Macro Roundup

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

Abowd, J, F Kramarz and D Margolis (1999), “High Wage Workers and High Wage Firms”, Econometrica 67(2): 251-333.

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.

Hecker, D (2005), “High-technology employment: a NAICS-based update”, Monthly Labor Review 128(7).

Kroszner, R and P Strahan (2014), “Regulation and Deregulation of the US Banking Industry: Causes, Consequences, and Implications for the Future”, in N L Rose (ed), Economic Regulation and Its Reform: What Have We Learned?, Chicago: University of Chicago Press.

Luo, T, A Mann and R Holden (2010), “The expanding role of temporary help services from 1990 to 2008”, Monthly Labor Review 133(8): 3-16.

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

Mion, G, L D Opromolla and G Ottaviano (2020), “Dream jobs: A comparison of career prospects in Portuguese firms”, VoxEU.org, 28 August.

Song, J, D Price, F Guvenen, N Bloom, and T von Wachter (2019), “Firming Up Inequality”, Quarterly Journal of Economics 134(1): 1-50.

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

“…“If you take the U.S. as a whole,” says Edward Glaeser, chairman of Harvard’s economics department, office attendance is “down about 19%, relative to pre-pandemic levels.”That average masks some startling variations among major cities. While Houston sits squarely on the mean at 19% and Los Angeles is “looking pretty good” at 21%, New York and Boston are both down 32%. “And then you go to San Francisco,” Mr. Glaeser says with an almost unseemly gusto. “San Francisco is still down 52%. The tech hub is the most extreme.” The impact of virtual work was very different “at different parts of the education distribution,” Mr. Glaeser points out—and that may exacerbate economic and social inequality. He cites data from the Bureau of Labor Statistics: The share of people with advanced degrees who were working remotely in May 2020 was 68.9%, compared with only 15% of remote-working high-school graduates and 5.2% of high-school dropouts. “This was a fairly elite phenomenon.”…”

Tunku Varadarajan, "Can America’s Cities Make a Post-Pandemic Comeback?"Wall Street Journal, May 27, 2022, https://www.wsj.com/articles/big-cities-will-suffer-if-workers-stay-home-remote-labor-young-old-finance-college-11653664888

Can America’s Cities Make a Post-Pandemic Comeback?

Before Covid came to America in January 2020, only 5% of the U.S. labor force worked remotely all the time. Within a few months of the pandemic setting in, however, nearly every American who could work from home was doing so. Today remote work is a white-collar norm.

“If you take the U.S. as a whole,” says Edward Glaeser, chairman of Harvard’s economics department, office attendance is “down about 19%, relative to pre-pandemic levels.” That average masks some startling variations among major cities. While Houston sits squarely on the mean at 19% and Los Angeles is “looking pretty good” at 21%, New York and Boston are both down 32%.

“And then you go to San Francisco,” Mr. Glaeser says with an almost unseemly gusto. “San Francisco is still down 52%. The tech hub is the most extreme.”

These figures are based on data drawn from Google Maps, which tracks users’ comings and goings by monitoring the location of their phones. “They’re pretty good, a reliable indicator,” he says. But numbers from Kastle Systems, a Falls Church, Va., property-management company that provides swipe technology for clocking into fancy office buildings, are disconcerting. “Those numbers are down by over 50%” in America’s top 10 cities, Mr. Glaeser says. “They’re telling you that in the highest end of the commercial real-estate market, the picture is more severe.”

Mr. Glaeser has mixed feelings about the long-term effects of all this. The perspective of the economist “tends to be one that favors choice, rather than thinking there’s only one way to live or work,” he says. “For many workers, some form of hybrid will be desirable.” Some, perhaps those in the later stages of their careers, may settle only for going fully remote. He doesn’t think it’s “absolutely necessary” that every employee get back into an office. “But I will say that it’s going to be very difficult for cities if it doesn’t happen.”

Mr. Glaeser, 55, is arguably the foremost economist who studies cities. It’s a growing tribe, he says in a Zoom interview from his office on the Harvard campus, to which, he’s keen to stress, he’s been going in person since August 2021. The “hallmarks” of the urban economic models he studies involve space—“recognizing that people and firms make choices about where to locate.” Zoom and hybrid work may be here to stay, he allows. “But for most of us the most important interactions of our lives will occur in the real world and, consequently, location remains absolutely critical.”

The way Mr. Glaeser tells it, cities are among mankind’s finest creations—engines of entrepreneurship, inventiveness and economic growth. His intellectual inspiration as an urban economist is Alfred Marshall (1842-1924), who formulated the hypothesis that Mr. Glaeser says has been central to his own career. “Great are the advantages,” Marshall observed of cities, “which people following the same skilled trade get from near neighborhood to one another. The mysteries of the trade become no mystery but are, as it were, in the air.”

That explains why young people are drawn to cities—and why, in Mr. Glaeser’s view, in-person work is vital in the early stages of a career. Cities—and face-to-face contact at work—have “this essential learning component that is valuable and crucial for workers who are young,” he says. The acquisition of experience and improvement in productivity, “month by month, year by year,” ensures that individual earnings are higher in cities than elsewhere.

Mr. Glaeser commends a “superb paper,” published in the Review of Economic Studies in 2017, that documents how people learn by working in big cities. The authors show that workers in Madrid earn 55% more than those in rural Spain. “These wage benefits don’t appear magically when workers come to Madrid or Barcelona,” Mr. Glaeser says. “Instead, a new worker in the big city earns only about 10% more than a worker in a mid-sized city.” After 10 years, that earnings gap grows to 35%.

“The sort of young people who don’t want to come back to the office,” he says, “don’t really know what they’ve missed.” They think that the experience of “working from a Starbucks is all there is, and that they’re having just as much career development as they would have if they were surrounded by mentors.” They aren’t. He cites a study that finds remote workers face “a 50% reduction in their probability of being promoted.”

Mr. Glaeser describes the pandemic as a direct and withering assault on cities. “If you take the definition of urbanism,” he says, “a city is the absence of physical space between people. A city is density, proximity, closeness.” Social distancing amounted to “the rapid-fire deurbanization of the world.” Americans went from willingly paying a “hefty premium, whether we were an advertising company on Madison Avenue or an ordinary 22-year-old who just wanted to be in New York, to all of a sudden wanting to have no one around us whatsoever.”

The world did connect virtually, “which essentially changed the rules of the game.” However exhausting we find Zoom and other technologies, we’d have incurred greater economic or health damage without them: “We would have either gone ahead and not social-distanced, in which case we would have had more deaths, or we would have lost more economically—lost more from our productivity.”

The impact of virtual work was very different “at different parts of the education distribution,” Mr. Glaeser points out—and that may exacerbate economic and social inequality. He cites data from the Bureau of Labor Statistics: The share of people with advanced degrees who were working remotely in May 2020 was 68.9%, compared with only 15% of remote-working high-school graduates and 5.2% of high-school dropouts. “This was a fairly elite phenomenon.”

But now that the threat from Covid has abated, will employees go back to work in their city offices? Those who ask the question range beyond employers who yearn to have them back, and include everyone from city mayors to the humblest vendors of coffee and hot dogs who ply their trade on urban sidewalks.

Service workers in the retail, leisure and hospitality sectors accounted for 32 million American workers in 2019—or a fifth of the American labor force. They may be hit particularly hard. “Those workers thrive by providing services to people that come to big offices,” Mr. Glaeser says. He had believed that the pandemic would be “absolutely catastrophic” for them. “As it turns out, any decrease in labor demand was shielded by the trillions of dollars that flowed out in federal largess.” As that money dries up, the effect is likely to be acute.

There’s an irony in all this. “There tends to be a very strong correlation with political beliefs,” he says. “The difference in mobility—people moving around—was very clear early on between red and blue. There was much more Covid fear in blue states, where there were also more knowledge workers.” Although liberals also profess concern about economic inequality, the effects of remote work are “probably more problematic for upward mobility in blue states than red states,” he says. “The well-heeled in San Francisco not going to the office is bad for demand for urban services, which are provided by less-well-educated San Franciscans.”

How would Mr. Glaeser make the case for a nationwide return to the workplace? “If I were making it to Mayor Adams of New York or Mayor Wu of Boston, it would be pretty easy.” The job would be one of civic and moral “suasion,” as well as talking up the vital importance of cities. “The city depends upon being a thriving place, where people buy and sell to each other, and interact with each other. It’s terrible for a city that many of its key urban office markets feel like wastelands during the daytime.”

Cities, Mr. Glaeser says, must recognize their “traditional function of providing economic opportunity, even more so helping poor people become middle-income people.” He’d recommend making it as easy as possible for businesses to reopen and offering “permit holidays” for new businesses in currently dormant downtown areas. Most of all, he’d like America’s urban mayors to tackle crime, which increased alarmingly during the pandemic. Homicides in 2021, he points out, were up 44% “in major American cities” compared with 2019.

He would be “more circumspect” in making the case to a business owner. “I would say, ‘I understand that a lot of your employees don’t like coming in, so let’s look at some data.’” He points to an article in Nature that examines the impact on more than 61,000 Microsoft employees of working remotely over the first six months of 2020. “Firm-wide remote work,” the study found, “caused the collaboration network of workers to become more static and siloed, with fewer bridges between disparate parts.” Employees found it much harder to acquire and share information. Mr. Glaeser would counsel employers to ask themselves whether they were “causing long-term problems for the company by not bringing people together into the office.”

Which raises another question: “Can they get their workers to come back? Certainly. They can use their wage power to get anyone to come back.” There’s a price at which “almost all of us will come into the office—the question is, just how much?” But there has, he says, been a great “clarifying” that has led us to think about “the nature of work, and the nature of offices.” And he thinks the pace and incidence of return will differ by the task an employee performs.

He cites the example of his own workplace. “I consider it incredibly important to have a community of scholars in this building,” he says, “one that is there and available for our graduate students, that runs into each other in the halls and is a living, breathing community.” By contrast, he doesn’t think the support staff “who work on our financials” need to be in the building. “They need a little bit of time to onboard, and then it’s really OK for them to go home.”

Remote connection has been calamitous for education, Mr. Glaeser says: “The unmitigated disaster that remote learning has been for American children—and children throughout the world—has been confirmed in study after study, and particularly for the most disadvantaged kids.” College instruction has suffered too. Delivering a lecture to 100 students on Zoom, he says, is “just a bad movie, a really bad movie. None of the magic that comes from live lecturing and live interaction with students is there when you’re doing it via Zoom.”

As for individual employees, Mr. Glaeser would tell them that his own return to his Harvard office nine months ago was “just fantastic for my mental health”: “I have been a much happier person for being around young people again, for being around my colleagues. And that has been not only productive but also joyful.”

We show that, for all occupation groups, quits are mostly driven by job-to-job transitions—indicative of what we call the Great Reallocation—rather than quits to unemployment, as the term the Great Resignation may suggest.Meanwhile, job-to-job transitions, and thereby total quits, increased the most in the type of occupations that are typically found in the service sector. The next four figures plot the normalized (January 2021 = 100) number of total quits (gray dashed lines), quits to unemployment (orange lines) and job-to-job transitions (blue lines) for nonroutine cognitive, nonroutine manual, routine cognitive and routine manual occupations, respectively. Nonroutine cognitive: managers, doctors, engineers, etc. Nonroutine manual: child care workers, janitors, food service workers, etc. Routine cognitive: cashiers, sales agents, postal service workers, etc. Routine manual: construction laborers, electricians, flight attendants, etc. Following a voluntary separation, a worker can flow into either unemployment or employment with another employer. A careful consideration of this movement reveals that the latter—that is, job-to-job transitions—drives most of the change in the total number of quits during 2021. The so-called Great Resignation may therefore be more appropriately dubbed the Great Reallocation, as quits largely don’t reflect a desire to stop working altogether, but to find new (and presumably better) employment.

Serdar Birinci and Ngân Trần, "The Great Resignation vs. the Great Reallocation Revisited,"Federal Reserve Bank Of St. Louis, June 2, 2022, https://www.stlouisfed.org/on-the-economy/2022/jun/great-resignation-vs-great-reallocation-revisited

The Great Resignation vs. the Great Reallocation Revisited

The Great Resignation—a term referring to the historically high levels of voluntary job separations during the economic recovery of 2021—continues to garner interest among journalists and social analysts. In a previous Economic Synopses essay, Serdar Birinci and Aaron Amburgey examined the Great Resignation hypothesis at the industry level. In this blog post, we continue this examination by looking at quits and job-to-job transitions across four broadly defined occupation groups.

For each group, we consider the total number of quits and two components: quits to unemployment and job-to-job transitions (or quits to other jobs). We show that, for all occupation groups, quits are mostly driven by job-to-job transitions—indicative of what we call the Great Reallocation—rather than quits to unemployment, as the term the Great Resignation may suggest. Meanwhile, job-to-job transitions, and thereby total quits, increased the most in the type of occupations that are typically found in the service sector.

efining Occupation Groups

Data for our analysis come from the Current Population Survey, which tracks employment status, occupation, change of employer and reason for unemployment of U.S. workers on a monthly basis.

Our focus is 2021, when quit rates climbed above pre-pandemic levels.1 We broke down occupations into four groups along two dimensions: whether an occupation is manual or cognitive and whether it is routine or nonroutine.2

Occupations are considered manual if they involve more physical activities and cognitive if they involve more mental tasks. Occupations are considered routine if the tasks involved can be accomplished by following a set of well-defined procedures and nonroutine if these tasks require more complex problem-solving and communication skills. Below are examples of occupations in each group:

Nonroutine cognitive: managers, doctors, engineers, etc.
Nonroutine manual: child care workers, janitors, food service workers, etc.
Routine cognitive: cashiers, sales agents, postal service workers, etc.
Routine manual: construction laborers, electricians, flight attendants, etc.

Job-to-Job Transitions and Quits to Unemployment by Occupation Groups

The next four figures plot the normalized (January 2021 = 100) number of total quits (gray dashed lines), quits to unemployment (orange lines) and job-to-job transitions (blue lines) for nonroutine cognitive, nonroutine manual, routine cognitive and routine manual occupations, respectively.

We found that total quits in nonroutine manual occupations experienced the sharpest rise during 2021, while the growth in quits was more modest in other occupation groups. We also showed that across occupation groups, movements in the number of job-to-job transitions very closely track movements in the total number of quits, indicating that job-to-job transitions are the main driver behind changes in total quits. As a result, this trend is more indicative of labor market reallocations, i.e., the Great Reallocation, rather than quits to unemployment, as the term the Great Resignation may suggest.

Focusing on job-to-job transitions, all occupation groups except nonroutine manual did not see sizable increases until December 2021. Between January 2021 and November 2021, job-to-job transitions increased by only about 7% for nonroutine cognitive occupations, about 3% for routine cognitive occupations and about 9% for routine manual occupations. December, however, saw jumps in the number of job-to-job transitions for all three groups, with increases from January 2021 of approximately 14%, 11% and 16%, respectively.

On the other hand, in nonroutine manual occupations, job-to-job transitions were on a steady and significant increase throughout 2021. Job-to-job transitions grew by about 18% between January 2021 and November 2021 for this group—approximately twice the corresponding increase for its routine counterpart. By December, they had grown by almost 24% compared with January 2021.

Conclusion

In 2021, the Great Resignation narrative gained traction in the discourse on how COVID-19 permanently changed workers’ relationship to work. Following a voluntary separation, a worker can flow into either unemployment or employment with another employer. A careful consideration of this movement reveals that the latter—that is, job-to-job transitions—drives most of the change in the total number of quits during 2021. The so-called Great Resignation may therefore be more appropriately dubbed the Great Reallocation, as quits largely don’t reflect a desire to stop working altogether, but to find new (and presumably better) employment.

Consistent with our previous work using industry-level evidence, the most sizable increase in job-to-job transitions comes from nonroutine manual occupations, of which service industry jobs make up a large proportion. Job-to-job transitions, in turn, typically lead to higher wage growth, as we discussed in a previous blog post on job-switching rates during the COVID-19 recession. Taken together, these two pieces of empirical evidence are consistent with another empirical observation that recent average wage increases have been relatively high for workers in the service industry.

Notes and References

1 In January 2020, the quit rate for workers excluding farm laborers was 2.4%. By April, it had plummeted to 1.6%, but quickly rebounded and returned to its pre-pandemic level by the end of 2020. Throughout 2021, the quit rate continued its upward trend, reaching 3% in December. See this FRED figure of the U.S. quit rate.

2 We used the methodology found in Autor, David H.; and Dorn, David. “The Growth of Low-Skill Service Jobs and the Polarization of the U.S. Labor Market.” American Economic Review, August 2013, Vol. 103, No. 5,pp. 1553-97.