The first chapter studies the rate of long-term unemployment, which spiked during the Great Recession. To help explain this, I exploit the systematic and counter-cyclical differences in unemployment duration across occupations. This heterogeneity extends the tail of the unemployment duration distribution, which is necessary to account for the observed level of long-term unemployment and its increase since 2007. This chapter introduces a model in which unemployment duration and occupation are linked; it measures the effects of occupation-specific shocks and skills on unemployment duration. Here, a worker will be paid more for human capital in his old occupation but a bad shock may make those jobs scarce. Still, their human capital partly ``attaches'' them to their prior occupation, even when searching there implies a longer expected duration. Hence, unemployment duration rises and becomes more dispersed across occupations. Redistributive shocks and business cycles, as in the Great Recession, exacerbate this effect.
For quantitative discipline, the model matches data on the wage premium to occupational experience and the co-movement of occupations' productivity. The distribution of duration is then endogenous. For comparison's sake, if a standard model with homogeneous job seekers matches the job finding rate, then it also determines expected duration and understates it. That standard model implies just over half of the long-term unemployment in 1976-2007 and almost no rise in the recent recession. But, with heterogeneity by occupation, this chapter nearly matches long-term unemployment in the period 1976-2007 and 70% of its rise during the Great Recession.
The second chapter studies the link between wage growth and the match of a worker's occupation and skills. The notion here is that if human capital accumulation depends on match quality, poor matches can have long-lasting effects on lifetime earnings. I build a model that incorporates such a mechanism, in which human capital accumulation is affected by imperfect information about one's self. This informational friction leads to matches in which a worker accumulates human capital more slowly and has weaker earnings growth.
To get direct evidence, the chapter pieces together two sets of data on the skills used by an occupation and the skills a worker is particularly good at. Data on occupations describes occupations by the intensity with which they use many dimensions of workers' knowledge, skills and abilities. To pair, we have data on tests taken by respondents in a panel that tracks occupations and earnings. The test designers created a mapping between their tests and the occupational descriptors, which allows us to create two measures. The first measure of match quality is just the dot product between the dimensions of workers' skills and utilization rate of these skills by occupations. The second measure mismatch relative to an optimal matching computed using the Gale-Shapley algorithm for stable pairs. In both, worse matches have significantly slower returns to occupational tenure. With the most conservative estimate, plus or minus one standard deviation of mismatch affects the return to occupational tenure by 1% per year.