Predictors Of Long Term Unemployment In Sweden

The availability of huge government collected data sets facilitated by a well functioning government and public trust that allows privacy concerns to be overcome due to a history of non-corruption makes Scandinavia one of the best places in the world to do large sample size social science research. This makes this research valuable despite the great differences in culture and economic organization between places like Sweden and societies like the United States.

In a nutshell, a recent paper on long term unemployment in Sweden concludes that some individuals who are long term unemployed are at much greater risk of ending up that way, especially in economic downturns, than others. This is best predicted by their past history of unemployment.

This paper studies the predictability of long-term unemployment (LTU) and analyzes its main determinants using rich administrative data in Sweden. Compared to using standard socio-demographic variables, the predictive power more than doubles when leveraging the rich data environment. The largest gains come from adding job seekers' employment history prior to becoming unemployed. Applying our prediction algorithm over the unemployment spell, we show that dynamic selection into LTU explains at least half of the observed decline in job finding. While the within-individual declines are small on average, we find substantial heterogeneity in the individual-level declines and thus reject the commonly used proportional hazard assumption. Applying our prediction algorithm over the business cycle, we find that the cyclicality in average LTU risk is not driven by composition but rather by within-individual cyclicality and that individual rankings are relatively persistent across years. Finally, we evaluate the implications of our findings for the value of targeting unemployment policies and how these change over the unemployment spell and the business cycle.

Andreas I. Mueller and Johannes Spinnewijn, "The Nature of Long-Term Unemployment: Predictability, Heterogeneity and Selection" NBER WORKING PAPER 30979 (February 2023) DOI 10.3386/w30979.