Essays on Macroeconomics and Labor Economics
2019-05-14T18:34:34Z (GMT) by
This dissertation consists of three independent chapters at the intersection of macroeconomics and labor economics. The first chapter studies the job-search trade-offs between full-time employment, part-time employment, and multiple job holdings. The second chapter explores the macroeconomic relationship between property crime and output in a dynamic stochastic general equilibrium framework. The third chapter studies the causal effect of property crime on output.
The first chapter develops a search-matching model of the labor market with part-time employment and multiple job holdings. The model is calibrated to data from the CPS between 2001 and 2004. Workers are able to choose their search intensity and are allowed to hold two jobs while firms can choose what type of worker to recruit. When compared to the canonical Diamond-Mortensen-Pissarides model, this model performs quite well while capturing some empirical regularities. First, the model generates recruiting and vacancy posting rates that move in opposite directions. Second, part-time employment is up to 10 times more responsive than full-time employment. Third, the model suggests that multiple job holding rates are more flexible than observed in the data with the rate changing by as much as 4 percentage points compared to 0.1 percentage points in the data. Finally, the full model is able to capture compositional changes during recessions with the full-time rate declining and the part-time rate increasing. It also produces an empirically consistent increase in the unemployment rate as well as a decrease in output. The DMP model is more muted than in the data for both.
The second chapter explores how property crime can affect static and dynamic general equilibrium behavior of households and firms. I calibrate a model with a representative firm and heterogeneous households where households have the choice to commit property crime. In contrast to previous literature, I treat crime as a transfer rather than home production. This creates a feedback loop wherein negative productivity shocks increase property crime which further depresses legitimate work and capital accumulation. These responses by households are particularly important when thinking about the effect of property crime on the economy. Household and firm losses account for 24% of compensating variation (CV) and 37% of lost production. This suggests that behavioral responses are quite important when calculating the cost of property crime. Finally, on the margin, decreasing property crime by 1% increases social welfare by 0.19%, but the effect is diminishing suggesting that reducing crime entirely may not be optimal from a policymakers perspective.
The third chapter estimates the causal effect of property crime on real personal income per capita. Running system GMM on an unbalanced panel of MSA-year pairs suggests that property crime reduces real personal income per capita by a highly statistically significant 13.3%. This implies that the average person loses $4,869 (2009 dollars) per year with real annual personal income per capita totaling $36,615. The effect is driven primarily by larceny-theft and burglary with highly statistically significant coefficients of -0.179 and -0.110 respectively. Estimates for the effect of robbery are unstable, and the effect of motor vehicle theft is statistically significant, but smaller with a coefficient of -0.060.