Young Children and Parents’ Labor Supply During COVID-19

Covid-19 School Closures and Parental Labor Supply in the United States

This article examines changes in parental labor supply in response to the unanticipated closure of schools following the onset of the COVID-19 pandemic in the United States. The authors collect detailed daily information on school closures at the school-district level, which they merge to individual-level data on labor supply and sociodemographic characteristics from the monthly Current Population Survey spanning from January 2019 through May 2020. Using a difference-in-differences estimation approach, the authors find evidence of non-negligible labor supply reductions. Having a partner at home helped offset the negative effect of school closures, particularly for maternal employment, although respondents' job traits played a more significant role in shaping labor supply responses to school closures. Overall, the labor supply impacts of school closures prove robust to identification checks and to controlling for other coexistent social distancing measures. In addition, these early school closures seem to have had a long-lasting negative impact on parental labor supply.

Young Children and Parents' Labor Supply during COVID-19

Using Earnings Data From the Monthly Current Population Survey

Does participating in a longitudinal survey affect respondents' answers to subsequent questions about their labor force characteristics? In this article, we investigate the magnitude of panel conditioning or time-in-survey biases for key labor force questions in the monthly Current Population Survey (CPS). Using linked CPS records for household heads first interviewed between January 2007 and June 2010, our analyses are based on strategic within-person comparisons across survey months and between-person comparisons across CPS rotation groups. We find considerable evidence for panel conditioning effects in the CPS. Panel conditioning downwardly biases the CPS-based unemployment rate, mainly by leading people to remove themselves from its denominator. Across surveys, CPS respondents (claim to) leave the labor force in greater numbers than otherwise equivalent respondents who are participating in the CPS for the first time. The results cannot be attributed to panel attrition or mode effects. We discuss implications for CPS-based research and policy as well as for survey methodology more broadly.

:This paper addresses the economic impact of the COVID-19 pandemic by providing timely and accurate information on the impact of the current pandemic on income and poverty to inform the targeting of resources to those most affected and assess the success of current efforts. We construct new measures of the income distribution and poverty with a lag of only a few weeks using high-frequency data from the Basic Monthly Current Population Survey (CPS), which collects income information for a large, representative sample of US families. Because the family income data for this project are rarely used, we validate this timely measure of income by comparing historical estimates that rely on these data to estimates from data on income and consumption that have been used much more broadly. Our results indicate that at the start of the pandemic, government policy effectively countered its effects on incomes, leading poverty to fall and low percentiles of income to rise across a range of demographic groups and geographies. Simulations that rely on the detailed CPS data and that closely match total government payments made show that the entire decline in poverty that we find can be accounted for by the rise in government assistance, including unemployment insurance benefits and the Economic Impact Payments. Our simulations further indicate that of those losing employment the vast majority received unemployment insurance, though this was less true early on in the pandemic, and receipt was uneven across the states, with some states not reaching a large share of their out of work residents. Updated results during the pandemic for a subset of the tables in this article can be found at povertymeasurement.org.

Changes in the Employment Status of People With and Without Disabilities in the United States During the COVID-19 Pandemic

This paper addresses the economic impact of the COVID-19 pandemic by providing timely and accurate information on the impact of the current pandemic on income and poverty to inform the targeting of resources to those most affected and assess the success of current efforts. We construct new measures of the income distribution and poverty with a lag of only a few weeks using high frequency data from the Basic Monthly Current Population Survey (CPS), which collects income information for a large, representative sample of U.S. families. Because the family income data for this project are rarely used, we validate this timely measure of income by comparing historical estimates that rely on these data to estimates from data on income and consumption that have been used much more broadly. Our results indicate that at the start of the pandemic, government policy effectively countered its effects on incomes, leading poverty to fall and low percentiles of income to rise across a range of demographic groups and geographies. Simulations that rely on the detailed CPS data and that closely match total government payments made show that the entire decline in poverty that we find can be accounted for by the rise in government assistance, including unemployment insurance benefits and the Economic Impact Payments. Our simulations further indicate that of those losing employment the vast majority received unemployment insurance, though this was less true early on in the pandemic and receipt was uneven across the states, with some states not reaching a large share of their out of work residents. Updated information on the summary measures presented in this paper, using the latest data available, may be found at povertymeasurement.org.

In recent years, Congress has considered a variety of work-subsidy programs designed to encourage work among welfare recipients. Many of these programs would subsidize individuals only if they work some minimum number of hours. Commonly used techniques cannot give direct answers to relevant policy questions since a tied offer is involved, and hence the offer cannot be treated as a simple wage change. The essence of the problem involves utility comparisons between two or more discrete alternatives. Such comparisons inherently require information about consumer preferences in a way not easily obtained from ordinary labor-supply functions. To make such comparisons, I present a method for directly estimating consumer indifference surfaces between money income and nonmarket time. Once these surfaces are determined, they can be used to compare a variety of alternative programs to investigate whether or not there is scope for Pareto-optimal redistribution of income transfers and time, improving the general level of welfare of the community at large without reducing the welfare of individuals receiving income transfers. Knowledge of these indifference surfaces allows us to estimate reservation wages to estimate the value of nonworking-women's time (Gronau 1973), laborforce participation functions, hours-of-work functions, and welfare losses due to income tax programs (Harberger 1964). I demonstrate that direct estimation of indifference surfaces allows us, at least in principle, to relax

Does the Internet Help the Unemployed Find Jobs?

Several studies have documented the increase in retirement since the onset of the Covid-19 pandemic. I contribute to this literature by answering two key questions that show the pandemic excess retirement is caused primarily by demand-side factors: first, using the monthly Current Population Survey (CPS) data on employment and retirement of older workers and CPS’s panel structure, I explore if older workers left their jobs and the labor force voluntarily as a part of “the great resignation”. I find that very few workers quit their jobs voluntarily, and most retirements were preceded by involuntary job loss and unemployment. Second, using the Health and Retirement Study (HRS) with its rich data on wealth, employment, and the special Covid-19 related questions included in 2020 HRS (Wave 15), I examine if older workers who experienced job loss during the pandemic retired because they were financially prepared for retirement. I find that among the small group of older workers who quit their jobs because of the pandemic, the majority did not have adequate retirement assets and their decision is more likely to be based on their health status and vulnerability during the pandemic.

This paper presents some previously unpublished data from the Current Population Survey's 1957 interview sample on religion. All of the available data from this sample have now been published. Our review of these data indicates that there has been a structural convergence between white Protestants and Catholics, but that the Jews and blacks have not been parties to this convergence. The necessity for future questions on religion on both the monthly Current Population Survey and the decennial census is discussed.

To analyze whether there was an increase in retirement or in part-time work among older workers after January 2014, when new health insurance coverage options became available because of the Affordable Care Act (ACA). We analyze trends in retirement and part-time work for individuals aged 50-64 years in the basic monthly Current Population Survey from January 2008 through June 2016. We test for a break in trend in January 2014. We also test for differences in trends, both before and after 2014, in states that expanded their Medicaid programs in January 2014 under the ACA compared with those that did not. We find that there was no change in the probability of retirement or part-time work among older workers in 2014 and later, either overall or in Medicaid expansion states relative to nonexpansion states. Although many observers had predicted that an unintended consequence of health reform would be reduced labor supply, we find no evidence of this for older workers in the first 2.5 years after the law's major coverage provisions took effect.

A large and growing percentage of households are missed in the monthly Current Population Survey (CPS). For the survey as a whole, the rate of nonresponse is roughly 13 percent. This is higher for Blacks, with the share for young Black men being about 30 percent. The BLS’s current methodology effectively assumes that, with adjustment for various characteristics, people who are not included in a follow-up survey may not differ systematically from those who are included. The present paper, however, provides evidence that this may not be the case. With the rotation panel structure of the CPS data from 2003 to 2019, we investigate bias from nonresponse in CPS and its association with one’s prior labor market status, paying particular attention to how the relationship differs by race, ethnicity, and gender. Our analysis suggests that people are considerably more likely to be missing in a subsequent observation if they are unemployed or not in the labor force in the prior observation. We also estimate what the real labor market outcomes might have been when adjusting for nonresponse and undercoverage. Findings indicate that the current methodology may underestimate the national unemployment and labor force participation rates by about 0.7 and 1.2 percentage points, respectively. The gap between observed and adjusted unemployment rates tends to grow beginning in 2015. The unemployment rate is more understated for Blacks than for whites, particularly with a gap of about 3.3 percentage points for young Black men (age 16 to 34).

We investigate the impact of the COVID-19 epidemic on gender disparities in three employment outcomes: labor force participation, full-time employment, and unemployment. Using data from the monthly Current Population Survey, in this research note we test individual fixed-effects models to examine the employment status of women relative to that of men in the nine months following the onset of the epidemic in March of 2020. We also test separate models to examine differences between women and men based on the presence of young children. Because the economic effects of the epidemic coincided with the summer months, when women’s employment often declines, we account for seasonality in women’s employment status. After doing so, we find that women’s full-time employment did not decline significantly relative to that of men during the months following the beginning of the epidemic. Gender gaps in unemployment and labor force participation did increase, however, in the early and later months of the year, respectively. Our findings regarding women’s labor force participation and employment have implications for our understanding of the long-term effects of the health crisis on other demographic outcomes.