Sector

In a new paperwe 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.

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, Jim Spletzer, "Rising inequality: Industries and mega firms,"Center For Economic And Policy Research, May 30, 2022, 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).

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

“…One way American firms have improved their moats in recent times is through creeping consolidation. The Economist has divided the economy into 900-odd sectors covered by America’s five-yearly economic census. Two-thirds of them became more concentrated between 1997 and 2012 (see charts 2 and 3). The weighted average share of the top four firms in each sector has risen from 26% to 32%....These data make it possible to distinguish between sectors of the economy that are fragmented, concentrated or oligopolistic, and to look at how revenues have fared in each case. Revenues in fragmented industries—those in which the biggest four firms together control less than a third of the market—dropped from 72% of the total in 1997 to 58% in 2012. Concentrated industries, in which the top four firms control between a third and two-thirds of the market, have seen their share of revenues rise from 24% to 33%. And just under a tenth of the activity takes place in industries in which the top four firms control two-thirds or more of sales. This oligopolistic corner of the economy includes niche concerns—dog food, batteries and coffins—but also telecoms, pharmacies and credit cards….”

Economist Staff, "Too much of a good thing,"The Economist, March 26, 2016, https://www.economist.com/briefing/2016/03/26/too-much-of-a-good-thing

Too much of a good thing

AMERICA’S airlines used to be famous for two things: terrible service and worse finances. Today flyers still endure hidden fees, late flights, bruised knees, clapped-out fittings and sub-par food. The profit bit of the picture, though, has changed a lot. Last year America’s airlines made $24 billion—more than Alphabet, the parent company of Google. Even as the price of fuel, one of airlines’ main expenses, collapsed alongside the oil price, little of that benefit was passed on to consumers through lower prices, with revenues remaining fairly flat. After a bout of consolidation in the past decade the industry is dominated by four firms with tight financial discipline and many shareholders in common. And the return on capital is similar to that seen in Silicon Valley.

What is true of the airline industry is increasingly true of America’s economy as a whole. Profits have risen in most rich countries over the past ten years but the increase has been biggest for American firms. Coupled with an increasing concentration of ownership, this means the fruits of economic growth are being hoarded. This is probably part of the reason that two-thirds of Americans, including a majority of Republicans, have come to believe that the economy “unfairly favours powerful interests”, according to polling by Pew, a research outfit. It means that when Hillary Clinton and Bernie Sanders, the Democratic contenders for president, say that the economy is “rigged”, they have a point.

Opportunities

An intense burst of consolidation will boost their profits more. Since 2008 American firms have engaged in one of the largest rounds of mergers in their country’s history, worth $10 trillion. Unlike earlier acquisitions aimed at building global empires, these mergers were largely aimed at consolidating in America, allowing the merged companies to increase their market shares and cut their costs. The companies in question usually make no pretence of planning to pass the savings they make this way on to their customers; take their estimates of the synergies involved at face value and profits in America will rise by a further 10% or so.

Profits are an essential part of capitalism. They give investors a return, encourage innovation and signal where resources should be invested. Their accumulation allows investment in bold new ventures. Countries where profits are too low—Japan, for instance—can slip into morbid torpor. Firms that ignore profits, such as China’s state-run enterprises, lurch around like aimless zombies, as likely to destroy value as to create it.

But high profits across a whole economy can be a sign of sickness. They can signal the existence of firms more adept at siphoning wealth off than creating it afresh, such as those that exploit monopolies. If companies capture more profits than they can spend, it can lead to a shortfall of demand. This has been a pressing problem in America. It is not that firms are underinvesting by historical standards. Relative to assets, sales and GDP, the level of investment is pretty normal. But domestic cash flows are so high that they still have pots of cash left over after investment: about $800 billion a year.

High profits can deepen inequality in various ways. The pool of income to be split among employees could be squeezed. Consumers might pay too much for goods. In a market the size of America’s prices should be lower than in other industrialised economies. By and large, they are not. Though American companies now make a fifth of their profits abroad, their naughty secret is that their return-on-equity is 40% higher at home.

Most explanations of America’s high profits draw on national-accounts data which show that the fall in the share of output going to workers over the past decade is equivalent to about 60% of the rise in domestic pre-tax profits. Scholars typically have three explanations for this: technology, which has allowed firms to replace workers with machines and software; globalisation, which has made it easier to shift production to lower cost countries; and a decline in trade-union membership.

None of these accounts, though, explain the most troubling aspect of America’s profit problem: its persistence. Business theory holds that firms can at best enjoy only temporary periods of “competitive advantage” during which they can rake in cash. After that new companies, inspired by these rich pickings, will pile in to compete away those fat margins, bringing prices down and increasing both employment and investment. It’s the mechanism behind Adam Smith’s invisible hand.

In America that hand seems oddly idle. An American firm that was very profitable in 2003 (one with post-tax returns on capital of 15-25%, excluding goodwill) had an 83% chance of still being very profitable in 2013; the same was true for firms with returns of over 25%, according to McKinsey, a consulting firm. In the previous decade the odds were about 50%. The obvious conclusion is that the American economy is too cosy for incumbents.

In 1998, Joel Klein, who ran the antitrust operation at the Department of Justice (DoJ), declared that “our economy is more competitive today than it has been in a long, long time.” He may well have been right. In the post-war boom American firms grew into mighty conglomerates; in the 1960s J.K. Galbraith, a left-leaning economist, predicted the rise of a symbiotic “industrial state” in which large companies worked closely with the government. But in the 1980s deregulation opened some industries, such as telecoms and railways, to competition. And a new doctrine of shareholder value led big firms, such as RJR Nabisco, to be broken-up and sprawling conglomerates to become focused. In the 1990s American firms faced a wave of competition from low-cost competitors abroad (and, reciprocally, focused their energy on expanding overseas).

Since then the pendulum seems to have swung back. Huge companies, long the focus of American worries about competition, have not actually got any bigger. In 2014 the top 500 listed firms made about 45% of the global profits of all American firms, as they did in the late 1990s. Instead they, and other companies, have become more focused. The strategy can be seen as an amalgam of the philosophies of two deeply influential business figures. Jack Welch, the boss of General Electric for two decades at the end of the 20th century, advised companies to get out of markets which they did not dominate. Warren Buffett, the 21st century’s best-known investor, extols firms that have a “moat” around them—a barrier that offers stability and pricing power.

These data make it possible to distinguish between sectors of the economy that are fragmented, concentrated or oligopolistic, and to look at how revenues have fared in each case. Revenues in fragmented industries—those in which the biggest four firms together control less than a third of the market—dropped from 72% of the total in 1997 to 58% in 2012. Concentrated industries, in which the top four firms control between a third and two-thirds of the market, have seen their share of revenues rise from 24% to 33%. And just under a tenth of the activity takes place in industries in which the top four firms control two-thirds or more of sales. This oligopolistic corner of the economy includes niche concerns—dog food, batteries and coffins—but also telecoms, pharmacies and credit cards.

Concentration does not of itself indicate collusion. Other factors at play might include regulations that keep competitors out. Business spending on lobbying doubled over the period as incumbents sought to shape regulations in ways that suited them. The rising importance of intangible assets, particularly patents, has meant that an ability to manage industry regulators and the challenges of litigation is more valuable than ever.

The ability of big firms to influence and navigate an ever-expanding rule book may explain why the rate of small-company creation in America is close to its lowest mark since the 1970s (although an index of startups run by the Kauffman Foundation has shown flickers of life recently). Small firms normally lack both the working capital needed to deal with red tape and long court cases, and the lobbying power that would bend rules to their purposes. A lack of lobbying clout and legal savvy may also help explain foreign firms’ loss of momentum. In the 1990s adventurers from abroad piled into America, with the share of output from foreign-owned subsidiaries rising steadily. But foreign firms seem to have lost their mojo. Since 2003 their contribution has been flat at about 6% of private business output.

Another factor that may have made profits stickier is the growing clout of giant institutional shareholders such as BlackRock, State Street and Capital Group. Together they own 10-20% of most American companies, including ones that compete with each other. Claims that they rig things seem far-fetched, particularly since many of these funds are index trackers; their decisions as to what to buy and sell are made for them. But they may well set the tone, for example by demanding that chief executives remain disciplined about pricing and restraining investment in new capacity. The overall effect could mute competition.

Quantifying the effect of the corporate America’s defences is tricky. Profits are not the whole picture. In some industries—banking is a case in point—rent-seeking will result in high pay to an employee elite instead. But one can get a crude sense of what is going on by dividing the profits all firms generate into the “bog-standard” and the “exceptional”. Over the past 50 years return on capital has averaged about 10% (excluding goodwill) and that is what investors tend to demand, so let that represent bog-standard profits. The excess on top of that—which may reflect brilliant innovations, wise historic investments in intangible assets such as brands, or, perhaps, a lack of competition—is the exceptional bit. For S&P 500 firms these exceptional profits are currently running at about $300 billion a year, equivalent to a third of taxed operating profits, or 1.7% of GDP.

I love you, you pay my rent

About a quarter of America’s abnormal profits are spread across a wide range of sectors. Returns on capital, concentration and prices have risen in many pockets of the economy. The cable television industry has become more tightly controlled, and many Americans rely on a monopoly provider; prices have risen at twice the rate of inflation over the past five years. Consolidation in one of Mr Buffett’s favourite industries, railroads, has seen freight prices rise by 40% in real terms and returns on capital almost double since 2004. The proposed merger of Dow Chemical and DuPont, announced last December, illustrates the trend to concentration. After combining, the companies plan to split into three specialist companies each of which will have a higher share of its market than either original company had before the deal. They say the plan will yield $3 billion in cost savings. Since 2008 American mergers have sought to remove recurring annual costs of about $150 billion from industrial ledgers. Few firms that are not regulated utilities have public plans to pass these gains on to consumers.

Concentration is contagious. As firms become more powerful those elsewhere on associated chains of customers and suppliers bulk up in response. Google now dominates internet searches for flights and hotels. This has led Expedia, the leading internet travel-agent, to beef up by buying two of its main rivals over the past two years. The spectre of very big online travel sites dominating the purchase of hotel rooms has led the hotel firms to consolidate, too, with Marriott agreeing to buy Starwood this month. (A Chinese firm, Anbang, may make a counter-bid).

Roughly another quarter of abnormal profits comes from the health-care industry, where a cohort of pharmaceutical and medical-equipment firms make aggregate returns on capital of 20-50%. The industry is riddled with special interests and is governed by patent rules that allow firms temporary monopolies on innovative new drugs and inventions. Much of health-care purchasing in America is ultimately controlled by insurance firms. Four of the largest, Anthem, Cigna, Aetna and Humana, are planning to merge into two larger firms.

The rest of the abnormal profits are to be found in the technology sector, where firms such as Google and Facebook enjoy market shares of 40% or more. By Silicon Valley’s account such penetration reflects the popularity and inventiveness of the products on offer, some of which are free to consumers. Today’s dominant firms could be tomorrow’s Nokia or Blackberry: Apple now trades on just 11 times earnings, suggesting investors expect it to decline. Firms such as Uber and Airbnb are a rare source of disruption in the economy, competing fiercely with incumbents.

But many of these arguments can be spun the other way. Alphabet, Facebook and Amazon are not being valued by investors as if they are high risk, but as if their market shares are sustainable and their network effects and accumulation of data will eventually allow them to reap monopoly-style profits. (Alphabet is now among the biggest lobbyists of any firm, spending $17m last year.) A fall from grace in the tech world is not as bad as you might imagine. Microsoft’s operating profits today are twice what they were in 2000, when Mr Klein was prosecuting it in an antitrust trial. And the “sharing economy” startups that are being so highly rated by some investors mostly seek to dominate their markets. The large mountains of cash they are burning today can only be justified if they eventually mature to enjoy very high market shares and margins.

In the past, periods of high and stable profits have ended. Just three years after Mr Galbraith made his 1967 prediction of a cosy, collaborative business world, it was already toast: profits had collapsed by a third relative to GDP as recession struck. Today’s profits, too, may be more vulnerable than they look. If wages finally pick up it could crimp margins. The earnings-per-share of listed firms have fallen slightly in the past few quarters, though a strong dollar and declining oil revenues explain much of that. Some observers of the stockmarket argue that it is already signalling more decline. The gap between the real yield on equities and that on government bonds suggests that either firms are riskier than ever, bond yields are freakishly low, or that profits face a cyclical downturn.

Even so, it is hard to identify a mechanism by which profits might fall to more normal levels. Investors and managers continue to place extraordinarily high profit multiples on businesses with “moats”. The cable television industry is supposedly under pressure from the likes of Netflix and Amazon Prime. Yet in 2015 Charter Communications, a cable company, bought Time Warner Cable for $79 billion, or 26 times its free cash flow, which implies that it believes it will be in a position to raise prices. When Heinz (part-controlled by Mr Buffett) bought Kraft Foods in 2015, it paid 31 times the free cash flow and promptly slashed spending to boost margins, suggesting it felt the threat from rival makers of cheese slices was rather small.

Antitrust, but verify

Perhaps antitrust regulators will act, forcing profits down. The relevant responsibilities are mostly divided between the DoJ and the Federal Trade Commission (FTC), although some industries, such as railways and telecoms, also have their own regulators. The DOJ and FTC are busy trying to police the mergers-and-acquisition boom. Rather than contest every deal they select cases that set new precedents and argue them in court: of the 15,000 deals between 2005-14, about 3% have been subject to close scrutiny.

Together the two bodies have roamed far and wide. The DoJ has blocked domestic deals, such as the takeover by AT&T of T-Mobile USA in 2011, and cross-border combinations that would have caused concentration in global industries, such as the merger of two chipmakers, Applied Materials and Tokyo Electron, in 2015. The FTC spends a big chunk of its time looking at health care. It has blocked hospital mergers and fought “pay-for-delay” deals in which pharmaceutical firms try to stop generic competitors from launching rival products when patents expire. The DoJ is casting a beady eye over the airlines.

Yet the system suffers two limitations. One is constitutional. The two bodies’ job is to police infringements of a well-established and mature body of law through the courts. This leaves them admirably free of overt political interference and lobbying but it also limits their scope. Lots of important subjects are beyond their purview. They cannot consider whether the length and security of patents is excessive in an age when intellectual property is so important. They may not dwell deeply on whether the business model of large technology platforms such as Google has a long-term dependence on the monopoly rents that could come from its vast and irreproducible stash of data. They can only touch upon whether outlandishly large institutional shareholders with positions in almost all firms can implicitly guide them not to compete head on; or on why small firms seem to be struggling. Their purpose is to police illegal conduct, not reimagine the world. They lack scope.

The second limitation is intellectual. America’s antitrust apparatus has gone through periods of leniency (1915-35) and stridency (1936-72). By the 1980s the Chicago school of free-market thought was ascendant. Its insistence that the efficiency benefits of big mergers should not be dismissed had a big influence on the courts. Antitrust guidelines which held that any deal involving a firm with a market share of 35% or more should be considered suspect on principle have been set aside in favour of a more granular approach, with regulators looking ever more closely at the specific effects of a deal. To work out if a deal lowers consumers’ level of choice or lets firms hike prices they will study micro-markets in specific regions.

Who does not prefer the rifle to the blunderbuss, the scalpel to the axe? Such sophistication allows regulators to demand clever remedies, such as the disposal of subsidiaries. But with their heads deep in data and court rulings that set fine precedents, the scientists of antitrust are able to sidestep some troubling questions. If markets are truly competitive, why do so many companies now claim they can retain the cost synergies that big deals create, not pass them on to consumers? Why do investors believe them? Why have returns on capital risen almost everywhere?

These legal and intellectual limitations of the antitrust apparatus raise the question of competition to the political sphere—currently, alas, a realm well supplied with blunderbusses and axes wielded haphazardly and at the wrong targets. Americans’ mistrust of their economic system and the companies that make so much money in it has so far been channelled into calls for protectionism and government intervention. Free trade should be limited. Health-care firms should be more regulated. Foreign firms—particularly Chinese ones—should be discriminated against. Wages should be forced up. Taxes on companies should be raised.

Memories of the future

Nowhere has the alternative approach been articulated. It would aim to unleash a burst of competition to shake up the comfortable incumbents of America Inc. It would involve a serious effort to remove the red tape and occupational-licensing schemes that strangle small businesses and deter new entrants. It would examine a loosening of the rules that give too much protection to some intellectual-property rights. It would involve more active, albeit cruder, antitrust actions. It would start a more serious conversation about whether it makes sense to have most of the country’s data in the hands of a few very large firms. It would revisit the entire issue of corporate lobbying, which has become a key mechanism by which incumbent firms protect themselves.

Large firms no longer employ all that many people in America: the domestic employee base of the S&P 500 is only around a tenth of total American employment. New firms would invest more, employ more staff, and force incumbents to invest more in order to compete. If this sounds pie in the sky, consider the shale revolution over the past decade. Although the industry is now suffering from low oil prices, it is a rare example of entrepreneurial spirit taking on a stodgy industry to the benefit of all. A new commitment to competition could be the source of optimism that America is desperately searching for. After all, it is only a healthy dollop of greed and a belief in a better future that prompts people to start from scratch and try to cross the moat that has been dug around corporate America.

Some good factoids on concentration from JPM’s Michael Cembalset, "...Industry concentration is at an all-time high and is attracting antitrust attention. But the tech/communications services opportunity set is much bigger than the 5 megacap firms: secular growth tech stocks we discussed in our 2021 Outlook are performing in line with the megacap stocks this year, and carry a lot less antitrust baggage. As long as wages are the largest component of overall business expenses (last chart), the corporate sector will look to tech companies to help reduce their unit labor costs and improve productivity. In other words, we expect strong demand for technology goods and services to continue, and do not expect any mean reversion in sector profit margins...."

"...The world’s unicorn herd is multiplying at a clip that is more rabbit-like. The number of such firms has grown from a dozen eight years ago to more than 750, worth a combined $2.4trn. In the first six months of 2021 technology startups raised nearly $300bn globally, almost as much as in the whole of 2020. That money helped add 136 new unicorns between April and June alone, a quarterly record, according to cb Insights, a data provider. Compared with the same period last year the number of funding rounds above $100m tripled, to 390. A lot of this helped fatten older members of the herd: all but four of the 34 that now boast valuations of $10bn or more have received new investments since the start of 2020....The torrent of cash can be explained by two factors. The first is a divestment spree by the startups’ early venture-capital (vc) backers. These stakes command top dollar from investors desperate for exposure to the pandemic-era digitisation wave. Exits, via public listings and acquisitions, more than doubled globally year on year, to nearly 3,000. The proceeds are flowing back into new vc funds, which have so far this year raised $74bn in America alone, nearing the record $81bn in 2020 in half the time. The venture capitalists cannot spend the dough fast enough. In the three months to June Tiger Global, a particularly aggressive New York investment firm, made 1.3 deals on average every business day. The second reason for soaring valuations is more competition among investors. Relative newcomers to tech-investing, such as pension funds, sovereign-wealth funds and family offices, are encroaching on the private markets that used to be dominated by vc firms from Sand Hill Road in Palo Alto. In the past quarter “non-traditional” investors in America took part in nearly 1,800 deals that together raised $57bn. Many may have been encouraged by the success of earlier forays by dabblers from outside the vc world. Their annual returns from exited investments in a first round of financing have averaged 30% in the past decade, reckons PitchBook, another data firm. That is more than double the 10-15% for veteran vcs...."

Economist Staff, "Technology unicorns are growing at a record clip,"The Economist, July 22, 2021, https://www.economist.com/business/2021/07/19/technology-unicorns-are-growing-at-a-record-clip

Technology unicorns are growing at a record clip

Aileen lee, a venture capitalist who founded an investment firm called Cowboy Ventures, coined the term “unicorn” in 2013 to refer to what was then a rare, mythical species: privately held startups valued at $1bn or more. Any magical attributes aside, today they are commonplace—and becoming ever more so. Consumers, who stand to benefit from an array of novel, often cheap products and services, can expect to enjoy the ride. Investors betting on the unicorn derby should tread more carefully.

The world’s unicorn herd is multiplying at a clip that is more rabbit-like. The number of such firms has grown from a dozen eight years ago to more than 750, worth a combined $2.4trn. In the first six months of 2021 technology startups raised nearly $300bn globally, almost as much as in the whole of 2020. That money helped add 136 new unicorns between April and June alone, a quarterly record, according to cb Insights, a data provider. Compared with the same period last year the number of funding rounds above $100m tripled, to 390. A lot of this helped fatten older members of the herd: all but four of the 34 that now boast valuations of $10bn or more have received new investments since the start of 2020.

The latest tech darlings are no longer primarily Uber-esque marketplaces for matching services with consumers. Instead, they offer, or are developing, sophisticated products, often in more niche markets. Some 25% of the funding in the second quarter went to financial-technology firms, with lots also flowing into artificial intelligence, digital health and cyber-security (see chart).

The torrent of cash can be explained by two factors. The first is a divestment spree by the startups’ early venture-capital (vc) backers. These stakes command top dollar from investors desperate for exposure to the pandemic-era digitisation wave. Exits, via public listings and acquisitions, more than doubled globally year on year, to nearly 3,000. The proceeds are flowing back into new vc funds, which have so far this year raised $74bn in America alone, nearing the record $81bn in 2020 in half the time. The venture capitalists cannot spend the dough fast enough. In the three months to June Tiger Global, a particularly aggressive New York investment firm, made 1.3 deals on average every business day.

The second reason for soaring valuations is more competition among investors. Relative newcomers to tech-investing, such as pension funds, sovereign-wealth funds and family offices, are encroaching on the private markets that used to be dominated by vc firms from Sand Hill Road in Palo Alto. In the past quarter “non-traditional” investors in America took part in nearly 1,800 deals that together raised $57bn. Many may have been encouraged by the success of earlier forays by dabblers from outside the vc world. Their annual returns from exited investments in a first round of financing have averaged 30% in the past decade, reckons PitchBook, another data firm. That is more than double the 10-15% for veteran vcs.

This winning streak may yet end in tears. That is what happened two years ago, when richly valued firms with shaky business models either fizzled after their initial public offerings (like Uber and Lyft, two ride-hailing rivals) or never got that far (WeWork, an office-rental firm whose flotation was shelved after investors got cold feet). Many recently listed unicorns continue to bleed cash. According to The Economist’s calculations, those that went public in 2021 made a combined loss of $25bn in their latest financial year.

Assessing whether the remaining ones are worth their lofty valuations looks harder than ever. Like their predecessors, they do not disclose financial results. At the same time, extrapolating from the earlier unicorns, which tended to pursue growth at all costs in winner-takes-all markets, offers little help because today’s lot often aim to capture good margins by selling genuinely unique technology. This could be a more sustainable strategy—if the technology works. But it is harder for non-experts to evaluate, especially based on what is often little more than a prototype. Nikola and Lordstown, two electric-vehicle companies that listed in 2020 via reverse mergers with special-purpose acquisition companies (spacs), are under investigation by American authorities over allegedly exaggerating the viability of their technology.

Another risk comes from politics. Authorities around the world are growing warier of letting tech firms get too big or entering regulated markets such as finance or health care. As part of a broader crackdown against big tech firms China’s government recently sabotaged the operations of Didi, by banning its app from Chinese app stores days after the firm’s $68bn initial public offering in New York, ostensibly over misuse of users’ data. Such moves have chilled investors’ appetite for Chinese startups, funding for which has actually declined in the past two quarters. In America the Securities and Exchange Commission is beginning to scrutinise the use of cryptocurrencies. Many crypto-exchanges set investors’ pulses racing in last year’s bitcoin rush. Now the market capitalisation of Coinbase, one of the biggest, has shrunk by half, or $56bn, since peaking after its listing in April.

Investors, then, had better beware. For everyone else, the unicorn stampede is a boon. Because venture investments involve mostly equity and little debt, even flops such as WeWork or cautionary tales like Didi pose little risk to the financial system. So long as venture capital is bankrolling lossmaking startups while they offer subsidised services or develop clever new products, consumers have no reason to look the gift horned horse in the mouth.

McKinsey data on intangibles, core findings, "...Even through economic disruptions, intangibles investments have increased. Over the past 25 years, the United States and ten European economies (Austria, Denmark, Finland, France, Germany, Italy, the Netherlands, Spain, Sweden, and the United Kingdom) achieved 63 percent growth in gross value added (GVA), a measure of economic growth. During this period, the investment share of intangibles increased by 29 percent. Rising investment in intangibles has been associated with increasing total factor productivity of entire economies. Growth in investment in intangibles slowed after the global financial crisis, and productivity growth decelerated, too, suggesting a link. Investing in intangibles correlates with productivity and sector growth. In the past quarter century, intangibles investment has risen in all sectors, and data from INTAN-Invest indicates that there is an observable link between investment in intangibles and GVA growth. It also indicates a strong association with total factor productivity. Sectors that have invested the most in intangibles— more than 12 percent of their GVA— achieved 28 percent higher growth than other sectors in GVA, at more than 2.7 percent per year between 1995 and 2019. The relationship is strongest in knowledge-intensive services such as financial services and in innovation-driven services such as telecommunications, media, and technology. This apparent correlation reflects the synergistic nature of different types of intangibles. Companies with top-notch digital analytics attract the best talent, and that talent improves the quality and scope of the analytics. Reflecting such synergies, companies and sectors that invest across intangible categories post higher GVA than their peers. Regardless of the sector, companies that invest more in intangibles grow more. The new survey indicates that top growers, defined as companies in the top quartile of GVA growth by sector in 2018-19 (whose median growth was 20 percent) are investing 2.6 times more in intangibles than low growers, defined as the bottom 50 percent of companies for GVA growth in 2018-19 (whose median growth was 3 percent).The gap increases to between five and seven times in sectors such as financial services where competitive advantage is anchored in knowledge..."

“…Most other sectors invested less than 12 percent of their GVA in intangible capital and have achieved below-average rates of GVA growth, at 2.1 percent per year. In general, a relationship can be observed between higher intangibles investment and higher growth, although there are some exceptions. One exception is manufacturing, which has invested 15.3 percent of its GVA in intangibles but has experienced broadly flat growth. This appears to reflect the fact that manufacturing has invested particularly heavily in innovation capital, specifically R&D, which entails large sunk costs as companies seek to develop the right new products. Another example is healthcare (Exhibit 6)….”

“…Three economies stand out for having achieved both high investment in intangibles and robust growth in GVA: Sweden, the United Kingdom, and the United States. Sweden achieved annual 2.4 percent GVA between 1995 and 2020, the United States 2.3 percent, and the United Kingdom 2.2 percent. All three economies have larger shares of knowledge and innovation sectors, in which intangibles investment is prominent. Conversely, economies that posted lower rates of growth in GVA over this period were more exposed to resource- or labor-intensive sectors that tend to invest less in intangibles. Many other factors determine growth, of course, and they should be examined in detail, but there does appear to be a link between investment in intangibles and GVA growth at the whole-economy level. An intangibles-rich economic model is not the only way for an economy to promote productivity and growth. Nevertheless, economies that are experiencing growth in intangibles investment are also posting growth in total factor productivity (Exhibit 4…”

New note from Michael Mauboussin highlight the impact of intangible assets, intangible intensive firms grow faster than their tangible counterparts but also face a greater risk of being overtaken by their competitors. Implying a higher standard deviation in the distribution of growth rates for intangible-based versus tangible-based industries.

Implication for base rates is the past distributions are not prologue given the impact of intangibles.

"...suggests two hypotheses that we can test. The first is that intangible-based businesses can grow faster than what the base rate data show...the right tail of the distribution of growth rates is extending outward from the average. Amazon’s results provide anecdotal evidence for this. The second is that we should observe greater variance in the distribution of growth rates for intangible-based businesses. That means that the left tail of the distribution of growth rates is also spreading further from the average. BlackBerry’s 26.7 percent average annual revenue decline in the past decade through February 2021 is a case in point....Companies grow by generating a return on investment. The nature of investment has changed markedly in recent decades, from one dominated by tangible assets to one mostly in the form of intangible assets. Intangible assets have some characteristics that distinguish them from tangible assets, including greater potential economies of scale and higher risk of obsolescence. The good news is that intangible-intensive companies can grow faster than their tangible counterparts. The bad news is they can also become irrelevant and shrink fast. As a consequence, we should see two effects in the data: higher growth and more dispersion in the outcomes. Our analysis of the results from companies in the Russell 3000 from 1984-2020 reveals both of these. The base rate of sales growth is getting stretched from the average in both the positive and negative direction. There are two main lessons for investors. First, it is important to be mindful of the potential shift in the base rate as the result of the rise of intangibles. Second, skillful investors may be able to identify the companies that will grow faster than expected, hence providing the potential for attractive returns...."

Evidence: “.. We calculated the median sales growth rate for companies in each of those categories using the constituents of the Russell 3000 from 1984-2020. We also examined the standard deviation, a measure of the dispersion, of the distributions. Exhibit 1 showsthe results for the full sample.

Michael Mauboussin and Dan Callahan, "The Impact of Intangibles on Base Rates," Morgan Stanley, June 23, 2021,
https://www.morganstanley.com/im/publication/insights/articles/article_theimpactofintangiblesonbaserates.pdf

The sales growth rates, measured either as the median or average, consistently go from highest for companies that are most intangible-asset intentive to lowest for those that are least intensive. While the short-term numbers are noisy, the relationship holds true over 1-, 3-, 5-, and 10-year periods. Consider the median compound annual sales growth rates over five-year periods. Growth was 10.4 percent for healthcare, 7.9 percent for technology, 6.0 percent for consumer, and 5.0 percent for manufacturing. The median across all companies was 6.5 percent. This supports the first hypothesis. The standard deviation of the growth rates follows the same pattern. Where intangible asset intensity is high, the standard deviation is also high. Assuming these distributions are normally distributed, an imperfect but illustrative assumption, about two-thirds of healthcare companies had 5-year sales growth rates between -18.0 and 43.2 percent. The comparable figures for manufacturing companies were -7.0 and 19.2 percent. This is consistent with the second thesis....One silver lining was the ability of digital companies, built largely on intangible assets, to thrive in the chaos. We examined the sales growth rates of the companies in the Russell 1000, the largest one thousand companies in the U.S., to see which companies fared well. Healthcare and technology, the industries with the highest intangible asset intensity, represented over 60 percent of the top 20, 50, and 100 growers despite being only 29 percent of the universe (see exhibit 2)...."