Workers and occupations in a changing labour market

Abstract

Essay I: Sickness insurance guarantees employees the right to take leave from work when they are sick, but is vulnerable to excessive use. This paper studies which workers react to changes in monitoring by physicians in a large-scale randomised experiment. I use a causal forest to identify heterogeneous effects on the duration of workers’ sickness absence spells. Those who are most sensitive to monitoring have a history of extensive sick leave uptake, low socioeconomic status, and male gender. A targeted monitoring policy is estimated to be 40 percent more efficient than a random one.

Essay II: Routine-biased technological change has depressed prospects for workers in exposed occupations, with those displaced in mass layoffs particularly affected. I compare labour market outcomes of displaced routine workers to those of displaced non-routine workers using Swedish microdata. The results show substantial and persistent routine penalties among displaced workers. A possible channel is the loss of occupation- and industry-specific human capital, as routine workers are unable to find jobs similar to those they had before displacement. I do not find evidence that switching to a non-routine occupation reduces routine workers’ losses.

Essay III (with Susan Athey, Lisa Simon, Oskar Nordström Skans and Johan Vikström):
We study heterogeneity in the impact of job loss in mass layoffs using generalized random forests. We identify the groups of workers who are hit the hardest and document substantial and persistent variation in displacement losses. Worker attributes and semi-aggregate local and industry conditions interact to generate this heterogeneity. Old and less-educated workers lose six times as much as young and highly educated workers. Nevertheless, there is overlap among
the losses of these two groups, much of which is related to industries and locations. Working in manufacturing and living in a rural area are strong predictors of severe displacement losses. No simple rule is as effective at identifying vulnerable workers as the more flexible generalized random forest.

Essay IV (with Adrian Adermon, Simon Ek and Georg Graetz):

Using a new identification strategy, we jointly estimate growth in occupational wage premia and time-varying occupationspecific lifecycle profiles for Swedish workers in 1996–2013. We document a substantial increase in between-occupation wage inequality due to differential growth in premia. The association of wage premium growth and employment growth is positive, suggesting that
premium growth is predominantly driven by demand side factors. Wage growth due to occupation-specific skill acquisition was more dispersed in the early years of the sample period. Our results are robust to allowing for occupation-level changes in returns to cognitive and psycho-social skills.

Keywords: Structural change; Mass layoffs; Sickness absence; Causal forest

Yaroslav Yakymovych, Department of Economics, Box 513, Uppsala University, SE-75120, Uppsala, Sweden. © Yaroslav Yakymovych 2022, ISSN 0283-7668, ISBN 978-91-506-2967-5
URN urn:nbn:se:uu:diva-481848 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-481848)