Yaroslav Yakymovych

Research 

Medical Certificates and Sickness Absence: Who Stays Away From Work if Monitoring Is Relaxed? 

Paid sick leave can cause absenteeism, which is controlled through costly monitoring by medical professionals. I study whether a targeted relaxation of monitoring can reduce costs without affecting absenteeism by leveraging a large-scale experiment which randomised Swedish workers into different monitoring regimes. Using machine learning, I identify systematic heterogeneity in the causal effects of monitoring on the absence of different worker groups. Individuals strongly affected by monitoring have high previous sick leave, low socioeconomic status, and are mostly men. A targeted relaxation of monitoring would be cost-efficient and would halve the increase in sickness absence compared to an untargeted relaxation.

IFAU working paper  Report in Swedish  Link to latest version

Media coverage:  Dagens ETC, Han vill se minskade krav på läkarintyg vid sjukfrånvaro – för vissa

The heterogeneous earnings impact of job loss across workers, establishments, and markets (with Susan Athey, Lisa Simon, Oskar Nordström Skans and Johan Vikström)

Using generalized random forests and rich Swedish administrative data, we show that the earnings effects of job displacement due to establishment closures are highly heterogeneous. We find as much heterogeneity within as across closing establishments, and within as across worker types defined by age and schooling. We display the potential of market-based policy interventions by showing that much of the heterogeneity across establishments is shared within markets. Several results suggest that the effect heterogeneity disfavors already vulnerable workers. Thus, targeted policy interventions may be justified to a larger extent than suggested by estimated average earnings effects.

IFAU working paper  Report in Swedish 

Consequences of Job Loss for Routine Workers (accepted for publication in Empirical Economics)

If displaced in mass layoffs, routine workers are hit by the combination of a severe shock and a long-term decrease in demand for their skills due to automation. I use matched employer-employee data from Sweden to show that displaced routine workers have worse labour market outcomes than displaced non-routine workers. Furthermore, a significant share of their earnings losses is passed through to disposable income, meaning that routine workers are not compensated by social insurance. I also find evidence in favour of routine workers’ larger losses being caused by the irrelevance of their pre-displacement occupation-specific human capital. Routine workers are more likely to switch occupations and industries upon re-employment. Their wages upon re-employment are lower compared to non-routine workers and compared to the mean wage in their new occupation. I do not find evidence that switching to a non-routine occupation reduces losses, with switchers instead appearing to do worse in the short-to-medium run.

IFAU working paper  Report in Swedish  Link to latest version

Understanding occupational wage growth (with Adrian Adermon, Simon Ek and Georg Graetz)

Using a new identification strategy, we jointly estimate the growth in occupational wage premia as well as time-varying occupation-specific life cycle profiles for Swedish workers 1996–2013. We document a substantial increase in between-occupation wage inequality due to differential growth in premia as well as due to shifts in life-cycle profiles. However, this increase is not apparent in raw wage data, because of strong sorting responses. The association of wage premium growth and employment growth is positive, suggesting that premium growth is predominantly driven by demand side factors. Our results are robust to allowing for occupation-level changes in returns to cognitive and psycho-social skills.

Families, Neighbourhoods and Children's Educational Outcomes (with Matz Dahlberg, Torsten Santavirta and Majken Stenberg)

Understanding whether differences in the outcomes of children who grow up in different locations represent location effects or residential sorting is an important question in economic research and policy. We estimate location effects by controlling for differences in observed family characteristics across locations using machine learning and rich Swedish administrative data. We focus on university enrolment, and find that observed family characteristics explain 70-80 percent of the differences between children who grow up in different locations. The remaining unexplained gap is an upper bound for the size of location effects, as it also includes the effects of unobserved family characteristics. We systematically analyse heterogeneity in the size of the unexplained gap for children from different types of families, finding that it is larger for children of low-educated parents. The unexplained rural-urban gap is larger for boys and second-generation immigrants, while the unexplained gap between rich and poor neighbourhoods in cities is larger for girls and those with native-born parents. Overall, the results suggest that differences in university enrolment across locations are mostly due to residential sorting of families rather than location effects. 

Parental separations and children’s outcomes (with Raoul van Maarseveen)