Macro Roundup

“…The high cost of bringing up children gobbles up a big share of disposable income in some countries, particularly in Asian countries such as Japan, South Korea and China….”

For all the many rewards that children bring, they are financially draining. They bring economic benefits, but these are not reaped by the parents who bear the brunt of the costs. Boosting the former may require more sharing of the latter.

Countries that provide generous financial support appear to have more births. Nordic countries, noted for their gender equality and generous welfare states, have birth rates that are close to replacement levels. Providing financial support for childcare, making it easier to combine work and parenthood, is particularly effective, according to the OECD.

Sovereign bond investors are increasingly mindful of demographic risks. Rapidly ageing populations increase public spending on health, social care and pensions, while the tax burden is spread over fewer workers. Rating agency Fitch calculates that the impact of ageing costs on sovereign ratings could result in a 10.5-notch downgrade for Slovakia, the most extreme case, by 2070 assuming no policy changes.

But helping parents financially is one obvious way to lower barriers. The high cost of bringing up children gobbles up a big share of disposable income in some countries, particularly in Asian countries such as Japan, South Korea and China.

For equity investors, ageing populations present opportunities as well as risks. Rising demand for healthcare and automation should benefit companies such as Japan’s Fanuc and Germany’s Siemens. Even nappy manufacturers like Japan’s Daio Paper, Nippon Paper and Unicharm can adapt to demographic trends. Sales of adult diapers in Japan have exceeded those for babies since 2011, says Jefferies.

FT Staff, "Fertility: childcare costs exacerbate the demographic drought,"Financial Times, May 15, 2022, https://www.ft.com/content/fcb40906-3f1d-4962-9ae8-4943271af9f5

Fertility: childcare costs exacerbate the demographic drought

The Covid-19 baby bust — marked by falls of up to a fifth in December 2020 birth rates — was shortlived. But a reversion to pre-pandemic trends still leaves about half the world’s population below the replacement rate of fertility. The economic consequences are a big issue for politicians and portfolio managers alike.

Nearly 64 percent of adults between the ages of 55 and 64 were working in April, essentially the same rate as in February 2020. That’s a more complete recovery than among most younger age groups. The share of Americans reporting that they were retired did rise sharply in the spring of 2020. But retirement is not an irreversible decision. And research from the Federal Reserve Bank of Kansas City has found that at the pandemic’s onset, there was a steep drop in the number of people leaving retirement to return to work, attributable at least partly to fear of the virus and a lack of job opportunities, swelling the ranks of the retired. As the economy has reopened and the public health situation has improved, these “unretirements” have rebounded and have recently returned roughly to their prepandemic rate, according to an analysis of government data by Nick Bunker of the Indeed Hiring Lab. The return of older workers has been concentrated among those in their late 50s and early 60s, people who were still several years or more away from retirement when the pandemic began. The employment rate among those 65 and older fell more sharply and has been much slower to recover.That suggests that the pandemic might have led some people who were already closer to retirement to accelerate those plans, and that the greater health risks they faced may have made them less likely to return to work while the virus continues to circulate.

Ben Casselman, "‘I Had to Go Back’: Over 55, and Not Retired After All,"New York Times, May 19, 2022, https://www.nytimes.com/2022/05/19/business/economy/older-workers-labor-force.html

‘I Had to Go Back’: Over 55, and Not Retired After All

When Kim Williams and millions of other older Americans lost their jobs early in the coronavirus pandemic, economists wondered how many would ever work again — and how that loss would weigh on the economy for years to come.

Ms. Williams, now 62, wondered, too, especially when she struggled for months to find work. But in January, she started a new job at an AAA office near her home in Waterbury, Conn.

“I’m too young to retire, so I had to go back,” she said.

Whether by choice or financial necessity, millions of older Americans have made the same move in recent months. Nearly 64 percent of adults between the ages of 55 and 64 were working in April, essentially the same rate as in February 2020. That’s a more complete recovery than among most younger age groups.

“The bottom line is that older workers have gone back to work,” said Alicia Munnell, director of the Center for Retirement Research at Boston College.

For many people, retiring early was never an option. Ms. Williams spent more than 25 years in manufacturing, working for a Hershey’s plant making Almond Joy and Mounds bars. The job paid reasonably well, and offered a retirement plan and other benefits. But in 2007, Hershey’s closed the factory, moving production partly to Mexico.

Ms. Williams, then in her 40s, went back to school, earning an associate degree in hospitality and eventually finding a job as a supervisor at a local hotel. But the position paid significantly less than her factory job, and she drew down her retirement savings to cover medical expenses and other bills. When she was laid off again in June 2020, just a few weeks after her 60th birthday, Ms. Williams had little in savings.

Ms. Williams tried to change careers again, this time going back to school to train as a medical secretary. But she has been unable to find work in her new field. In January, with her savings gone, she took a job at AAA for $16.50 an hour, $2 an hour less than she earned at the factory in 2007, before accounting for inflation. She says she will have to work at least until she can start drawing her full Social Security benefits at age 67.

“If I could’ve left at 62, I would’ve left at 62, but I can’t,” she said. “Not all of us made that money where I could move down to Florida and get a $400,000 house.”

The fastest inflation in decades has added to the pressure on people of all ages to return to work. More recently, so has the turmoil in financial markets, which has taken a bite out of retirement savings.

But even some people who could retire are choosing to return to work as the pandemic ebbs.

When the Long Island fitness studio where she worked as a spinning instructor shut down early in the pandemic, Jackie Anscher lost both a job and a part of her identity. In an interview with The New York Times that summer, she described what seemed at the time like an abrupt end to her career as “a forced retirement.”

But after spending the beginning of the pandemic reorganizing her life and re-evaluating her priorities, Ms. Anscher, 60, has begun teaching spin classes again as a substitute instructor at a local gym, and she is looking for a more regular gig. Her husband is already retired — “he’s been waiting for me to go fishing,” she said — and the couple could afford for her to stop working. But she isn’t ready to hang up her cycling shoes.

“I liked what I had. I loved who I was in front of the room,” she said. “It’s about my mental health. For me, it’s about preserving me.”

Older workers weren’t any more likely than younger workers to leave the labor force early in the pandemic. But economists had reason to think they might be slower to return. Unemployed workers in their 50s and 60s typically have a harder time finding jobs than their younger counterparts, because of ageism and other factors. And unlike after the 2008-9 recession, when depressed housing prices and high debt levels left many people with little choice but to keep working, in this crisis prices of both homes and financial assets kept rising, providing a financial cushion to some people nearing retirement age.

The share of Americans reporting that they were retired did rise sharply in the spring of 2020. But retirement is not an irreversible decision. And research from the Federal Reserve Bank of Kansas City has found that at the pandemic’s onset, there was a steep drop in the number of people leaving retirement to return to work, attributable at least partly to fear of the virus and a lack of job opportunities, swelling the ranks of the retired.

As the economy has reopened and the public health situation has improved, these “unretirements” have rebounded and have recently returned roughly to their prepandemic rate, according to an analysis of government data by Nick Bunker of the Indeed Hiring Lab.

Still, the return of early retirees to the labor force is a reminder that rising wages and abundant job opportunities can draw in workers who might otherwise remain on the sidelines, Mr. Bunker said. The labor force shrank during the last recession, too, and some economists were quick to declare that workers were gone for good. But many people eventually came back during the strong job market that preceded the pandemic: It provided opportunities to people with disabilities and criminal records, to people with little formal education and to people who had taken time away from work to raise children or to care for ailing parents.

That pattern may be repeating itself, but on a much more compressed timeline.

“Don’t underestimate labor supply,” Mr. Bunker said. “Don’t count out the possibility that people want and need work. It has happened much more quickly than what we saw after the global financial crisis, but the broad principle is the same.”

When Tad Greener lost his job managing utilities for a Utah university in late 2019, he wasn’t worried at first about finding a new one — the unemployment rate, after all, was near a 50-year low. But Mr. Greener had hardly begun his search when the pandemic hit and the bottom fell out of the economy. Suddenly, he was 60 years old, unemployed and facing the worst labor market in nearly a century.

Mr. Greener eased up on his job search during the first phase of the pandemic, in part because of some health issues unrelated to the coronavirus. By spring of 2021, he was ready to work again, but he had little luck applying for jobs. He thinks many prospective employers were turned off by the combination of his age and his time out of the work force.

“It’s a daunting task to be 62 years old, to be unemployed for over a year and to try and find work,” Mr. Greener said. “There were times where I didn’t think I was ever going to be able to go back to work.”

As the economy reopened, however, many businesses struggled to hire enough workers to meet the surge in demand. That prompted employers to consider candidates they might otherwise have dismissed, or to look for ways to attract people who could work but weren’t looking.

In Mr. Greener’s case, he learned about a new “returnship” program from the State of Utah that was meant to help people who had been out of the labor force get back to work. Last fall, he was accepted into the program, landing a part-time job in the state Office of Energy Development, which quickly turned into a permanent, full-time job. Now that he is back at work, Mr. Greener says he plans to stay until he is 67, or perhaps longer if he stays healthy.

“Every day I hear about how there aren’t enough workers available,” Mr. Greener said. “There are a lot of older workers that are being written off, or at least finding it much more difficult to get back into the workplace, who have a lot of years and things to offer.”

During the nation’s job recovery (April 2020 to April 2022), the overall civilian unemployment rate fell from 14.7% to 3.6%. Payroll employment returned almost to its pre-pandemic level. The employment-to-population ratios of some noncollege-educated men and women have returned to or exceeded their pre-pandemic levels; however, this literally happened since December 2021.* For example, as the following figure shows, the employment-to-population ratio of young Black men reached 62.1%, exceeding its pre-pandemic value of 56.3% and its December 2021* level of 56.1%.3 In recent months, the ratio for young Latino men recovered to its pre-pandemic level.The next figure reports that the employment-to-population ratio of young Black women recovered to 60.0%, 7.4 percentage points above its pre-pandemic value of 52.6%. The ratios of both young white men and young white women were 2.1 percentage points and 3.2 percentage points, respectively, below their pre-pandemic values. The average employment gap—the difference between their employment-to-population ratios—between young Black men and young white men since January 2020 to March 2022 is 15.0 percentage points. Over the same period, the average employment gap between adult Black men and adult white men was 10.9 percentage points, and it was 8.6 percentage points between young Black women and young white women. The only group near parity with its white counterpart is adult Black women. The figure below shows that, throughout the jobs recovery following the COVID-19 recession, the employment gap between young Black men and young white men stayed between about 8.2 percentage points and 21.0 percentage points. The gap between adult Black men and adult white men ranged from 8.1 percentage points to 13.1 percentage points.

William Rodgers III, "Young Adults without College Education See Uneven Jobs Recovery,"Federal Reserve Bank Of St. Louis, May 17, 2022, https://www.stlouisfed.org/on-the-economy/2022/may/young-adults-without-college-see-uneven-jobs-recovery

Young Adults without College Education See Uneven Jobs Recovery

Relief and recovery investments during the pandemic have made this most recent job recovery unique relative to others in past decades. In fact, major efforts to restore the economy’s health and commitments to lessen racial inequality, combined with widespread labor shortages, have helped to create a “moment of opportunity” for young noncollege-educated adults.1 This moment is especially crucial for young noncollege-educated adults because early career opportunities are key for their long-term earnings prospects. They tend to have fewer skills and credentials and weaker job search networks, and face geographic isolation from job opportunities, making a potential toehold in the labor market all the more important to securing their economic futures. Young Black and Latino adults also face additional hurdles, such as discrimination.

The challenges are even greater for the current cohort of noncollege-educated youth who have entered the labor force during the pandemic. Research has shown that in the aftermath of a recession, youth who enter labor markets experience lower income over their lives. The COVID-19 pandemic is the latest example of a public health and economic shock that could adversely impact their futures. This blog post, the first in a two-part series, assesses how well young noncollege-educated adults, especially young Black adults and young Latino adults, are faring during the recovery.2 Simply put, their recovery is one of unevenness.

Noncollege-Educated Youth Experienced an Uneven Recovery

During the nation’s job recovery (April 2020 to April 2022), the overall civilian unemployment rate fell from 14.7% to 3.6%. Payroll employment returned almost to its pre-pandemic level. The employment-to-population ratios of some noncollege-educated men and women have returned to or exceeded their pre-pandemic levels; however, this literally happened since December 2021.* For example, as the following figure shows, the employment-to-population ratio of young Black men reached 62.1%, exceeding its pre-pandemic value of 56.3% and its December 2021* level of 56.1%.3 In recent months, the ratio for young Latino men recovered to its pre-pandemic level.

The average employment gap—the difference between their employment-to-population ratios—between young Black men and young white men since January 2020 to March 2022 is 15.0 percentage points. Over the same period, the average employment gap between adult Black men and adult white men was 10.9 percentage points, and it was 8.6 percentage points between young Black women and young white women. The only group near parity with its white counterpart is adult Black women.

These gaps are surprising because of the relative gains we would expect young Black and Latino adults to experience as a result of relief and recovery efforts (e.g., the American Rescue Plan and CARES acts) during the pandemic that were particularly impactful for families with the greatest need as well as the “Great Resignation” and its contribution to labor shortages. Furthermore, this most recent recovery is inconsistent with the historical link between overall economic conditions and outcomes for young noncollege-educated adults. In the past, strong economic recoveries have had a disproportionate positive impact on employment among young, noncollege-educated Black men.

Unfortunately, as of March 2022, these conditions did not lead to a narrowing in racial and ethnic employment gaps, and Black and Latino people have experienced persistent disadvantages. The figure below shows that, throughout the jobs recovery following the COVID-19 recession, the employment gap between young Black men and young white men stayed between about 8.2 percentage points and 21.0 percentage points. The gap between adult Black men and adult white men ranged from 8.1 percentage points to 13.1 percentage points.

The second part of this blog series will explore how strong the macroeconomy must be to improve not only the absolute but also the relative economic status of noncollege-educated youth. The post will also discuss ways to narrow the employment gap.

*Editor's Note: This article was updated to correct the year.

Notes and References

1 These young adults are 16- to 24-year-olds who are not enrolled in school, have no more than a high school degree and have no more than 10 years of potential labor market experience. Potential labor market experience equals age minus years of schooling minus 6.

2 This series is published as part of the vulnerable workers initiative from the Institute for Economic Equity. The initiative examines the challenges facing vulnerable workers during the recovery and discusses potential ways to improve their economic security and resiliency in an economy reshaped by the pandemic.

3 These estimates are through March 2022 because the micro data from which these estimates are calculated are only publicly available through March 2022 and not April 2022.

Core Findings

First, “…we develop an instrument that exploits variation in the proximity of districts to coal power plants and year-to-year fluctuations in power production at these plants, as in Duque and Gilraine (2020). Specifically, we instrument for air quality using the yearly coalbased energy production occurring within 60km of a school district. The choice of 60km is informed by prior research which finds a strong relationship between coal power production and PM2.5 concentrations only for locations within roughly 40-60km of the plant (Levy et al., 2002; Clay et al., 2016). We confirm this relationship, showing that increased power production within 60km of a school district significantly raises PM2.5 concentrations and correspondingly lowers student test scores; increased power production located further than 60km away has limited impacts on air quality and no effect on test scores. Our IV estimates indicate that each microgram per cubic meter (μg/m3) increase in PM2.5 concentrations causes a 0.02 standard deviation decline in test scores. These estimates are larger in magnitude compared to our OLS estimates, as expected given we suspect OLS regressions will feature severe attenuation bias in this context….”

We combine satellite-based pollution data and test scores from over 10,000 U.S. school districts to estimate the relationship between air pollution and test scores. To deal with potential endogeneity we instrument for air quality using (i) year-to-year coal production variation and (ii) a shift-share instrument that interacts fuel shares used for nearby power production with national growth rates. We find that each one-unit increase in particulate pollution reduces test scores by 0.02 standard deviations. Our findings indicate that declines in particulate pollution exposure raised test scores and reduced the black-white test score gap by 0.06 and 0.01 standard deviations, respectively….Figure 2(a) displays the PM2.5 concentrations among low- (dashed line) and high-coal (solid line) districts over time. As expected, there is a large drop in PM2.5 concentration in 2011-12 for high- relative to low-coal districts, which coincides with the significant decrease in U.S. coal production that year….Our two empirical strategies indicate that each µg/m3 reduction in PM2.5 increases test scores by 0.02 standard deviations. Panel A of Table 2 places these estimates into perspective by reporting the PM2.5 exposure an average student in the United States faced in 2002-03, 2010-11, and 2018-19. We see a large decline in pollution exposure over the last two decades: the average student saw their PM2.5 exposure drop a full 3µg/m3 from 2002-03 to 2018-19. Our estimates therefore indicate that air quality improvements raised test scores nationwide by a full 0.06 standard deviations. This nationwide test score impact is large; in comparison a nationwide policy that releases the bottom 5% of teachers according to value-added would only raise test scores by 0.02-0.025 standard deviations. Given that the decline in pollution exposure over the past two decades has disproportionately benefited areas where African Americans reside...it is also natural to wonder whether test score equity has also been impacted. Panel B reports PM2.5 exposure for the average white and black student in the United States. From 2002-03 to 2018-19 the black-white PM2.5 exposure gap declined by 0.50µg/m3. This suggests that changes in pollution exposure decreased the black-white test score gap by 0.01 standard deviations, or 6 percent of the 0.18 standard deviations decline in the black-white test score gap over this period. Using instrumental variables that leverage exposure to nearby power production, we find that air pollution significantly lowers student test scores. Given that, the large 3µg/m3 drop in PM2.5 concentrations experienced by the average student materially raised test scores nationwide. Substantial improvements to student performance and equity through cleaner air are still possible, however. For example, decreasing average PM2.5 to that of the first quartile district would raise nationwide test scores by 0.036 and completely eliminating black-white differences in particulate exposure would further decrease the black-white test score gap by 0.024 standard deviations.

In a new paper…we document an important determinant of how much an employer pays: the industry. An employer’s industry is determined by its production processes or its products. Pay differentials across industries are large and have widened over time, leading to increases in labour earnings inequality. For example, restaurants tend to be low-paying employers. Our data indicate that in recent decades in the US, about one-tenth of the rise in labour earnings inequality can be attributed to restaurants employing more people and paying them less. We use matched employer-employee data for 18 US states to study labour earnings inequality (hereafter, simply ‘inequality’; note that this excludes self-employment and capital income). Thirty industries (out of a total of 301) can account for nearly all of the rise in inequality from the late 1990s to the late 2010s. In these 30 industries, ‘mega firms’ (which employ 10,000 or more) have seen a massive surge in employment. This column describes some of our main findings. Industries drive increasing inequality. Figure 1 reports our measure of inequality, which is the variance of log (annual) labour earnings. The contributions of different industries to between-industry inequality are shown in Figure 2. Recall from Figure 1 that between-industry inequality (the green region of Figure 1) accounts for 61.9% of the total increase in inequality. The 30 industries that contribute most to increasing inequality explain nearly all (98.1%) of the between-industry contribution to inequality - despite employing less than 40% of the workforce. Of these 30 industries, 19 are high-paying and account for 54.0% of the increase in between-industry inequality; 11 are low-paying and account for 44.1% of this increase. The other 271 industries account for only 1.9% of rising inequality. Is increasing inequality driven by changes in employment or earnings? Figure 3 shows that there are sizeable differences in the answer to this question depending on whether the industry is high- versus low-paying. Among high-paying industries, 83.9% of the contribution to inequality is due to strong increases in earnings, while any employment increases in these industries only account for only 16.1% of this rise. In contrast, the employment among the 11 most important low-paying industries surged, accounting for 68.3% of their contribution to between-industry inequality. A much more modest decline in earnings in these low-paying industries explains the remaining 31.7%. Industries drive recent changes in inequality in the US. Thirty out of the 301 industries in our classification can explain nearly all of the rise in between-industry inequality. Continued study of these industries can help shed light on the mechanisms by which inequality continues to increase.

High-paying jobs are paying more; low-paying jobs are hiring more

We now address the question of how much of increasing inequality is due to employment versus earnings. Note that changes in either employment or earnings can contribute to earnings inequality. For example, restaurants tend to be among the lowest-paying employers. When (relatively) more workers are employed by restaurants, inequality will increase. A decline in the pay of this industry would also increase inequality.

Is increasing inequality driven by changes in employment or earnings? Figure 3 shows that there are sizeable differences in the answer to this question depending on whether the industry is high- versus low-paying. Among high-paying industries, 83.9% of the contribution to inequality is due to strong increases in earnings, while any employment increases in these industries only account for only 16.1% of this rise. In contrast, the employment among the 11 most important low-paying industries surged, accounting for 68.3% of their contribution to between-industry inequality. A much more modest decline in earnings in these low-paying industries explains the remaining 31.7%.

Following the pathbreaking work of Abowd et al. (1999), Card et al. (2013), and Song et al. (2019), we consider the question of the extent to which changes in inequality are driven by workers or firms, which is especially important for understanding the contribution of high-paying firms and industries to inequality. Do high-paying firms offer larger premia? Or are they hiring highly paid (i.e. more costly) workers? As Song et al. (2019) demonstrate, there are three channels through which differences among employers contribute to inequality. These are:

1. Pay premia: some firms offer greater earnings to any worker

2. Sorting: high-paying firms employ more highly paid workers

3. Segregation: more highly paid workers concentrate among each other

We explore the relative contributions of these three phenomena to between-industry inequality in Figure 4. Sorting has the greatest role in increasing inequality, and its contribution to the variance of log annual labour earnings increased from 0.078 to 0.112 from 1996-2002 to 2012-2018. In other words, high-paying industries increasingly employ highly paid workers. Highly paid workers tend to be employed in the same industries (to the exclusion of workers with low earnings), and this rises from 0.059 to 0.089. Industry-level pay premia have also widened somewhat over time. This latter channel has had a smaller contribution to the variance of log earnings and rose from 0.033 to 0.044.

‘Mega firm’ employment has surged in the 30 industries that drive increasing inequality

We now explore the role of ‘mega firms’ in increasing inequality. By mega firms we mean those that employ at least 10,000 workers. Mega firms have been increasing as a share of employment, as documented by Bloom et al. (2018) and Autor et al. (2020). In Figure 5, we break this increase down according to our four industry groups.

The rise in mega firm employment is quite dramatic in the 30 industries that drive increasing inequality. It is especially apparent in the low-paying firms that drive inequality through increasing employment. The employment share of mega firms in the 11 low-paying industries that drive between-industry inequality increased by 2.5 percentage points, from 3.1% to 5.6%. Note that this implies that millions of additional workers were employed in mega firms in low-paying industries: the total number of people employed in the US in 2018 was more than 150 million (Bureau of Labor Statistics 2019). The employment share of mega firms in the 19 high-paying industries increased by 1.4 percentage points, from 3.2% to 4.5%. The employment share of mega firms in the other 271 industries fell.

Industries drive recent changes in inequality in the US. Thirty out of the 301 industries in our classification can explain nearly all of the rise in between-industry inequality. Continued study of these industries can help shed light on the mechanisms by which inequality continues to increase.

References

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Autor, D, D Dorn, L F Katz, C C Patterson, and J Van Reenen (2020), “The Fall of the Labor Share and the Rise of Superstar Firms”, Quarterly Journal of Economics 135(2): 645-709.

Berlingieri, G, P Blanchenay, C Criscuolo (2017), “Great Divergences: The growing dispersion of wages and productivity in OECD countries”, VoxEU.org, 15 May.

Bloom, N, F Guvenen, B S Smith, J Song and T von Wachter (2018), “The Disappearing Large-Firm Premium”, AEA Papers and Proceedings 108: 317-322.

Bloom, N, T Hassan, A Kalyani, J Lerner and A Tahoun (2021), “How disruptive technologies diffuse”, VoxEU.org, 10 August.

Card, D, A Cardoso and P Kline (2016), “Bargaining, Sorting, and the Gender Wage Gap: Quantifying the Effect of Firms on the Relative Pay of Women”, Quarterly Journal of Economics 131(2): 633-686.

Card, D, J Heining and P Kline (2013), “Workplace Heterogeneity and the Rise of West German Wage Inequality”, Quarterly Journal of Economics 128(3): 967-1015.

Cooper, Z, S Craig, M Gaynor and J Van Reenen (2019), “The Price Ain’t Right? Hospital Prices and Health Spending on the Privately Insured”, Quarterly Journal of Economics 134(1): 51-107.

Decker, R A, A Flaaen and M D Tito (2016), “Unraveling the Oil Conundrum: Productivity Improvements and Cost Declines in the U.S. Shale Oil Industry”, FEDS Notes.

Dey, M, S Houseman and A E Polivka (2006), “Manufacturers’ Outsourcing to Employment Services”, Working paper.

Dey, M, S Houseman and A Polivka (2010), “What Do We Know About Contracting Out in the United States? Evidence from Household and Establishment Surveys” in K G Abraham, J R Spletzer and M Harper (eds), Labor in the New Economy, Chicago: University of Chicago Press.

Dorn, D, J Schmieder and J Spletzer (2018), “Domestic Outsourcing in the United States”, Unpublished.

Fernald, J (2014), “Productivity and Potential Output before, during, and after the Great Recession”, NBER Macroeconomics Annual 29(1): 1-51.

Foster, L, J Haltiwanger and C J Krizan (2006), “Market Selection, Reallocation, and Restructuring in the U.S. Retail Trade Sector in the 1990s”, Review of Economics and Statistics 88(4): 748-758.

Foster, L, J Haltiwanger, S Klimek and C J Krizan (2016), “The Evolution of National Retail Chains: How We Got Here”, in E Basker (ed), Handbook on the Economics of Retailing and Distribution, Northampton, MA: Edward Elgar Publishing.

Fulton, B (2017), “Health care market concentration trends in the United States: Evidence and policy responses”, Health Affairs 36(9): 1530-1538.

Goldschlag, N and J Miranda (2016), “Business Dynamics Statistics of High Tech Industries”, US Census Bureau Center for Economic Studies.

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Marin, D (2016), “Inequality in Germany: How it differs from the US”, VoxEU.org, 23 June.

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John Haltiwanger, Henry Hyatt and Jim Spletzer, "Rising inequality: Industries and mega firms,"Center For Economic and Policy Research, https://voxeu.org/article/rising-inequality-industries-and-mega-firms

Rising inequality: Industries and mega firms

Why has earnings inequality been increasing? Evidence from an increasing number of countries indicates that where people work is of key importance (Card et al. 2013, 2016, Marin 2016, Berlingieri et al. 2017, Song et al. 2019, Mion et al. 2020). Some employers pay more than others, and these pay differentials have widened in recent decades. An important question is why employer-driven inequality has been rising over time.

In a new paper, we document an important determinant of how much an employer pays: the industry (Haltiwanger et al. 2022). An employer’s industry is determined by its production processes or its products. Pay differentials across industries are large and have widened over time, leading to increases in labour earnings inequality. For example, restaurants tend to be low-paying employers. Our data indicate that in recent decades in the US, about one-tenth of the rise in labour earnings inequality can be attributed to restaurants employing more people and paying them less.

We use matched employer-employee data for 18 US states to study labour earnings inequality (hereafter, simply ‘inequality’; note that this excludes self-employment and capital income). Thirty industries (out of a total of 301) can account for nearly all of the rise in inequality from the late 1990s to the late 2010s. In these 30 industries, ‘mega firms’ (which employ 10,000 or more) have seen a massive surge in employment. This column describes some of our main findings.

Industry-level differences drive increasing inequality

Industries drive increasing inequality. Figure 1 reports our measure of inequality, which is the variance of log (annual) labour earnings. We estimate inequality in three seven-year intervals: 1996-2002, 2004-2010, and 2012-2018. This is broken down into the inequality within firms and between firms. The between-firm component of inequality is further broken down into that which occurs between firms in the same industry versus that which occurs at the industry level.

Inequality in the US has risen over time. In Figure 1, we show that the variance of log earnings has increased from 0.794 in 1996-2002 to 0.916 in 2012-2018. The variance of log earnings is the sum of three components. Most inequality occurs within firms, and this dispersion increased from 0.512 to 0.531, accounting for 14.9% of the increase in inequality. Among firms in the same industry, dispersion increased from 0.112 to 0.140, accounting for 23.1% of the rise in inequality. Dispersion between industries increased from 0.170 to 0.245, accounting for 61.9% of the increase in inequality. Therefore, most of the rise in inequality has occurred across industries.

About 10% of industries account for all of the between-industry rise in inequality. We consider 301 industries defined using the North American Industrial Classification System (NAICS) at the 4-digit level. We classify industries based on whether they tend to pay more or less than the average, as well as their contribution to inequality. Thirty industries account for at least 1% (positive) of the rise in between-industry inequality. Another 271 industries each contribute less than 1% to between-industry inequality.

The contributions of different industries to between-industry inequality are shown in Figure 2. Recall from Figure 1 that between-industry inequality (the green region of Figure 1) accounts for 61.9% of the total increase in inequality. The 30 industries that contribute most to increasing inequality explain nearly all (98.1%) of the between-industry contribution to inequality - despite employing less than 40% of the workforce. Of these 30 industries, 19 are high-paying and account for 54.0% of the increase in between-industry inequality; 11 are low-paying and account for 44.1% of this increase. The other 271 industries account for only 1.9% of rising inequality.

We start with the 19 high-paying industries. At least ten of these industries have been defined as high-tech in terms STEM intensity according to the criteria of Hecker (2005) and Goldschlag and Miranda (2016). Two high-paying industries are in mining, including support activities such as drilling oil wells. The contribution of these industries is likely related to the shale oil boom (e.g. Decker et al. 2016) Five high-paying industries involve finance, insurance, or corporate headquarters. The outsized role of these industries may reflect restructuring and consolidation that have followed financial deregulation (e.g. Krozner and Strahan 2014).

Health care and social assistance includes both low-paying and high-paying industries. Two high-paying industries include physician offices and hospitals, in which considerable consolidation has occurred (e.g. Fulton 2017, Cooper et al. 2019). Of the three low-paying industries, two deal primarily with care for the elderly, including both in-home care (e.g. hospice) and retirement homes. The remaining low-paying industry, Individual and Family Services, includes adoption and foster care, services for persons with disabilities, and crisis hotlines.

The nine remaining low-paying industries can be divided into roughly two groups. First, there are two industries that provide support to businesses and facilities. This includes temporary help, where employment has increased (e.g. Luo et al. 2010), as well as Professional Employee Organisations (Dey et al. 2006). It also includes cleaning and other support services in which there has been a substantial amount of outsourcing (e.g. Dorn et al. 2018). The remaining six industries include restaurants, retail trade, and gyms, which have been transformed in recent decades with the rise of national chains (e.g. Foster et al. 2016).

Rising earnings inequality is dominated by rising between-firm inequality. Our analysis as well as the recent literature emphasizes that this largely reflects how firms are organizing themselves in terms of their workforce. High (low) earnings workers are more likely to work with each other (increased segregation), and high (low) earnings workers are more likely to work at high (low) firm premia firms (sorting). Our contribution is to highlight the dominant role of industry effects in accounting for this structural change of how firms organize their workforces. Most of rising between-firm inequality is accounted for by rising between-industry dispersion in earnings. The between-industry component accounts for 61.9% of total increasing earnings inequality, and 72.8% of between-firm inequality growth. This changes the narrative of the sorting and segregation contributions. High (low) earnings workers are more likely to work with each other in specific industries and high (low) earnings workers are more likely to work in high (low) average firm premia industries. Not only do industry effects dominate but it is a relatively small share of industries that account for virtually all the increasing dispersion in earnings across industries. We find that about ten percent of the 301 detailed 4-digit NAICS industries account for almost 100% of the rising between-industry dispersion, while accounting for less than 40% of employment. The ten percent of industries that account for virtually all of the increase are drawn from the top and bottom of the earnings distribution in terms of industry-level averages. For those industries at the top of the earnings distribution, their contribution is dominated by rising inter-industry earnings differentials. For industries at the bottom of the earnings distribution, their contribution is dominated by shifts in employment to these very low earnings industries. For both sets of industries at the top and the bottom of the earnings distribution, increased sorting and segregation between industries dominates but increased dispersion in between industry firm premia also plays an important supporting role. Increased sorting is relatively more important for the rising between-industry dispersion from the industries at the bottom of the earnings distribution. In contrast, increased segregation is relatively more important for the rising between industry dispersion from the industries at the top of the earnings distribution. The dominance of industry effects is closely linked to the rising importance of mega (10,000+) firms in the U.S. economy. The increasing share of employment accounted for by mega firms is concentrated in the thirty 4-digit industries that account for virtually all of rising between-industry dispersion. This rising share of employment at mega firms is accompanied by a declining size-earnings premium in the eleven low-paying industries. For mega firms in the nineteen high-paying industries in the top 30, earnings premia rise sharply relative to other industries (albeit not as rapidly as other large but not mega firms in these industries). Our findings imply that understanding rising earnings inequality during the last several decades requires understanding the restructuring of how firms organize themselves in a relatively small set of industries. Moreover, since it is the between-industry contribution that dominates, it is the common effects of re-organization across firms in the same industry that matter.Many mechanisms such as changing technology, market structure, and globalization likely underlie rising earnings inequality. The focus of future research on the impact of such changes on rising earnings inequality should be on the uneven and concentrated impact of such mechanisms across industries. The top ten percent of industries that account for virtually all of rising between-industry inequality are not randomly spread across the distribution of industries but concentrated in specific industry clusters in the tails of the earnings distribution. At the high end, dominant industries are drawn from high-tech and STEM intensive industries, finance, mining, and selected industries in health. At the low end, dominant industries are drawn from selected industries in retail and health. Notably absent are the vast majority of industries in manufacturing. The top thirty industries are in industry clusters that have exhibited structural transformations that have been the subject of independent study. Our findings imply that the role of inter-industry earnings differentials and the changing composition of employment across industries is much more important for understanding earnings inequality than suggested by the recent literature.

“…The largest contribution is from Restaurants and Other Eating Places (7225), which alone accounts for 16.9% of between-industry variance growth. The second-largest contribution occurs among Other General Merchandise Stores (4529), which accounts for 6.8%. While the most important two industries to increasing inequality tend to offer low-paying jobs, the other three industries that account for more than 5% of between-industry variance growth are high-paying: Software Publishers (5112), Computer Systems Design (5415), and Management of Companies (5511). What about the other 271 4-digit NAICS industries? The contributions of these industries to between-industry variance growth are summarized in Table 2. There are 145 industries that each contribute approximately 0.0% (to be precise, greater than −0.05% and less than 0.05%) to betweenindustry variance growth. This says that almost half of all 4-digit NAICS industries contribute essentially nothing to inequality growth. There are 71 industries that contribute between 0.05% and 1.0%, accounting for 22.3% of between-industry variance growth. These industries are basically offset by another 55 industries that have a negative contribution (< −0.05%), accounting for −20.3% of between-industry variance growth. As seen in Table 4, the top thirty industries include nineteen high-paying industries that account for 54.1% of between-industry variance growth, and eleven low-paying industries that account for 44.1% of between-industry variance growth. The other 271 industries that have small contributing and offsetting contributions to increasing inequality do not occur systematically among high-paying vs. low-paying industries. 146 high-paying industries account for 1.3% of between-industry variance growth, and 125 low-paying industries account for only 0.6% of between-industry variance growth…”

Characteristics of the top thirty industries

“…The top thirty industries reflect a small number of industry clusters that are notable for undergoing structural transformations that have been the subject of independent analysis. Eleven of the nineteen high-paying industries have been defined as high-tech industries in terms of STEM intensity by Hecker (2005) and Goldschlag and Miranda (2016).13 These innovative industries in combination account for about one-third of the between-industry increase in earnings dispersion. The transformation of the retail sector accounts for another one-third of the increase.Other industry clusters evident in Table 3 include four of the nine 4-digit industries in Finance and Insurance (NAICS sector 52), Management of Companies (NAICS sector 55), two of the eleven 4-digit industries in Administrative and Support Services (NAICS sector 56, e.g., Employment Services (5613)), and two of the five 4-digit industries in Mining (NAICS sector 21, e.g., Oil and Gas Extraction (2111)). Finance and Insurance (NAICS sector 55) industries have undergone tremendous restructuring and consolidation following deregulation (see, e.g., Kroszner and Strahan (2014)). Management occupation differentials have risen dramatically over our sample period (see Haltiwanger and Spletzer (2020a)). The Employment Services industry (5613) is a low-paying industry that has experienced dramatic growth and change with the growth of Professional Employee Organizations (NAICS 561330, see Dey et al. (2006)). Oil and Gas Extraction (2111) has long been a high-paying industry and underwent dramatic expansion and innovation with the shale oil boom starting in 2007 (Decker et al. (2016))….”

John Haltiwanger, Henry R. Hyatt and James R. Spletzer, "Industries, Mega Firms, and Increasing Inequality,"Institute of Labor Economics, March 2022, https://docs.iza.org/dp15197.pdf

“…There are five industries that each contribute more than 5% of between-industry variance growth, accounting for 40.7% of between-industry variance growth. These five industries have 8.8% of total employment. An additional twenty-five industries each contribute between 1% and 5% of between-industry variance growth, accounting for 57.4% of between-industry variance growth. In total, the top thirty industries - about ten percent of all 4-digit NAICS industries - account for 98.1% of between-industry variance growth and 39.3% of employment. As nearly two-thirds of the growth in U.S. earnings dispersion has occurred between industries rather than within them, these thirty industries account for most of increasing inequality. We provide detail about these thirty industries in Table 3 (the industries in Table 3 are sorted by NAICS)….”

“…Our contribution is to explore and emphasize that rising between-firm dispersion mostly occurs at the industry level. Rising between-industry dispersion accounts for most of the overall increase in earnings inequality, and is driven by a relatively small number of industries. About ten percent of 4-digit NAICS industries account for virtually all of the increase in between-industry dispersion, while accounting for less than 40% of employment. These industries are in the tails of the earnings distribution including high-paying industries such as Software Publishing (5112) and lowpaying industries such as Restaurants and Other Eating Places (7225). Remarkably, the remaining ninety percent of 4-digit industries individually contribute little to rising between-industry earnings inequality. We provide further insights about rising between-industry inequality using an Abowd, Kramarz, and Margolis (1999, hereafter AKM) decomposition of earnings. Changing composition of workers across industries through sorting (high wage workers are more likely to work in industries with high average firm effects) and segregation (high wage workers are more likely to work together in the same industry) account for most but not all of the industry effects - industry-specific pay premia play a smaller but nontrivial role. Importantly, it is increased sorting and segregation between industries, rather than between firms within industries, that primarily matters for rising earnings dispersion. We find differences in the roles of sorting, segregation, and pay premia based on whether the industries tend to be low-paying vs. high-paying. The top ten percent of industries that contribute to rising inequality include nineteen that are high-paying. These industries account for 54.1% of the increase in between-industry inequality. The top three of these are high-paying, high-tech service industries - Software Publishers (5112), Computer Systems Design (5415), and Other Information Services (5191) - and, in total, eleven of these nineteen high-paying industries are high-tech.As discussed in Oliner, Sichel, and Stiroh (2007) and Fernald (2014), these industries are characterized as the source of rapid technological advances. These industries play an outsized role in the tendency for high-paid workers to work both for high-paying firms (sorting) and with each other (segregation). More generally, we find a dominant role for segregation - employees with high worker effects concentrated among each other - in the contribution of these nineteen high-paying industries to increasing inequality. Eleven low-paying industries are in the top ten percent of industries that dominate rising earnings inequality. These industries in combination account for 44.1% of the increase in between-industry inequality. More than one-fourth of the increase is accounted for by just three of these eleven: Restaurants and Other Eating Places (7225), Other General Merchandise Stores (4529), and Grocery Stores (4451). These industries have gone through substantial changes in recent decades, moving away from single establishment firms to large, national chains, see Foster, Haltiwanger and Krizan (2006), Foster et al. (2016), and Autor et al. (2020). In all three of these industries, sorting provides the largest contribution to rising inequality. The dominant role of sorting holds more generally among the eleven low-paying industries that have contributed to rising inequality. A distinctive feature of the dominant ten percent of industries is that they exhibit a sharp increase in the share of employment at mega firms, which we define as firms with more than 10,000 employees. Strikingly, the remaining ninety percent of industries exhibit small declines in the share of employment at mega firms. For the low-paying dominant industries, there is a sharp decline in the earnings of mega firms relative to earnings of the average industry (averaging over all 301 industries). This sharp decline is accompanied by a decline in the size-earnings premium within these low-paying industries. For the high-paying dominant industries, the mega firms experience a substantial increase in earnings relative to both small firms in the same industry and to earnings of the average industry. Thus, we find that the rise in “superstar” firms (see, e.g., Autor et al. (2020)) is concentrated in these dominant industries with accompanying systematic changes in the size-earnings premia…”

“…There are a total of 301 4-digit NAICS industries in our LEHD data. A natural starting point is to group industries by their contributions to increasing inequality, which we explore in Table 2…”

Evidence

The industries that drive increasing inequality