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The Wild Ride of Pandemic Employment Status and Why Race and Gender Matter

It was back in August 2020 that Mina Kim and I first started looking at the (kinda-secret yet publicly-available) Bureau of Labor Statistics data on Asian female employment status and noticed that the Asian female pictures were like holding a magnifying glass to the more general stories documenting the so-called “She-Cession.” It turns out that the one race-and-gender category with the largest absolute increases in unemployment for several months from July through November were Asian women. I continued to track the data releases each month (not always remembering to update my blog I now realize, but always sharing on Twitter). I saw the overall female unemployment rate finally drop back below the male one back in October, while the Asian female unemployment rate has remained higher than the Asian male one even through last Friday’s data release for February 2021.

Now, employment status is a notoriously imprecise thing to measure, and responding to a short survey question with a short, categorical answer about something as complicated as what kind of work you’re doing during the pandemic is difficult. I know, because over the summer I got surveyed by the Census Bureau’s “household pulse survey” and was often not sure which answer to pick and wanted to add “but let me explain…” I had taken a buyout/severance package offer from my last full-time job, so I was not working that job anymore but was continuing to get paid (for another month or two at the time, at least). While the “loss” of my job was of my own doing, at the same time it certainly felt like it was a pandemic-related job loss that certainly hadn’t been completely my choice, and my lack of success in finding a new full-time role really made me feel like a legitimately involuntarily (and now long-term) unemployed person.

I started to imagine how many other people struggle to respond to those household surveys, maybe get confused about the different labels for employment status, maybe get worn out by the end of the questions and start answering without careful thought. This, of course, is the challenge of collecting data through fielded surveys, rather than letting people self-report what’s on their minds based on their Google searches or their other means of asking for help and interacting and transacting with the world.

But the BLS data remain the best data we have to study what’s going on with US employment, so that’s what I’ve been sticking with (while urging agencies and research organizations to expand efforts to collect more disaggregated data to better understand the macroeconomy as well as the disparities that aggregate and average statistics hide, per one of President Biden’s Day One executive orders). Now that a full year has passed since the economic starting point of the “pandemic recession” (the last employment peak in February 2020), I’ll put aside the more erratic unemployment story and the messiness of defining “labor force participation” (which also defines the denominator of the unemployment rate) and focus on the cleaner picture of the employment-to-population ratio.

Here are some charts that go back to January 2019 to get a bit of a running start so you see what “normal times” looked like in terms of employment-to-population. I first separate by race, then by gender, then by race *and* gender –to make the point that each way of cutting that data reveals a pattern you couldn’t see in the others.

First, by race:

Note: The onset of the pandemic (as seen in April 2020 employment) collapsed differences across the races while everyone’s employment rate suffered. Asian employment (in blue) was slowest to recover but is now (Feb 2021) as high as Hispanic employment (in red), a relative improvement from pre-pandemic.

Next, by gender:

Note: while overall female employment (in red) declined more in absolute terms than male employment (in blue) at the start of the pandemic, it has also recovered faster since the fall, so the *relative* differences between male and female E/pop are essentially back to pre-pandemic levels.

And now, women by race:

Note: Asian women (in blue) had the second highest employment rates for much of 2019 (close to keeping up with Black women, in green) but have been on the lowest end among the race categories (sticking close to Hispanic women, in red) over the course of the pandemic. In the most recent data (Feb 2021), the female-by-race categories have converged except for Hispanic women.

And now, men by race:

Note: differences across the men by race in employment-to-population also collapsed at the start of the pandemic except for Black men (who suffered a similarly bad fall from the worst starting point). The disparity between Black men and other men is even higher now (Feb 2021) than pre-pandemic.

The last two charts showing E/pop for women by race vs. men by race make obvious that gender plays a different role in employment status depending on one’s race. Hispanic and Asian women are less likely to be in paid employment than other women, while Hispanic and Asian men are more likely to be in paid employment than other men. If you look at Census data on the composition of households by race, it shows that Hispanic and Asian households are more likely to be opposite-sex married couples with children. This suggests cultural explanations for the gendered roles of women vs. men in providing household work and caregiving vs. working in the labor market.

You can go back to my August blog with Mina to see more about what we believe makes Asian women “different” from other women. On top of being raised by our immigrant mothers to do everything well (to pursue lots of education and succeed in our careers but be ever-attentive mothers), Asian women also tend to be more cautious about health and safety (and our behaviors/practices during something like a public health pandemic–just think about who were the only people you would occasionally see wearing face masks pre-pandemic?) and more “picky” (discriminating) about the quality of our children’s experiences and education. This explanation is supported by this recent Washington Post story about Asian families being slowest to send their kids back to in-person school:

As school buildings start to reopen, Asian and Asian American families are choosing to keep their children learning from home at disproportionately high rates. They say they are worried about elderly parents in cramped, multigenerational households, distrustful of promised safety measures and afraid their children will face racist harassment at school. On the flip side, some are pleased with online learning and see no reason to risk the health of their family.
By Moriah Balingit, Hannah Natanson and Yutao Chen in the Washington Post, March 4, 2021.

If Asian Americans are such a small fraction of the US population that the government statistical agencies don’t even collect enough data on them to get a statistically-reliable sample (for statistical weighting and seasonal adjustment purposes), then why should researchers and policymakers care about them? Well, Asians are the single fastest growing racial category in the US today. Asian women are the closest to closing the gender pay gap with White men among all women. Asians work disproportionately in the sectors of our economy that were growing the fastest pre-pandemic–sectors like leisure/hospitality, healthcare, and computer science/data analytics. Asian Americans, in other words, have been on the leading edge of movement in our economy–the most influential population in terms of the dynamics of the US macroeconomy. Study what’s going on with Asian women and Asian men at the most granular level you can, and you will better understand how our entire macroeconomy is doing and how to improve public policies and business practices to truly get our economy to close the potential “output gap” –which honestly we do not yet know how to measure because we don’t even yet “see” actual productivity, let alone our potential.

While waiting for today’s BLS report…

I never posted my tables and charts through the December report (released first week of January) here. In a few minutes we get the January numbers, so here’s the bottom line as of the end of 2020: the so-called “She-cession” with female unemployment exceeding male only still held among *Asian* women as of the end of 2020! Here’s the table which underscores how we can’t generalize about how women vs. men are doing–or how anyone in the economy is doing–based on our usual aggregate and average statistics. I’ll have more to say on this topic of the need for more granular data in the future, maybe even this coming week when I next update employment stats based on the numbers we’re about to see.

Women Still Working a Lot of Jobs During the Pandemic

Last summer –as in all the way back in 2019–I wrote this post about how multiple jobholding had become more common in the labor force overall and how this seemed to be a “new normal” for the (very strong 2019) economy–something no longer limited to recessionary times when people might be forced to piece together several smaller jobs when they couldn’t get a full-time job. I underscored my finding that multiple jobholding was becoming especially more prevalent among working women, and I hypothesized on some reasons why, including: (i) women need multiple jobs to add up to the pay of one job (because women typically earn less than men); (ii) women choose to work multiple jobs to add up to the hours of one full-time job–yet with the greater flexibility/control over work schedule that multiple part-time jobs allow; and (iii) women often choose to “work” not to maximize earnings but for personal fulfillment, which often calls for a variety of work whether paid or underpaid or unpaid, rather than just one job.

In 2019 all these reasons were already, in the slow-but-steady recovery from the Great Recession, becoming part of the “new normal” of an economy and labor market increasingly dominated by women. Last week I learned of this new Census analysis based on a new measure of multiple jobholding:

We create a measure of multiple jobholding from the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics data. This new series shows that 7.8 percent of persons in the U.S. are multiple jobholders, this percentage is pro-cyclical, and has been trending upward during the past twenty years. The data also show that earnings from secondary jobs are, on average, 27.8 percent of a multiple jobholder’s total quarterly earnings. Multiple jobholding occurs at all levels of earnings, with both higher- and lower-earnings multiple jobholders earning more than 25 percent of their total earnings from multiple jobs. These new statistics tell us that multiple jobholding is more important in the U.S. economy than we knew.
“A New Measure of Multiple Jobholding in the U.S. Economy,” by Keith A. Bailey and James R. Spletzer, Working Paper #CES-20-26, September 2020.

So the Census researchers conclude that multiple jobholding is pro-cyclical (no longer just a “make ends meet in hard economic times” phenomenon), has been rising in prevalence over the past 20 years (not even just since the Great Recession), and “is more important in the U.S. economy than we knew.” They might as well have said that the role of women in the workforce, and how women choose to work, is more important than we knew.

This prompted me to go back to my summer 2019 blog post and update the data through September 2020 (the latest monthly employment statistics from household survey data) to see what the past year looks like in terms of multiple jobholding and its prevalence among workers overall and men vs. women. Here are some charts that look at multiple jobholding as share of people who are employed, in the workforce (“labor force” meaning employed or unemployed/looking for work), and relative to total population–and then comparing each of the three shares for men vs. women. All charts show BLS unadjusted monthly household employment survey data for populations age 16+, from January 2018 through September 2020:

Shares of total (men+women) population measures: Overall, multiple jobholding dropped dramatically from February to April 2020 at the start of the Pandemic Recession as the number of jobs in the overall economy plummeted, and as of September is still below what had been the “new normal.”

As share of total employed, comparing men (blue) vs. women (orange). Note that multiple jobholding in the recovery (thus far) from the Pandemic Recession first rose for both men and women, but since July has decreased among working men while increasing among working women.

As share of those in the labor force, we see basically the same story as among those employed; since July, women’s multiple jobholding has increased while men’s has decreased.

And even as a share of their total population, women are more likely to be multiple jobholders than are men.

In summary, we see that multiple jobholding has come down and then back up during the pandemic, along with jobs and employment in general. Women still juggle a lot of jobs, whether paid work in the labor market or unpaid work at home. Even as workers have lost some of their multiple jobs and have yet to fully regain them, we see that multiple jobholding is a significant phenomenon in the U.S. economy and one that is not necessarily a bad thing if it has made it easier for workers–especially working women–to better tailor their work opportunities to their personal circumstances and preferences. Women have always chosen multiple part-time jobs so that work fits into the rest of their lives better, even pre-pandemic. With the pandemic placing only more burdens and constraints on women’s time (with kids and elderly parents to care for), multiple jobholding will become an even more important way for women to stay connected to the workforce. But multiple part-time jobs have never added up to the level of economic reward one can get from one full-time job–whether it be in the form of wages or benefits (such as subsidized health insurance, childcare assistance, and paid leave). Women are too easily relegated to “secondary earner” status (read: “you should be the one to stay at home now”) which makes it too common for them to disengage from market work when family circumstances change. The way women juggle their different jobs at home and at work will require a lot more attention and focus from economic policymakers if we want our economy to not just “survive” this pandemic recession but to actually “thrive” over the longer term.

Update on Pandemic Recession vs. Great Recession Unemployment by Race and Gender (incl. Asian Women)

10/22/20: Belated UPDATE through September data to charts below, originally shared on Twitter here. (I’ll make future updates here as well in order to keep in one place where some might look thanks to the new Center for American Progress report by Mike Madowitz and Diana Boesch published today.)

Today the BLS released their monthly “Employment Situation” report –and the report’s underlying data (which you can access here), including numbers on Asian women separated from Asian men which are not included in the report. Here’s a small, mostly quantitative update to my original post (with Mina Kim) from last month. First, the bottom-line/main-story bar chart:

… and below, the updated table with detailed unemployment stats by race and gender:

One thing I decided to adjust in the bar chart and add as note to the table is that April 2020 was when the unemployment rate peaked for the workforce overall as well as for most of the race-gender categories–except that the peak for Asians (both men and women) and for Black women (and Black overall but not Black men) happened in May. So the bar chart compares the absolute change in unemployment rate during the (aftermath of the) Great Recession compared with the change from this February to either April or May–whichever was the worst point for each race-gender category.

Some findings worth highlighting (or repeating):

Asian women fared the best in the Great Recession in that their absolute change in unemployment rate from the start of the recession to peak unemployment was the smallest of all race-gender categories;
From the start of the Pandemic Recession (Feb. 2020) to either peak unemployment in April or May, or to the latest data for September, White men have fared the best (their September unemployment rate is just 2.8 percentage points higher than in February);
Hispanic women fared the worst in the Pandemic Recession through this spring (April)–but up through September, Asian women have seen the largest increase in unemployment (+6.7 percentage points);
While Asian unemployment peaked in May at 16.6% for women and 13.2% for men, both had failed to recover as much as for other race-gender categories through August. Unemployment rates in August were 11.5% for Asian women and 10.0% for Asian men, still higher than they ever got even in the depths of the Great Recession. By September, Asian male unemployment (at 8.0%) was lower than at its peak in the Great Recession, yet Asian female unemployment (at 9.7%) was still higher than at any time in the last recession–and in fact, higher than ever measured in the BLS data.

As I hinted at in my first post on this subject, there are many different possible explanations for why this Pandemic Recession has been so hard on women (it’s the “She-cession”) and Asian women in particular. The intersection of Asian and female provides a uniquely-focused lens into what’s so different about this recession and how it’s affecting all workers through the roles and demands that Asian women just happen to disproportionately represent. To be continued!

Seeing Asian Women to Better Understand the Pandemic ‘She-cession’

By Mina Kim and Diane Lim (with assistance from Taylor D. Jones), August 2020

This pandemic recession has been like nothing we’ve ever seen in our lifetime. The U.S. has experienced the largest contraction since the Great Depression. Every week, we eagerly await each data release for any sort of good news, only to be met with confirmation after confirmation of the calamitous impacts of the coronavirus pandemic. Economists were quick to point out early in the pandemic that increases in unemployment have been more severe for women than men and more severe for people of color than for White people. But in all the analyses, Asian women have been missing. Given the goal of national representativeness in most surveys, the “invisibleness” of Asian women is understandable. We represent a very small percentage of the population. We are also probably not as responsive to surveys given how 1^st and 2^nd generation Asian American women are taught to blend in and not stand out. (Both of us reflect on how this “blend in” behavior was reinforced in us very early on in our childhoods, by our teachers, classmates, and even our immigrant parents.) As Asian female economists, we had personal incentive to take a good look at the data—we just wanted to find ourselves there! What we found is that not only have Asian women experienced the largest increase in unemployment during the pandemic recession, but that the case of Asian working women probably best shines a spotlight on how women’s employment depends as much or more on what’s going on at home (and the demands for our time there) than on what’s going on with the market demand for our paid work. What follows is a story of what has happened to women in the labor market during this Pandemic Recession and how this recession is different from the last, made clearer by our ability to dive deeper into details across race categories.

NOTE: For this analysis we used the BLS “One-Screen Data Search” tool here. While the monthly employment reports of the Bureau of Labor Statistics (BLS) present breakdowns of the unemployment measure by race and by race-gender categories, they only show data for the Asian category as a whole because of the valid concern that the Asian sub-sample sizes are too small to be statistically comparable with other race-gender categories. But the BLS does publish and make publicly available the data on Asian women separated from Asian men on its website. All data are monthly and unadjusted (not seasonally adjusted), for the populations age 16 and over. The “Asian” category includes only people who identify themselves as (only) Asian on the Census survey, not those who identify as two or more races; it also does not include Pacific Islanders. (A helpful BLS report which explains their categorization of people by race and ethnicity is here.)

The “Pandemic Recession” is a “She-cession” (Figure 1). C. Nicole Mason, CEO of the Institute for Women’s Policy Research, first dubbed the pandemic recession the “She-cession” back in early May in this New York Times story, when it had become apparent that more women than men had become unemployed since February. At the time, the leading explanation was that women make up a disproportionately large share of the workforce in the industries that have been most adversely impacted from the “stay-at-home” orders: the leisure/hospitality sector (think restaurants, bars, sports venues, concert halls, movie theatres), other people-intensive personal services businesses (think beauty salons/barbers, gyms, yoga studios), and non-emergency medical care offices (think general/family practices, dentists, eye doctors).

Now there is recognition of the role childcare (and in-person school), or the lack thereof, plays in affecting the ability of women to work (see Alon, Doepke, Olmstead-Rumsey and Tertilt; Mongey, Pilossoph, and Weinberg; Dingel, Patterson, and Vavra; Modestino, Ladge and Lincoln; and Heggeness). Compared with a typical economic recession that impacts male employment more than female, the Alon et al. paper stresses that the “She-cession” has more severe short-term/cyclical impact on economic activity due to diminished “within-household insurance”—a reduced ability for parents to trade off/coordinate roles at home vs. work—and will also lead to a longer-term (persistent) widening of the gender wage gap even after the recession is over.

This recession is very different from the last one (Figure 2). The “Great Recession” (left panel) impacted male-dominated jobs in the economy more than the female-dominated ones. Before the start of that recession, goods-producing jobs (particularly in the durable goods/”heavy” manufacturing sectors) had already begun a longer-term loss due to both offshoring and automation. As the economy began its long, slow recovery from the recession (officially in “recovery” by June 2009), unemployment rates continued to rise, peaking for men in early 2010 at a much higher rate than for women. Overall female unemployment peaked later (in the middle of 2010) but remained below male unemployment until the middle of 2011. Many of the men who had been laid off from their manufacturing jobs during the Great Recession never came back to those jobs after the economy recovered. These were disproportionately middle-aged, White men who came out on the other side of the recession saying “well, I guess I’m retired now.” (These were also the White men disproportionately represented in the “deaths of despair” that Anne Case and Angus Deaton revealed.)

This time (right panel) the economic impact was felt suddenly due to a non-market event and has fallen disproportionately on the employment of women. After several years of very low unemployment where women and men switched ranking but stayed very close to each other, female unemployment very quickly overtook male unemployment at the onset of the pandemic and has retained its “lead” thus far.

Pandemic unemployment has been dominated by the laying off and rehiring of largely part-time workers (Figures 3 and 4). The part-time share of total employed fell as unemployment peaked (in April) and rose as unemployment has declined (since May). The timing is consistent with job losses concentrated in the leisure/hospitality, personal services, and retail sectors, where workers are disproportionately young, part-time, and low-wage (no benefits). (This is also why government assistance has been critical for households to be able to maintain basic consumption and prevent a more severe “reverse multiplier” effect on the economy more broadly.)

While the trends in unemployment of part-time workers impact women more than men across all race categories, Black and Asian women more often work full-time jobs than do White or Hispanic women.

If pandemic unemployment were mainly driven by occupational and industry factors (layoffs from part-time jobs in the leisure/hospitality, retail sales, and personal services sectors), we might see Hispanic and White women suffering the greatest unemployment, and Asian and Black women, less so. In fact, it’s not that simple…

The “She-cession” story differs across race (Figures 5 and 6). It appears to have come and gone among Black Americans but has persisted for other race categories. By June, Black male unemployment was back above Black female unemployment, while in all other categories female unemployment has exceeded male unemployment since April. In the prior recession, men overall faced more severe unemployment than women, and it was especially severe for Black men. In this recession, Black men have not had as large an increase in unemployment as many of the women, but only because they started so high. Before the pandemic, in February, the Black male unemployment rate (at 7.3%) was the highest of all the race-gender categories. As of July, the Black male unemployment rate (at 15.6%) was still the highest of all the categories.

The “She-cession” experience has had biggest impact on Asian women (Figures 7 and 8). Among all women, Asian women had the lowest unemployment rate in February, yet their unemployment rate has been above average and in line with the unemployment rate of other women of color during the pandemic. In terms of the change in unemployment rates during this pandemic recession so far, Asian women have experienced the single worst change in unemployment through July—not just among women but across all the race-gender categories. (Hispanic women suffered the largest increase in unemployment from February to April; besides figures below, see details in the Appendix Table at the end.)

**Figure 8.** (Note: Worst point in the Great Recession is going to differ for each race category. See the Appendix Table for the exact dates.)

Caveats, Take-Aways, and “To Be Continued”s:

We’ve provided a one-step-more-granular analysis here, simply by including Asian females in our look at employment outcomes by race and gender. But this still doesn’t include everyone! “Asian” doesn’t include those who identify as only part-Asian (of multiple races), nor the “Pacific Islanders, American Indians, Alaska Natives, Native Hawaiians, and Other Pacific Islanders” who also tend to get left out of the “Asian” category in analyses by race—due to even smaller sample sizes than that of Asian females.
Yet, this race-gender intersectional analysis is still meaningful in that we do see clear differences in employment outcomes across both race and gender and all the race-gender categories. The analysis “works” because as broad as these race-gender categories are, and as heterogenous as the characteristics of the populations within each category, there are still enough characteristics that are common (or prevalent) within each category yet distinct enough from those in other categories. We suspect, for example, that the “Asian Female” category contains a sizable group of women who have relatively low educational attainment and work relatively low-skilled and low-paid personal services jobs (think nail salon), which would partially offset whatever we see that is driven by the high-skilled, higher-paid Asian women working in professional service jobs. Yet the average characteristics and outcomes within the Asian Female category still put the group as a whole in higher-skilled occupations and higher-paid jobs compared with women in the other race categories.
And those distinct characteristics and outcomes are not merely random differences. Race is a social construct, and that’s why it is useful in studying social and economic behaviors, influenced by social and cultural norms. Asian women look “special” in this analysis of unemployment during the Pandemic Recession because their roles in the labor market and at home and the resulting tradeoffs they face in making decisions about work vs. home are likely different enough from other women. For example, Asian women are less likely to experience the gender wage gap, probably because of their high educational attainment and occupational choices—and that’s a plus for holding onto jobs during this pandemic. (Distinct from other women, Asian women are more likely to work high-paid professional service jobs than low-paid leisure/hospitality or retail jobs.) But Asian women are also more likely to be in a traditional family structure, married with children, and married to a man who has even higher market-earning power. (We hope to do a follow-on piece on the family structure and marriage/”assortative mating” factors later.) So, as highly productive as an Asian woman might be in her job, she is likely to be the household’s “secondary earner” and also the household’s “primary caregiver.” This is why a deeper study of what’s going on with Asian women in this pandemic recession would shine a light on the struggles of all working mothers, who all face demands on their time at home with the kids. Those demands (regardless of the financial opportunity costs) too easily tip the scales in favor of staying at home and cutting back on or quitting their paid work.
For Asian women, the cultural factors influencing our labor market decisions are perhaps stronger than for other women. (See this paper on “Babies, Work, or Both?” by Mary Brinton and Eunsil Oh.) Many working-age Asian women are immigrants or children of immigrants. Diane’s parents immigrated to the U.S. from Korea (dad) and the Philippines (mom, of Chinese ethnicity) in the 1950s, both to pursue PhDs in Chemistry. Diane’s mom finished her PhD in 1962, just months after Diane was born, and spent nearly a decade staying at home with Diane and her younger sister Janice and was never able to make up for that lost first decade of her career. Mina’s parents immigrated from Korea in the 1970s to pursue a PhD in Physics (dad) and a Masters in Nursing (mom). Mina’s mom put her career on hold to take care of her and her two sisters when family could not help. Yet both moms constantly coached their daughters to “do it all”: to both stay ever-active in the workforce and take primary responsibility for the proper care of the kids on the home front.
This “first look” at Asian women’s employment outcomes has underscored the importance of considering how social and cultural expectations about the roles of women at home vs. at work influence female employment outcomes, especially in this pandemic with kids learning from home and daycare and elder care facilities closed or downsized (or viewed as undesirable/unsafe by families). It also suggests that the increased demands placed on women’s time on the home front does not just force women who earn relatively low wages (below the cost of child care) to give up their paid job, but is likely causing many higher-earning women (who can afford to pay for child care even as it has become scarcer) to cut back on or even drop out of the labor force, even when the financial and economic opportunity cost of doing so is high.
Further investigation is needed, going still more granular and down to more household-level data, to better understand what truly drives women’s choices and outcomes concerning work. These are questions fundamental to the study of Economics and requires research methods that have largely remained outside the Economics profession. Further research should: (i) start with interviews and focus groups; (ii) use these findings to develop surveys; and (iii) field the surveys to intentionally oversample the population subgroups that best represent and emphasize the decisions we are trying to better understand.

Appendix Table: detailed unemployment rates and changes, comparing previous to current recession

Women Work A Lot of Jobs (Not Even Counting At Home)

The latest Bureau of Labor Statistics employment report (for July 2019), which came out on Friday, was kind of “same old, same old”: service-providing jobs (particularly in healthcare) strong, goods-producing jobs weak, the pace of job growth holding steady, the unemployment rate still really low (3.7%), and wages still rising decently. We’re now in the slowest, steadiest economic expansion on record (since June 2009 according to the NBER), and economists are constantly looking for signs we are finally due for a downturn and pondering if the signs we’re used to looking for aren’t so obvious anymore. With our mix of people who are the foundation of our economy, and the things we people (vs. robots and other countries’ people) do, changing all the time, I often wonder how economists can assume that the patterns the U.S. macroeconomy has followed in the past are well-informing our assessments of where we are headed. How can we predict or even recognize what we haven’t really seen before–something that might be called a “new normal” (clearly, an oxymoron).

One way we know that the economy is different from the last expansion, just from living and looking around us, is the changing nature of work. There are more “gig” or independent workers in the economy now compared with the last expansion, facilitated by the development of mobile online labor platforms that have made it possible for people to supplement their incomes from their main or “real” job with the income from being a Lyft or Uber driver, or a Rover dog walker, or a Thumbtack handyman, or a Grubhub restaurant-food deliverer. Technological advancements alone have likely made this economic recovery’s labor market look different from that of the last one–before such “gigs” ever existed.

So I decided to dig a little deeper into the latest jobs report to see what I could see as any evidence that this was not entirely just a “same old, same old” employment report during a typical (but long) economic expansion. It seemed to me that given the rise of alternative forms of work over the past 10 years, we might expect this to show up in the BLS data on workers who hold multiple jobs. An in-depth analysis of the household data on multiple jobholders was published in the BLS’s Monthly Labor Review in April 2015 on data that ran (only) through 2013. The article by Etienne Lale concluded that:

“Multiple jobholding declined in the United States during the past two decades [1994-2013]. Data from the Current Population Survey show that the trend reflects a lower propensity to moonlight among single jobholders. Multiple jobholders, by contrast, did not become more likely to return to single jobholding…

In 2013, 6.8 million workers in the United States held more than one job. Twenty years before, the figure was 7.5 million, although the total number of workers with a job was lower by 15.9 million. The multiple-jobholding rate— the proportion of multiple jobholders among all employed workers—rose from 6.2 percent in 1994 to a high of 6.8 percent during the summer of 1995. It has declined steadily since then and was at 5.0 percent by the end of 2013. Inspection of data from the Current Population Survey…reveals that the downward trend holds across various sociodemographic groups of the working-age population (those 16 to 64 years old).“

The above analysis had indeed picked up an apparent downward long-term (20-year, 1994-2013) trend in the rate of multiple jobholding among all workers, male and female, and across different age groups and educational attainment levels. The historical time series also revealed the short-term blips up in multiple jobholding that happened during the Great Recession which caused a temporary deviation or at least pause in the downward trend. This reminds us that people who hold multiple jobs often do so because they need the extra income or because one job alone (whether part- or full-time) doesn’t pay enough to pay one’s bills. Was the general downward trend from 1994-2013 due to more people having access to better-paying jobs because of long-term (not just cyclical) improvements in the economy–such that at least in non-recessionary times we should see continued declines in multiple jobholding?

I decided to take a quick spin through the most recent data available (through July 2019), to split the women from the men, and to look at multiple jobholding as a share of some broader populations–not just as % of employed (as BLS calculates for us in their report), but as % of the labor force and as % of overall population. The charts below show (really simple) annual averages of the unadjusted monthly data for all years from 2000 through 2019 (with 2019 showing the average of only the first 7 months in the year, which I can think of reasons why that would bias the number upward as well as downward).

Multiple jobholders as % of employed, annual averages of unadjusted (not seasonally-adjusted) monthly numbers, for employed women (age 16+) and employed men (age 16+), from BLS historical tables in the July 2019 employment report (https://www.bls.gov/cps/cpsatabs.htm).

Multiple jobholders as % of labor force, annual averages of unadjusted (not seasonally-adjusted) monthly numbers, for women (age 16+) in the labor force (i.e., employed or unemployed and looking for work) and men (age 16+) in the labor force. Calculated from BLS historical tables in the July 2019 employment report (https://www.bls.gov/cps/cpsatabs.htm).

Multiple jobholders as % of population, annual averages of unadjusted (not seasonally-adjusted) monthly numbers, for women (age 16+) and men (age 16+). Calculated from BLS historical tables in the July 2019 employment report (https://www.bls.gov/cps/cpsatabs.htm).

You can see why I titled this post “Women Work A Lot of Jobs”–because the difference between the story for men and the story for women is stark. In the 2015 article cited above, it was already apparent that multiple jobholding was more common for women than for men; since the late 1990s the fraction of employed women working multiple jobs has always exceeded–and increasingly so–the fraction of employed men working multiple jobs. The longer-term downward trend in multiple jobholding that first started in the late 1990s for both women and men appears to have bottomed out after the Great Recession and even started to rise again–a new trend that was hard to spot in the prior analysis that went only through 2013. For women in particular, something about the current economic expansion and whatever the “new normal” might be is causing multiple job holding to be a more common state. As of July 2019, 5.7% of employed women (age 16+) and 5.5% of women in the labor force were multiple jobholders–both the highest monthly shares since 2009 (when the overall unemployment rate hit 10%)! And from the last % of population chart, this says that any woman is now as likely to be a multiple jobholder as any man–regardless of whether they are in the labor market or not! (3.16% of total population for women and 3.25% for men as of July 2019; the chart shows only annual averages.) And that’s multiple jobs in the formal labor market–not even counting all the jobs we do at home before and after “work.”

Why is this happening to women–so differently from men? I can make up several different explanations (below), none of which could be confirmed or rejected without further breakdowns (“crosstabs”) of the data–such as women by age, educational attainment, and marital status–and/or (ideally, and) by conducting survey and focus group research on women vs. men who work multiple jobs and asking them “why?”

They need multiple jobs to add up to the pay of one job. Women are more likely to earn less than men in even a main, full-time job, holding constant their level of human capital (such as holding a Ph.D. in economics), for all the different reasons that could fill up an entire book: career interruptions having children, having more responsibility in caring for (young or old) family members, being less inclined to ask for raises and promotions, as well as outright (unfair) discrimination.
They need multiple jobs because their best single job opportunity would still leave them underemployed. Many women reentering the workforce after raising children may find it hard to get hired into a full-time role after so much time out of the labor market. They may also choose to intentionally signal to employers that they need some flexibility in their schedule to attend to their family when needed. This gives the employer an excuse to underpay and underemploy them. (Women who reveal they are not only mothers but also wives receive the double whammy of signaling that they don’t need a full-time job because they have a spouse to support them. Economists know this result from recent research showing the effects of the birth of a child on a father’s vs. a mother’s earnings.)
They choose to work multiple jobs not because of the money but because of how they want to spend their time and use their talents. I recently got remarried, and it kills me when women say “congratulations, you don’t have to work anymore!” (Men wouldn’t dare say that to me.) I’ve always worked multiple jobs even while having one really good full-time job, because I like doing things other than my main job, and sometimes I even get paid a little for those other things (like teaching economics one evening a week and teaching yoga on Saturday mornings). Did you know that women 55 and over are projected to be the single fastest growing segment of the labor force over the next decade? Many of us have high levels of human capital and have had productive careers thus far, but now that the kids are grown, we have more time and energy and wisdom and can actually work harder in the market economy than ever before–but we can also afford to be choosy about how we work, and often a combination of part-time roles that are customized to our skill sets and talents will be more attractive to us than one full-time, cookie-cutter role (which maybe any old man or younger person could fill).

So the phenomenon of holding more than one job is definitely not a thing of the past. It’s also not a thing that necessarily gets worse/more prevalent during recessionary times, given that its rates as a share of whatever denominator you use–employed, labor force, or total population–are as high now as during the Great Recession. It’s also not easy to blanket-characterize as a bad thing (forced by economic hardship) or a good thing (more opportunities to be productive in more ways). I think it’s a great example of something important happening in our economy which to better understand, we need to do more quantitative and qualitative research on–by analyzing more disaggregated (“granular”) data, and by having conversations with more individual people (those grains in granular!) who I know will have stories to tell, because I do, too.