Publications
The Long-Lived Cyclicality of the Labor Force Participation Rate (joint with John Coglianese and Joshua Montes), The Review of Economics and Statistics, forthcoming.
Abstract: How cyclical is the U.S. labor force participation rate (LFPR)? We examine exogenous state-level business cycle shocks, finding that the LFPR is highly cyclical, but with significantly longer-lived responses than the unemployment rate. After a negative shock, the LFPR declines for about four years—substantially lagging unemployment—and only fully recovers after about eight years. Our main specifications use age-sex-adjusted LFPR, and we show that using unadjusted LFPR is problematic because local shocks spur changes in the population of high-LFPR age groups. Cyclicality varies across groups, with larger and longer-lived responses among men, younger workers, less-educated workers, and Black workers.
Gross Worker Flows over the Life Cycle (joint with İlhan Güner and Toshihiko Mukoyama), Journal of Money, Credit and Banking, 2025, pp. 757-791.
Abstract: We analyze the gross worker flows over the life cycle by constructing a quantitative general equilibrium model. Using U.S. data, we first document the life-cycle patterns of flows across different labor market states (employment, unemployment, and not in the labor force), as well as job-to-job transitions. We then build a model of the aggregate labor market that incorporates the life cycle of workers, consumption-saving decisions, and labor market frictions. We estimate the model and use it to examine the effects of policies on aggregate labor market outcomes. In particular, we analyze a taxes-and-transfers policy and an unemployment insurance policy.
Widening Health Gap in the US Labor Force Participation at Older Ages (joint with Javier Fernández-Blanco and Virginia Sánchez-Marcos), CESifo Economic Studies, 2024, pp. 195-206.
Abstract: Using microdata from the CPS and the HRS, we document changes in labor force participation at older ages in the USA since the mid-1990s. Our main finding is that the over two-decade increase in participation is solely driven by individuals in good health, and does not differ across either educational or occupational groups. This phenomenon may importantly affect the results of social security reforms aiming at raising the mandatory retirement age and may exacerbate the health gap in lifetime earnings.
Payroll Employment at the Weekly Frequency (joint with Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz), AER Papers and Proceedings, 2023, pp. 145-150.
Abstract: Nontraditional data can provide critical economic insights in real time. We document the development of weekly employment indexes based on microdata from the payroll processor ADP. These data have provided insights into labor market developments in the fast-moving pandemic environment of recent years, as well as in more quiescent periods. We describe processes for weighting and benchmarking, seasonally adjusting weekly data, and ultimately combining the signals from ADP and official data. Finally, we show how the weekly employment indexes have fared during the pandemic jobs recovery, focusing on revisions to official data for the second half of 2021.
Lessons Learned from the Use of Nontraditional Data during COVID-19" (joint with Laura Feiveson, Christopher Kurz, and Stacey Tevlin), in Wendy Edelberg, Louise Sheiner, and David Wessel, eds., "Recession Remedies, Lessons Learned from the U.S. Economic Policy Response to COVID-19", Brookings/Hamilton Project/Hutchins Center.
Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data (joint with Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, Christopher Kurz), in Katharine G. Abraham, Ron S. Jarmin, Brian Moyer, and Matthew D. Shapiro, eds., "Big Data for 21st Century Economic Statistics", NBER Book Series Studies in Income and Wealth, University of Chicago Press, 2022.
Abstract: This paper combines information from two sources of U.S. private payroll employment to increase the accuracy of real-time measurement of the labor market. The sources are the Current Employment Statistics (CES) from BLS and microdata from the payroll processing firm ADP. We briefly describe the ADP-derived data series, compare it to the BLS data, and describe an exercise that benchmarks the data series to an employment census. The CES and the ADP employment data are each derived from roughly equal-sized samples. We argue that combining CES and ADP data series reduces the measurement error inherent in both data sources. In particular, we infer “true” unobserved payroll employment growth using a state-space model and find that the optimal predictor of the unobserved state puts approximately equal weight on the CES and ADP-derived series. Moreover, the estimated state contains information about future readings of payroll employment.
The US Labor Market during the Beginning of the Pandemic Recession (joint with Leland D. Crane, Ryan A. Decker, John Grigsby, Adrian Hamins-Puertolas, Erik Hurst, Christopher Kurz, Ahu Yildirmaz), Brookings Papers on Economic Activity, Summer 2020, pp. 3–33.
Abstract: Using weekly administrative payroll data from the largest US payroll processing company, we measure the evolution of the US labor market during the first four months of the global COVID-19 pandemic. After aggregate employment fell by 21 percent through late April, employment rebounded somewhat through late June. The reopening of temporarily shuttered businesses contributed significantly to the employment rebound, particularly for smaller businesses. We show that worker recall has been an important component of recent employment gains for both reopening and continuing businesses. Employment losses have been concentrated disproportionately among lower wage workers; as of late June employment for workers in the lowest wage quintile was still 20 percent lower relative to mid-February levels. As a result, average base wages increased between February and June, though this increase arose entirely through a composition effect. Finally, we document that businesses have cut nominal wages for almost 7 million workers while forgoing regularly scheduled wage increases for many others.
Human Capital and Unemployment Dynamics: Why More Educated Workers Enjoy Greater Employment Stability (joint with Isabel Cairó), Economic Journal, 2018, pp. 652–682.
Abstract: Why do more educated workers experience lower unemployment rates and lower employment volatility? Empirically, these workers have similar job finding rates but much lower and less volatile separation rates than their less educated peers. We argue that on-the-job training, being complementary to formal education, is the reason for this pattern. Using a search and matching model with endogenous separations, we show that investments in match-specific human capital reduce incentives to separate but leave the job finding rate essentially unaffected. The model generates unemployment dynamics quantitatively consistent with the data. Finally, we provide novel empirical evidence supporting the mechanism studied in the article.
Labor Force Participation: Recent Developments and Future Prospects (joint with Stephanie Aaronson, Bruce Fallick, Felix Galbis-Reig, Christopher Smith, William Wascher), Brookings Papers on Economic Activity, Fall 2014, pp. 197–255.
Abstract: Since 2007, the labor force participation rate has fallen from about 66 percent to about 63 percent. The sources of this decline have been widely debated among academics and policymakers, with some arguing that the participation rate is depressed due to weak labor demand while others argue that the decline was inevitable due to structural forces such as the aging of the population. In this paper, we use a variety of approaches to assess reasons for the decline in participation. Although these approaches yield somewhat different estimates of the extent to which the recent decline in participation reflects cyclical weakness rather than structural factors, our overall assessment is that much of the decline is structural in nature. As a result, while we believe some of the participation rate’s current low level is indicative of labor market slack, we do not expect the rate to substantially increase from current levels as labor market conditions continue to improve.
Working Papers
Beyond the Streetlight: Economic Measurement in the Division of Research and Statistics at the Federal Reserve (joint with Carol Corrado and Arthur Kennickell), FEDS WP 2025-019.
Abstract: This paper was written for the academic conference held in celebration of the 100th anniversary of the Division of Research and Statistics (R&S) of the Federal Reserve Board. The work of the Federal Reserve turns strongly on empirical efforts to understand the structure and state of the economy, and R&S can be thought of as operating a large factory for discovering and developing data and analytical methods to provide evidence relevant to the mission of the Board. This paper, as signaled by its title, illustrates how the measurement research component of the R&S factory often looks far beyond current conventions to meet the needs of the Board—and has done so since its earliest days. It would take a far longer paper to provide a complete history and evolution of measurement activities in R&S; here, we provide an indicative review focusing on selected areas from which, we believe, it is easy to conclude that R&S has been—and likely will continue to be—an important innovator in economic measurement.
Manufacturing Sentiment: Forecasting Industrial Production with Text Analysis (joint with Leland D. Crane, Christopher Kurz, Norman Morin, Paul E. Soto, and Betsy Vrankovich), FEDS WP 2024-026.
Abstract: This paper examines the link between industrial production and the sentiment expressed in natural language survey responses from U.S. manufacturing firms. We compare several natural language processing (NLP) techniques for classifying sentiment, ranging from dictionary-based methods to modern deep learning methods. Using a manually labeled sample as ground truth, we find that deep learning models--partially trained on a human-labeled sample of our data--outperform other methods for classifying the sentiment of survey responses. Further, we capitalize on the panel nature of the data to train models which predict firm-level production using lagged firm-level text. This allows us to leverage a large sample of "naturally occurring" labels with no manual input. We then assess the extent to which each sentiment measure, aggregated to monthly time series, can serve as a useful statistical indicator and forecast industrial production. Our results suggest that the text responses provide information beyond the available numerical data from the same survey and improve out-of-sample forecasting; deep learning methods and the use of naturally occurring labels seem especially useful for forecasting. We also explore what drives the predictions made by the deep learning models, and find that a relatively small number of words--associated with very positive/negative sentiment--account for much of the variation in the aggregate sentiment index.
Measuring Job Loss during the Pandemic Recession in Real Time with Twitter Data (joint with Anbar Aizenman, Connor M. Brennan, Cynthia Doniger, and Jacob Williams), FEDS WP, 2023-035.
Abstract: We present an indicator of job loss derived from Twitter data, based on a fine-tuned neural network with transfer learning to classify if a tweet is job-loss related or not. We show that our Twitter-based measure of job loss is well-correlated with and predictive of other measures of unemployment available in the official statistics and with the added benefits of real-time availability and daily frequency. These findings are especially strong for the period of the Pandemic Recession, when our Twitter indicator continues to track job loss well but where other real-time measures like unemployment insurance claims provided an imperfect signal of job loss. Additionally, we find that our Twitter job loss indicator provides incremental information in predicting official unemployment flows in a given month beyond what weekly unemployment insurance claims offer.
Are Manufacturing Jobs Still Good Jobs? An Exploration of the Manufacturing Wage Premium (joint with Kimberly Bayard, Vivi Gregorich, and Maria D. Tito), FEDS WP, 2022-011.
Abstract: This paper explores the factors behind the disappearance of the manufacturing wage premium—the additional pay a manufacturing worker earns relative to a comparable nonmanufacturing worker. With substantially larger declines across union members, we quantify the role of unionization by exploiting the heterogeneity in membership status across manufacturing industries. We find that the decline in union membership explains more than 70 percent of the decline in the wage premium since the 1990s for union members but does not affect nonunion premia. Our findings suggest that the erosion of “good” manufacturing jobs has contributed to the increase in overall wage inequality.
Reconciling Unemployment Claims with Job Losses in the First Months of the COVID-19 Crisis (joint with Andrew Figura, Brendan M. Price, David Ratner, and Alison Weingarden), FEDS WP 2020-055.
Abstract: In the spring of 2020, many observers relied heavily on weekly initial claims for unemployment insurance benefits (UI) to estimate contemporaneous reductions in US employment induced by the COVID-19 pandemic. Though UI claims provided a timely, high-frequency window into mounting layoffs, the cumulative volume of initial claims filed through the May reference week substantially exceeded realized reductions in payroll employment and likely contributed to the historically large discrepancy between consensus expectations of further April-to-May job losses and the strong job gains reflected in the May employment report. Analyzing the relationship between UI claims and underlying employment, we argue that insured unemployment—an alternative high-frequency indicator that responds to gross job gains as well as gross job losses — offers important advantages as a barometer of labor market conditions. Adjusting for reporting artifacts and for time lags between employment flows and associated claims, we show that insured unemployment comoved strongly with payroll employment throughout the first months of the pandemic, as it did during the Great Recession.
Using Payroll Processor Microdata to Measure Aggregate Labor Market Activity (joint with Leland Crane, Ryan Decker, Adrian Hamins-Puertolas, Christopher Kurz, and Tyler Radler), FEDS WP 2018-005.
Abstract: We show that high-frequency private payroll microdata can help forecast labor market conditions. Payroll employment is perhaps the most reliable real-time indicator of the business cycle and is therefore closely followed by policymakers, academia, and financial markets. Government statistical agencies have long served as the primary suppliers of information on the labor market and will continue to do so for the foreseeable future. That said, sources of "big data" are becoming increasingly available through collaborations with private businesses engaged in commercial activities that record economic activity on a granular, frequent, and timely basis. One such data source is generated by the firm ADP, which processes payrolls for about one fifth of the U.S. private sector workforce. We evaluate the efficacy of these data to create new statistics that complement existing measures. In particular, we develop a set of weekly aggregate employment indexes from 2000 to 2017, which allows us to measure employment at a higher frequency than is currently possible. The extensive coverage of the ADP data--similar in terms of private employment to the BLS CES sample--implies potentially high information value of these data, and our results confirm this conjecture. Indeed, the timeliness and frequency of the ADP payroll microdata substantially improves forecast accuracy for both current-month employment and revisions to the BLS CES data.
Racial Gaps in Labor Market Outcomes in the Last Four Decades and over the Business Cycle (joint with Tyler Radler, Ivan Vidangos, and David Ratner), FEDS WP 2017-071.
Abstract: We examine racial disparities in key labor market outcomes for men and women over the past four decades, with a special emphasis on their evolution over the business cycle. Blacks have substantially higher and more cyclical unemployment rates than whites, and observable characteristics can explain very little of this differential, which is importantly driven by a comparatively higher risk of job loss. In contrast, the Hispanic-white unemployment rate gap is comparatively small and is largely explained by lower educational attainment of (mostly foreign-born) Hispanics. Regarding labor force participation, the remarkably low participation rate of black men is largely unexplained by observables, is mostly driven by high labor force exit rates from employment, and has shown little improvement over the last 40 years. Furthermore, even among those who work, blacks and Hispanics are more likely than whites to work part-time schedules despite wanting to work additional hour s, and the racial gaps in this involuntary part-time employment are large even after controlling for observable characteristics. Our findings also suggest that the robust recovery of the labor market in the last few years has contributed significantly to reducing the gaps that had widened dramatically as a result of the Great Recession; however, the disparities remain substantial.
Labor Market Frictions and Bargaining Costs: A Model of State-Dependent Wage Setting, November 2011
Abstract: This paper develops a search and matching model with endogenous separations and costly wage bargaining. In particular, I introduce into an otherwise standard model a fixed wage bargaining cost, which endogenously generates infrequent wage adjustments, but nevertheless leaves wages in new job matches perfectly flexible, consistent with some recent microeconometric evidence. The steady-state version of the model provides a theoretical link between wage bargaining institutions and the unemployment level, illustrating how higher wage bargaining costs lead to higher unemployment. The dynamic version of the model shows how unemployment volatility increases with wage bargaining costs, primarily due to enhanced volatility at the job destruction margin. The model can thus explain why job destruction plays a bigger role for unemployment fluctuations in Continental Europe than in the United States. Finally, the model can rationalize the empirical observation that many firms in recessions do not avoid layoffs by cutting pay.
Job-Embodied Growth and the Decline of Job Tenure (joint with Jan Grobovšek), May 2012
Abstract: We argue that a fraction of the increase in aggregate productivity is related to productivity growth embodied in new job vintages. We propose a model economy to measure indirectly the rate of job-embodied technical change by linking it to job tenure. We find that in the US, job-embodied technical change is an important component of aggregate growth, and that its growth rate must have significantly increased in the mid-nineties to match the concomitant sharp drop in job tenure over the last two decades. Our measure appears robust in the sense that it replicates the decline in job tenure in Europe over the same time period, which we do not target. We also show that labor market frictions present a larger obstacle to productivity growth the higher is the rate of job-embodied technical change, but that their quantitative impact is negligible and hence unlikely to explain the large productivity gap that has opened up between Europe and the US since the mid-nineties.
Policy Notes
Tracking the Labor Market with "Big Data" (joint with Leland Crane, Ryan Decker, Adrian Hamins-Puertolas, and Christopher Kurz), FEDS Notes, September 20, 2019.
A Cautionary Note on the Help Wanted Online Data (joint with David Ratner), FEDS Notes, June 23, 2016.
The Recent Decline in Long-Term Unemployment (with David Ratner), FEDS Notes, July 21, 2014
Why is Involuntary Part-Time Work Elevated? (with Dennis Mawhirter, Christopher Nekarda, and David Ratner), FEDS Notes, April 14, 2014