Special Lecture Series (en) Archive - IAB - Institut für Arbeitsmarkt- und Berufsforschung

The evolution of gender in the labour market: Inequality, (in)efficiency and stereotypes

The study is about gender inequality. Are women held back in the labour market because they are less willing to engage in competitive careers?

Gender inequality is rife. Women make systematically different education choices from men, are under-represented in high-earning careers, and bear the bulk of the earning penalty from parenthood. Moreover, the disproportionate role women play in unpaid work in the home more than offsets the gap in paid work in the labor market, resulting in less leisure time and possibly lower life satisfaction.

Are women held back in the labour market because they are less willing to engage in competitive careers and/or they especially value caring for their children? In fact, research has shown that men and women are on average similar in their preferences and attitudes but face different career-family trade-offs. Besides valid arguments of social justice, differential entry barriers into certain professions raise obvious questions about the efficient allocation of women’s talent. While it would be tempting to conclude that family-friendly workplaces could provide a solution to women’s quest for the desired work life balance, there is a risk that this solution may further entrench gender segregation across firms and professions.

Conversely, research has established that, if women’s involvement in the labour market is shaped by prevailing gender norms (actual or perceived) around their primary role in raising children, progress towards gender equality requires redressing gender stereotypes via policy, education, and information interventions.

Prosociality and Layoffs

This study is about layoffs by U.S. public firms using NLP techniques, which allows to establish several facts that are jointly hard to reconcile with existing models.

Many managers describe layoffs as the hardest and most painful decisions of their careers; yet,
standard economic models treat human and physical capital adjustments alike. We collect novel
data on layoffs by U.S. public firms using NLP techniques, which allows us to establish several
facts that are jointly hard to reconcile with existing models. First, firing decisions have large
adverse healthof effects on CEOs, with distress-induced layoffs estimated to reduce CEO lifespan
by 1.85 years. Second, CEOs become more reluctant to make layoffs over their tenure as they
form more connections inside the firm. After plausibly-exogenous CEO changes, instead, new
CEOs make more and shareholder value-increasing layoff decisions. Third, CEOs’ increasing
reluctance to lay off employees intensifies when layoffs are more painful for employees, such as
during recessions or the holiday season, or more painful for managers to witness, such as when
they affect socially or geographically close employees. Fourth, the documented layoff reluctance is
substantially more pronounced among CEOs with higher empathy-related traits. We also show
that long-tenured CEOs are more likely to cut R&D spending during recessions, offsetting forgone
savings from layoffs. Our results imply the need to adjust models of managerial decision-making
for a “human” component of layoff avoidance. Prosocial, or empathy-related, motives provide a
unifying explanation.

Does automation replace experts or augment expertise? The answer is yes.

Yes, automation replaces experts or augment expertise.

Patterns and Paradoxes of Migrant and Ethnic Inequalities in the UK

This lecture examines the patterns and paradoxes in observed educational and labour market attainment of migrants and minorities.

The inequalities faced by immigrants and ethnic minorities are topics of substantial salience across Europe, reflected in an every-growing body of research. Despite the insights shed by this burgeoning literature there remain a number of outstanding debates and puzzles about what leads to better or worse outcomes and the underlying mechanisms.

The UK is an interesting case for illuminating some of these debates and puzzles, due to: the diversity of its immigrant and ethnic minority population, the richness of research base, and certain counter-intuitive findings that are at odds with theoretical expectations as well as evidence from other European countries. It thus has the potential to shed further light on what drives more or less unequal outcomes in different contexts.

Drawing on a range of research from across the last 20-years, in this lecture I examine the patterns and paradoxes in observed educational and labour market attainment of migrants and minorities, with reference to the role of class background, educational aspirations, neighbourhoods and social networks, cohort change and return migration, discrimination and policy to. I explore the gendered differences in such outcomes, and what that implies for our understanding of wider national ‘gender orders’. I reflect on what this body of work can tell us about the factors that shape economic outcomes in different settings, the need for greater attention to both migrant success as well as migrant disadvantage; and I assess the key outstanding questions and implications for future research.

Measuring and Predicting New Work in the US: the role of local factors and global trends

"New work" - we develop an algorithm that identifies new job-titles in the US economy based on their vector distance from the closest existing job title in the previous census.

"New work", namely the introduction of types of jobs that did not exist earlier, is an essential part of innovation and employment growth for advanced economies. Using text analysis, we develop an algorithm that identifies new job-titles in the US economy based on their vector distance from the closest existing job title in the previous census. We use this method to generate a measure of "new work" from 1980 to 2010 in each of 354 occupations and we construct its distribution across 766 commuting zones. We first show how this measure of "new work" is associated to task and skill characteristics of workers in the occupations and to employment growth, skill bias and innovation in the commuting zones. Then we analyze whether local population density, human capital and manufacturing intensity in the 1980, and/or local exposure to structural "shocks" in the 1980-2010, relating to trade competition, technological change, immigration and age changes predict the creation of new work.

Our main findings are that the share of college educated and the density of population in 1980 are the strongest predictors of New Work creation in the 1980-2010 period. The aging of population and exposure to computer adoption were also associated to New Work creation, while robot adoption was negatively associated to it. The exposure to immigration and trade had a more nuanced and differentiated correlation to new work.

Navigating Vulnerabilities: Exploring Trust Dynamics in Citizens-State Interactions for Access to Public Services

This lecture delves into the multifaceted dynamics of the state-citizen relationship within the realm of service delivery in vulnerable contexts.

This lecture delves into the multifaceted dynamics of the state-citizen relationship within the realm of service delivery in vulnerable contexts, with a specific focus on the crucial roles played by health workers, teachers, and social workers. Examining these interactions against the backdrop of high vulnerabilities, characterized by factors such as limited trust, resource constraints, perceived lack of state legitimacy, and pervasive inequalities, our discussion aims to uncover the nuanced impact of contextual challenges on encounters between citizens and frontline service providers. Drawing on various research studies concerning frontline workers in Brazil, we will explore the underlying mechanisms that either reduce or reproduce existing inequalities when implementing policies in contexts of high vulnerabilities.

Quantifying Non-Sampling Variation: College Quality and the Garden of Forking Paths

The literature on alternative methods for accounting for sample-independent variability is reviewed, a typology of sources of sample-independent variation is developed, and an empirical investigation is conducted estimating the relative and absolute importance of the different types of sample-independent variation.

Empirical economics papers report standard errors to take into account uncertainty associated with sampling variation but rarely consider non-sampling variation from researcher choices about measurement of key variables, functional form choice, identification strategy, and data set. In this paper, we review the literature on alternative methods for taking account of non-sampling variability, develop a typology of sources of non-sampling variation, and conduct an empirical exercise in which we estimate the relative and absolute importance of different types of non-sampling variation. The empirical exercise proceeds in the context of the literature that seeks to estimate the causal effect of college quality on educational and labor market outcomes.

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.