We develop a task-based model of occupational sorting to identify and quantify the effect of discrimination, racial skill gaps, and aggregate task prices on Black-White differences in labor market outcomes over time. At the heart of our framework is the idea that the size and nature of racial barriers faced by Black workers vary by the task requirements of each job. We define a new task that measures the extent to which individuals interact with others as part of their job. We show that this measure is a good proxy for the extent of discrimination in the economy. (JEL J15, J23, J31, J71, M51)

A household’s vehicle purchases are among its largest expenditure outlays. Moreover, unlike housing purchases, which a typical household may make once or twice over a lifetime, a household may well buy several cars over the same interval. The magnitude and relative frequency of vehicle purchases suggest that differential treatment by race in the vehicle market may have important implications for differences in wealth and financial wellbeing by race. Yet, whereas a robust literature in economics has studied virtually all aspects of racial treatment in the housing market, corresponding work about vehicles has been relatively sparse, with most work focusing on racial differences in prices paid (Pinelopi Goldberg (1996) and Fiona Scott-Morton, Florian Zettelmeyer, and Jorge Silva-Risso (2003)). Very little previous attention has been paid to whether there is differential racial treatment in another important outcome in the vehicle market: the interest rates that households pay on the loans used to purchase vehicles.1 Calculations using data from the Survey of Consumer Finances indicate that loans for vehicle purchases are primarily obtained from one of two sources. Roughly two-thirds of vehicle loans originate from the traditional banking sector: commercial banks, savings institutions, or credit unions. Vehicle manufacturers finance the remaining one-third of auto

We use scanner data and time diaries to document how households substitute time for money through shopping and home production. We document substantial heterogeneity in prices paid for identical goods for the same area and time, with older households shopping the most and paying the lowest prices. Doubling shopping frequency lowers a good's price by 7 to 10 percent. We estimate the shopper's price of time and use this series to estimate an elasticity of substitution between time and goods in home production of roughly 1.8. The observed lifecycle time allocation implies a consumption series that differs markedly from expenditures. (JEL D12, D91)

Regional shocks are an important feature of the US economy. Households' ability to self-insure against these shocks depends on how they affect local interest rates. In the United States, most borrowing occurs through the mortgage market and is influenced by the presence of government-sponsored enterprises (GSE). We establish that despite large regional variation in predictable default risk, GSE mortgage rates for otherwise identical loans do not vary spatially. In contrast, the private market does set interest rates which vary with local risk. We use a spatial model of collateralized borrowing to show that the national interest rate policy substantially affects welfare by redistributing resources across regions. (JEL E32, E43, G21, G28, L32, R11, R31)

Using administrative payroll data from the largest US payroll processing company, we measure the extent of nominal wage rigidity in the United States. The data allow us to define a worker’s per-period base contract wage separately from other forms of compensation such as overtime premiums and bonuses. We provide evidence that firms use base wages to cyclically adjust the marginal cost of their workers. Nominal base wage declines are much rarer than previously thought with only 2 percent of job-stayers receiving a nominal base wage cut during a given year. Approximately 35 percent of workers receive no base wage change year over year. We document strong evidence of both time and state dependence in nominal base wage adjustments. In addition, we provide evidence that the flexibility of new hire base wages is similar to that of existing workers. Collectively, our results can be used to discipline models of nominal wage rigidity. (JEL E24, E32, J31, J41)

Using data from the American Time Use Survey between 2003 and 2010, we document that home production absorbs roughly 30 percent of foregone market work hours at business cycle frequencies. Leisure absorbs roughly 50 percent of foregone market work hours, with sleeping and television watching accounting for most of this increase. We document significant increases in time spent on shopping, child care, education, and health. Job search absorbs between 2 and 6 percent of foregone market work hours. We discuss the implications of our results for business cycle models with home production and non-separable preferences. (JEL D31, E32, J22)

Using time use survey data we document a hump-shaped profile of job search time in the United States across the life-cycle. The middle-aged unemployed spend roughly three times as much time in job search as the youngest group of unemployed. The hump-shaped profile of job search time is relatively stable across demographic groups. However, the profile of job search time appears to be declining in non-US countries. We discuss how standard life-cycle models with incomplete markets have difficulty in accounting for the hump-shaped profile found in the US data.

When a city experiences a decline in income or population, do all neighborhoods within the city decline equally? Or, do some neighborhoods decline more than others? What are the characteristics of the neighborhoods that decline the most? We answer these questions by looking at what happened to neighborhoods within Detroit as Detroit experienced a sharp decline in income and population from the 1980s to the late 2000s. We find patterns of changes in income and population that are consistent with the model and empirical patterns of gentrification presented in Guerrieri, Hartley, and Hurst (2011), only playing out in reverse.

The (S, s) model has enjoyed tremendous popularity over the past decade. It has been employed almost everywhere that discrete adjustment is observed. Today microeconomic rigidities are seen as an important influence on aggregate dynamics. In this paper we quickly characterize the microeconomic evidence for the model. To narrow the scope of our discussion, we will focus our attention on real variables, and we will comment on price inertia where appropriate. We conclude that, in spite of its popularity, the evidence for the importance of the (S, s) adjustment is surprisingly weak. We argue that discrete adjustment is, in and of itself, of little macroeconomic interest. To be important these frictions must coordinate agents to act together, thereby exacerbating deviations from the neoclassical benchmark. To date there have been few attempts at empirically identifying such interactions. In the last section, we present some results of our own. We test one of the main implications of (S, s) adjustment, that a greater variance in the forcing process leads to more frequent adjustment. Using data on automobiles from the Consumer Expenditure Survey, we find that more variable income leads to less frequent adjustment. We speculate that this correlation is indicative of a link between discrete adjustment and imperfect capital markets. This interaction could provide an important role for (S, s) frictions.

We study how the recent housing boom and bust affected college enrollment during the 2000s. We exploit cross-city variation in local housing booms, which improved labor market opportunities for young men and women. We find that the boom lowered college enrollment, with effects concentrated at two-year colleges. The decline in enrollment during the boom was generally reversed during the bust; however, attainment remains persistently low for particular cohorts, suggesting that reduced educational attainment is an enduring effect of the recent housing cycle. The housing boom can account for approximately 25 percent of the recent slowdown in college attainment. (JEL I23, I25, J24, J31, R21, R31)