Job Polarization, Skill Mismatch and the Great Recession

occupations by 3.5 percentage points. This fall is mainly due to women, and workers from the first two cohorts. The only possible occupation in which the share of HS employment could grow during the recovery was a routine one (Panel B), with younger workers more likely to downgrade during the recovery and women during the following expansion. This evidence is consistent with the idea, introduced by Beaudry et al. (2016), that a “great reversal” in the demand for skills is pervading the abstract market, thus pushing HS workers down the ladder. As Panel C shows, manual jobs could not significantly absorb HS workers during the recovery and the next expansion phase. It is important to notice that -within local labor marketsthe e↵ect of the shift of the occupational structure experienced during the recession has significant impact on the reallocation of HS workers only during the economic recovery but vanishes (at least at State-level) in the next expansion. This corroborates the fact that the reallocation down the ladder is only temporary for HS workers. Table 6 shows results for LS workers. As from model (1) of Panel A, higher state-level polarization did not allow LS workers to recover through abstract occupations in the aftermath of the GR. In fact, for a 1 percentage point increase in polarization, LS employment share in abstract jobs fell by 2 points during the recovery. Such a decline is mostly explained by women and the first two cohorts. Panel B shows results for LS employment in routine jobs. These jobs were mostly destroyed during the recession, but -once the economy reorganized and started expanding againthe polarization trend dominated with LS employment share declining by 6 percentage points with respect to the end of the recession. This suggests that local markets that polarized more tend to polarize also in the future at the expense of LS workers who, indeed, are dismissed abundantly from the routine market. In the aftermath of the Great Recession, the bulk of LS workers could recover only through manual occupations, with a 2.2 points increase for states that polarized more (Panel C). Such expansion of the LS employment share in the manual market continued afterward, with a further rise by 3.3 points, thus reaching an overall increase of 5.5 points from the end of the recession. This is mostly explained by women and the younger cohort. In conclusion, the disruptive e↵ects of polarization and its interaction with the cycle lead to di↵erent patterns of human capital reallocation across jobs. The degree at which a local market polarizes a↵ects the size of future reallocation. In fact, stronger polarization magnifies movements from the top to the bottom of the job ladder, with HS workers only temporarily reallocating in routine jobs rather than abstract ones during the recovery, and LS workers persistently more likely to get manual jobs from the recovery onwards. 36 Electronic copy available at: https://ssrn.com/abstract=3544689 5.4. Skill-Requirements and the Great Recession In this section I loose the classification of skill-groups commonly used in the literature (HS vs. LS) when mapping education to job tasks. In practice, I introduce a broader spectrum of educational attainments and study which schooling degree matters the most and when for an unemployed individual to be hired. This will help to show (i) which skills mattered the most during the GR and for which jobs, and (ii) if there was cyclical up-skilling within each occupation. To do so, I use the longitudinal component of CPS data to look at flows from unemployment to employment. In particular, I consider a sample of 25 to 55 years old unemployed individuals interviewed in two consecutive months and whose unemployment spell is below or equal to 4 weeks. Each individual unemployed in the first month can flow to one of the three jobs, remain unemployed or flow to non-participation in the next month. Therefore, a discrete choice model (i.e. a multinomial logit) can account for the odds of each individual to flow out of unemployment as a function of individual characteristics and business cycle phases. With the baseline choice normalized to remaining unemployed, the unconditional probability for individual i to flow (F) from unemployment (U) to abstract (A), routine (R), manual (M) or non-participation (NLF) between t and t+1 can be written as: Pr(F j i,s,t+1|Ui,s,t) = exp( 0