Amanda M Michaud

To challenge and improve business cycle models of labor markets, we use data on separations into non-employment by destination and reason. Standard models where layoffs are random and quits are selective send too many laid-off workers to unemployment– empirically, a third leave the labor force– and they get the cyclical direction of labor force attachment backward. Adding selective layoffs and random quits resolves these issues and clarifies two channels that affect how labor markets evolve during recessions: (i) labor supply increases as marginal workers hoard their jobs and the displaced keep searching, and (ii) selection in layoffs shifts from marginal toward attached workers. Consequently, output per worker falls through composition rather than TFP, and the welfare cost of recessions is less than half that of the standard model. Through these channels, the volatility of the jobless who want to work is further disconnected from measured unemployment, a prediction we confirm in the data.

We construct monthly Current Population Survey series that classify every separation into non-employment by both its reason (quit or layoff) and its destination (unemployment or non-participation) from 1978 on. Standard destination flows mischaracterize churn: over a third of laid-off workers exit the labor force, leading employment-to-unemployment (EU) flows to understate true layoffs by 20% to mask a stabilizing negative correlation between quits and layoffs. Properly measured, layoffs account for 62% of employment-population volatility and 35% more of unemployment volatility than EU flows, amplified by a new channel: laid-off workers’ rising propensity to flow to unemployment during recessions.

There is substantial variation in Social Security Disability Insurance (DI) across the United States, with DI beneficiaries accounting from less than one percent to more than one-fifth of a county's working-age residents. We combine county-level data on DI applications and awards with measures of labor market characteristics, living costs and population health to model the geographic dispersion of DI. We find that local differences in health and income levels are about equally important in determining geographic differences, with living-cost differences also playing an important role. The DI program redistributes across space, delivering ex ante welfare gains to residents counties at the 90th percentile in terms of DI receipt that are more than twice as large as in counties at the 10th percentile. The place-based effects of DI are larger than most public policy initiatives specifically designed to support local economic activity. NBER SI 2023 Slides

We describe the small amount of wage information in online job posts in the U.S. and how te information that is available is systematically correlated with occupation wages, firms wages, and local labor market tightness. Only 14% of posts contain any information, and 75% of all posts are accounted for by firms with pay information in less than 5% of their posts. More than half of posts with pay information list a range that, on average, is wide, spanning 28% of the midpoint (e.g. $21-28/hr or $32,000-$42,000/yr). The lack of information is not ‘missing at random’. First, postings for high wage occupations and at high wage firms are less likely to provide any wage information, more likely to use ranges when they do, and wider ranges at that. Second, average wages in job postings are higher than workers’ wages in CPS and OES data, especially at the bottom of the wage distribution, by around 40 percent. In low wage salaried positions this jumps to 80 percent. Our results are exemplified by the top 20 posting private, which are representative of the U.S. economy, but have wage information in less than 2% of their posts, while the top 20 firms with wage information are unrepresentative of the U.S. economy.

As married women's labor force participation has increased in the United States, the cyclical volatility of their employment has also increased. We provide a unified theory that can reconcile these facts. Lower volatility of married women's employment over the business cycle is driven by counter-cyclical motives to remain in the labor force to provide insurance against their spouse's income risk. Changes in fundamentals that increase attachment subsequently lower the ability of wives to adjust labor supply to provide insurance in recessions-- they are more likely to have permanently high labor supply anyway. The model predicts that some forces driving the growth in female participation-- increasing returns to tenure and decreasing fixed cost of work-- increase attachment and cyclical volatility. The closing gender wage gap, by contrast, reduces both. A quantitative evaluation predicts that the former two forces have dominated in the United States with some ebbing for recent cohorts. Microeconomic evidence support both this prediction and the specific mechanism of precautionary spousal labor supply. Implications for welfare and intra-household insurance are discussed.

I develop a quantitative framework to study the impact of Unemployment Insurance (UI) expansions to workers earning below eligibility thresholds. A model of UI is calibrated to replicate experiences of the eligible and ineligible, including consumption after job loss. Eligibility rules distort labor supply in costly ways and removing them would benefit workers across the income spectrum, even with consideration of funding costs. The model captures job-finding rates by eligibility that I document during the GFC and COVID-19 recessions. Although expanded eligibility during COVID-19 lowered job-finding rates, other changes in UI policy were more impactful.

In recent decades, Social Security Disability Insurance (SSDI) claims have risen rapidly. We evaluate the importance of changing macroeconomic conditions in shaping this trend. Our quantitative framework considers that economic conditions interact with individuals' health status in their decisions to apply for SSDI. Crucially, these factors are correlated through the nature of work: multiple sectors differentially expose workers to health and economic risks. Decomposing factors driving SSDI growth in a calibrated model, we find the secular deterioration of economic conditions concentrated in populations with high health risks accounts for about half of the increase in SSDI claims predicted by the model, about a third overall.

Involuntary job separation causes earnings to fall 15.4% initially and remain depressed two decades later. We quantify how much these scars shape the income distribution: incorporating the empirical separation hazard and scar pro le into a standard income process, we nd that job loss reduces average life-cycle income growth by 14.7% and raises cross-sectional dispersion by 17.8% relative to a counterfactual without separations. We then highlight a simple but underappreciated implication of life-cycle concavity for reducedform income processes: any speci cation in which displaced workers restart along the same concave path mechanically delivers rapid recovery, missing the persistent scar in the data. We use a parsimonious learning-by-doing framework to show that this tension cannot be resolved by selection on wages or serially correlated separations alone; some channel that yields lower post-separation income growth is required. We provide a calibrated process matching both scars and life-cycle moments, intended for researchers who need a portable income process with realistic job-loss risk.

Living in a married household typically mitigates income risk. Is this true during a pandemic? On the one hand, the presence of two potential earners reduces the household income risk associated with a cut in hours, job loss, or a stay at home order. On the other, married couples are more likely to have children to care for during a stay at home order and larger households are more likely to have a member with a health condition that makes them vulnerable to severe illness if they contract the virus. Using a structural model of labor supply, we measure how these factors affect the welfare outcomes of different household types and the aggregate dynamics of employment in response to the COVID-19 pandemic.