I present three studies on wages and employment over the business cycle. In Chapter 1, I provide quasi-experimental evidence that downward nominal wage rigidity causes firms to destroy jobs and that this effect is empirically relevant for the macroeconomy. Given the unanticipated nature of the financial collapse in Q3 of 2008, differences across firms in their patterns of seasonal nominal wage adjustment generated heterogeneity in firms’ exposure to downward nominal wage rigidity in Q4 of 2008. I find that exposure to downward nominal wage rigidity generated by firms’ seasonal wage adjustment patterns accounts for 23% of the spike in aggregate job destruction that occurred in Q4 of 2008.

In Chapter 2, I present descriptive work with Leland Crane and Henry Hyatt regarding variation over the business cycle in both: i) the composition of employment by worker and firm productivity, and ii) the degree of assortative matching between workers and firms. Using employer-employee linked data for the U.S., we implement a battery of methods for ranking workers and firms by their productivity.

Across all these methods, we find three consistent patterns. First, the changes in the composition of employment by worker and firm productivity types move in opposite directions over the business cycle. During and immediately after recessions, low-productivity workers are less likely to work, whereas the employment share of low-productivity firms increases. Second, we find evidence of positive assortative matching between workers and firms (high-productivity workers are more likely to work at high-productivity firms). And third, the degree of positive assortative matching between workers and firms strengthens during the early stages of labor market downturns as low-productivity workers are disproportionately laid off from high-productivity firms.

In Chapter 3, I present a methodological advancement in the measurement of workers’ base wages, variable compensation, and weeks worked in large administrative employer-employee linked data sets that only report workers’ earnings. I develop a set of machine learning methods that identify each worker’s unobserved persistent base wages, paydays weeks, and annual bonuses from the worker’s observed quarterly earnings. I then implement and evaluate the quality of these methods using quarterly earnings data in the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) dataset, an employer-employee linked dataset for the United States. Using the estimated nominal wages of workers in 30 U.S. states, I document three patterns of nominal wage adjustment: i) estimated persistent wage changes exhibit downward nominal wage rigidity, ii) optimal real wage cuts are suppressed by downward nominal wage rigidity, and iii) workers’ nominal raises follow a Taylor-like pattern, with the probability of a wage raise spiking every four quarters.