Education : risk enhancing or insurance mechanism?

Autor: Delaney, J. M.
Rok vydání: 2017
Předmět:
Druh dokumentu: Electronic Thesis or Dissertation
Popis: In the first chapter, I examine the returns to education for both males and females with a particular focus on the effect of wage risk and periods of non-employment. I also account for selection in to the labour market using a Heckman selection equation and decompose earnings in to permanent and transitory components in an effort to understand the components of wage risk. My results suggest that failure to account for periods of non-employment, wage risk and selection in to the labour market when calculating returns to education leads to biased estimates. In the second chapter, along with my co-author, Paul Devereux, we look at the causal effect of education on earnings uncertainty and volatility and the effect of education on sheltering workers from the adverse effects of recessions. We use the 1973 change in compulsory schooling law to provide exogenous variation in education. Our regression discontinuity estimates suggest that men whose education was increased by the law subsequently had lower earnings volatility, less pro-cyclical earnings, and were less likely to experience real pay cuts. In the third chapter, I analyse the role of risk, family background, cognitive and noncognitive skills in determining college attendance. I use a structural life cycle model explicitly capturing the decision to go to college and incorporating important features which impact the returns to college such as savings, labour supply, human capital accumulation and depreciation, wage risk and employment risk. It is estimated that grants, parental background, non-cognitive skills and risk significantly impact the decision to go to college. However, the biggest factor in determining college attendance is cognitive skills. This is driven both by differences in returns to college conditional on cognitive skills and by the larger psychic costs faced by those with low cognitive skills.
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