This dissertation studies the role of occupation-specific human capital in explaining the long-run decline in labor market dynamics observed in the United States for the past four decades.
Chapter 1 presents empirical facts on labor market outcomes by required occupation-specific training. This is to provide evidence that (i) required length of occupation-specific training is a proxy for the specificity of human capital to perform the occupation and that (ii) increasing occupation specificity has led to the decline in labor market dynamics. First, I find from the Dictionary of Occupational Titles and O*NET that for the past four decades, within occupations, there has been an increase the amount of time needed to become trained in the occupation. I then find from the Survey of Income and Program Participation that the average wage loss experienced by occupation switchers after unemployment increases when their occupation held before unemployment has faced over time an increase in occupation-specific training. I take this as evidence that the observed increase in occupation-specific training over time has made human capital less transferable across occupations. I then proceed to use the Monthly Current Population Survey, combined with the required length of occupation-specific training by occupation from the Dictionary of Occupational Titles and O*NET, to do a shift-share decomposition of the decline in labor market outcomes. The decline in the aggregate job separation rate and the increase in unemployment duration is accounted for mostly by the increase in specific training within occupations.
Motivated by my empirical analysis, in Chapter 2, I then build a search-and-matching model to learn how the increase in specificity within occupations explains the decline in the aggregate job separation rate. The main ingredients are endogenous job separations and occupation-specific human capital that workers acquire during employment and lose when they switch occupations. My model has two occupation specificity parameters: (i) the average duration of occupation-specific training and (ii) the output gap by which nontrained workers are less productive because they have not yet acquired the occupation-specific capital. To ask my model how much of a decline it predicts in the aggregate job separation rate when occupations become more specific, the occupation specificity parameters in the model are increased to match the increase in occupation specificity in the data. The increase in the average duration of occupation-specific training matches the required length of occupation-specific training from the Dictionary of Occupational Titles and O*NET. The increase in the output gap is informed by the estimated increase in the wage penalty faced by occupation switchers (relative to non-occupation switchers) when their previously held occupation requires more occupation-specific training, obtained from the Survey of Income and Program Participation. The model predicts 60% of the decline in the aggregate job separation rate.
Chapter 3 relaxes the assumption that occupation switching is exogenous in Chapter 2, endogenizing occupation switching in addition to job separations. The model predicts a greater increase in the average unemployment duration in line with the data. In the model, the longer unemployment spells are due to the unemployed trained workers, whose human capital has become more specific to their previous occupation, choosing not to switch occupations. If they switch occupations, they could quickly end their unemployment spell. This would however come at the cost of larger wage cuts because their human capital has become less transferable to a different occupation. Occupation switchers would also have to earn these lower wages for a longer period of time until they become trained in their new occupation. Hence, despite a low probability of getting reemployed in the same occupation as before, previously trained workers increasingly choose not to switch occupations, which increases the average unemployment duration.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/871t-0324 |
| Date | January 2022 |
| Creators | Shin, Joo-Hyung |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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