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Archives: IAB-Veranstaltungen

The conference aims to bring together experiences and research results on different aspects of practice integration.

Including practical work and work-based learning in higher education curricula has become increasingly popular, both to increase graduate employability and to improve the permeability between vocational and university education.

The implementation of practical experience in higher education is country-specific and takes different forms, from internships to integrated curricula as in the “dual-study” model of German universities of cooperative education.

The conference aims to bring together experiences and research results on different aspects of practice integration from various countries. We are particularly interested in:

  • Stocktaking: What forms of practice integration exist in the higher education systems of different countries? What are their characteristics, advantages and disadvantages? Is practice integration increasing, and how do the developments compare between different countries?
  • Student characteristics: Which types of students (e.g., high-achieving; non-academic background) are attracted to practice-oriented study programmes? What are their motives for choosing them?
  • Effects: How does work experience and practice orientation in higher education affect students’ skills, confidence, and motivation? Compared to less practice-oriented study programmes, are there differences in final grades, study-to-work transitions, job prospects, and income?
  • Internationalisation: How can internationalisation be implemented with regard to practice orientation in higher education? What are the special needs of international students?
  • Measurement and recognition of achievements: How can student achievements in practice phases be measured and integrated into the academic system of exams and grades? What are the problems in aligning practical and academic evaluation?
  • Cooperation of stakeholders: How can the cooperation between universities and stakeholders, e.g. vocational schools and companies, be improved? What formal framework is required?

Moreover, the conference offers sessions with a more general perspective on “Higher Education and the Labour Market”, for example on returns to tertiary education, university dropout, graduates’ placement on the labour market, and regional mobility of graduates.

This two-day conference seeks to bring together researchers addressing different aspects of social policy.

The Standing Field Committee on Social Policy (Ausschuss für Sozialpolitik) of the German Economic Association (Verein für Socialpolitik), the Institute for Employment Research (IAB), and the Labor and Socio-Economic Research Center (LASER) of the Friedrich-Alexander-Universität Erlangen Nürnberg (FAU) are pleased to announce a workshop on “Social Policy”.

This two-day conference (starting at Thursday noon and ending on Friday afternoon) seeks to bring together researchers addressing different aspects of social policy, e.g. migration and integration, unemployment insurance, welfare system, pension policy, education policy, family policy, and health policy.

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.

This lecture investigates the most durable positive consequences of tight labor markets and focus on the mechanisms that produce positive outcomes.

Most research on poverty focuses on the damage caused by persistent unemployment.  But what actually happens when jobs are plentiful and workers are hard to come by? Moving the Needle examines how very low unemployment boosts wages at the bottom, improves job quality, lengthens job ladders, and pulls the unemployed into a booming job market. Drawing on over seventy years of quantitative data as well as interviews with employers, jobseekers, and longtime residents of poor neighborhoods, this lecture investigates the most durable positive consequences of tight labor markets and focus on the mechanisms that produce positive outcomes: matching processes that include the dispossessed, job ladders that grow within the low wage sector, and increasing human capital that can be parlayed into internal and external upward mobility.  Dr. Newman will also consider the downside of overheated economies, which can fuel surging rents and ignite outmigration. She will conclude with a discussion of policies and practices that can sustain the benefits of tight labor markets when unemployment begins to rise.

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.

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.