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Veranstaltungsformat: Online

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 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.

Social science research demonstrates that dispersal policies and restrictions on the freedom of residence have inhibited refugees’ socio-economic integration, presumably because such policies prevent refugees from moving to places where they can employ their skills most fruitfully. However, studies of refugees’ actual residential choices provide little evidence that good economic prospects attract refugees, and some even suggest that refugees often move to deprived cities with frail labor markets. The combination of negative effects of residence restrictions and emerging evidence of disadvantaging secondary migration forms what we call the ‘refugee mobility puzzle’. In this study, we aim at unpacking this puzzle by analyzing the inner-German migration patterns of recent refugees. Specifically, we ask: What attracts refugees to deprived areas, and can their seemingly unfortunate residential choices be understood as moves to opportunity and increased prospects of labor market integration after all? Empirically, we draw on the IAB-BAMF-SOEP Survey of Refugees and track the location of more than 2,000 refugee respondents who were exogenously allocated a place of residence and subsequently became free to move. Based on linear-probability discrete choice models across all German counties and postcodes, we confirm that refugees tend to move to areas with high unemployment. We show that major attractors like housing availability, co-ethnic networks, and service-oriented labor markets are clustered in areas with high unemployment. Taken together, our results complicate recent critiques of dispersal policies and restrictions. On the one hand, our findings show that seemingly disadvantaging relocations into high unemployment areas can conceal potentially improved economic perspectives in relevant labor markets. On the other hand, refugees’ search for affordable housing may turn into an unintended lock-in factor in the mid- and long-run.

This paper studies the interplay between how much workers value workplace flexibility, whether they have such amenities, and how the presence of amenities affects their wages. To overcome the challenge of eliciting quantitative measures of willingness to pay (WTP) at the individual level, we propose the use of dynamic choice experiments, a method which we call the Bayesian Adaptive Choice Experiment (BACE). We implement this method to collect data on the joint distribution of wages, work arrangements, and WTP for different forms of flexibility. We then introduce and estimate a model in which workers may face different prices for job amenities depending on their productivity, extending the Rosen (1986) model of compensating differentials. The model captures key patterns in the data, including (i) the relationship between wages and having amenities, (ii) inequality in workplace amenities across the earnings distribution even when workers value these amenities similarly, and (iii) the tradeoffs across different forms of flexibility. We use the estimates to explore the welfare consequences of workers facing different amenity prices.

Social disparities in track choices are a well-known mechanism for the intergenerational reproduction of inequality. School guidance may help reducing such disparities by narrowing information gaps and by reducing the family influence on students’ decision making. We investigate the potential equalizing role of guidance programs by analysing an intervention carried out in Italy, where students are tracked at age 14 and teacher recommendations are non-binding. The intervention took place in 2018 in the city of Turin and involved 40% of all eighth-grade students, shortly before their transition from comprehensive to tracked education. The students attended four two-hour sessions designed to provide them with information about the educational system and related job market opportunities, and to raise their awareness of their aptitudes and inclinations. We expected the programme to be of particular benefit to low socio-economic status (SES) and migrant students and thus to reduce social gaps in track choices. We adopted a mixed-method research design: with quantitative analyses based on a combination of propensity-score-matching and differences-in-differences techniques, we compared the outcomes of comparable students from the 2017 and 2018 cohorts who were or were not exposed to the intervention in order to assess its impact on inequality; additionally, we use qualitative non-participatory observation to unveil the actual content and implementation of the program and the behaviour of the key actors. We find that while the program contributed to reducing indecision, probably by compelling students to reflect more carefully about their decisions during this crucial transition, it did not have any major effect on social inequalities. Results from the qualitative analysis help us shed light on the mechanisms at play behind this lack of effect. In particular, the heavy emphasis placed on current achievement records, dropout risks, and (short-term) labour-market outcomes may counteract the equalizing potential of the program by pushing low-SES and migrant students towards vocational tracks.

We use French data over the 1994-2013 period to study how imports of industrial robots affect firm-level outcomes. Guided by a simple model, we develop various empirical strategies to identify the causal effects of robot adoption. Our results suggest that, while demand shocks generate a positive correlation between robot imports and employment at the firm level, exogenous exposure to automation leads to job losses. We also find that robot exposure increases productivity and some evidence that it may increase the relative demand for high-skill professions.