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Archives: IAB-Veranstaltungen

Results from the first ever study to examine short- and long-term returns to special education programs with causal machine learning and computational text analysis methods.

Leveraging unique insights into the special education placement process through written individual psychological records, I present results from the first ever study to examine short- and long-term returns to special education programs with causal machine learning and computational text analysis methods. I find that special education programs in inclusive settings have positive returns in terms of academic performance as well as labor-market integration. Moreover, I uncover a positive effect of inclusive special education programs in comparison to segregated programs. This effect is heterogenous: segregation has least negative effects for students with emotional or behavioral problems, and for nonnative students with special needs. Finally, I deliver optimal program placement rules that would maximize aggregated school performance and labor market integration for students with special needs at lower program costs. These placement rules would reallocate most students with special needs from segregation to inclusion.

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.

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.

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.

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.

This paper develops a macroeconomic model that combines an incomplete-markets overlapping-generations economy with a job ladder featuring strategic wage bargaining and endogenous search effort of employed and non-employed workers. The model is able to capture the empirical relationships between search activity, labor market transition, earnings and wealth that we document in German data. We use the calibrated model to analyze the determinants of job mobility, earnings and wealth dynamics over the life cycle. We further examine the impact of unemployment insurance and progressive taxation for labor market dynamics, wage inequality and macroeconomic outcomes.

This workshop aims to improve the knowledge on welfare and unemployment dynamics and social security under different institutional settings. It is also about the question of how benefit recipients can be helped to leave benefit receipt permanently.

Demographic change, digitalisation and the need to achieve carbon-neutral growth not only have macro-economic consequences, but also have an impact on individual employment prospects and careers. Flexible employment might offer additional employment opportunities, but might also lead to interrupted employment careers with workers being less well protected against social risks and against old-age poverty. Technological change might decrease the labour demand particularly for medium and low-skill occupations. This might affect individual employment stability. Changing working conditions may demand new requirements on employees' qualifications and skills, leading to qualification policies reacting more flexibly to new requirements. The recent crises have also shown that certain population groups have limited access to benefits in existing social security systems. This particularly holds for those with non-standard employment (i.e. solo-self-employed, marginally employed). Conditionality and demanding elements are prevalent in most social security and minimum income systems. It is vital to understand consequences of these principles for the take-up of benefits as well as the employment prospects and social mobility of recipients.

Against this background, this workshop aims to improve the knowledge on welfare and unemployment dynamics and social security under different institutional settings. It is also about the question of how benefit recipients can be helped to leave benefit receipt permanently.

The workshop is open to empirical and policy-oriented single country studies or international comparisons from sociology, economics or political science based on quantitative empirical data. Contributions using different methods, for example sequence data analysis, duration analysis, causal analysis, and methods of policy analyses and microsimulation on one or more of the following questions are very welcome:

  • How do the mentioned structural changes (e.g. technological change) affect individual employment prospects and economic situation? What is the impact on social inequality?
  • What are typical labour market trajectories for different groups of unemployed individuals (e.g. vulnerable groups)?
  • What role does atypical employment play? Have atypical employment relationships proved successful? How can upward mobility succeed?
  • What role do education and training play? What are their long-run effects?
  • Which experiences did welfare states make with the strategies of activation and social investment?
  • Is providing a basic income instead of insurance based social security an adequate response to the trends?

We argue that skill-biased technological change not only affects wage gaps between skill groups, but also increases wage inequality within skill groups, across workers in different firms. Building on a heterogeneous firm framework with labor market frictions, we show that an industry-wide skill-biased technological change shock will increase between-firm wage inequality within the industry through four main channels: changes in the skill wage premium (as in traditional models of technological change); increased employment concentration in more productive firms; increased wage dispersion between firms for workers of the same skill type; and increased dispersion in the skill mix that firms employ, due to more sorting of skilled workers into more productive firms. Importantly, a simultaneous increase in the supply of skilled workers does not offset the technology- induced rise in inequality. Using rich administrative matched employer-employee data from Germany, we provide empirical evidence of establishment-level adjustments that are in line with the predictions of the model. We further document that industries with more technological adoption exhibit particularly pronounced adjustment patterns along the dimensions highlighted by the model. 

We study a Dutch reform that raised the retirement age by 13 months and nearly tripled employment at targeted ages.

Government policies are encouraging older workers to delay retirement, which may curb younger workers' career advancement. We study a Dutch reform that raised the retirement age by 13 months and nearly tripled employment at targeted ages. Using monthly linked employer-employee data, we show that affected firms delay and decrease replacement hiring, and coworkers' earnings fall via reductions in hours worked, wages, and promotions. The hiring and coworker spillovers offset most of the additional hours worked by older workers. These spillovers exacerbate within-firm earnings disparities, redistributing earnings from low to high earners, young to old workers, and women to men.