Essays on the Economics of Labor Demand and Policy Incidence
CitationGarin, Andrew. 2018. Essays on the Economics of Labor Demand and Policy Incidence. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractThis dissertation studies social incentives in pro-social behavior and its various implications, including but not limited to disclosure policies, fundraising strategies and geographic polarization. In Chapter 1, I (with Filipe Silverio) test how sensitive wages are to firm-level labor demand by estimating the incidence of idiosyncratic export demand shocks on the wages of incumbent workers in Portugal during the Great Recession (2008-2010). Using detailed export records, we construct measures of firm exposure to unanticipated shocks to the demands of different countries for specific products. The shocks predict changes in output and payroll at affected firms, but not at other similar firms. We combine the export demand measures with firm balance sheet data and matched longitudinal administrative employer-employee records to estimate the impact of idiosyncratic firm-level demand shocks on employee outcomes. We find that idiosyncratic shocks that decreased sales or value added by 10 percent caused wages of incumbent workers who were employed by affected firms in 2007 to decrease by 1.5 percent relative to trend. These causal effects are large enough to explain nearly all of the variance in wages that can be attributed to observational firm pay differentials. Furthermore, we find that these pass-through effects are stronger in industries with higher durability of employment relationships and lower employee turnover rates. These results support a model in which barriers to replacing incumbent workers give rise to internal labor markets within the firm, exposing workers to their employers' idiosyncratic conditions.
Chapter 2 studies whether local infrastructure construction an effective way to boost employment in distressed local labor markets. I use new geographically-detailed data on highway construction funded by the American Recovery and Recovery Act to study the relationship between construction work and local employment growth. I show that the method for allocating funds across space facilitates a plausible selection-on-observables strategy. I find that highway impacted construction employment at the county level: a dollar of additional Recovery Act spending on local construction increased local construction payrolls by thirty cents over 2009-2013. The magnitude of this effect matches the national labor share of construction revenues, suggesting that targeted spending did not crowd out other local construction. These effects are most pronounced among counties with smaller populations and smaller fractions of residents that commute to outside counties for work. However, when I test for general equilibrium effects on local employment and payroll aggregates, I find effects close to zero with very wide confidence intervals across all specifications. Although the Recovery Act was a significant enough intervention to have a sizable impact on the construction sector in counties with low mobility, these findings suggest that the local variation in highway spending was too small relative to baseline regional volatility to detect a local employment “multiplier.”
Chapter 3 examines the long-run local labor market effects of the publicly-financed construction of large manufacturing facilities during World World War II. I focus on a subset of large, new plants that the military was not able to incentivize private firms to stake any capital on, and likely would not have been built if not for the war. I compare recipient counties to counties that were similar but for conditions engendered by the war. After establishing an absence of pre-trends across a number of outcomes, I show that recipient counties experienced a large post-reconversion boost in manufacturing employment and wages that persisted for several decades. I show how these effects impact broader labor market outcomes in the post-war period and discuss methods for distinguishing between causal mechanisms using plant-level data.
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