Carter Bryson

The employment rate for Black men worsened significantly relative to White men during the second half of the 20th century. We explore the role of broad sectoral shifts in labor demand over this period in explaining this trend. We first quantify changes in local employment rates and population in response to local labor demand shifts for both groups of workers. We then combine our estimates with a stylized model that incorporates frictional local labor markets and imperfect mobility across markets. Our framework enables us to aggregate local responses while accounting for geographic mobility and regional employment composition. We find that sectoral reallocation can account for at most, one-fifth of the total exacerbation in the employment rate differential between Black and White men over 1970–2010. Out-migration from harder-hit markets, while large, only slightly mitigates the impact of negative labor demand shifts. We also find that most of the predicted change in the employment rate differential is due to differential response rather than differential exposure to sectoral shifts across groups.

Younger workers tend to sort into younger firms, suggesting that the decline in U.S. business dynamism differentially affected labor market outcomes over the worker life cycle. In this paper, I develop an equilibrium labor market sorting model with both on-the-job search and two-sided, life-cycle heterogeneity to quantify the career consequences of firm aging. I find that it accounts for about 45 percent of the decline in the employer switching rate and about 15 percent of the decline in the employment-to-population ratio since the 1990s. Total welfare of employed workers declines by about 0.9 percent and younger workers experience larger losses.

This paper quantifies the contribution of unemployment inflows and outflows to cyclical changes in the unemployment rate. I show that the time series behavior of worker flows implies that they exhibit a specific dynamic structure. I then implement a simple identification strategy motivated by this evidence in order to empirically separate changes in job separation and job finding. I find that both margins contribute significantly to unemployment volatility and that their interaction is important for understanding the business cycle dynamics of the unemployment rate. My results suggest that models of the labor market should aim to capture this interaction as well as cyclical variation in job loss.

The United States experienced a broad reallocation of economic activity across sectors in recent decades. The manufacturing sector accounted for about a quarter of total value added in 1990, but now comprises less than 15%. At the same time, the firm entry rate fell dramatically from about 15% in 1990 to under 9% in 2019. This paper investigates whether and how these two important macroeconomic trends are related. Using data from the Business Employment Dynamics (BED) program of the Bureau of Labor Statistics (BLS), I first document several facts about establishment dynamics over the life-cycle. Notably, average establishment size at birth declined in recent cohorts, especially in the manufacturing sector, while cohort-specific growth and exit rates remained largely unchanged. Then, I build a multi-sector firm dynamics model in which within-sector shocks to firm fundamentals spill over across sectors, shifting the equilibrium composition of demand for sectoral goods. The model features a realistic firm life cycle that can capture the empirical patterns of firm birth, death, and growth within sectors. I use the model to quantify and decompose the contributions of within-sector adjustments in firm dynamics – driven by changes in fixed costs of production, fixed costs of entry, and the persistence and dispersion of idiosyncratic shocks to productivity/demand – versus across-sector shifts in demand to aggregate sectoral reallocation. I find that absent changes in sector-level firm fundamentals, the decline in manufacturing value added would have been more than twice as large, indicating that within-sector firm dynamics attenuated structural change.

A large literature examines the business cycle dynamics of labor market sorting between heterogeneous workers and firms. Though these studies typically consider a fixed distribution of firm types, firm dynamics such as entry, exit, and growth are strongly cyclical in the data. This paper develops a framework to jointly analyze worker and job flows in which entrepreneurs face fixed startup costs, such that firm entry and exit are endogenously determined in the tradition of Hopenhayn (1992). Tractability is achieved by using a joint surplus representation of the worker-firm problem and by assuming Bertrand competition for workers between incumbent and poaching firms. Ongoing work examines the model’s ability to match cross-sectional empirical patterns of worker flows and firm dynamics, as well as its business-cycle implications for job ladder dynamics across worker groups. When calibrated to labor market aggregates, model simulations show that procyclical firm entry amplifies and propagates fluctuations in unemployment, job switching, and mismatch.

What is seasonality? To date, there is not yet a consensus definition, as over a dozen unique definitions of seasonality can be found in the economics and statistics literature. This lack of consistency complicates the identification of, and adjustment for, seasonal effects in economic time series. In this paper, we propose a new definition of seasonality as well as how to detect and adjust for it, ultimately aiming to build consensus around a more unified approach. We first review the literature and identify common themes across definitions with an eye towards formulating a foundational definition – expressed in both plain language and mathematical terms – that can be used by academics, data providers, policymakers, and practitioners alike. We then propose to classify a series as seasonal if, among all its possible repeating cycles, the ones associated with the fundamental periodicity are demonstrably larger than the others. In other words, we define seasonality as a measure of relative peak dominance in the spectral density function, following Granger (1978). Next, we develop a new methodology for detecting seasonality and performing seasonal adjustment that follows directly from our definition and leverages properties of the sample periodogram, the empirical counterpart to the spectral density function. We illustrate the performance of our test statistic through simulation studies and find that it is well-sized and has good power. Lastly, we introduce Stochastic Spectral Imputation (SSI), an adjustment procedure that outperforms standard methods currently used by national statistical offices when applied to prominent macroeconomic time series.