Election-Week Updates on Women and Work

With Joe Biden having won the presidency as of yesterday (11/7/20), the new labor market data that came out the day before was amazingly aligned with voting patterns, which show men more often voting for Trump, women and especially women of color more often voting for Biden, and Trump (vs. Biden) voters more likely to put the economy (vs. the pandemic) as their biggest concern. The Bureau of Labor Statistics’ report on the monthly employment situation, and their underlying survey data (not all published in the report but accessible here and here) show that while the labor market continues to improve since its worst point this spring, the continued severity of the pandemic continues to weigh on women’s work far more than men’s. The “SheCession” (or is it “She-session” as Heather Long of the Washington Post recently called it… either works to me) is still an acute condition, particularly for women of color.

Below are the latest numbers (in table and charts) on unemployment by race and gender categories and comparing the current Pandemic Recession to the Great Recession. Through September, only Asian women were still at a higher level of unemployment than at the worst point of the Great Recession. As of the latest data for October, all race-gender categories have seen unemployment continue to fall to the point where even Asian women are now (slightly) better off (at 8.3% unemployment) than they were at the worst point of their Great Recession experience (which was 8.4%).

October is the first month since the start of the pandemic that we saw the overall female unemployment rate (at 6.5%) fall below the overall male unemployment rate (at 6.6%). But the differences across race are stark: among Hispanics and Asians, female unemployment still exceeds male unemployment. Factors driving the “SheCession” on both the demand and supply sides of the labor market make the explanations complicated and impossible to generalize. (Further interdisciplinary study–starting with interviews and focus groups, then moving to more detailed surveys and analysis of collected data–is needed.)

The larger toll of the pandemic on working women doesn’t just show up in the unemployment rate measure–which is still nearly double its February pre-pandemic rate and which can be misleadingly reduced when people drop out of the labor force entirely, which reduces the numerator (# of unemployed) by a larger proportion than the denominator (# in labor force = # unemployed + # employed). The “SheCession” also shows up in the employment-to-population (E/Pop) ratio:

And the SheCession shows up in the multiple jobholding data, where working women more commonly than men work two or more jobs (as has always been true because women more commonly work part-time rather than full-time jobs whether by preference or not), but in the pandemic recession have had to piece together and juggle multiple jobs along with their unexpected and unpredictable caregiving responsibilities at home. Note that during the pandemic we have seen the distribution of multiple jobholding among women widen across both race and age categories–with Black women and the youngest women most likely to be working multiple jobs:

Multiple Jobholding Among Women In the Pandemic Economy

To follow up on last week’s post showing how women are still more likely to be working multiple jobs than men (both among the employed and among their total populations), here’s a reminder that not all women are the same. Let’s look at multiple jobholders as a share of employed, across race and age categories.

Black women are substantially more likely to hold multiple jobs than any other race categories of women. Notably, while multiple jobholding fell for all groups in the spring when unemployment peaked (and number of overall jobs in the economy cratered), multiple jobholding as share of employed has already (as of September) returned to a “normal” level for Black women but not for other women.
By age categories, the youngest of working women (ages 20-24) have always been the most likely to hold multiple jobs, because they are most likely to have to piece together multiple part-time jobs (often in the leisure/hospitality sector) to make a living. These women were most likely to lose at least one of their multiple jobs at the start of the pandemic, and regain work as businesses reopened in the summer.

There are many factors that could explain the differences by race, probably most significantly that Black women are more likely to be sole earners in their households (as well as single parents) yet also more likely to earn lower hourly wages. The different trends by age reflect that multiple part-time jobs are often the closest a young adult (even a college-educated one) can come to a full-time job–and that the human-service-intensive jobs many young women work in were the ones that disappeared the most at the start of the pandemic and have not and will not likely fully come back even when the public health crisis eventually wanes. The Pandemic Recession — or “She-cession”– has not just been hard on women because of the severity of the lowest depths of job loss experienced, but because it’s really “jerked around” the women who were already the most economically vulnerable.

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!

More Pictures on the Pandemic “She-cession” by Race

Here are a bunch of charts that illustrate why the Pandemic Recession is also sometimes referred to as the “She-cession.” These are based on BLS monthly data through August (released last Friday, Sept. 4). I’m also re-posting the bar charts I put up on Twitter and here last Friday, so the latest figures are in one place. (See original post on “Seeing Asian Women…” here and the table with all the unemployment data through August by race-gender categories here.)

First, compare male minus female unemployment during the Great Recession to during the Pandemic Recession (thus far):

During the Great Recession (Dec. 2007-2009/10 recovery period), male unemployment relative to female unemployment grew dramatically–and in general/overall male unemployment stayed higher than female even years after.
During the Pandemic Recession (since Feb. 2020), female unemployment increased dramatically and surpassed male unemployment in all racial categories, and only among Black workers did male unemployment again exceed female unemployment this summer.

The differences in unemployment across racial categories have been stark. It’s not just a recession with disproportionate impact on women, but one with disproportionate impact on people of color. Zooming in a bit more recently, here’s what the unemployment rates by race since January 2019 look like:

Unemployment rates by race categories, men and women combined. Note that Asian unemployment started the very lowest in February 2020 and is the second highest in August 2020.

Going back to looking at the employment outcomes among women by race over the past year and a half (since January 2019), here are charts showing the unemployment rates, the employment-to-population ratios, and the labor force participation rates:

Female unemployment rates, by race, January 2019 through August 2020. Note that like for race categories overall, Asian females had the lowest unemployment rate in February 2020, but in August 2020 have the second highest–as Hispanic female unemployment has shown the best recovery since its peak in April.

Female employment-to-population ratios by race, January 2019 through August 2020. Hispanic women had the lowest e/pop at worst part of the Pandemic Recession and have traded places with Asian women a couple times since. Black women had had the highest e/pop going into the pandemic but in August, white women had surpassed black women.
Female labor force participation rates by race. Asian female participation was relatively low pre-pandemic, but is second highest this summer–showing that many Asian women consider themselves still “in the labor force” even if currently unemployed.

And below is the headline bar chart I previously posted comparing absolute changes in unemployment rates for women by race in the Great Recession vs. during this Pandemic Recession (through August):

The Pandemic Recession is a “She-cession” and has had disproportionate impact on women of color, particularly on Asian women–who face a still higher unemployment rate in August (at 11.5%) than at their worst point in the Great Recession (8.4%).

And looking at Asians alone (both men and women), the “double whammy” of being both Asian and female in this Pandemic Recession is underscored:

Asians (especially the women) had very low increases in unemployment during the Great Recession relative to other race categories. This time is totally different.

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 1st and 2nd 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.)

  1. 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.

Figure 1.
  1. 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.

  1. 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.)
Figure 3.

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.

Figure 4.

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…

  1. 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.
Figure 5.
Figure 6.
  1. 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 7.
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

Thoughts on Coronavirus Economic Policy, Thinking as “Economist Mom”

L to R: Johnny, Diane (Mom, holding Taco), Grace, Allie, Becca, Bill (holding Tammy), Emily, and Danny

I know I haven’t been here very often since I “rebirthed” my Economist Mom blog!  But every glass half empty is also half full.  The coronavirus crisis and its required “social distancing” have provided me both the calling and the opportunity to think about the possible economic policy responses, not just relying on the same old economist’s toolkit but with the benefit of wisdom from my own children—who are experiencing this crisis much more than I.  I have come to the conclusion that the fastest, most helpful policy the federal government could implement would be to immediately put in place a tax deduction for donations to service-sector small businesses adversely impacted by the coronavirus shutdowns, which tax filers could claim off their 2019 returns that they are filing now.  How I got there is explained below—in my Economist Mom kind of way.

My husband Bill and I are really lucky.  We work as economists—policy “experts” they call us—in salaried positions where we can easily work from home or take paid sick leave.  The coronavirus pandemic affects us only in truly superficial ways that I am ashamed to even bring up: I am bummed that our planned trips to see family were cancelled and that we can’t otherwise spend our leisure time going out around town, and Bill is pretty lost without March Madness.  Today we took advantage of having few of our usual weekend pastimes to work on our 2019 tax return—a full month ahead of the April 15 “Tax Day” deadline.

Our own children: not so lucky.  We have six adult children between us.  Danny (Bill’s son) works for Amazon in Seattle.  A co-worker who works in an office near his has been diagnosed with the coronavirus, and Danny has been on mandatory work from home (WFH).  Becca (Bill’s daughter) works at Cedars Sinai in LA where several coronavirus patients are being treated.  Allie (my eldest daughter) works for Coursera in Mountain View, CA –another “hotspot” for coronavirus cases; she and her boyfriend, who together share an apartment with another roommate, are all on mandatory WFH in a really-too-small apartment to serve as an office for three.  Emily (my second daughter) and Grace (my youngest daughter) both live in Brooklyn, NY.  Emily is a Ph.D. student at NYU, adjunct teaching this semester at her undergrad alma mater, Sarah Lawrence College, in Bronxville, NY.  (Look that up in a map, and you’ll understand why we stayed in a hotel in New Rochelle when we attended Emily’s college graduation—and you’ll understand why Emily started teaching online last week and why she’s at high risk for being exposed.)  Grace is a 2019 graduate from NYU and works part time at the service counter and as wait staff of a high-end bakery in Brooklyn on a combination of low hourly wage and tips.  Her other part-time job is with a consultant who plans and stages events.  (They have no events planned now.)  She commutes to the bakery by subway and is face to face with customers all day long.  Her bakery is complying with the 50% capacity restriction by having removed half the tables in the café, but otherwise business is booming; on Saturday the customers lined up to buy out all the bread they had made that morning –so Grace had plenty of contact with many, many customers.  She tweeted out this morning that she cried while she was at work yesterday, so scared about how difficult it is for her to stay safe from this pandemic.  And that broke my heart.  Even my youngest, my son Johnny, a junior attending William and Mary, has not come out unscathed despite having gone straight from an indoor track championship last weekend in Boston to a spring break beach week in Charleston, SC with his track buddies.  While on break, he and his teammates learned their entire spring season was cancelled—which broke all their hearts.  (The ruling of the NCAA that spring season athletes will be granted another year of eligibility provides some compensation and consolation, but now we can expect to see a lot of track athletes with master’s degrees!)

I tell you about all the hardships on our children to provide context for the reason I write this post: their situations have made me see that the debilitating effects of the coronavirus pandemic on the economy require a very different kind of economic policy response from anything (“boomer”?) policy “experts” have thought about and worked on before.  The whole reason for the economic shutdown is because we’re trying to prevent the spread of the health problem.  If we could easily tell when someone was sick with or carrying the virus (i.e., if we had universally available and quick/instant testing), then we wouldn’t have to tell everyone to stay home.  But the invisible, stealthy nature of this virus and how fast it spreads requires that we all stay at home and practice safe “social distancing.”  This is not a problem for me and Bill who can work in social isolation all the time (as many economists like to do, but not me).  It is a much bigger hardship for our children—especially Grace who only gets paid when she shows up for work, relies more on tips than her hourly wage, and who works in an intensely face-to-face, hand-to-hand job.

Congress and the Administration are trying to come up with an economic policy response to the coronavirus crisis by dusting off policies from past economic crises.  (OK, Boomer…)  But those policies such as payroll tax cuts or enhanced unemployment benefits are not going to help hourly-wage workers like Grace who will either continue working as usual, worried about being or getting sick, or will work fewer (or no) hours and earn lower (or no) wages and tips.  What we really need is for workers like Grace to be paid their usual wage to stay at home.  But this is a totally radical concept for economists who overly obsess about price incentives in normal times: what, pay people to not work?  These are not normal times or a “normal” economic crisis.

DC’s most beloved restauranteur and chef, Jose Andres, just announced today that he is closing all his restaurants to reduce the risk of spreading the virus among staff and customers, and that he is increasing production of to-go meals in his community kitchens (many of which will operate inside his restaurant kitchens, keeping his healthy staff working).  The DC community is fortunate to have such a successful business owner be also such a generous man.  But other food service businesses cannot afford to keep their workers working and earning as usual when people aren’t coming out or the business is required to cut the number of customers they serve.  At the same time, people like me and Bill who are lucky enough to keep our usual paychecks and who would normally go out to eat and drink still have the discretionary income, and the desire, to support our favorite food and drink establishments—even if it means helping them make payroll and rent while they are forced to shut down.

Now, anyone could choose to help out their favorite local bar, restaurant, or bakery, by donating to that establishment’s Go Fund Me page if they were to set one up to help continue to pay their hourly workers even as their business is shuttered or reduced.  (I do recommend that small service-industry businesses do that; your usual patrons will support you!)  I’m here to help out my daughter Grace if she is forced to or wants to stop working at the bakery, and certainly if Bill and I were to benefit from some new tax cut we don’t need, we would most likely immediately hand it over to Grace and our other kids.  And while Congress may be trying to come up with legislation that better gets at the fundamental problem of steering dollars to the workers who will have to stay home because food and entertainment establishments and schools are shut down, let’s face it, nothing that government can do will happen soon enough to get people to stay home soon enough (as in right away) and stop spreading the virus. 

So that brings me back to Bill and I working on our 2019 tax return today.  Why not allow people to make individual financial donations to small service-providing businesses that have to shut down operations, and why not let government subsidize this by making such donations tax deductible?  (Current law allows deductions only for donations to charitable or nonprofit organizations, not any for-profit businesses.)  And why not take advantage of being in tax filing season by letting people immediately write off coronavirus donations (made now, in March 2020) from their 2019 taxes?  This is a way a government policy could have immediate impact.  No figuring out how to cut checks or send debit cards!  No need to send those checks to everyone (including people like me and Bill, who don’t need it).

If service-providing businesses reduce or shut down their human-facing operations, it provides a public good (reduced spread of the coronavirus) but at private cost (lost revenue).  Internalizing this positive externality requires that the good-for-society behavior (shutting down face-to-face, hand-to-hand services) be subsidized.  That subsidy can come from private individuals who care about the social good, but ideally comes from government as well.  Doing policy through the tax system can be handy when it sets up a public-private partnership of sorts: private citizens choose to make charitable contributions to the organizations they wish to help, and government matches the donation at a fraction equal to their marginal tax rates.  (Doesn’t have to be limited to the itemized deduction mechanism though—in fact, this is a perfect occasion to bring in the “above the line deduction.”)  And I just said all that as an economist, but it’s the mom in me (and the dad in Bill) that really motivates this policy idea.  We need to quickly get relief to businesses like the bakery Grace works for.  It’s critical to addressing not just the economic fallout from coronavirus but the underlying root of this crisis: the uncontrolled, invisible, and rapid spread of the virus.

Talking about “paying for stuff”–like an economist who’s also a mom

Sorry I have been pretty AWOL for awhile. It took a nice article in today’s NYTimes and last night’s Saturday Night Live show to spur me to post again. I loved last night’s SNL “cold open” (and Kate McKinnon’s brilliant rendition of Elizabeth Warren talking about how she would pay for “Medicare for All”) for its reference to how moms talk about stuff (vs. dads), and how economists explain things (see back of whiteboard in the skit). And a bonus for me: in this morning’s New York Times, Jim Tankersley quotes me about how little economists *actually* know about what the economic effects of a 6% tax on the highest wealth of the very wealthy would be:

Perhaps the biggest unknown is how the capitalist American economy would function with levels of taxation and spending more comparable to the social democracies of Scandinavia.

“It’s as much an art as a science, trying to figure out the economic effects of policies we haven’t seen before,” said Diane Lim, a former economist for congressional Democrats and senior economist at the White House Council of Economic Advisers, who now works for the Penn Wharton Budget Model. “I’m worried it’s unrealistic. It’s just unknown.”

Jim Tankersley, New York Times, Nov. 3, 2019, Section A, Page 1, “Trillion-Dollar Pledges Cement Democrats’ Bet on Taxing Rich.”

I’ve been working for Penn Wharton Budget Model as a “senior advisor” for a few months now–hoping to better connect their highly sophisticated, academically “rigorous” economic models to real-world policymaking, especially as the presidential campaigns heat up and begin to include (more specific) policy platforms. The policy ideas we’re already hearing about–like Warren’s Medicare for All and the taxes to pay for it–are really big and bold but also huge in terms of economists’ unfamiliarity with them. We will be relying on a lot of economic theory but not a lot of “economic practice” in offering our “expert” perspective. (In other words, we’re not exactly “experts” on this.)

Here is where the “art” within an economic model can actually inform the science, because where we don’t have previous real-world experience with such a policy, we can kind of “make up” and “try on” different kinds of behavior at the individual household and business levels and see how they add up to different outcomes for different types of households and the entire macroeconomy–and we can evaluate how likely the candidates’ tax proposals would actually raise the levels of revenue needed to pay for the candidates’ spending proposals.

I’ll try to keep my blog updated with news of Penn Wharton Budget Model’s analyses on presidential candidate proposals; we are still working on them now. My take here will always have that perspective that comes from my being a mom first (who will talk about the hard choices and realities very openly) and an economist second (who can translate the “econ-speak” of my profession into plain(er) and hopefully more useful English).

The (Desperate) Need for Economists to Consider “Intersectionality” and Our “Multiple Identities” in Our Work

This fascinating article by Duke psychology professor Sarah Gaither in the July 2019 issue of the American Psychological Association came across my radar because it was featured in the Behavioral Science & Policy Association e-newsletter I subscribe to.  As Sarah introduces her work:

“We all have multiple identities — race, gender, age, sexual orientation, occupation — the list goes on and on. However, psychology research has traditionally focused on the effects stemming from one identity (i.e., race OR gender), rather than trying to measure how belonging to multiple groups may actually shift our behavior or even perhaps change our results. The question I asked — does thinking about one’s self from a multifaceted angle shift your flexible thinking?”

Apparently being aware of one’s multiple identities (as opposed to where you might peg into a mono-dimensional category) makes a person more flexible and creative in one’s thinking.  As Sarah explains, it also reminds a person—even a child—that there a “more social categories in their world beyond just race and gender” and encourages the person “to move beyond their default thinking of either/or categories.”

This got me to thinking about one of my complaints about the economics discipline, that we economists are too often focused on “social well-being” as an aggregate concept (as if it could be representative of the well-being of any single one of us), or on the “distributional effects” of economic policies as a mere divvying up of the aggregate economic pie.  In economic analyses, even when we try to look more deeply at how a trend or a policy may affect some people differently from other people, we almost always only consider one dimension/attribute at a time—e.g., the distribution of the income tax burden by categories of household income levels (rich vs. poor), how government deficits and debt affect older people vs. younger people, how trade wars affect one country’s GDP or well-being over another country’s, how immigration policy affects immigrant vs. native-born employment and wages, how labor-market discrimination manifests itself in a gender pay gap (as in, only by gender and only according to pay).  Too often these analyses will only fuel a taking-sides phenomenon as soon as we are led to put ourselves into the “us” or the “them” category in the “us vs. them” dichotomy.  And we’re off to the partisan, polarized, combative, competitive races, where protecting one’s own interests means squashing someone else’s—or at least covering your ears or turning a blind eye to them.

What if economists could help people understand that in our society—and even in our dollar-measured economy—we each hold multiple roles and identities?  I am an Asian (Korean and Chinese) American, a professional economist, a volunteer English teacher, an adjunct professor, a part-time yoga teacher, a taxpayer, a mother of four 20-somethings, a daughter of immigrants, a (newly-remarried) wife, a middle-aged “baby boomer,” a northern Virginian, a native of Michigan, a consumer, a homeowner, a dog owner/lover.  I think about the economy and public policy—and lots of other things that affect my life—from all those perspectives, even more than based on where I got my Ph.D. or what political party I tend to vote for.  Expressing the full intersection of my perspectives, the full complement of identities in me, with the standard economist’s toolkit has always been challenging, and I find myself turning increasingly to qualitative descriptions expressed in words (which might make other economists view me as not as “technical” or “rigorous” as I was in my younger years when I built beautifully mathematical economic models).  Yet people of all types seem to be more likely to listen to and engage with my stories which highlight that I have an identity (or several identities) that maybe they have, too than to pay attention to any quantitative analysis of mine that shows precise debt-to-GDP ratios with calculated effects on aggregate GDP or even the economic income or “well-being” of one’s particular age cohort or income category. Being a mom has always been a more persuasive and compelling identity of mine than being an economist. (As the Wall Street Journal put it 10 years ago in featuring my blog: “How can you quibble with EconomistMom? What would your mother say?”)

Could economic analysis become more “relatable”—more often?  Could we do better at highlighting (and even measuring/quantifying) the commonalities, synergies, and interdependencies among and across people’s many and intersecting identities, so that we could become more likely as a society to pursue economic policies that are truly in our greatest individual and mutual interests?  Could we stop wasting resources and pursuing counter-productive policies with all our rhetoric and YELLING?

There’s plenty of evidence that people are more likely to be prejudiced, discriminatory, or outright hostile and hateful to “other” groups of people the less experience they have interacting with these “other” types of people.  (See factor #9 in this article, for example.)  But that “otherness” is often just the most superficial, obvious characteristic that makes someone appear very different from you (their race, their age, their gender, their social-media-posted politics).  People of different superficial categories can have many common identities as parents, community members, workers in a particular industry or occupation, etc., so maybe it’s not so much that we need more interactions with “other” people (“them”) but that we need to recognize that in the grand scheme of life, those “others” are us–and we are “them.” We are all in this together.

Sarah Gaither concludes her article, pitched mainly to her own psychology discipline, with this (my emphasis added):

“With rises in immigration, increases in interracial marriage, and shifts in language surrounding biracial and transgender populations, it is essential for research to acknowledge that we are all lots of things at the same time. Therefore, my work has both theoretical and practical relevance in highlighting that belonging to multiple groups — and acknowledgement of that membership — not only impacts our behavior and perceptions of others, but it also suggests that that variability that exists both between and within groups may have been overshadowed in research. By considering our multiplicity of belonging that has always existed we can push our fixed thinking about social groups to be more reflective of the flexibility that we all possess. Thus, I argue that as we continue to study behavior within this evolving cultural landscape, we must be aware of how multiple identity mindsets may impact our findings.”

Yes, it’s a far bigger reach to push this perspective of flexibility, intersectionality, and multiple identities onto economists, but it’s something I’d like to try.

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.