Women’s Labor Force Exits During COVID-19: Differences by Motherhood, Race, and Ethnicity

Abstract

While the descriptive impacts of the pandemic on women have been well documented in the aggregate, we know much less about the impacts of the pandemic on different groups of women. After controlling for detailed job and demographic characteristics, including occupation and industry, we find that the pandemic led to significant excess labor force exits among women living with children under age six relative to women without children. We also find evidence of larger increases in exits among lower-earning women. The presence of children predicted larger increases in exits during the pandemic among Latina and Black women relative to White women. Overall, we find evidence that pandemic induced disruptions to childcare, including informal care from family and friends. Our results suggest that the unique effect of childcare disruptions during the pandemic exacerbated pre-existing racial and income inequalities among women.

Appendix

Trends in Exits Among Men

In order to compare the trends we see across women to pandemic patterns among men, we show labor force exits among previously employed men broken out by the presence of children in the household in Appendix Fig. 5 and by race and ethnicity in Appendix Fig. 6.

Fig. 5

Data are from the CPS downloaded from IPUMS (Flood et al., 2020)

Male labor force exits, by presence of children. Plotted are 3-month moving average changes in labor force exits for prime-working-age workers, by the presence of children aged 0 to 5 and 6 to 12 before the pandemic among workers who were employed 1 year prior. Each is adjusted for monthly seasonality based on average monthly values from January 2003 to February 2020. Statistics are weighted using sampling weights.

Fig. 6

Data are from the CPS downloaded from IPUMS (Flood et al., 2020)

Male labor force exits, by race and ethnicity. Plotted are 3-month moving average changes in labor force exits for prime-working-age workers, by race and ethnicity among workers who were employed 1 year prior. Each is adjusted for monthly seasonality based on average monthly values from January 2003 to February 2020. Statistics are weighted using sampling weights.

These figures show that differences in exit rates between men with children and those without are much smaller than the differences for women. Men of color had larger exit rates during the pandemic relative to White men, but the differences decreased later in the pandemic. This convergence did not occur for women.

Women Outside the Labor Force: Caregiving Trends

The labor force patterns for women described in the paper are mirrored by increases in the share of women not in the labor force stating that caregiving is their primary reason for non-participation. In particular, we find that there were large increases in the share of women who were living with children who exited the labor force and continued to be out of the labor force because of caregiving, as shown in panel A of Appendix Fig. 7. Women who were living with children under age 6 saw a roughly 4 percentage point increase that persisted through early 2021. Women who were living with children aged 6 to 12 saw a highly persistent jump of around three percentage points. While it is reassuring that the largest jumps are among women with children, the graph also shows a slight increase of around half a percentage point for other women. This could reflect additional caregiving responsibilities, for example for elderly relatives.³⁴

Latinas and Black women also had larger increases in the share of exits that women report as being due to caregiving. Panel B of Fig. 7 shows a roughly two percentage point increase in the share of Latinas and Black women who exited the labor force and said that they were not in the labor force because of caregiving reasons. The two percentage point increases among these women of color was roughly double the one percentage point increase among White women. This pattern provides additional support for our main finding that the burden of caregiving affected women of color more than it did White women, at least in terms of women’s ability to remain in the labor force.

Fig. 7

Data are from the CPS downloaded from IPUMS (Flood et al., 2020)

Previously employed women not in the labor force: caregiving reasons. Plotted are three month moving average changes in the rates of respondents not in the labor force stating caregiving as a reason among prime-working-age women who were employed 12 months ago by the presence of children aged 0 to 5 and aged 6 to 12 before the pandemic and by race and ethnicity. Each is adjusted for monthly seasonality based on average monthly values from January 2003 to February 2020. Statistics are weighted using sampling weights.

Additional Results

In this section, we report additional results from our main specifications in Table 2 and the marginal effects from a logit specification rather than a linear probability model.

Full Results of Baseline Specification

As briefly discussed in the main text, labor force exits decline as educational attainment increases both before and during the pandemic. Excess pandemic exits exhibit the same pattern both when compared with pre-pandemic exits and Great Recession exits although many of the point estimates are not statistically different from 0. Generally the occupation and industry measures are not predictive of excess exits. They would likely be predictive of employment losses in the initial months of the pandemic, but these results suggest that occupation and industry COVID-19 effects didn’t drive excess exits as measured in September 2020 to February 2021. Interestingly, occupations that were hard hit by employment losses during the pandemic had greater base levels of exits during the years prior to the pandemic while occupations and industries that used more working from home had fewer exits. These correlations likely reflect existing differences above and beyond women’s education and earnings in the occupations and industries that were affected by COVID-19.

Age does not appear to be predictive of excess exits, but it was predictive of pre-pandemic exits. Latinas and Black women were more likely than White women to exit the labor force both before and during the pandemic, but when we compare their excess exits we see positive but statistically insignificant effects after controlling for many observable covariates.

Table 5 Effects of children on labor force exits: additional variables

Results from Logit Specification

An alternative specification would be to use a non-linear logit model to predict labor force exits. We show the marginal effects at the mean of each variable in Table 6. The estimates are qualitatively similar to those from the linear probability model. Women with small children were more likely to leave the labor force during the pandemic than observably similar women without children. The wage gradient for women with older children remains while the wage gradient for women with younger children is economically small and statistically insignificant. Women with older children who earned lower wages before the pandemic had larger increases in their exits than women with older children who earned higher wages.

Table 6 Effects of children on labor force exits; logit specification

Additional Decomposition Results

Summary Statistics

Appendix Table 7 shows summary statistics for our sample by race and ethnicity, providing context for the decomposition results.

The first big difference is that Latinas and Black women had higher rates of exits relative to White women during the pandemic. And these larger differences are likely to be expected because Latinas and Black women generally were working in occupations and industries that were harder hit by the pandemic than White women. Latinas and Black women additionally had lower levels of education than White women.

Relevant to our results, we also find differences in fertility, marital status, and pre-pandemic earnings. Latinas were much more likely to have children—particularly small children—than were White women. Black women were also slightly more likely to have children than were White women. Black women were also much less likely to be married than White women or Latinas. Latinas and Black women also earned less per week than White women. Each of these differences points to the channels of how childcare interruptions could differentially affect these groups of women.

Table 7 Summary statistics by race and ethnicity

Full Decomposition Results as a Table

Appendix Table 8 shows the results from our Oaxaca style decomposition. Reported in the table are the proportions of the difference in exit rates explained by each group of variables. Columns one though three show the results for the Latina-White gap while columns 4 through 6 show the results for the Black-White gap. The Before column explains differences in exit rates prior to the pandemic, the During column is for pandemic era exits, and the Difference column shows the difference in explanatory power for the variables between the two time periods.

Factors relating to employment explain a large share of the differences in exit rates before and during the pandemic for both Latinas and Black women. When we look at the differences between the two time periods we see that the household interactions stand out as being much more important in explaining exits during the pandemic than prior to the pandemic. For Black women their lower rates of marriage actually predict lower exit rates so this covariate actually increases the unexplained portion. A woman’s state of residence is predictive of exits before the pandemic but is not predictive during the pandemic. While we include this control for completeness, one could argue that states with higher levels of workers of color may have higher exit rates due to discrimination and therefore the state itself is not a good control.

Finally a larger share of the Latina-White gap is explained than the Black-White gap. Mechanically, much of the difference in the explained effects is due to the explanatory power of the larger differences in educational attainment and earnings between Latinas and White women. Higher rates of fertility as well as lower earnings (interacted with higher fertility) also make the variables relating to children more explanatory of the gaps between Latinas and White women.

Table 8 Decomposition of gaps in exits during the pandemic

Alternative Decomposition Using Linear Regression

Another conceptually similar approach to understanding the role of covariates in explaining racial and ethnic differences in exits is to break down our baseline regression into steps where we first include the race/ethnicity coefficients alone, and then we add categories of covariates to examine how they change the mean differences between race/ethnicity groups.

Equation 1 formalizes this approach in terms of coefficients on an indicator of whether someone is a Black woman ($\alpha _1$) or a Latina ($\alpha _2$). Changes in $\alpha _1$ and $\alpha _2$ show to what extent correlations with controls in the matrix X are able to account for different rates of exits by race and ethnicity. Conceptually, if differences in the covariates themselves explained all of the racial and ethnic differences in exits, then the coefficients on race and ethnicity would go to 0

$$\begin{aligned} \text {Exit} = \alpha _1 \text {Black} + \alpha _2 \text {Latina} + \beta X + \epsilon \end{aligned}$$

(1)

Table 9 presents this approach both in the pre-pandemic period and during the pandemic. When looking across the table at how the coefficients change, we can again see the importance of education, industry and occupation, and earnings in explaining differences in exits both before the pandemic and during between women of color and White women. Looking further across the columns, we see some role for marital status, children, and children interaction variables.

We include this approach for completeness, but we prefer the Oaxaca−Blinder−Fairlie decomposition because the exact contribution of each covariate is difficult to assess as it heavily depends on the order that the coefficients are added in. In contrast, the main results in Table 8 are averaged so that the order variables are introduced does not matter.³⁵

Table 9 Excess exits sequentially adding covariates as decomposition

Differential Effects of Children by Race–Ethnicity

Our Oaxaca−Blinder−Fairlie decomposition shows that a large proportion of the gap in excess exits between women of color and White women remains unexplained. An underlying assumption of the decomposition is that the effect of the covariates on exits is the same across racial and ethnic groups. In order to test whether the effects of children were larger for Black women and Latinas, we augment our baseline model to include interactions between the presence of children and the race and ethnicity of the woman. Unfortunately we lack precision to estimate these interaction terms definitively. The point estimates suggest that the effect of living with a child under 6 was greater on women of color’s excess exits, while living with a child aged 6 to 12 was either the same or perhaps less important. Interpreting these effects; however, requires us to hold constant marital status and earnings, which we know differ by race and have their own effects on excess exits by race.

Table 10 Effects of children on labor force exits interacted with race–ethnicity