. In a large-scale field experiment, we provide job seekers with vacancy information and occupational recommendations on an online dashboard. A two-stage randomized design with regionally varying treatment intensities allows us to account for treatment spillovers. Our results show that online advice is highly effective when the share of treated workers is relatively low: in regions where less than less than 50% of job seekers are exposed to treatment, working hours and earnings of treated job seekers increase significantly in the year after the intervention. At the same time, we find substantial negative spillovers on other treated job seekers for higher treatment intensities, resulting from increased competition between treated job seekers who apply for similar vacancies.

This paper questions the design of job recommender systems (RS).

This paper questions the design of job recommender systems (RS). We argue that state of the art ML-based algorithmic recommendations aimed at identifying a hiring score from past successful matches do not always result in improved outcomes for job seekers (JS). This is because first, the objectives of these recommendations do not align with the ones of the JS and second, they are usually generated independently of each other, without considering competition. Using a theoretical model of a two-sided market with an application stage, we discuss the needs that RS should meet. We then show that the ML-based hiring score, from which recommendations are typically derived, is only one of the necessary ingredients to meet these needs. Additionally, a matching score between JS and job offer profiles must be considered, and the two should be combined to form a criterion that reflects the expected utility. Our empirical analysis confirms this quantitatively matters, using the RS designed as part of a long-term project in collaboration with the French Public Employment Service. This project leverages extensive and detailed data on applicants, firms, and past job searches. Moreover, we discuss how optimal transport can be leveraged to design RS that avoid congestion, viewing the recommendations as a collective problem rather than a series of individual programs.

EU Eastern Enlargement elicited a rise in (temporary) labour market oriented immigration to Germany starting in May 2011. Taking into account that not all immigrants stay permanently and that outmigration flows are selective, this paper classifies recent EU immigrants into “new arrivals” and “stayers” drawing on administrative social security data (2005-2017). This novel strategy allows us to separately identify their potentially opposing short- and medium-run effects on labour market outcomes in Germany. We find a transitory negative wage effect among German nationals, particularly at the bottom of the wage distribution; and a permanent positive effect on full-time employment.

The Institute for Employment Research (IAB) is pleased to host an international workshop on recent developments in wage determination, distribution, and job skills from 14-15 June.

The traditional human capital model of wage determination fails to explain why wage disparities exist within or between firms, as firms themselves are deemed irrelevant. However, the availability of new data, such as employer-employee matched data sets, makes it possible to better explore issues of wage inequality. Consequently, models examining the sorting of workers across firms with varying productivity levels have gained importance. Our international conference aims to contribute to a better understanding of wage determination, distribution, and job skills.

Our outstanding speakers will address the significant rise in earnings inequality witnessed across numerous countries and the factors contributing to these developments. They will discuss the role of individual determinants of wage inequality, including tenure and job mobility, as well as firm characteristics and labor market institutions, and they will delve into the effects of wage losses following job displacement and the wage elasticity of recruitment.

We combine novel micro data with quasi-random timing of patent decisions over the business cycle to estimate the effects of the Great Recession on innovative startups. After purging ubiquitous selection biases and sorting effects, we find that recession startups experience better long-term outcomes in terms of employment and sales growth (both driven by lower mortality) and future inventiveness. While funding conditions cannot explain differences in outcomes, a labor market channel can: recession startups are better able to retain their founding inventors and build productive R&D teams around them.

We address the question whether diversity in the workforce increases in the absence of diversity-targeted policy interventions when academic labor markets become tighter.

Increasing diversity is one of the challenges in modern labor markets. Increased representation, participation and inclusion in the workplace may not only desirable from a societal or political, but also from an economic perspective. The lack of diversity has been documented to be particularly salient in the academic sector. In this paper, we address the question whether diversity in the workforce increases in the absence of diversity-targeted policy interventions when academic labor markets become tighter. We focus on the German academic labor market for professors which has been characterized as a slack labor market with an over-representation of males in which closed networks play an important role. To study market forces, we explore two natural experiments that unexpectedly increased labor demand and led to tighter labor markets for professors. First, using newly digitized data from the German Federal Statistical Office on academic staff during the German university expansion in the 1960s and 70s, we document an increase in the share of female professors from 0.62 percent in 1960 to 4.39 percent in 1977. Second, we explore between-discipline variation in staff replacements at universities in East Germany after the reunification. Using administrative data on university staff, we find that nine years after the fall of the German wall professors are significantly younger in the Social Sciences (strongly affected by replacements) compared to STEM subjects (barely affected), in the East relative to the West. There is no respective significant change in the share of female professors. However, professors have a more diverse academic background as measured by their university of habilitation. Taken together, our analyses demonstrate that positive labor demand shocks indeed have the ability to contribute to more diversity in academia in some dimensions and, by market force and in the absence of targeted policy interventions, break up some of the "Old-Boys' Club’'.