The opening of refugee shelters is regularly met with protest from the surrounding community. Often, such opposition is driven by the fear that the presence of a shelter devalues the neighbourhood, either because of a concrete decrease in the quality of local amenities and public life, or because of neighbours and prospective residents’ prejudicial beliefs (or a combination of both). At the same time, it is unclear whether protests by individual residents reflect the preferences of the entire community, and whether fears over the arrival of refugees are held strongly enough to affect residents’ concrete decisions over where to live. In this article I combine information on property listings between 2012 and 2019 with data on all refugee accommodation facilities in Munich, Germany to examine whether the opening of a refugee shelter affects the desirability of the surrounding neighbourhood, decreasing local property prices relative to elsewhere. Results from the staggered difference-in-difference design find no evidence that the presence of a shelter impacts the value of surrounding properties, or changes the demand for or supply of local housing. Complementary survey findings suggest that increased contact may be driving this null effect: the presence of a nearby refugee shelter increases casual encounters between natives and refugees, which may reduce prior fears over refugees’ negative impact on the local community.
Archives: IAB-Veranstaltungen
Perspectives on (Un-)Employment
Cross-Border Commuting, Gender Differences, and the Outside Option
We study a cross-border commuting reform that granted German workers in the German-Swiss border region access to the high-wage Swiss labour market. This exogenous increase in German workers‘ outside option led to an increase in average wages paid by German establishments in the border region. But this wage increase is not homogenous across worker types. First, high-skilled workers enjoyed a higher wage increase than low-skilled workers, consistent with a stronger increase in Swiss-labor demand for high-skilled German workers. Second, the positive wage effects only accrue to men in the border region, but not women, consistent with gender differences in the willingness to commute. The outside option clearly seems to play an important role in wage negotiations and its wage effects can be heterogeneous.
The role of overconfidence in the gender pay gap
Recent evidence on the gender pay gap has shown that while it is narrowing for the least educated, it has remained stagnant for those with a university degree and is largest for those at the top of the earnings distribution. Attempts to explain the gap using non-cognitive traits have been limited despite a literature highlighting the fact that some of the gap may be attributable to women not “leaning in” while men are more overconfident in their abilities. We probe this hypothesis using longitudinal data from childhood into mid-career and construct a measure of overconfidence using multiple measures of objective cognitive ability and subjective estimated ability. Our measure confirms previous findings that men are more overconfident than women. We then use linear regression and decomposition techniques to account for the gender pay gap including our measure of overconfidence. Our results show that overconfidence captured in adolescence explains a significant portion of the gender wage gap at age 25, which decreases in importance by age 34 and age 42. This highlights the importance of overconfidence in helping individuals to get on a trajectory of higher earnings early in career.
4th Forum “Higher Education and the Labour Market” (HELM)
Changing Gender Status Beliefs? Implications for Gender Inequality in the Labor Market
We draw on research on status processes and cultural change to develop predictions about gender status beliefs in the United States. We expect that
- while explicitly men and women may not distinguish competency and worth by gender, they do so implicitly,
- that younger respondents, especially women, hold less consensual gender status beliefs, and
- men are less likely to alter their gender status beliefs due to loss aversion.
We conduct two studies to assess these arguments. The first uses novel nationally-representative data to describe the distributions of status beliefs in the US population; the second demonstrates the importance of these beliefs for allocating rewards by gender. Combined, the studies demonstrate the distribution of gender status beliefs by age and gender, and the implications for gender inequality, thereby illustrating the role of cultural status beliefs for maintaining gender stratification and the potential role of cohort change for changing such beliefs. Finally, we discuss promising approaches to reduce the impact of gender status beliefs in labor market processes.
Labor Market Search with Imperfect Information and Learning
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.
European Meeting of the International Microsimulation Association 2022
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)
TASKS VI: The Digital and Ecological Transformation of the Labour Market
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
Designing labor market recommender systems: the importance of job seeker preferences and competition
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".