Last hired, first fired, and first to know

Black unemployment has run at roughly twice the White rate for as long as the series has existed. That gap in levels is well documented. This paper asks a different question: whether the timing of a recession differs, so that Black workers’ labor market turns down before the aggregate does and recovers after it.

If it does, an indicator built on the headline rate is, in effect, waiting for a recession to reach White workers before it calls one. The paper traces that lag across every recession since 1982 and asks how much of it is about race itself.

Composition is what a group’s mix explains; the residual is what it does not

The composition piece prices Black workers’ own mix of income, education, sex, age and region at White workers’ unemployment rates within each of those cells. It answers: if Black workers faced White workers’ labor market, cell by cell, how much earlier would their recession still start?

The residual is what remains. It is not a direct measure of discrimination, since it also absorbs anything the cells leave out, but it is an upper bound on how much of the timing gap the observed characteristics can explain away.

The Racial Inequality of Recession

The full paper sets out the data, the turning-point framework and the decompositions, with robustness checks across smoothing windows, thresholds and reference groups.

The bottom of the distribution is the leading edge

Lower-income workers are concentrated in cyclically sensitive jobs and absorb hours cuts and early separations first, so their unemployment departs from its trough before the aggregate does.

Read from the bottom up, the income distribution is an early-warning system. By the time the headline rate has moved enough to trigger a conventional indicator, the poorest tenth of families has often been in a recession for months.

Separating the paycheck from the person

Low-income workers are also younger, less likely to hold a degree and more likely to work in industries that shed jobs early. The split below prices each decile’s demographic mix at economy-wide rates, so the composition piece is what that mix alone would predict and the residual is the head start that remains.

The Income Inequality of Recession

The full paper documents the decile series, the turning-point framework and the decompositions, and shows how the results change with the smoothing window, the threshold and the reference group.

Time, not only safety

A degree lowers the chance of losing a job in a downturn. It also changes when the downturn arrives. Workers without a high school diploma tend to see their indicator turn first, while those with a bachelor’s degree are often the last to register a recession and the first to leave it.

That ordering matters for policy timing: support keyed to the headline rate reaches the least-educated workers after their recession is already under way.

The Educational Inequality of Recession

The full paper sets out the education series, the turning-point framework and the decompositions, with robustness checks across parameters and reference groups.

What each measure adds

Each U-measure widens the one before it. U-1 and U-2 are narrower than the official rate; U-4 through U-6 add people the official rate leaves out: workers too discouraged to search, people who want a job but have not looked recently, and workers who are employed but working fewer hours than they want. U-4, U-5 and U-6 begin in 1994, when the redesigned Current Population Survey made them possible, so they cover the 2001, 2007–09 and 2020 recessions.

The intensive margin moves first

When demand weakens, firms usually cut hours before they cut jobs: shifts shrink, overtime disappears and full-time workers are moved to part time. Those workers are still employed, so the official rate does not count them, but U-6 does. Layoffs follow later, and the long-term unemployment captured by U-1 builds last.

The workers whose hours are cut first are disproportionately the same workers the rest of this site shows entering recession first: lower-income, less-educated, younger, Black and Hispanic workers. U-6 is, in effect, a composite of the leading edge of the labor market.

Discouraged workers, or shorter hours?

U-6 differs from the official rate in two steps: U-5 adds discouraged and other marginally attached workers, and U-6 then adds people working part time for economic reasons. The split below asks how much of U-6’s head start over U-3 comes from each step.

The answer is clear. In every recession since 1994, U-5 crossed the threshold in the same month as U-3: counting discouraged workers adds no early warning. All of U-6’s head start over U-3 comes from adding workers whose hours were cut. As the decomposition below shows, though, much of that is because adding them makes the series larger.

Scale, composition, or the intensive margin?

U-6 is about one and a half to two times the size of U-3, so the same 0.5-point rise is easier for it to reach. It also gives more weight to the groups that are most often underemployed. To separate these from genuinely earlier movement, the split below rebuilds U-3 and a U-6-style rate from 32 race, sex and education cells of the CPS microdata and asks three questions. How much earlier would U-3 fire if it were simply as large as U-6 (scale)? How much earlier if each group’s U-3 were scaled by that group’s own underemployment (composition)? And how much earlier does the actual U-6-style rate fire than that (within groups, the intensive margin)?

Most of U-6’s head start at a fixed threshold is scale. Composition adds little, and within groups part-time work does not consistently move before unemployment. When the threshold is scaled to each measure’s level (the Threshold scaling control in the full analysis below), U-6 and U-3 fire within a month of each other. The case for U-6 is less that it is faster than that it is larger: it shows how much more slack a recession creates, and for whom.

Who is underemployed, beyond their characteristics?

Two decompositions of the same CPS microdata, with the controls used across the site: demographics (age, sex, education, family, region, metro status, veteran status, nativity), then industry, occupation and sector, then income. The first asks who is underemployed but not unemployed: the gap between two groups in the share working part time for economic reasons, and how much of it their characteristics explain. The second asks whether a gap grows when the intensive margin is counted, by decomposing the same comparison with U-3 and with the U-6-style rate side by side.

The Inequality of Recession

The paper shows that, in a search across thousands of candidate recession indicators, a rule built on U-6 sits on or near the frontier that trades early detection against accuracy, and explains why: it captures adjustment on the intensive margin and the workers who feel a downturn first.

A recession is not a switch

The NBER’s Business Cycle Dating Committee decides, after the fact, whether the economy as a whole was in a recession and when it began and ended. That is a judgment about the aggregate, and it is binary: a month is either in a recession or it is not.

Workers do not experience the aggregate. A downturn reaches some industries, places and groups of workers months before it reaches others, deepens unevenly and lingers for some long after the official trough. The same indicator, run on different groups, shows that a recession has a composition (who is in it), a distribution (how deep it is for each) and a continuous shape (how much of the labor force is in it at any moment).

Never called, but not ruled out

The Dating Committee never declared a recession in this period. But the indicators built to detect recessions in real time were not unanimous. In July 2024 the Sahm Rule reached its 0.5-point threshold. The Michez Rule of Pascal Michaillat and Emmanuel Saez, which requires unemployment to rise and job openings to fall together, entered its recession band in early 2024; in the working-paper version of “Has the Recession Started?”, they put the probability that the US was already in recession at about one in two with August 2024 data, and dated a possible start to March 2024.

Read as a yes-or-no question, the answer was no. Read as a distribution, the more useful question is: for whom was it a downturn, and how much of the labor force was in one?

The aggregate held; parts of the labor market did not

Run separately by race, income and education, the same turning-point rule shows which groups crossed into a downturn during these years even though the headline rate never did so convincingly. Pick a dimension below; the table counts the months each group spent above its threshold since January 2022.

Was it who these workers are, or something else?

A group can show a deeper downturn because of its mix: younger, lower-income or less-educated workers are more cyclical wherever they appear. The split below asks how much of each group’s gap over the period is predicted by that mix, priced at the reference group’s rates within each cell, and how much remains. The measure is continuous: the average level of the indicator over the period, or the number of months spent above the threshold.

Recessions are compositional, distributional and continuous

The vibecession is a useful test case because the aggregate answer and the lived answer diverged. A binary dating rule has to wait until a downturn is broad enough to move the whole economy. A distributional reading can say, month by month, how much of the labor force is in a downturn and who they are, which is closer to what people mean when they say the economy feels like a recession.

None of this says the NBER was wrong. It says that the aggregate is one summary of a distribution, and that indicators built on marginal workers carry information the aggregate averages away.

A mentor beyond measure

I owe my deepest thanks to Trevon Logan, who has been a fantastic mentor in every sense of the word. His generosity with his time, his sharp and honest reading of every draft, and his steady belief in this project have shaped it from its earliest form to the papers it has become. It was his encouragement to decompose the gaps, to ask how much of each group’s recession is composition and how much is something more, that gave this work its center. Any scholar would be fortunate to have a mentor like him; I am grateful beyond words that I do.

The rule, and the guidance behind it

To Claudia Sahm, whose rule is the foundation of everything on this site: thank you for providing so much guidance to a young scholar venturing into a subfield that has been written about far less than it deserves. Your patience, your openness and your willingness to engage with a newcomer’s questions made it possible for me to build on your work with confidence. This project stands, quite literally, on what you created.

Thoughtful, forthcoming and transparent

Thanks to Pascal Michaillat for being an incredible mentor. He has been unfailingly thoughtful, forthcoming and transparent, and remarkably generous in meeting with me and answering so many of my questions as this research developed. His own work on recession detection set the standard I have tried to meet, and his candor and care have made me a better economist.

Thanks to Steven N. Durlauf of the University of Chicago for some of my earliest and most thoughtful feedback on this topic. He encouraged me to think deeper, broader and more ambitiously, and he would not let me settle for a simple statistical question when the work called for deeper theory.

Thanks to Emmanuel Saez for seeing the value in this research, for encouraging me to pursue further papers in this field, and for recognizing the promise of the spatial research. I am grateful for his lectures and for the guidance he gave in office hours during the Stone Center Summer Fellowship at the University of California, Berkeley.

Thank you to William A. Darity Jr. for being so incredibly helpful and so incredibly giving, for seeing the value in this research, and for inviting me to speak on it at the Journal of Stratification Economics Conference. Above all, thank you for creating space for me in the study of inequality. I will always be thankful.

Thank you to Miles Corak for coming to my early brown-bag seminars, when this research was in its infancy, and for pointing out that, whether or not I knew it yet, what I was really trying to do was characterize the economy. That advice, like so much of his advice, was sage.

Thank you to Matthew J. Baker for letting me pick his brain about the microeconomic theory behind this work.

Thanks to Justin Wolfers for helping me think clearly about the international panel, about how to index workers and the quality of the labor market across countries, and about how to respond to journal feedback.

To Wim P. M. Vijverberg, thank you for teaching me the econometrics I know, and for allowing me to bring this project into the classes you teach so well.

Thank you to Janet Gornick for always pointing me in the right direction, even when a research question sat outside her own area of expertise.

Thank you to Aaron Sojourner for his feedback on the general topic and for his early interest in what this work could mean.

Thanks to my professors at the CUNY Graduate Center. To Guillaume Haeringer: although you are a microeconomic theorist, you have been one of the most encouraging voices for this project, always supportive of the story I was telling with these macroeconomic statistics. To Thomas Herndon: thank you for our long conversations about the paper and for your counsel on how to navigate economics and academia. To Lilia Maliar: thank you for suggesting the machine-learning techniques in which you are an expert, to find which indicator among trillions was the right one, and for so generously offering your expertise. To Sebastiano Manzan: thank you for your insights into forecasting and how this work relates to it.

I also thank Matthew J. Baker, Miles Corak, William A. Darity Jr., Steven N. Durlauf, Janet Gornick, Guillaume Haeringer, Thomas Herndon, Lilia Maliar, Sebastiano Manzan, Aaron Sojourner, Wim P. M. Vijverberg, Bryan Weber and Justin Wolfers for their comments, insight and advice. Thank you to Marcus Cundiff of the City University of New York Graduate Center, and to Ricardo Martin, Angel Martinez, Joshua Velez and Ryan Lamidi of the City College of New York, for valuable research assistance. Thanks to the National Bureau of Economic Research Conference on the Economics of Race and Stratification, the Journal of Stratification Economics Conference and the American Economic Association Mentoring Program. Lastly, special thanks to Giorgi Ghviniashvili for his help building the interactive Modified Sahm Rule tool.

This work received no financial support from any outside agency or organization. The views expressed here, and any mistakes, are my own.