IAB-Colloquium (en) Archive - Page 8 of 17 - IAB - Institut für Arbeitsmarkt- und Berufsforschung

Big Data and Start-up Performance

Big Data and the analysis thereof is considered of increasing importance, not only for big (tech) companies.

Big Data (BD) and the analysis thereof (i.e., Big Data Analytics, BDA) is considered of increasing importance, not only for big (tech) companies but also for small/medium-sized companies and, in particular, for new ventures. Despite or precisely due to its generally presumed relevance, the question arises whether applying BDA leads to better firm performance. Based on a large, representative sample of 3,700 German start-ups, we specifically study the adoption of BDA among start-ups and analyze its economic effects using various short- and longer-term performance measures. We show that start-ups adopting BDA significantly differ from non-adopters regarding their founders' age, education, team composition, and experience. Accounting for these differences, we then investigate the effect of adopting BDA on the new ventures' operating costs, sales, profits, survival rate, and (employee) growth. Our findings show that using BDA does not lead to a competitive advantage in terms of the classical short-term performance measures but is rather associated with greater sales/profit uncertainty, higher (personnel) costs, and a higher probability of failure. Yet, the increased risk of adopting BDA is at the same time compensated by a prospect for higher excess performance -- BDA-adopting start-ups perform significantly better than their peers at the 90%-quantile -- as well as by better expected longer-term performance, as measured by the start-ups' growth and by their ability to secure Venture Capital (VC). Our findings support the concept of a few Schumpeterian Entrepreneurs who adopt technology at the frontier of innovation and found high-risk start-ups with the prospect of high rewards.

The costs and benefits of work after the retirement

A proposal for an occupation-specific end-of-life and late-life perspective on retirees.

Retirement marks a significant life transition, signaling the end of one's active work engagement and the beginning of a new phase of life. In the best-case scenario post-retirement life is characterized by increased time spend with friends and family, and a purpose in life that is perceived as meaningful. The reduced work load should be associated with improved health, because sources of physical and psychological stress should have far less impact. However, reality paints a different picture: The research on retirement shows a lot variation in retirement experiences, financial well-being, mental and physical health, social engagement, and overall life satisfaction.
The importance of pre-retirement occupations on individuals' lives cannot be overstated. However, the impact of occupations on people’s lives does not end with their retirement. Hence, I will propose a new perspective on employment and occupations by focusing on the retirees’ late life and end of life. In my presentation I will provide the theoretical foundations, why occupations (should) have a lasting impact. I will present a data source with which such analyses could be done and discuss outcome variables of interest. Examples for occupation-specific outcomes of interest are: years of life remaining after retirement, years in good health after retirement, years spend in loneliness, wealth, poverty, depression, political attitudes, decrease in cognitive capabilities, social networks, free-time activities, volunteering, housing, life satisfaction, etc.
From a policy perspective, understanding how different occupations influence post-retirement lives can inform retirement planning initiatives and social safety nets as well as occupation-specific training and safety regulations. On an individual level, insights gained from such research can assist individuals in making informed decisions about their vocational aspirations, careers, planned changes of occupations, retirement age, and financial preparations.

The Gender Wage Gap Among Those Born in 1958: A Matching Estimator Approach

Exploiting prospective data on a cohort born in 1958 we estimate the gender wage gap over the life-course between age 23 and 63, departing from the literature in two ways.

Most studies estimating the gender wage gap rely on linear regression of log hourly earnings. Estimates often condition on potentially endogenous regressors such as labour market experience and family formation. Exploiting prospective data on a cohort born in 1958 we estimate the gender wage gap over the life-course between age 23 and 63, departing from the literature in two ways. First, we use matching estimators which are rarely used in the literature. Like regression analyses matching relies on observed data to recover the effect of gender on earnings, but by explicitly considering the issue of common support it is more transparent in its treatment of men as counterfactuals for women. We examine the importance of the common support issue for the size of the gender wage gap. Second, we condition on pre-labour market variables to avoid conditioning on endogenous choice variables, such as family formation, which are made, in part, with knowledge regarding one’s potential earnings. We argue our data are well-suited to the task because they contain a wide array of prospective data collected at birth, then at ages 7, 11 and 16, which might conceivably confound estimates of the GWG. In contrast to findings in the literature in which the regression-adjusted GWG is considerably smaller than the raw gap, we find differences in log hourly mean earnings between men and women are of roughly similar size and, in some cases, wider than raw gaps conditioning on pre-labour market variables. This is the case whether we use matching or linear estimation techniques. However, the PSM estimated GWG is above the raw gap when cohort members are in their 40s, 50s and 60s. The implication is that women have pre-labour market traits which reduce their earnings later in life relative to men. The gap follows an inverted-u shape over the life-course, reaching its maximum of around. .45 log points at age 42, after which it begins to decline, though it remains large among cohort members in their 60s.

Joint work with: Francesca Foliano, Heather Joshi, Bozena Wielgoszewska and David Wilkinson

Hiring and the Dynamics of the Gender Gap

In this study, we examine how the same vacating opportunity translates differently for male and female full-time workers.

In this study, we examine how the same vacating opportunity translates differently for male and female full-time workers. By utilizing matched employer-employee data from Germany, our empirical approach leverages 30,000 unforeseen worker deaths spanning from 1980 to 2016 which enables us to explore how firms react to exogenous vacancies. We find that when a position becomes vacant, female replacements have starting wages that are 20 log points lower compared to their male counterparts. Even after considering the pre-hire wage of replacement workers, half of this gap persists. The gender disparity in opportunities cannot be attributed to workload redistribution among other coworkers. Over time, the gap tends to widen on average and remains stable even for those who remain employed full-time in the subsequent five years after being hired.

How can AI improve search and matching? Evidence from 59 million personalized job recommendations

This lecture is about how artificial intelligence can be used to reduce friction in markets.

We explore how Artificial Intelligence can be leveraged to help frictional markets to clear. We design a collaborative-filtering machine-learning job recommender system that uses job seekers' click history to generate relevant personalised job recommendations. We deploy it at scale on the largest online job board in Sweden, and design a clustered two-sided randomised experiment to evaluate its impact on job search and labour-market outcomes. Combining platform data with unemployment and employment registers, we find that treated job seekers are more likely to click and apply to recommended jobs, and have 0.7 percent higher employment within the 6 months following first exposure to recommendations. At the job-worker pair level, we document that recommending a vacancy to a job seeker increases the probability to work at this workplace by 10 percent. We propose a decomposition exercise of the net employment effects into three channels. The most important channel corresponds to the increase in the number of applications due to recommendations (first channel), partly offset by the lower conversion into employment of marginal applications (second channel). Congestion effects (third channel) are not a significant contributor to the overall effect. We also find larger employment effects when recommended vacancies are less popular, and for recommendations that broaden search further away in geographical and occupational distance.

The Direct and Indirect Effects of Online Job Search Advice

We study how online job search advice affects the job search strategies and labor market outcomes of unemployed workers.

We study how online job search advice affects the job search strategies and labor market outcomes of unemployed workers. In a large-scale field experiment, we provide job seekers with vacancy information and occupational recommendations on an online dashboard. A two-stage randomized design with regionally varying treatment intensities allows us to account for treatment spillovers. Our results show that online advice is highly effective when the share of treated workers is relatively low: in regions where less than less than 50% of job seekers are exposed to treatment, working hours and earnings of treated job seekers increase significantly in the year after the intervention. At the same time, we find substantial negative spillovers on other treated job seekers for higher treatment intensities, resulting from increased competition between treated job seekers who apply for similar vacancies.

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.

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’'.

Estimating returns to special education: combining machine learning and text analysis to address confounding

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.

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.