Hybrid Archive - Page 13 of 15 - IAB - Institut für Arbeitsmarkt- und Berufsforschung

Designing labor market recommender systems: the importance of job seeker preferences and competition

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 enlargement and (temporary) migration: Effects on labour market outcomes in Germany

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.

Labor Demand and Workforce Diversity: Evidence from Two Natural Experiments

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’'.

Climate Change, Migration, and Inequality

Can weather events predict migration choices of 140,000+ individuals?

Existing work presents mixed findings on the impact of weather events on international mobility. Relying on fine-grained data over 1980-2018 in the Mexico-U.S. setting, we turn to machine learning (ML) tools to first determine if weather events can predict migration choices of 140,000+ individuals. We use random-forest models which allow us to include a comprehensive list of weather indicators measured at various lags and to consider complex interactions among the inputs. These models rely on data-driven model selection, optimize predictive performance, but often produce ‘black-box’ results. In our case, the results show that weather indicators offer at best a modest improvement in migration predictions. We then attempt to open the black box and model the linkages between select weather indicators and migration choices. We find the combination of precipitation and temperature extremes and their sequencing to be crucial to predicting weather-driven migration responses out of Mexico. We also show heterogeneity in these responses by household wealth status. Specifically, we find that wealthier households in rural communities migrate in the immediate aftermath of a negative weather shock (relative to the ‘normal’ weather in their community), while poorer households need to experience consecutive and worsening shocks to migrate to the United States. This pattern suggests that migration as an adaptation strategy might be available to select households in the developing world.

Marriage and Divorce under Labor Market Uncertainty

This paper analyzes these “labor market divorces” in a novel model of simultaneous search in labor and marriage markets.

Married women’s greater allocation of time towards household chores and childcare suggests that an increase in their labor supply may result in reduced marital surplus and stability. This mechanism can explain persistent gender gaps in labor supply if the potential reduction is considered in decisions about reservation wages and job search efforts. An implication is that divorces may be caused by transitions into employment. This paper analyzes these “labor market divorces” in a novel model of simultaneous search in labor and marriage markets. Labor market search intensity choices depend on marital status and the partner’s type. The model matches key trends in German household survey data: declining marriage rates, increasing employment rates of married women, and a reduction of married women’s domestic time inputs. Our laboratory to quantify the role of labor market divorces is a period of rapid employment growth in Germany that started in the mid-2000s. This development in the labor market was not neutral with respect to marriage. Although more married women entering employment led to more divorces, the decrease in divorces caused by job loss among married men was greater, resulting in a net decrease in the overall divorce rate.

The Care-Dependent are Less Averse to Care Robots: An Empirical Comparison of Attitudes

Using a large-scale experimental vignette study, we investigated people’s affective attitudes toward care robots.

A growing gap is emerging between the supply of and demand for professional caregivers, not least because of the ever-increasing average age of the world’s population. One strategy to address this growing gap in many regions is the use of care robots. Although there have been numerous ethical debates about the use of robots in nursing and elderly care, an important question remains unexamined: how do the potential recipients of such care perceive situations with care robots compared to situations with human caregivers? Using a large-scale experimental vignette study, we investigated people’s affective attitudes toward care robots. Specifically, we studied the influence of the caregiver’s nature on participants’ perceived comfort levels when confronted with different care scenarios in nursing homes. Our results show that the care-robot-related views of actual care recipients (i.e., people who are already affected by care dependency) differ substantially from the views of people who are not affected by care dependency. Those who do not (yet) rely on care placed care robots’ value far below that of human caregivers, especially in a service-oriented care scenario. This devaluation was not found among care recipients, whose perceived level of comfort was not influenced by the caregiver’s nature. These findings also proved robust when controlled for people’s gender, age, and general attitudes toward robots.

Performance costs and benefits of collective turnover: A theory-driven measurement framework and applications

We model the effect of collective turnover on workplace performance as the sum of its costs and possible benefits occurring through changes in workforce match quality.

Building on job matching theory, we model the effect of collective turnover on workplace performance as the sum of its costs and possible benefits occurring through changes in workforce match quality. The resulting theoretical turnover-performance relationship is generally curvilinear, nesting all the hitherto known patterns -- linear, ``U-shape'' and ``inverted U-shape'' -- as special cases. We show how one can estimate this relationship empirically, for matched worker-plant data, and calculate the implied costs and benefits of turnover. Applications to data from two retail networks reveal that turnover is more costly than beneficial.

Automated classification for open-ended questions + Hammock Plots

We investigate whether BERT is more effective at automated coding of answers to open-ended questions than non-pre-trained statistical learning approaches.

Answers to open-ended questions are often manually coded into different categories. This is time consuming. Automated coding uses statistical/machine learning to train on a small subset of manually coded text answers. The state of the art in NLP (natural language processing) has shifted: A general language model is first pre-trained on vast amounts of unrelated data, and then this model is adapted to a specific application data set. After reviewing some earlier results, we empirically investigate whether BERT, the currently dominant pre-trained language model, is more effective at automated coding of answers to open-ended questions than non-pre-trained statistical learning approaches. In the second part of the talk, I discuss the hammock plot for visualizing categorical or mixed categorical data.

Household Labor Supply Elasticities: Evidence from Cross-Border Workers

We provide evidence for intensive margin adjustments in hours worked consistent with estimates.

After the Swiss National Bank unexpectedly abandoned a minimum exchange rate policy in 2015, the Swiss franc appreciated by more than 10 percent against the Euro. The appreciation implied a sudden increase in real wage incomes for over 40,000 German cross-border commuters into Switzerland. We use this exchange rate shock to estimate the own-wage and cross-spouse labor supply elasticities from administrative tax returns data and find a 5% drop in taxable income for cross-border workers and a 1.5% reduction in taxable income for cross-border worker spouses. We provide evidence for intensive margin adjustments in hours worked consistent with these estimates.