Amanda M Michaud

We characterize the little wage information contained in online job posts. Wage information is rare: only 14% of posts contain any information. Of these, wage ranges are more common than point wages, and are wide on average, spanning 28% of the midpoint (e.g. $32,000-$42,000/yr). Posted wages are highly selected in low income occupations: 40% higher than wages of employed workers. High wage firms are more opaque, with more and wider ranges. We find zero correlation between wage information and local labor market tightness. We provide an example of bias in econometric inference that worsens as wage information falls.

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.

This paper develops a quantitative framework to study the impact of Unemployment Insurance (UI) expansions to workers earning below eligibility thresholds. A model of how UI affects welfare and labor supply is developed and calibrated with microeconomic data, including consumption. The model predicts that the current ineligible would choose to stay on UI longer than the current eligible and the margins of why this is the case are quantified. The model is applied to the Great Recession by identifying ineligible workers in the data using machine learning and to an actual expansion during COVID-19 using administrative data. The UI duration for newly eligible under the expansion was 1.7 times longer than the previous eligible but is one-third shorter than the model's economic incentives predict. This suggests caution in extrapolating from the COVID-19 data and the model is used to predict impacts of smaller scale expansions during non-pandemic times.

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.

We argue that dynamics play a critical role in the evaluation of punitive incarceration reform on crime, inequality and the Macroeconomy. Individuals' past choices related to crime and employment under old policies have persistent consequences that limit their contemporaneous responses to future policy changes. Novel cohort evidence is provided in support of this mechanism. A quantitative model of this theory generates nuanced, non-monotone dynamics of crime and incarceration similar to the U.S. experience following a single permanent increase in punitive incarceration in the 1980s. Increased inequality and declining employment accompany these changes and are borne unequally across generations.

A large literature shows workers who are involuntarily separated experience wage scars: their hourly earnings fall initially by an average of 15.4% and remain much lower than their non-separated counterparts more than 20 years later. We find that this reduces average life-cycle wage growth by 14.7% and increases cross-sectional wage dispersion by 17.8%. We research variants of human capital theory capable of replicating scars, highlighting a tension in producing large, persistent wage scars alongside average life-cycle wage dynamics. An examination of labor market and demographic characteristics of workers who never recover suggests many theories of wage scars are operational, but on different groups of workers.

A pre-existing health condition makes 18.1% of the labor force highly vulnerable to severe illness or death from COVID-19. While some can work remotely, 11% of the labor force is COVID vulnerable and in a job that cannot be done remotely. I study the labor supply decisions of these individuals in an equilibrium model of labor markets where participants can choose whether to search for remote or non-remote jobs and non-participants can apply for disability. In the worse case scenario, the disability rate rises from 5% of the working age population to 10%. More realistically, a pandemic lasting up to 3 years raises the disability rate to 5.5% of the working age population. Increases in unemployment benefits lower this number, but competition for work from home jobs increases it.

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.