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Archives: IAB-Veranstaltungen

We investigate the role of information frictions in the US labor market using a new nationally representative panel dataset on individuals' labor market expectations and realizations. We find that expectations about future job offers are, on average, highly predictive of actual outcomes. Despite their predictive power, however, deviations of ex post realizations from ex ante expectations are often sizable. The panel aspect of the data allows us to study how individuals update their labor market expectations in response to such shocks. We find a strong response: an individual who receives a job offer one dollar above her expectation subsequently adjusts her expectations upward by $0.47. We embed the empirical evidence on expectations and learning into a model of search on- and off- the job with learning, and show that it is far better able to fit the data on reservation wages relative to a model that assumes complete information. We use the framework to gauge the welfare costs of information frictions which arise because individuals make uninformed job acceptance decisions and find that the costs due to information frictions are sizable, but mitigated by the presence of learning.

The conference is open to all areas of microsimulation, including static and dynamic microsimulation, agent-based models, behavioural models, and all applied and methodological contributions related to microsimulation. Moreover, there will also be thematic streams during the conference (organised together with partners in brackets):

  • Labour markets and welfare policies (Dr. Kerstin Bruckmeier, Institute for Employment Research IAB)
  • Comparative analysis on taxes and benefits (Salvador Barrios, PhD, Joint Research Centre, European Commission)
  • Dynamic microsimulation (Prof. Ralf Münnich, MikroSim FOR2559)
  • Health (Ieva Skarda, PhD, Centre for Health Economics at the University of York)
  • Agriculture and environment (Prof. Cathal O’Donoghue, National University of Ireland, Galway; University of Maastricht) 

Digital technologies can be both labour-saving and labour-augmenting, thereby changing the division of labour between humans and machines. While an increasing range of tasks can be automated, new tasks arise at the same time. This digital transformation is likely to interact with the ecological transformation towards a climate-friendly economy, both of which will shape the future of work. On top of that, the Covid-19 pandemic induced fast changes in the organisation and location of work. The aim of this conference is to bring together economists, sociologists and researchers from related fields to discuss frontier research on labour market effects of processes associated with the digital and ecological transformation. Special focus lies on the following questions:

  • How does the division of tasks between workers and machines develop?
  • Do green jobs differ from non-green jobs in terms of skills and human capital?
  • How does the digital and ecological transformation affect labour market, firm and individual outcomes?
  • How do job contents and tasks evolve and how do workers adapt?
  • What is the role of education and training in preparing the workforce for new knowledge and skills requirements?
  • How does the Covid-19 pandemic affect both types of transformations? And what does the pandemic reveal about the interactions between gender, education, work requirements and tasks?
  • How can policy cushion potential negative outcomes r

We examine the properties of a recommender system we developed at the Public Employment Service (PES) in France, prior to its implementation in the field. The algorithm uses past matches and a very large set of covariates to produce, for each job seeker, a ranking of the available offers and score each pair jobseeker-offer. Using a calibration step that takes advantage of the observation of application sequences, it gives a predicted "matching probability" for each pair.  After a theoretical discussion about the possible strategies to design a recommender system, we compare this new machine learning (ML) algorithm with another matching tool, mimicking the one currently used at the PES, based on a score measuring the "closeness" between the jobseeker's search criteria or preferences and the characteristics of the offer. We quantify the trade-off between the matching probability and the later "preference score" when switching from one system to the other. Next, we examine the issue of congestion.  We show that, on the one hand the ML algorithm based on past matches tends to increase congestion and on the other hand that this strongly reduces its performance. Finally, we show that the use of optimal transport to derive recommendations from the matching probability matrix significantly alleviates this problem. The main lesson at this stage is that an algorithm ignoring preferences and competition in the labor market would have very limited performances but that tweaking the algorithm to fit these dimensions substantially improves its properties, at least "in the lab".

We quantify the effects of wage bargaining shocks on macroeconomic aggregates using a structural vector auto-regression model for Germany. We identify exogenous variation in bargaining power from episodes of minimum wage introduction and industrial disputes. This narrative information disciplines the impulse responses to a wage bargaining shock of unemployment and output, and sharpens inference on the behaviour of other variables. The implied transmission mechanism is in line with the theoretical predictions of a large class of search and matching models. We also find that wage bargaining shocks explain a sizeable share of aggregate fluctuations in unemployment and inflation, that their pass-through to prices is very close to being full, and that they imply plausible dynamics for the vacancy rate, firms' profits, and the labour share.

We study how Americans respond to idiosyncratic and exogenous changes in household wealth and unearned income. Our analyses combine administrative data on U.S. lottery winners with an event-study design that exploits variation in the timing of lottery wins. Our first contribution is to estimate the earnings responses to these windfall gains, finding significant and sizable wealth and income effects. On average, an extra dollar of unearned income in a given period reduces pre-tax labor earnings by about 50 cents, decreases total labor taxes by 10 cents, and increases consumption by 60 cents. These effects are heterogeneous across the income distribution, with households in higher quartiles of the income distribution reducing their earnings by a larger amount.

Our second contribution is to develop and apply a rich life-cycle model in which heterogeneous households face non-linear taxes and make earnings choices along both intensive and extensive margins. By mapping this model to our estimated earnings responses, we obtain informative bounds on the impacts of two policy reforms: an introduction of UBI and an increase in top marginal tax rates. Our last contribution is to study how additional wealth and unearned income affect a wide range of behavior, including geographic mobility and neighborhood choice, retirement decisions and labor market exit, family formation and dissolution, entry into entrepreneurship, and job-to-job mobility.

The conference focuses on technology, trade, and demographic changes and the ways they interact with employment, wages, and participation in the labor market, with a particular emphasis on the role of institutions and on labor markets during the COVID-19 crisis. Understanding these relationships is key in assessing the performance of the labor market and for the design of effective labor market policies. We invite empirical and theoretical contributions on these topics from all areas of economics and sociology with a focus on labor, education, health, or human resource management.

The conference will be held in-person. It is sponsored by the German Research Foundation (DFG) as part of the Priority Program 1764 “The German Labor Market in a Globalized World” and will also mark the end of the program.

The Covid crisis revived the interest in the topic of short-time work (sometimes also known as furlough schemes or work sharing). In many countries, the schemes were utilised in unprecendented ways. The Institute for Employment Research organises a one-day online workshop on May 13, 2022 that focuses on current research on short-time work. Contributions may address the Covid crisis or previous economic crises. Both theoretical and applied papers with both micro- and macroeconomic approaches are welcome.

The workshop provides the opportunity for timely exchange on cutting-edge research on a specific topic. Presentations and discussions should spur the debate on usage, effects and design of a crucial labour market instrument.

COVID-19 drove a mass social experiment in working from home (WFH). We survey more than 30,000 Americans over multiple waves to investigate whether WFH will stick, and why. Our data say that 20 percent of full workdays will be supplied from home after the pandemic ends, compared with just 5 percent before. We develop evidence on five reasons for this large shift: better-than-expected WFH experiences, new investments in physical and human capital that enable WFH, greatly diminished stigma associated with WFH, lingering concerns about crowds and contagion risks, and a pandemic-driven surge in technological innovations that support WFH. We also use our survey data to project three consequences: First, employees will enjoy large benefits from greater remote work, especially those with higher earnings. Second, the shift to WFH will directly reduce spending in major city centers by at least 5-10 percent relative to the pre-pandemic situation. Third, our data on employer plans and the relative productivity of WFH imply a 5 percent productivity boost in the post-pandemic economy due to re-optimized working arrangements. Only one-fifth of this productivity gain will show up in conventional productivity measures, because they do not capture the time savings from less commuting.

We investigate the role of information frictions in the US labor market using a new nationally representative panel dataset on individuals' labor market expectations and realizations. We find that expectations about future job offers are, on average, highly predictive of actual outcomes. Despite their predictive power, however, deviations of ex post realizations from ex ante expectations are often sizable. The panel aspect of the data allows us to study how individuals update their labor market expectations in response to such shocks. We find a strong response: an individual who receives a job offer one dollar above her expectation subsequently adjusts her expectations upward by $0.47. We embed the empirical evidence on expectations and learning into a model of search on- and off- the job with learning, and show that it is far better able to fit the data on reservation wages relative to a model that assumes complete information. We use the framework to gauge the welfare costs of information frictions which arise because individuals make uninformed job acceptance decisions and find that the costs due to information frictions are sizable, but mitigated by the presence of learning.