Low-income workers in the United States, especially those in the service sector, face highly unpredictable work hours and pay. Many workers report chaotic employer practices that make balancing work and personal responsibilities nearly impossible. Some of these practices include not receiving notice of when they are scheduled to work until days or even hours before their shift starts, making stable day care arrangements impossible, having shifts extended while they are already at work, which leads to a scramble to arrange child pick-up from school or day care, and being sent home early when things were slow, meaning a slashed paycheck and unpaid bills.
There is a new innovation in labor policy aimed at reducing these types of unpredictability in service workers’ schedules. Known as Schedule Stability or Fair Workweek laws, these regulations are gaining traction in local governments and state legislatures. Seven cities and one state have already enacted such laws to ease burdens on workers or ensure they are compensated for last-minute schedule changes.
But what do economists and other social scientists know about the effects of such policies? In a recently released working paper, we help answer this question by studying the effects of Emeryville, California’s 2017 schedule stability policy, the Fair Workweek Ordinance, on working parents and their families. This law requires large food and retail businesses to provide hourly employees with 2 weeks’ advanced notice of their schedules and to compensate workers if their schedules were later changed by paying them for half of any cut hours and paying a premium for any added hours, similar to overtime pay.
In our study, we compare workers at regulated businesses to similar workers at retail and food-service businesses that fell below the size cut-off for regulation—55 employees across these firm’s entire U.S. business operations for retail and more than 55 employees across the United States and 20 or more employees in Emeryville-based food-service companies. We recruited participants at their places of work, focusing on those who have young children since these working parents face particular strains in balancing work and family life and are thus of great interest to policymakers.
The nearly 100 workers who enrolled in our study—1 in 6 hourly service workers with young children in Emeryville—answered daily survey questions via text message about their expected and actual hours for the day, along with daily questions about their well-being and family functioning. We followed these workers both before and after the ordinance went into effect, surveying them over three waves lasting 30 days each. The surveys also occurred prior to the onset of the COVID-19 pandemic.
Before the ordinance was enacted, schedule unpredictability was quite frequent for Emeryville workers, with those in large firms reporting at least one schedule change on 13.8 percent of days, or around once a week; a last-minute schedule change on 9.2 percent of days, or nearly 1 in 10 days; and a surprise shift (being called into work unexpectedly) on 1.5 percent of days. These problems were greater among those working for larger businesses—precisely those who were targeted by the policy reform—than those working for small businesses, who reported these events on 10.4 percent, 6.8 percent, and 0.7 percent of days, respectively.
We find that the Emeryville Fair Workweek Ordinance succeeded in reducing schedule unpredictability for workers with young children. In particular, it successfully lowered the frequency of changes in shift start times and end times and in surprise shifts that got added to workers’ schedules at the last minute. The regulatory success of the ordinance also resulted in health benefits for those workers in regulated jobs, notably improved sleep quality—an aspect of well-being that service workers highlight as particularly important and especially affected by unpredictable work schedules. (See Figure 1.)
Figure 1
Importantly, we also find that the Fair Workweek policy had no effect on total hours worked per week, contrary to some concerns raised prior to implementation. This implies that workers who become subject to scheduling ordinances end up benefiting from the stability and predictability guaranteed by the law without necessarily losing out on income from reduced shifts.
All told, the scheduling regulation brought the experiences of workers in large firms in line with the experiences of workers in smaller firms, who were already benefiting from their employers’ more predictable scheduling practices. For surprise shifts, the policy left workers in newly regulated firms even better off than workers in smaller firms, nearly eliminating this highly disruptive practice: As seen above in Figure 1, workers in regulated firms reported that the rate at which they experienced last-minute shift additions to their schedule fell to 0.3 percent of days post-implementation.
Our working paper underlines the existing research on how stable schedules benefit workers and why Fair Workweek laws can provide the necessary support to improve economic and health outcomes across the U.S. labor force. Emeryville’s scheduling regulation—and others like it that have been enacted across U.S. cities and states—successfully reduces schedule unpredictability, maintains employment and hours, and improves well-being for working parents of young children, a group of workers that faces particular challenges in balancing work and family.
These challenges have plausibly only worsened amid the COVID-19 pandemic. That’s another reason why work schedule unpredictability is a particularly salient and ongoing challenge highlighted by workers, labor organizers, and social science scholars, and is of policy interest and concern. Fair Workweek laws could provide a pathway for policymakers to increase predictability for low-income families, improving their economic and physical well-being and, in turn, boosting the overall U.S. economy.
The increasing visibility of artificial intelligence and other advanced technologies in the workplace is drawing new attention to how employers integrate technology into work processes and what effects these decisions may have on the future of work, workers, and the U.S. economy at large. Economists and other researchers are exploring the rise of these new technologies and what it means for the future of work in the United States through research that points the way to more nuanced labor market policy solutions.
In a 2015 article, titled “Why Are There Still So Many Jobs? The History and Future of Workplace Automation,” for example, Equitable Growth grantee and member of Equitable Growth’s Research Advisory Board David Autor of the Massachusetts Institute of Technology explains that popular narratives over the past two centuries have dramtically overstated automation’s ability to replace workers. The degree to which employers use machines to substitute or complement human labor, he writes, depends on a variety of interlocking factors.
Indeed, employers’ decisions about how to invest in capital—such as buying more machinery or implementing new technologies—can have different effects on productivity, wages, and overall employment levels. One area of particular interest is the outcomes of capital investment in manufacturing, an industry that has experienced falling employment in recent years and is the site of policy concerns about the impact of employers’ use of industrial robots and other technologies.
A working paper published earlier this year by E. Mark Curtis of Wake Forest University, Daniel G. Garrett of the University of Pennsylvania, Eric C. Ohrn of Grinnell College, Kevin A. Roberts of Duke University, and Equitable Growth grantee Juan Carlos Suárez Serrato of Duke University provides additional evidence on how these dynamics play out within manufacturing firms, adding a new facet to this debate. The working paper, “Capital Investment and Labor Demand,” explores whether business tax incentives promoting capital investment affect labor demand in manufacturing plants.
The co-authors use data on manufacturing plants from the U.S. Census Bureau to examine the effects of a large corporate tax incentive for capital investment on employment and wages from 2001–2011. The tax incentive they focus on—bonus depreciation—allows businesses to deduct capital investments from their taxable income almost immediately instead of over a series of years and has been in place almost continuously since 2001.
The researchers find that bonus depreciation did stimulate capital investment, but the increase in capital investment neither increased productivity nor lowered employment. Instead, it led to increased employment, but with lower average earnings: Employment increased by 11.5 percent among production workers in industries that benefit the most from bonus depreciation and 8.1 percent among nonproduction workers, but average earnings decreased by 2.7 percent.
Their analysis suggests this positive effect on employment is due to the “scale effect,” which happens when plants increase both output and demand for inputs, as opposed to incentivizing them to replace workers with machines. This led to a greater demand for labor, and thus greater employment at plants with greater capital investment. The effect appears to have increased employment more at plants that also increased the use of industrial robots.
Why did the investment increase employment but decrease wages? The researchers’ analysis suggests that the lower earnings due to the tax incentive are connected to changes in the types of workers hired. The capital investments led to employers hiring a greater share of groups of workers who tend to have lower wages and lower bargaining power—employees who are women, who are Latino or Black workers, who have a high school education or less, and who are 35 years or younger.
The co-authors also find that employers increased capital investment more in plants with lower levels of unionization, as well as in plants located in so-called right-to-work states and in areas with higher labor market concentration—all factors that decrease worker power and weaken wages, and thus lower labor costs for employers.
These findings add additional depth to the discussion around the interplay between capital investment and labor outcomes. The findings also highlight the need for economic research that looks at outcomes for workers beyond overall employment levels—outcomes such as wages, health and safety, and other factors related to job quality and working conditions. This multifaceted approach to research can inform nuanced policy solutions that ensure employers are implementing automation and other new technologies in ways that build high-road supply chains and improve outcomes for U.S. workers in the years ahead.
A new National Academy of Sciences panel convened publicly for the first time this week to discuss how statistical agencies should unify and improve the measurement of inequality in income, wealth, and consumption in the United States. This consensus study will debate how these concepts should be measured, how to harmonize these connected concepts, to what degree it should be possible to disaggregate them by geography and demographic characteristics, how they could be constructed using current data or data that are yet to be created, and more.
Equitable Growth’s Jonathan Fisher offered a six-point wish list before the event that covers some of the possibilities discussed during the first convening of the new NAS panel. But this is just the beginning of the panel’s work, which should wrap up sometime in 2023.
1. Income, consumption, and wealth measured in one survey with no need to impute any of the measures. While we did research with imputed consumption in the SCF, better research can be done when all three are reported.
The first public meeting featured presentations by three prominent economists. Angus Deaton is a Princeton University professor and Nobel prize winning co-author of Deaths of Despair alongside Anne Case, the Alexander Stewart 1886 professor of economics and public affairs, emeritus at Princeton University. Emmanuel Saez is an economist at the University of California, Berkeley, a professor, a John Bates Clark Medal winner, and a MacArthur Genius fellowship winner. Raj Chetty is a Harvard University professor and also a John Bates Clark Medal winner and MacArthur fellow winner who has transformed empirical economics with his big data research.
The three economists presented alongside representatives from U.S. statistical agencies, who spoke on their current distributional data projects. The first meeting highlighted three debates that will likely be central to the panel’s work.
How much guesswork is acceptable?
The meeting started out on a technical note, with Deaton expressing hesitancy over what we might broadly call statistical modeling to create government statistics, which includes techniques such as imputation and model-assisted estimation. He pointed specifically to a field of research known as the Distributional National Income Accounts, popularized by economists Thomas Piketty at the Paris School of Economics and UC Berkeley’s Saez and Gabriel Zucman. Distributional accounts research seeks to take an aggregate income concept, such as Gross Domestic Product, National Income, or Personal Income, and distribute it among persons or households in an economy.
A common step in the production of such accounts is called “scaling.” Researchers start with a survey or administrative data source that provides information on incomes, among them IRS tax returns or the Current Population Survey. They add up all income in their base data to get an aggregate for the economy. But, for known and unknown reasons, these aggregate totals are almost always smaller than the total recorded in national accounts. To reconcile them, researchers simply scale up, inflating the wages or business income of each person in their data by a set amount so the new aggregate in the dataset matches what is reported in the national accounts.
This is the approach the U.S. Bureau of Economic Analysis takes in its Distribution of Personal Income prototype data series. Deaton is rightly concerned that scaling is a bit of a Band-Aid that ignores what the underlying causes of these aggregate discrepancies might be. Better source data, using a combination of administrative data and survey data, should be leveraged to try to explain why these gaps exist. It may be difficult, however, to eliminate these gaps altogether.
Deaton also expressed skepticism about other assumptions that are commonly made in the construction of distributional national accounts. These assumptions should be examined, but Deaton is overly pessimistic. There are several reasons we should be willing to use statistical modeling to construct official statistics.
Perhaps the most convincing argument is that these statistical Band-Aids are already being employed in many of our federal statistics and have been since the federal government first started recording them. A significant fraction of Gross Domestic Product and other national account aggregates are already being imputed because there is no other basis for estimating them. Early estimates of these metrics include more imputed information, as they are published before all data are received and they are revised several times.
Modeling can make incredibly useful contributions to our national statistics. Consider the U.S. Bureau of Labor Statistics’ recent experimental release of estimates that extend the Job Openings and Labor Turnover Survey to metropolitan statistical areas. These estimates are not possible, given the existing JOLTS sample sizes. The agency’s experimental release uses a modeling technique known as small area estimation to create estimates for these small geographical areas. This allows it to report statistics for small geographical areas without adding expensive oversamples to the survey itself. That’s incredibly valuable for statistical agencies, where resources are often tight.
Nor should we let a fear of imputation and modeling stand in the way of reporting important concepts. The underlying logic of Distributional National Accounts is compelling: Integrating microdata into our economic aggregates allows the statistical agencies to fully account for economic income and answer questions about who is benefitting from economic growth. Knowing the answers to those questions is a compelling public good.
No economic metric is without error. The role of our statistical agencies is to balance the utility of a measurement for informing the public and steering policy against the amount of error that might be present. Statistical agencies should continue to study model-assisted estimations because, in many cases, they are the only options for constructing a metric. Moreover, this research can yield valuable insights into sources of error in both our micro- and macro-data sources.
How ambitious should the federal government be?
Closely related to the debate over imputation is the question of how ambitious federal agencies should be. In the second half of the meeting, we heard from a number of statistical agency economists about projects they are working on that will expand the U.S. statistical capacity considerably. I have written extensively about BEA’s landmark distributing personal income product, presented by BEA’s Dennis Fixler, the only product from a statistical agency that tries to measure inequality using a comprehensive definition of income.
Jonathan Rothbaum of the U.S. Census Bureau presented his team’s efforts to link administrative and survey data to create more comprehensive databases of economic outcomes. And statisticians from the U.S. Bureau of Labor Statistics and Statistics of Income, the IRS’s statistical agency, likewise showcased impressive projects to improve the measurement of consumption and income.
There is always more to be done, and the meeting highlighted some areas of work that would be quite new to statistical agencies. Harvard’s Chetty, for example, advocated for a national metric on mobility. This may be slightly outside the panel’s purview, but the panel’s work might make such a metric more feasible.
This metric would probably be controversial, however. There are relatively few longitudinal datasets that allow for direct observation of mobility in the United States. Chetty instead uses cross-sectional data and some clever assumptions to generate estimates of mobility over time. Jonathan Davis and Bhashkkar Mazumder at the Federal Reserve Bank of Chicago do use a longitudinal dataset—the National Longitudinal Survey of Youth—and find similar trends, providing some support for Chetty’s methods.
Mobility may also strike some economists as outside the traditional purview of federal statistics. Nonetheless, I agree with Chetty, who remarked that providing official measures of mobility would “be incredibly valuable for the public discourse.” U.S. residents should know if the nation’s economy is putting the American Dream further and further out of reach, as Chetty’s research demonstrates. That is valuable information that will make voters more informed and more able to hold their elected officials to account. It is equally important for elected officials themselves, who hopefully want to strengthen one of the nation’s foundational values.
Measuring consumption is hard
Consumption was, in some ways, the least discussed of the three measurement concepts this panel is tackling. The Bureau of Labor Statistics presented briefly on some of their work to modernize the Consumer Expenditure Survey, but most other presenters mentioned consumption only fleetingly if at all. Princeton’s Deaton suggested that focusing on wealth was paramount. Chetty advocated for mobility, which generally means income or wealth, although he did mention that it would be useful to measure mobility using consumption. UC Berkeley’s Saez largely focused on wealth and income.
There is an active debate about whether consumption inequality is more or less important than wealth and income inequality. In some ways, that debate is waiting for better data, because measuring consumption is very difficult, and most economists would probably agree that of these three concepts, we know the least about consumption. The Consumer Expenditure Survey has known problems related to how people answer questions that complicate interpretation.
The cutting edge of consumption research in academia uses transaction data from banks and credit card companies to track what people are spending money on. Sources such as Earnest Research make it possible for Equitable Growth grantee Jacob Robbins of the University of Illinois at Chicago, for instance, to track how consumption responded to a wave of retail restrictions during the pandemic.
Unfortunately, there are numerous obstacles to government agencies using these kinds of data in the production of official statistics. These datasets are not representative of the entire U.S. population since they track only credit card users and individuals with bank accounts. They don’t include cash transactions and can miss transactions if a household uses more than one credit card provider. The datasets are expensive, a problem for cash-strapped federal agencies. And working out terms of use with banks and credit card firms can be a significant challenge.
Finally, there is no guarantee that a private dataset will be consistent over time. Either the data provider or the underlying producer of the data (banks and credit card companies) could make changes to data collection that break time series. Consistent time series is one of the greatest achievements of the federal statistical system, giving researchers treats such as an uninterrupted 74-year data series of unemployment rates measured in nearly the same way in every period.
Ultimately, government statistics might rely on a mix of survey and transaction data, or perhaps significant changes to how the Consumption Expenditure Survey is administered to improve respondent accuracy and develop a more complete set of U.S. consumption data to measure this aspect of U.S. economic inequality.
The National Academy of Sciences assembled an outstanding panel of scholars to consider these issues. Equitable Growth is especially proud that there are seven Equitable Growth grantees, a member of Equitable Growth’s Steering Committee, and three current or former members of our Research Advisory Board on the panel.
The work of this panel is incredibly important. As several speakers noted, inequality in the United States increasingly means that the rich and poor live dramatically different lives. This panel, and the work of our federal statistical agencies, will lead to a better understanding of inequality that informs U.S. residents and provides actionable information for policymakers.
For decades, the U.S. child care market has been on the verge of a crisis. As any parent knows, child care in the United States has long been too expensive, hard to access, and of inconsistent quality. It hasn’t been working for families—but it also hasn’t been working for providers and their staff, who scrape by on razor-thin profit margins and some of the lowest wages in the U.S. economy. It is, as the U.S. Treasury Department concluded in September 2021, a failed market.
While many prominent economists have called for increased government investment in child care to correct this market failure, such investment has not materialized. Some policymakers express concerns that further government investment in the U.S. child care sector will exacerbate already-high inflation and child care costs, and indeed, in this current inflationary environment, higher child care costs are the last thing families need.
But it is exactly what they might get without sufficient public investment.
To recognize why, we must understand the current U.S. child care market and what it might be like in the future depending on the actions policymakers take now. This column first summarizes the supply crisis facing the industry and then previews three possible scenarios that could play out in the U.S. child care market with or without the necessary public support. All of the potential child care futures have one thing in common: rising costs. But only one path forward—the one built on public investments in the system—will best shield U.S. families from rising costs, control increasing child care prices, and build a more resilient child care sector.
The present: An impending child care supply crisis
One cause of the failing child care market is its primary source of funding. Unlike the publicly funded education system that kicks in when a child reaches Kindergarten or pre-K age, the child care system requires most parents to pay high out-of-pocket fees without any public or private system of borrowing to help cover the costs, unlike for higher education. Families incur these costs at a moment in their working lives when expenses are typically high and income is usually low.
This liquidity constraint puts a limit on how parents will tolerate increasing child care prices. When child care prices are too high, parents will seek other alternatives, which may include dropping out of the labor force to care for their children, even though this limits future income growth. Such constraints won’t stop prices from rising outright, as many parents will place a higher value on the time that child care provides them to engage in work than on the money spent on care. But it does prevent providers, many of whom genuinely care about the families they serve, from raising prices—and, in turn, their own and their employees’ wages—too quickly.
To keep prices from rising even higher when most families already struggle to afford child care, providers must keep their wages and those of their employees as low as possible. The average U.S. child care provider’s wage is just $13.22 an hour, or $27,490 per year for a full-time employee. These low wages may be keeping prices in check, but they are unsustainable and contribute to the inconsistent quality of care available to families.
Low wages also are strangling the U.S. child care market. Many smaller home providers, where the owner may be the sole employee, have already been forced to close. And, in the wake of the COVID-19 pandemic and ensuing recession, those providers that did not shutter their doors have struggled to hire new staff members. Employment in the child day care sector, for example, remains 116,000 jobs below its pre-pandemic level, and the sector added fewer than 3,000 jobs in April 2022, even as the broader labor market has recovered. (See Figure 1.)
Figure 1
That child care prices are currently rising at a slower rate than the general Consumer Price Index should provide little comfort to policymakers and is not indicative of where child care prices are likely to go. Over the past two decades, child care prices have risen faster than the average for all other products, by approximately 1.1 percentage points. Once supply chain challenges ease and broader inflation cools, it is possible child care prices could once again rise faster than broader inflation, as they have in nearly 16 out of the past 20 years. (See Figure 2.)
Figure 2
Long-term employment declines, coupled with a lethargic U.S. recovery from the pandemic in the child care sector, means that parents will have an even harder time finding safe, quality child care for their children. These supply challenges could spell an impending crisis for this already-struggling industry.
Currently, there are two potential futures for the U.S. child care market absent government investment. If wages stay low and the supply of child care stays constant or decreases, then parents will be left to fight for limited child care slots. This could force those parents who highly value child care—because it is a necessity for them to work—to pay higher prices. It may also drive other parents to exit the labor force entirely because they can’t find acceptable care or because rising prices have made paid work no longer sufficiently profitable.
Alternatively, child care employers could attract workers to enter the sector by offering higher wages and thus increasing the supply of care. These higher wages, however, would be passed on to families in the form of higher prices because most providers’ profit margins are already so low.
Either way, families in the United States would see higher prices that threaten their economic security. Evidence shows that higher prices compel parents to drop out of the labor force or reduce their work hours to focus on child rearing, which, in turn, dampens broader economic growth.
The potential first future: Providers leave wages low, yet costs for families still rise
The first possible future scenario for the U.S. child care market is a continuation of the status quo, in which parents’ liquidity constraints incentivize providers to keep prices—and, in turn, wages—as low as possible.
In this scenario, child care prices may not rise immediately, but providers would continue to struggle with hiring and retaining workers. With fewer workers to care for children and meet the child-to-staff ratios necessary to provide safe, quality care, providers will be required to reduce available child care slots.
The number of licensed small in-home providers is already declining steadily, either because they are leaving the industry entirely or they are entering the unlicensed “underground” market, where subsidies aren’t available, and quality and safety standards cannot be enforced. (See Figure 3.)
Figure 3
There are several potential outcomes should the child care market follow this path forward. As the supply of care decreases, families will compete for fewer and fewer available slots, and competition for rare high-quality slots could be even worse. Over time, such competition may cause prices to rise.
If and how much prices increase depends on how willing parents are to tolerate them. Parents who strongly value high-quality care may be willing to pay a premium for a product in short supply. Many may begrudgingly accept higher prices up to a point because they are still better off paying for expensive child care than dropping out of the labor force. Because there is some elasticity in parents’ demand for child care, others will deem child care too expensive and drop out of the labor market to focus on child rearing.
If, however, parents’ liquidity constraints are powerful enough that prices cannot rise significantly despite a low-supply environment, then there will still not be enough child care slots to meet demand. Parents who cannot find care may be forced to stop working or reduce their hours regardless of their willingness to pay.
Essentially, in this future scenario, the costs of caring for their children increase among all U.S. families. For those that continue to purchase formal child care, those higher costs come in the form of child care prices. For those that stop buying care, it is the opportunity cost of forgone work.
The potential second future: Providers raise wages for workers—and prices for families
The second possible future scenario for the U.S. child care market is one in which providers respond to current labor market pressure and quickly raise wages to compete with other sectors to attract talent amid rising costs of living.
Such a move would not only bring child care workers back to the sector after the COVID-19 recession but also help keep workers in the jobs they already have. Recent research from the Minneapolis Federal Reserve finds that child care centers that pay $15 per hour or less lose approximately 1 in 5 staff members per year, while centers that pay $20 or more per hour have a turnover rate of 1 in 10 employees or fewer per year. (See Figure 4.)
Figure 4
Such an industrywide shift would expand the supply of child care across the country. Yet it also would inherently require providers to raise their prices. According to an analysis by the Center for American Progress, salaries and benefits account for 65 percent to 76 percent of providers’ costs. Without sufficient cash reserves, investments, or profits to cushion these rising costs, providers must pass them on to the families they serve.
How this increase in provider costs translates to the real costs that families pay varies by state earnings averages and regulations. In West Virginia, for example, raising the salaries of child care center workers to parity with Kindergarten teachers would result in a 24 percent to 27 percent increase in what families pay, according to the Center for American Progress’ Cost of Child Care Tool. Even closing the pay gap between child care workers and teachers by just half would result in a 12 percent to 13 percent price increase. While such an increase would represent a one-time bump in prices as wages are brought up to close this pay gap, it also would raise the “floor” from which all future prices will grow.
In this second future scenario, the supply of child care will stabilize, but families are set to be rocked by steeply increasing prices—absent government investment in the industry.
Fortunately, government investment opens the door to a third potential future. This potential third way forward would lead to a greater supply of child care, stable costs for families, and, ultimately, broad-based growth for the whole economy.
The third potential future: Government investment in child care expands supply and stabilizes costs for families
The third potential future for child care in the United States is one in which the federal government makes a sustained, robust investment in child care and early education for the first time. Recent policy proposals envision an expanded subsidy system on a sliding scale, in which most families pay between 0 percent and 7 percent of their household income, with subsidies picking up the leftover price.
For some families, child care costs would drop to $0, but even families required to pay the maximum 7 percent of their income would likely see a meaningful decrease in their child care costs under this system. On average, U.S. families currently spend approximately 10 percent of household income on child care. Continuing with figures from West Virginia, middle-class families’ child care spending in that state would fall by more than half, from an average of 10.4 percent of income to just 5 percent—a savings of more than $100 per week.
Such a system would also allow child care providers the breathing room they need to raise their workers’ wages. The new subsidy system would shield most families from ever paying more than 7 percent of their income on care. Even if the formal price that providers charge were to rise, public spending would eventually make up the difference. This has the added benefit of attracting new talent into the child care field, expanding the supply of child care, and easing upward pressure on prices due to insufficient supply to meet demand.
If, in keeping with smart policy design, a subsidized system is gradually phased in over a few years, then in the short term some families who are not yet eligible for subsidies may experience modest price increases due to increased demand for care from the expanded subsidies. In a phased-in plan, however, all eligible families will receive subsidies in a few years, and at that point, nearly all families will pay significantly less for child care.
Further, smart policy choices, such as using federal grant funds to cover the cost of increasing child care workers’ wages during the phase-in, can shield the families who are next in line for subsidies from experiencing cost increases arising from the higher wages.
Well-designed policy can also keep prices aligned with their fair market value. Theoretically, the expansion of the government’s role in the child care market could allow providers to raise prices higher than the market would otherwise dictate without losing many customers because most families will never pay more than 7 percent of their income on care and the government would make up the difference. But public investment does not mean a “blank check” from the government.
Recent proposals require states to submit payment rates and cost-estimation models to access federal child care money. Subsidy amounts are thus calculated on the anticipated real-world cost of providing quality care in local markets and not on potentially arbitrary prices set by the more egregious profit-motivated actors. Indeed, this is the only future in which the government’s regulatory authority and deliberative process for assessing costs as a buyer can help keep price increases in check.
Some policymakers point to the possibility that child care prices may still rise following government investment, as well as the impact on those families who are ineligible or not yet receiving subsidies, as reasons not to invest in child care. Such concerns miss an important counterfactual: Without such government investment, costs are going to rise for all families. This is the hard truth about the future of child care in the United States—and, if history proves instructive, prices are likely to rise faster than the rate of inflation in the long term.
The question for policymakers, therefore, is not whether child care costs will continue to go up—they will—but rather how policymakers can help the most families meet these rising costs while building a stronger, more stable child care industry. Helping families face the child care crisis will boost the U.S. economy in the short term by freeing up parents’ time and resources to enter the labor market and in the long term by supporting children’s educational and socio-emotional development, which will pay dividends for decades to come.
The future of child care remains uncertain, but one thing is not: Inaction in the face of the failed child care market will be too costly for families, and the economy, to bear.
Unstable scheduling is often framed as a problem for particular groups of workers who are more vulnerable to employer exploitation, whether because of a greater need for schedule accommodations, a lack of better job options, or any number of other possible factors. But this framing misses the broader extent and economic significance of the problem.
In prior research with Susan Lambert at the University of Chicago, I show that unpredictable and unstable schedules are widespread in the United States, affecting between 10 percent to 30 percent of civilian employees. While schedule instability is more prevalent among part-time workers, Black workers, and workers under the age of 35, it still affects a substantial share of full-time, White, and middle-aged workers. Unpredictable schedules also are common not only in low-wage service industries, such as hospitality and retail, but also across higher-wage construction, production, and transportation jobs.
Why do so many U.S. workers put up with unstable schedules? In a recent working paper, I shed light on this issue by answering a related question: What, if anything, do workers gain from these arrangements? I reframe schedule instability as a problem of allocating risk and reward in the employment relationship. In a fair, well-functioning market, those who accept greater risk can expect greater reward. So, I ask, do scheduling risks come with higher pay, flexibility, or other rewards for workers?
To answer these questions, I test three potential explanations for why workers accept unstable schedules. First, I test the claim that workers are compensated for scheduling risk with higher pay or advancement opportunities. Second, I test the claim that workers with unstable schedules benefit from better work-life balances or flexibility. Third, I test the claim that workers bear uncompensated scheduling risk because they have limited access to stable schedules.
The best available data on labor scheduling and compensation come from the National Longitudinal Survey of Youth, 1997 Cohort, or NLSY97 for short. This survey, sponsored by the U.S. Bureau of Labor Statistics, collects exceptionally detailed information on schedule arrangements, pay, and benefits for a nationally representative sample of employees born in the early 1980s. These workers were in their 20s and 30s during the period I studied, from 2011 to 2018.
For this period, the NLSY97 is the only national survey to include questions about the range of weekly hours, length of advance notice, and degree of control the worker or employer has over scheduling. The NLSY97 collects data every other year, providing repeated observations of workers as they move through different jobs and schedule arrangements. These data allow me to identify multiple types of scheduling risks—defined by different combinations of volatile hours, short notice, and little or no worker control—and to analyze the marginal effects of scheduling risk on compensation.
My analysis provides clear evidence that workers are worse off with scheduling risk. I find that jobs with unstable schedules offer workers no more pay and much less flexibility than otherwise-comparable jobs with stable schedules. For a typical worker with a stable and predictable schedule, the probability of having a flexible work schedule as a benefit of their job is 56 percent. That probability falls to 43 percent for those workers with an unstable schedule and 39 percent for those with an unpredictable schedule. (See Figure 1.)
Figure 1
I also find that scheduling risk lowers job satisfaction and reduces by 10 percentage points the probability that a worker will remain with the same employer over a period of 2 years. This negative effect on retention is consistent with prior research showing that schedule instability increases turnover at large retail and food-service firms. These results complement other recent studies calling into question whether unstable scheduling is as valuable as many employers believe. If unstable scheduling makes workers less satisfied, more likely to quit, and less productive at work, then it may not be the most efficient way of managing business risk.
Another set of findings from my working paper challenge the claim that uncompensated scheduling risk reflects the relative power of employers in the U.S. labor market. I find that compensation penalties for unstable schedules are not greater in the context of higher unemployment, where employers have more power to set compensation. Moreover, when I compare union workers and nonunion workers in the NLSY97 cohort, I find similar rates of unpredictable schedules. At least for U.S. workers in their 20s and 30s, low unemployment and a union contract are not enough to ensure fair compensation for scheduling risk.
Fair Workweek laws offer a more direct solution to the problem of schedule instability. While the details vary by jurisdiction—currently, seven cities and one state have Fair Workweek laws—these laws generally require employers to provide 2 weeks’ advance notice or extra pay for schedule changes. Research on Seattle’s scheduling regulation, for instance, found it was effective in reducing instability and improving health and economic security among covered employees.
My study strengthens the case for enacting Fair Workweek laws. I show that, on average, workers are penalized rather than rewarded for scheduling risk. Contrary to what opponents of scheduling regulations claim, unpredictable and unstable schedules lower job satisfaction and reduce flexibility for workers.
My recent research also provides a rationale for extending scheduling regulations beyond the narrow segment of retail and restaurant chains targeted by early Fair Workweek laws. Transportation and construction workers, for instance, have been largely absent from the public debate around this issue, yet they are exposed to some of the highest rates of scheduling risk.
By implementing scheduling regulations covering a wider range of workplaces across the country, policymakers can improve job quality for millions of workers. With more predictability or extra pay for schedule changes, workers will be better able to provide and care for themselves and their families.
What’s at stake is a precious resource—time—that shouldn’t be wasted with haphazard scheduling practices. If employers truly value schedule flexibility, it is only fair they compensate the workers who provide it.
The Washington Center for Equitable Growth is excited to welcome three scholars and students who will join the organization this summer through programs offered by the American Economic Association.
As part of the American Economic Association Summer Economics Fellowship program, Afrouz Jahromi, an assistant professor at Widener University, will join Equitable Growth as a scholar in residence for 12 weeks. Jahromi’s research interests lie in labor economics and microeconometrics. During her fellowship, she will pursue her research investigating the effects of having children on U.S. women’s income—in particular, looking at why the earnings divide between mothers and childless women is growing even as the pay gap between women and men shrinks.
This is Equitable Growth’s second year as a participating research institution with the AEA Summer Economics Fellowship program. As a host, we provide the selected scholar with a stipend, research and career mentorship, networking opportunities, and resources and training to engage with press and policymakers. Throughout the summer, scholars engage in a research project of their choice, which they then have the opportunity to present to members of our academic network for feedback and advice. We also help the fellow to identify research and career mentors.
The program is designed to increase the participation and advancement of women and people from underrepresented communities in economics by supporting postdoctoral scholars and junior faculty. Equitable Growth, through a competitive process, selects a scholar whose research explores how inequality affects economic growth and stability. The summer program is an opportunity for these early career scholars to access leading academics and policymakers to advance and elevate their research to a wider audience, receive critical input on their work, and strengthen their academic network.
Equitable Growth also will host two undergraduate students through the AEA Summer Training program: Ayesha Choudhery from Central Connecticut State University and Haroldo Nesbeth from Trinity College. During their 2 months with Equitable Growth, they will sharpen their research skills by examining literature and conducting quantitative analysis that advances Equitable Growth’s mission.
As an “experiential learning” host, Equitable Growth will introduce Choudhery and Nesbeth to professional work outside of academia for Ph.D. economists, build their quantitative skills, and provide mentorship and real-world experience to the students while they earn credits toward their undergraduate degrees. Students receive training on editing, writing, and networking, as well as the chance to meet thought leaders and other high-profile economists in our academic network.
Currently hosted at Howard University, participants in the training also undertake intensive coursework in microeconomics, math, econometrics, and research methods in order to prepare for graduate and doctoral programs in economics or a related discipline. The program is targeted to students of color and aims to increase diversity in economics and related fields. In fact, according to the AEA website, as many as 20 percent of students of color who received Ph.Ds. in the past 20 years participated in this program.
Equitable Growth is honored to participate in these two important summer programs offered by the American Economic Association that seek to diversify economics and the social sciences. Both are a continuation of our broader efforts to foster new pathways for students and scholars in the economics profession, as well as support organizations that steadfastly work to cultivate racial, ethnic, and gender diversity in academia. We look forward to welcoming Jahromi, Choudhery, and Nesbeth, and working with them to elevate their research, support their academic progress, and advance our mission to lift up historically marginalized voices.
The California “River Fire” of Salinas, in Monterey County, August 16, 2020.
The harmful consequences of climate change are broad, from extreme weather events making locations uninhabitable to increased temperatures delivering larger and more frequent downstream damages, such as drought and increased hurricanes. These effects are already imposing serious economic costs and limiting economic growth in the United States and around the globe.
A telling barometer for the consequences of climate change are wildfires. Global warming is increasing both the frequency and the size of wildfires, both of which take a direct human toll due to their rising destructive force and their damaging impact on economic growth and prosperity.
The effects of these wildfires are far-reaching and long-lasting. These include local homes and small businesses being consumed in smoke and flames, as well as cities and counties being forced to shift economic priorities from growth and development to fire suppression and infrastructure restoration. Evidence suggests that, of an estimated $500 billion in new costs, people of color and other historically disadvantaged groups are disproportionately affected by these crises.
The persistent threat facing fire-prone states needs to be addressed. Some of the downstream macroeconomic effects of increased heat within the continental United States are relatively well-understood, such as the cost of drought on agricultural production and the immediate costs of residential home damage resulting from increased wildfires. There also is a notable amount of well-informed research regarding the social costs of wildfires in their aftermath.
But there is still much to learn regarding the economic impacts of wildfires. This issue brief reviews the scope and intensity of economic risks imposed by droughts that exacerbate wildfires and the direct costs to housing and businesses of wildfires, and then details on how wildfire suppression and mitigation efforts are worthwhile investments that must be undertaken carefully based on well-informed research, so that equitable U.S. economic growth in fire-prone states is strong and sustainable.
Wildfires and drought
The downstream effects of rising heat continue to affect the United States. Since the 1980s, the nation has experienced 26 droughts. Drought, combined with hot weather, strong winds, and exceptionally dry vegetation, result in very active fire behavior.
In 2021, for instance, California experienced its 11th-driest year on record, breaking more than 1,500 daily high temperature records. This combination of weather forces led to an intense wildfire season. Consider the Dixie Fire. It started on July 13, 2021, burned on the Plumas National Forest, Lassen National Forest, Lassen Volcanic National Park, and across five Northern California counties: Butte, Lassen, Plumas, Shasta, and Tehama. Then, the Fly Fire started on July 22 and was managed under the Dixie Fire East Zone command as the two fires eventually merged into one.
California is not alone in its battle against the effects of rising temperatures. Several Western states are currently experiencing record-breaking levels of drought. Temperature increases continue to result in states, including Idaho and Wyoming, receiving minimal amounts of rainfall.
Consequently, these extended periods of drought increase the supply of dry lumber and dead vegetation, which serve as prime fuel for wildfires.
Economic costs to housing markets due to wildfires
Each year, as populations grow, individuals move farther into fire-prone lands. As new residential developments continue to urbanize fire-prone land, the risk of fire damage rises. These trends may have been accelerated by the COVID-19 pandemic, which led to many U.S. workers and their families migrating from traditionally dense metropolitan areas to less populated rural localities, such as those in Idaho and Arizona.
Conversely, the resulting population influx due to the pandemic may also be a contributing factor to increased wildfire risk. As homebuyers continue to expand into what the U.S. Fire Administration calls the“wildland urban interface”—inhabited lands that are known to have wildfires—these households also find themselves experiencing significantly higher levels of wildfire risk due to the surrounding vegetation being highly ignitable.
New research looks at the effects of wildfires on residential house prices. The study, by Nancy Wallace, Paulo Issler, and Richard Stanton at the University of California, Berkeley’s Haas School of Business and Carles Vergara-Alert of IESE Business School, features an empirical analysis using high-frequency geospatial data from ATTOM Data Solutions, a leading provider of nationwide property data. The study estimates both the wildfire exposure of residential single-family homes and determines the long- and short-run effects of these fires on insured properties. The main purpose of this work is to inform the current policy debate concerning residential fire-insurance regulation in California.
The study was conducted using two differing analytical strategies to understand how wildfires impact housing market dynamics. First, Wallace and her co-authors estimate the probability of wildfires in California to assess whether it is possible to estimate the actuarial risk of urban wildfires. Second, the researchers use a difference-in-difference methodology, which estimates how an event such as a wildfire shapes relevant variables for housing prices, to see whether there are there changes in housing quality and prices after large urban wildfires, whether there are changes in income and wealth after urban wildfires, and if there is a significant increase in mortgage defaults after an urban wildfire.
Wallace and her co-authors find that they can estimate the actuarial risk of urban wildfires. To date, their research finds a total of $265.6 billion worth of property currently at risk of wildfire damage. The researchers then estimate a back-of-the-envelope assessment of potential costs associated with wildfire risk. They use the previously described empirical estimates to test for the long-run post-fire effects between areas that had wildfires and the treatment group that consisted of homes in a wildland urban interface beyond the immediate risk of fire.
They find that there is a 2.1 percent increase in home prices within the treatment group—housing units within the fire radius 5 years after a wildfire event—versus homes outside of the treatment group. Results also show an income increase of 5.25 percent for workers residing within the treatment group after the same 5-year window following a fire, compared to 4.2 percent residing outside the treatment group. The upshot: This economic consequence of spreading wildfires will impact housing prices and family incomes in these new wildland urban interfaces.
The four co-authors of the new research also find potential signs of gentrification following fire damage. They find that home prices rebound to pre-fire rates just 2 years after wildfires. This rebound in property values keeps the prices of houses in wildland urban interface areas out of reach for less fortunate families looking to relocate. This means households that may have been able to previously afford a home in an area prone to wildfires may begin to experience difficulty relocating as property values continue to rise.
The co-authors, however, ultimately find that the size of wildfires is positively correlated with rising mortgage delinquency rates, which can cause prolonged financial hardship for low-income and low-wealth households in fire-prone areas. They hypothesize that these impacts may be due to a belief by homeowners that they will receive government assistance when there is a larger-than-average fire, and they may be missing their mortgage payments when less federal assistance is forthcoming.
Policy solutions to ensure fire safety and avoid the economic costs associated with wildfires
As policymakers attempt to understand both the costs and solutions for the economic damages of wildfires, a new paper by Patrick Baylis of the University of British Columbia and Judson Boomhower of the University of California, San Diego evaluates the effect of California’s wildfire building codes on the survival of a single property and it’s neighboring structures. Ultimately the paper seeks to uncover just how governments should go about supporting adaptation to worsening fire catastrophes through policies that mitigate some of the costliest outcomes.
The research examines the policy effectiveness of voluntary adaptation of fire prevention measures in buildings, relative to that of mandatory adaptations already included in building codes. To examine this, the researchers combine data from the CAL FIRE Damage Inspection, or DINS database, with property tax assessment data from the Zillow ZTRAX database for the years 2003 to 2020. Using a difference-in-difference fixed regression model, Baylis and Boomhower assess the effect of mandatory adaptations versus a counterfactual of voluntary building codes.
To do so, the analysis leverages a change in building-code policies following a deadly firestorm in Oakland, California in 1991, which resulted in a wave of mandated fire prevention building codes that led to California boasting some of the strictest fire-safety code measures in the country. This analysis was done using a counterfactual group of homes that predated the 1991 and 2008 building code mandates implemented in California. As such, the control group consisted of the same homes after the fire-prevention mandates were enacted.
The results of their study show the spillover benefits of mandated building codes on neighboring structures within 10 meters of one’s own space. The findings show that the benefit of having a retrofitted home that follows mandated building codes not only helps reduce the risk of exponential damage to the home by 40 percent, but also reduces the likelihood that neighboring homes experience catastrophic damage by 6 percent. This benefit is even stronger when several of the homes in the neighborhood are also retrofitted.
The resulting net-benefit calculations suggest that wildfire building codes yield unambiguous benefits in the most fire-prone areas of California, especially when homes are clustered closely together such that there are large risk spillovers. These findings suggest that mandated building codes should be a consideration to policymakers looking to curb rising wildfire risk.
Conclusion
Investing in climate change mitigation is an important long-term economic policy priority, and investing in wildfire mitigation is a critical immediate priority.Traditionally, when wildfires erupt, the most pressing issues are suppression and safety. Once managed, local governments are then able to focus on rebuilding and recovering the economic loss. The question is, should fire-prone localities focus on rebuilding or should they avoid building homes in high-risk areas altogether?
Climate models, regardless of carbon emission output, agree on increased durations of very hot days—more days with temperatures of 90 degrees Fahrenheit and above. This means that the present climate responsible for producing the third-largest wildfire in California history—the Dixie Fire—is not the worst that California will experience. It also means that attempting to rebuild infrastructure to its original pre-fire state may, in fact, be a moot effort as the risk of it being destroyed again increases with each year.
Making sure that homes avoid serious damages and that households can manage costs is a necessary step in managing the persistent threat of wildfires to families and the entire U.S. economy. This includes financial relief to affected families, as well as preventative measures to mitigate the worst costs.
As the United States faces the continued threat of increased wildfires due to rising heat levels, policymakers need to consider what steps should be taken. More data are needed to assess the long-term effects of wildfires to specific areas because the majority of the existing fire suppression data collection methods are designed to capture short-term impacts. Yet the available research on the long-term consequences demonstrates how disruption of livelihoods and economic activity can have lasting impacts. Understanding these long-term harms is critical to crafting effective policies that speak to the persistent and increasing threat of wildfires.
The violent effects of systemic racial segregation are ever-present in U.S. cities today, especially in the big Northern cities that were destinations for Black Americans fleeing racial oppression and violence in the rural American South during the Great Migration. The average Black resident in a Northern metro area today lives in a neighborhood that is at least 50 percent non-White, and most neighborhoods in metropolitan areas where violent crime is the highest are predominantly Black.
Indeed, homicides are the leading cause of death for young Black men in the United States. Young Black men accounted for nearly 40 percent of homicide victims in 2019, the most recent year for which complete data are available, about 20 times higher than young White men. This violence—itself largely borne by Black residents—is the result of decades of institutional racism. It is deeply linked with the history of racism resulting in residential segregation and accompanying “White flight” to the suburbs.
The more widespread consequences of segregation just in Northern American cities are found in rising poverty, collapsing local tax bases needed to maintain high-quality public education and effective public safety, and reduced economic mobility for Black residents. But the most immediate and life-threatening consequence certainly is the tragic rate of homicides.
Documenting this high cost of racial segregation
Our new research documents the roots of this tragic consequence—high non-White homicide rates in these highly segregated cities. In our working paper, “Black Lives: The High Cost of Segregation,” we sought to answer how residential segregation affected homicide victimization by race in Northern cities from 1970 to 2010. We find that higher levels of segregation are robustly and positively associated with higher levels of non-White homicide victimization in Northern cities, but unrelated to White homicide deaths.
In order to understand the link between residential racial segregation and crime, we used the configuration of railroad tracks within cities to analyze whether greater levels of segregation could explain higher levels of violent crime. Much of today’s residential segregation (outside of the South) is the result of the first wave of Black migration to the Northern and Western regions of the country between 1910 and 1970, but due to the availability of robust data, our analysis focuses on Northern cities.
We measure residential racial segregation in these cities using a variety of data. We employ the commonly used dissimilarity index applied to every U.S. census from 1970 to 2010. To capture the causal effect of segregation on homicides, we use the configuration of railroad tracks within a city or metropolitan statistical region as our instrumental variable. This variable proxies for the very real “other side of the tracks” idiomatic understanding of the consequences of redlining. Our research samples focus on select Northern cities where a railroad division index is available, and thus it can’t be assumed to apply to all cities. Yet other research suggests these findings would not be unique to these cities.
Our findings are stark. We document a robust causal link between residential racial segregation and the murder rate, and between segregation and non-White homicide victimization. We find that non-White residents bear the burden of violent crime due to segregation, particularly homicides, while finding no effect on White victimization. (See Figure 1.)
Figure 1
Our analysis of the findings points to several reasons residential racial segregation causes non-White homicide rates to be higher in more highly segregated metropolitan areas than in less segregated communities. Segregation lowers local government tax revenues from property taxes, which, in turn, reduces public safety expenditures per capita, including spending on police and fire safety workers. We find that inadequate police employment levels attributed to segregation account for approximately 56 percent of the non-White homicides. The same trend holds true for lower arrest rates for violent and property crimes in highly segregated areas.
These lower tax revenues impact economic mobility in these communities, too. Reduced school expenditures due to segregation are manifested in the negative relationship between segregation and per-pupil spending, which, in turn, inhibits the opportunities of children to benefit from social and economic mobility and drives ongoing inequality in Black communities.
What’s more, lower tax revenues result in disproportionate shifts of the remaining funding toward policing precisely because of higher crime rates in these communities. This more punitive policy approach in response to rising crime leads to higher Black imprisonment rates—yet another consequence of the legacy and the immediate impact of residential racial segregation.
Our new research builds on research showing the effects of residential racial segregation on poverty, inequality, and intergenerational mobility in Black communities, as well as effects of the Great Migration. These findings are similar to Princeton University economist Ellora Derenoncourt’s 2021 working paper, “Can you move to opportunity? Evidence from the Great Migration,” but we isolated the effects of residential racial segregation separate from Black migration. We also focused on metropolitan statistical areas and central cities (rather than the larger area of commuting zones), specific to Northern cities with railroad data.
The steadily accumulated evidence over the past several decades of the harmful consequences of residential racial segregation—particularly the most recent data-driven evidence published in the 21st century—drives home why policymakers at the federal, state, and municipal levels need to move beyond punitive “law and order” policy approaches if they want to address the devastating impacts of violent crime on Black communities.
The use of the pejorative euphemism “inner city” when discussing crime in the United States has been wielded for more than seven decades to stir up fears among mostly White voters that the mostly Black residents of big cities across the country are dangerous and out of control. Such racialized code words are often paired with demands for law and order, and recur in U.S. political campaigns again and again, beginning in the 1960s and replayed in different guises right up to today.
Yet, as we conclude in our new working paper, racial segregation undermines pluralist politics and allows politicians to make budget cuts in Black neighborhoods either because they expect minimal political fallout or to maintain the privileged status of White communities. These disincentives to collective action, in which all Americans have a vested interest in fighting crime in Black communities, remain the most corrosive to our nation’s political debates.
Residential racial segregation is multifaceted and a result of a deep-rooted and complex racial history, where structural racism continues to trap Black Americans in a permanent underclass. Targeted policies that improve socioeconomic conditions and increase opportunities for upward mobility would have long-lasting and persistent effects, decreasing non-White homicides in the long run.
—Jamein P. Cunningham is an assistant professor of economics at the Brook School of Public Policy and the Economics Department at Cornell University. Alberto Ortega is an assistant professor in the O’Neill School of Public and Environmental Affairs at Indiana University. Robynn Cox is an assistant professor at the University of Southern California’s School of Social Work. Kenneth Whaley is a lecturer in the Department of Economics at the University of Houston.
Between mid-March and mid-April, the U.S. economy added a strong 428,000 jobs, making it the 12th consecutive month in which employment growth surpasses 400,000 jobs. In addition, according to the U.S. Bureau of Labor Statistics’ Employment Situation Summary released this morning, the national unemployment rate remained unchanged at 3.6 percent—near the pre-pandemic rate of 3.5 percent. The share of 25- to 54-year-olds with a job dropped slightly to 79.9 percent in April from 80 percent in March, and the labor force participation rate fell, going to 62.2 percent from 62.4 percent in the same period.
This April also marks 2 years since the U.S. labor market shed a record 20 million jobs in a single month as the COVID-19 pandemic sent shockwaves through the economy. Since then, the recovery in employment has been exceptionally fast, compared to previous recessions. As of last month, there is now only a 1.2 million job deficit with respect to February 2020. (See Figure 1.)
Figure 1
But the recovery has been far from even. Across different demographic groups, joblessness is highest for Black workers, whose unemployment rate went to 5.6 percent in April from 6.3 percent in March. American Indian and Alaska Native workers are facing a 5.3 percent unemployment rate, Latino workers are at 3.8 percent, and White workers and Asian American workers at 2.9 percent. (The Bureau of Labor Statistics began publishing monthly data on American Indian and Alaska Native workers in February 2022, which means this statistic is not available on a seasonally adjusted basis.) (See Figure 2.)
Figure 2
While Asian American workers, along with White workers, are currently experiencing the lowest unemployment rate of any other major racial and ethnic group, their unemployment rate saw one of the biggest proportional increases at the onset of the pandemic and was slower to recover than the unemployment rate for White workers. In addition, both aggregate statistics and the topline unemployment rate fail to reflect a full picture of how this group of workers experiences the U.S. labor market.
Take long-term joblessness. According to BLS data, since the onset of the pandemic, Asian American workers in general, and Asian American men in particular, faced longer periods of unemployment than their Black, Latino, and White counterparts. In 2021, for instance, it took unemployed Asian American workers an average of 31.7 weeks to find a job—a full 3 weeks longer than the average unemployment spell for all U.S. workers. As of April 2022, the average unemployment duration was 46.2 weeks for Asian American men and 33.9 weeks for Asian American women. (See Figure 3.)
Figure 3
While the Bureau of Labor Statistics does not report monthly statistics on Native Hawaiian and Pacific Islander workers, annual data reflect that this group of workers faced big job losses as the COVID-19 pandemic hit. Available data show, for instance, that in 2021, the employment-to-population ratio of NHPI workers had yet to recover the most ground to return to its 2019 level. (See Figure 4.)
Figure 4
The barriers Asian American, Native Hawaiian, and Pacific Islander workers face in the U.S. labor market are often obscured by aggregate statistics
Asian American workers also experienced especially long periods of unemployment during the previous economic downturn. For instance, an analysis by Marlene Kim at the University of Massachusetts Boston shows that in 2010—when long-term joblessness was even more prevalent than during the Great Recession of 2007–2009—almost 49 percent of unemployed Asian American workers had been jobless for 6 months or more, a substantially higher proportion than for Latino workers and White workers and a slightly higher proportion than for Black workers.
Further, Kim found that Asian American workers’ concentration in states where long-term unemployment was especially prevalent and their higher likelihood of having been born outside the United States, alongside racial bias, were all likely explanations for why this group of workers was facing an especially difficult time finding a job after unemployment.
Indeed, a number of studies show that place of birth (about 3 in 4 Asian American, Pacific Islander, and Native Hawaiian workers are foreign born), as well as employment discrimination, hurt AANHPI workers’ employment outcomes, earnings, and opportunities for career advancement. For instance, research has found that getting an education abroad and English language ability can hurt AANHPI workers’ earnings. Further, analyses have found that workers within the AANHPI community can experience discriminatory pay penalties, where lower wages cannot be explained by workers’ level of formal education, years of work experience, and other characteristics believed to determine pay.
As the U.S. economy continues to add jobs, it will be essential to identify and address the obstacles facing AANHPI workers and communities
The jobs recovery continued in April, and the U.S. economy is now 1.2 million jobs away from its pre-pandemic level. Collecting and reporting detailed disaggregated data on AANHPI workers and communities will be crucial to ensure all workers share in the economic recovery, accurately capture experiences of these communities, and inform equitable policymaking. An analysis by Christian Edlagan and Raksha Kopparam at the Washington Center for Equitable Growth shows, for instance, that while AANHPI workers are overrepresented among front-line essential workers, Native Hawaiian and Pacific Islander workers, Thai workers, and Filipino workers were especially likely to work in occupations that put them at risk of contracting the coronavirus. More broadly, the big disparities in income, occupation, health, and educational attainment make it essential to understand how to target funds and policymaking efforts.
At the same time, policymakers need to invest in robust antidiscrimination enforcement and bolster existing laws to cover workers who are currently unprotected due to gaps in coverage, including independent contractors and others in nontraditional work arrangements. And a robust income support infrastructure is another important component to both reduce inequality and support AANHPI workers who may experience discrimination by improving their outside options.
Policies that support worker power—including raising the minimum wage, adopting a just cause employment standard, and supporting workers’ ability to form a union—are a vital foundation for addressing discrimination and improving earnings and working conditions for AANHPI workers, especially those in low-wage occupations. These changes, paired with innovative approaches to enforcing labor standards, including strategic enforcement and co-enforcement, are especially important to protecting vulnerable workers during times of economic upheaval.
On May 6, the U.S. Bureau of Labor Statistics released new data on the U.S. labor market during the month of April. Below are five graphs compiled by Equitable Growth staff highlighting important trends in the data.
The employment rate for prime-age workers decreased slightly to 79.9 percent in April from 80.0 in March as total nonfarm employment rose by 428,000.
The unemployment rate stayed at 3.6 percent in April, and remains higher for Black workers (5.9 percent) and Latino workers (4.1 percent) compared to White workers (3.2 percent) and Asian American workers (3.1 percent).
Private-sector employment continued to rise in April, while public-sector employment has recovered more slowly and remains well below pre-pandemic levels.
The proportion of unemployed U.S. workers facing long-term unemployment increased in April, as 34.6 percent of unemployed workers now have been out of work for more than 15 weeks.
Involuntary part-time work, which represents part-time workers who would prefer full-time work, has returned to below pre-pandemic levels.
Vision 2020
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