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Are US Industries Becoming More Concentrated? * [1]
['Grullon', 'Rice University', 'Larkin', 'York University', 'Michaely', 'Geneva Finance Research Institute', 'University Of Geneva']
Date: 2019-07-01
Abstract
Since the late 1990s, over 75% of US industries have experienced an increase in concentration levels. We find that firms in industries with the largest increases in product market concentration show higher profit margins and more profitable mergers and acquisitions deals. At the same time, we find no evidence for a significant increase in operational efficiency. Taken together, our results suggest that market power is becoming an important source of value. These findings are robust to the inclusion of (i) private firms; (ii) factors accounting for foreign competition; and (iii) the use of alternative measures of concentration. We also show that the higher profit margins associated with an increase in concentration are reflected in higher returns to shareholders. Overall, our results suggest that the US product markets have undergone a shift that has potentially weakened competition across the majority of industries.
1. Introduction
That competition promotes efficient allocation of resources is a fundamental argument of economic theory. In fact, in the late 20th century, this premise motivated governments around the world to institute a series of policy reforms, including tariff reductions, deregulations, and aggressive antitrust enforcement, whose transformation of industrial conditions for numerous markets facilitated increased competition (e.g., Shepherd, 1982; Graham, Kaplan, and Sibley, 1983; Pryor, 1994; Nickell, 1996; Rajan and Zingales, 2001; Irvine and Pontiff, 2009).
Around the turn of the 21st century, however, the nature of US product markets arguably began undergoing a new fundamental change. We find that over the last two decades the Herfindahl–Hirschman index (HHI) has systematically increased in more than 75% of US industries, and the average increase in concentration levels has reached 90%. Similarly, the market share of the four largest public and private firms has grown significantly for most industries, and both the average and median sizes of public firms, that is, the largest players in the economy, have tripled in real terms. This finding of increased concentration is robust to alternative measures of concentration, to the inclusion of private firms, to proxies for foreign competition, and to a variety of industry definitions.
We next examine whether increasing industry concentration has been accompanied by an increase in corporate profits.1 If markets are contestable, that is, few barriers to entry, then even firms operating in highly concentrated industries should behave as if they have many competitors (Baumol, 1982). Alternatively, significant barriers to entry, including economies of scale, technological barriers, and large capital requirements, should cause firms operating in increasingly concentrated industries to exercise market power and generate larger abnormal profits (e.g., Bain, 1951, 1956). Barriers to entry in the form of government regulations, for example, could increase the profitability and market value of incumbent firms (Bessen, 2016). However, the possibility exists that industry consolidation can lead to improvements in operational efficiencies, thereby increasing profitability. To address this question empirically, we examine whether the changes in industry concentration levels are linked to firm profitability, profit margins, and efficiency, as well as to the value created during mergers and acquisitions (M&A).
We find that over the past two decades profitability has risen for firms in those industries sustaining increases in concentration levels. Using various industry definitions, we document a positive correlation between changes in concentration levels and return-on-assets (ROA). When we decompose this profitability measure into operating efficiency, proxied by asset utilization (i.e., sales to assets ratio), and operating profit margins (i.e., Lerner Index), we find that the higher returns on assets are driven primarily by a given firm’s ability to extract higher profit margins. A change in concentration levels in the magnitude of its interquartile range, that is, 75th minus 25th percentile, increases profit margins by 142% relative to its median, whereas the same change increases Asset Utilization by only 6%. These findings demonstrate that firms in concentrated industries are becoming more profitable predominantly through higher profit margins, rather than via greater efficiency.
Importantly, we also consider the possibility that accounting profits do not fully capture firms’ payments for the use of capital. Since concentration levels have historically been higher in capital-intensive industries, results using ROA and profit margins may be driven by variation in cost of capital and/or in capital intensity across industries. To control for these factors, we obtain estimates of both the capital share and the cost of capital from the Bureau of Labor Statistics (BLS) to augment our analysis. While our findings for ROA and the Lerner Index remain economically and statistically significant, the positive correlation between concentration levels and Asset Utilization disappears. These results reinforce the conclusion that higher profits in concentrated markets result from the presence of markups; higher profits do not result from either increased reliance on capital or improvements in efficiency.
We also examine M&A transactions as an alternative way to test whether market power is the mechanism behind higher profitability in industries with increased concentration. If industry concentration impacts firms’ prospects, then the market should react more positively to announcements of transactions that further erode product market competition. We find that mergers of firms in the same industry have generally become more profitable to shareholders, as the market reaction to merger announcements is higher in industries with higher concentration levels.
Next, we perform a series of robustness tests to investigate whether our profitability and M&A findings are robust to accounting for other mechanisms and measures of market concentration. We first examine whether intensified foreign competition can provide an alternative source of rivalry to domestic firms, and ascertain, through additional tests, that cross-sectional differences in foreign competition cannot explain our main findings. Even after controlling for industry-level sales by foreign multinational firms in the USA, as well as for the level of import penetration, the relation between concentration measures and firm profitability remains positive and significant. Our results on M&A announcements are also unaffected by the inclusion of proxies for foreign competition.
Second, we confirm that private firms have not replaced public firms. When we use census-based measures of concentration, which include both public and private firms, we find the link between product market concentration and profitability remains positive and significant. Our M&A results are also robust to the use of census-based measures of concentration. We further ensure that the positive relationship between concentration levels and profitability holds when we include information on the profitability of private firms. Specifically, we repeat the analysis of profit margins and concentration using industry-level data from the NBER-CES manufacturing database and find similar results. This analysis also reveals that total factor productivity (TFP) is uncorrelated with industry concentration levels, indicating that if the efficiency channel is at work, it would be observable through factors unrelated to capital, labor, or materials.
Third, we examine whether our results are sensitive to the role played by large multisegment firms. To a significant extent, this concern is also mitigated by using census-based measures of concentration, which break down operations of multisegment firms into operations of component divisions sharing the same industry code. To provide additional evidence, we recalculate the Compustat-based HHI after removing multisegment firms, and obtain similar results. We also find the relation between concentration and profitability, as well as M&A returns, remains significant when we use text-based industry definitions (Hoberg and Phillips, 2010, 2016), which assign a unique set of peers to every firm.
In the final section, we examine whether changes in concentration levels are related to investors’ stock returns; we find that returns to shareholders increase as industries become more concentrated. We perform this analysis by estimating alphas on portfolios sorted on the change in concentration levels. We find that, in contrast to earlier periods, during period 2001–14, a zero-investment strategy of buying firms in industries with the largest increase in concentration levels, and shorting firms in industries with the largest decrease in concentration levels, generates excess returns of approximately 8.2% per year, after controlling for standard risk factors. This evidence suggests that the higher profit margins realized by firms during this recent increase in industry concentration have been reflected in higher returns to shareholders.
Our paper makes three important contributions to understanding product markets in the USA. First, our findings demonstrate that industry concentration over the last two decades has markedly increased. Notably, this increase in concentration has not been offset by the presence of private and foreign firms. Second, those industries that sustained increased concentration subsequently exhibit increases in profitability proportionate to the relevant increase in industry concentration. We also find that increased profits are driven primarily by wider operating margins rather than by higher operational efficiency, in line with the increased market-power explanation. Additionally, and consistent with this hypothesis, we show that related mergers are more profitable when markets are more likely to become highly concentrated. Finally, our third contribution entails the finding that increase in profitability stemming from increased market power has been transferred to investors by generating higher abnormal returns.
Our paper’s findings are relevant to both several strands of the academic literature and to the pragmatic interests of policymakers. First, we enhance existing research on the relation between concentration levels and profitability. Consistent with models positing that exogenous barriers to entry increase the likelihood of market power, we find that profit margins have been, both economically and statistically, positively related to several proxies of market concentration over the last two decades. To the best of our knowledge, this paper is the first in recent history to document a strong positive correlation between measures of concentration and profitability. Previous studies examining earlier periods find weak or no correlation between these variables (e.g., Domowitz, Hubbard, and Petersen, 1986a, 1986b, 1987; Schmalensee, 1989).
Second, our findings that product markets have become more concentrated in the last two decades, and that the firms affected by this secular trend generate higher profits and abnormal stock returns, augment a growing body of economic research marking a change in the nature of US product markets. This development has had a number of additional implications including: (i) higher labor market concentration and its impact on wages (Benmelech, Bergman, and Kim, 2018); (ii) a decline in business dynamism and entrepreneurship (Decker et al., 2014, 2016); and (iii) a decline in capital and labor share (Barkai, 2016). In our conclusion, we briefly discuss two possible reasons for the increased concentration, the first being technological changes that have increased barriers to entry, and the second being lax antitrust enforcement; however, we leave a formal investigation of this important question to future research.
2. Changes in Industry Concentration
2.1 Data
Our main sample consists of all firms in the CRSP-Compustat merged dataset over the period of 1972–2014. The main analysis entails firms incorporated in the USA trading on NYSE, AMEX, and NASDAQ, and for which information on ordinary common shares is accessible. To account for the role of private and foreign firms, we use information from the US Census Bureau and the US BLS. The precise definitions of all variables used in the paper, as well as their data sources, are summarized in Appendix A. For the main analysis, we use NAICS classification to define a firm’s industry, but consider alternative industry definitions in the Online Appendix.
2.2 General Trend
We first investigate changes in industry concentration levels over time. We examine the trend using several HHI concentration indices. The first HHI uses Compustat data, which contain information on US public firms. To construct the index, within every NAICS three-digit industry-year we sum up the squared ratios of firm sales to the total industry sales. We then aggregate the measure across industries by calculating a value-weighted average HHI, in which the weights are determined by the level of industry sales. This approach grants more weight to those industries with increasing relevance in the overall economy, and mitigates the effect of declining or disappearing industries.2
Figure 1 panel A shows the results for the Compustat-based HHI. Consistent with increased competition documented by prior studies (e.g., Rajan and Zingales, 2001; Irvine and Pontiff, 2009), the concentration index declines beginning in the 1980s and remains low until the late 1990s, reaching its lowest point in 1996–97. From the late 1990s, the HHI rises steadily until the end of the sample period in 2014. In aggregate, since 1997 the series has surged almost 70%. As we will demonstrate, this increase in concentration is widespread across industries.
Figure 1. Trends in industry concentration. This figure shows the time-series trend in measures of industry concentration. Panel A presents the HHI concentration index for all US publicly traded firms that appear in the CRSP-Compustat merged dataset (see Section 2.1 for details). HHI j in industry j is defined based on NAICS three-digit industry classification and is constructed as described in Appendix A. To aggregate the index across industries, we use a sales-weighted approach, where the weights are determined by the level of industry sales: 1 ∑ j = 1 T Sale j × ∑ j = 1 T Sale j × HHI j , (where Sale j is the total sales in industry j, and T is the total number of industries). Panel B shows the number of publicly traded firms in CRSP database since the beginning of its coverage in 1925. To be included in the sample, we require that the stock has share code 10 or 11, is traded on one of the three major exchanges, and has non-missing stock price information as of December of year t. Panel C reports the mean and median size for all US publicly traded firms in the CRSP-Compustat merged dataset (see Section 2.1 for details). Firm size is based on total sales in constant dollars of 1970. Panel D shows the share of employment in firms with 10,000 employees or more out of the total US employment (see Section 2.2 for details). Open in new tabDownload slide
In Figure 1 panel B we use the number of public firms as another proxy for concentration. Publicly traded firms tend to be much larger than private firms, and are therefore typically the key industry players. We use an extended period, including information from the earliest CRSP database coverage, to calculate the number of public firms. Consistent with the evidence in Gao, Ritter, and Zhu (2013); Doidge, Karolyi, and Stulz (2017); and others, the number of public firms has significantly declined since the late 1990s.3 The significance of this decline can be measured by the fact that the current number of publicly traded firms in the economy is even lower than that of the mid-1970s, when the real gross domestic product was one-third the current GDP. Significantly, after the late 1990s, the HHI increased in tandem with the drop in the number of firms, and the correlation between the number of firms and the HHI has strengthened from −0.72 during the 1972–90 period to −0.96 during the second half of the sample.
To analyze the link between the change in concentration and the number of firms over time, in Figure 1 panel C we examine changes in firm-size distribution over time. The chart reports the annual mean and median sizes of public firms based on total sales in constant dollars of 1970. While median firm size significantly declined from the early 1970s to the mid-1990s, it began increasing in the late 1990s. Currently the average US firm is almost three times larger in real terms than it was 20 years ago. These findings mirror the pattern of the decline in the number of public firms, and indicate that a driving force behind this systematic increase in industry concentration has been the disappearance of public firms combined with the significant increase in the scale of remaining firms.
Next, we go beyond sales-based measures to evaluate the relative importance of large US firms through labor market dynamics. Figure 1 panel D presents the results of calculating the share of employment in firms with 10,000 or more employees, which is the largest size-category classified by the Census Bureau.4 The trend corresponds to the sales-based analysis: Employment share by large firms in the overall economy began rising in the mid-90s and has recently exceeded previous historical peaks. In addition, this trend indicates that greater concentration in product markets, as measured by concentration in sales, correlates with increased concentration in labor markets. In other words, most jobs are currently being created by large and established firms, rather than by small entrepreneurial firms, consistent with the evidence in Decker et al. (2014, 2016).
We also investigate the possibility that the documented dominance of large firms over the past two decades is driven by a higher prevalence of multisegment firms. If a firm’s operations span multiple sectors, industry boundaries become blurred, and standard classifications such as NAICS or SIC do not identify the true set of product market competitors. We therefore recalculate the aggregate Compustat-based HHI after excluding firm-year observations in which the sales of the non-core segments, as reported in the Compustat Segment file, account for at least 30% of sales. Although the overall level of HHI is lower using the alternative definition, the pattern of a steep increase since 1997 remains unchanged.
As an alternative way to account for operations of multisegment firms across different geographic areas, we calculate the change in concentration measures using industry definitions derived from the text-based analysis of a firm’s product description in 10-K reports (see Hoberg and Phillips, 2010, 2016, for further details).5 According to this classification, each firm has a distinct group of competitors, thereby rendering industry definition firm-specific: Every firm in a given year has a distinct set of competing peers. In contrast to the standard approach for defining an industry, this method yields additional insight by classifying competitors of firms whose operations spread across different industries, and firms that change the mix of products offered. Using the text-based HHI, we find that between 1997 and 2014 industry concentration has increased in over 60% of the firm-specific industries (untabulated).
2.3 Industry Concentration—Cross-Industry Evidence
The previous subsection documents that over the past two decades, product market concentration levels have significantly increased. In this subsection, we examine whether the increased concentration has been widespread across industries, or whether the phenomenon has been limited to a few markets.
Our first test examines changes in concentration measures in each three-digit NAICS industry between 1997 and 2014. We use 1997 as our starting period because 1996 and 1997 are the years in which, during our sample period, the HHI was at its lowest level, and the number of public firms in our sample peaked.6 For every industry we use all public firms’ data from the merged CRSP-Compustat universe and calculate a percentage change in HHI from its 1997 level to its 2014 level. Figure 2 panel A reports the distribution of all changes. The concentration index has increased in 80% of the industries, and the magnitude of the change is concentrated in the extreme range of the spectrum. Specifically, the median increase in HHI is 41%, while the mean increase is 90%.
Figure 2. Change in measures of concentration across industries. Panel A (Panel B) depicts the distribution of percentage changes in the HHI Compustat-based index (HHI census-based index) across industries. Panel C shows the change in the share of the largest four firms in the industry (using census data), and Panel D shows the percentage change in the number of publicly traded firms across industries. Compustat-based HHI and the number of publicly traded firms are calculated for all US publicly traded firms that appear in the CRSP-Compustat merged dataset. The changes are calculated over the 1997–2014 period in the Compustat-based sample (i.e., for every industry we calculate the percentage change in concentration measure from its level in 1997 to its level in 2014), and over the 1997–2012 period in the census-based sample. The industries are defined based on NAICS three-digit classification. Open in new tabDownload slide
The absence of private firms in this measure is a potential weakness of the Compustat-based HHI. While private firms are on average very small ($1.3 million according to Asker, Farre-Mensa, and Ljungqvist, 2011), the possibility exists for a fraction of these firms to grow large enough to replace public firms. In this case, the measures of concentration based on only public firms would seem to point to an increase in concentration, despite the actual concentration having changed only slightly. We address this potential concern in three ways. First, we use the HHI provided by the US Census Bureau, which includes revenues of both public and private firms in the manufacturing sector. In addition to including private firms, another advantage of the census measure is its superior ability to account for the activities of conglomerates. Specifically, rather than assigning NAICS codes at a firm level, the census constructs measures of concentration based on NAICS classification of each individual facility. Consequently, sales of conglomerate firms are decomposed by divisions sharing the same NAICS code, and then grouped with the sales of stand-alone firms sharing the same NAICS code. Figure 2 panel B reports the changes in concentration measures using this alternative census-based measure of the HHI during the period 1997–2012 (2012 being the most recent year for which census data are available). We find an HHI increase in 76% of the manufacturing industries. Thus, the trend of increased concentration remains robust to including the share of sales generated by private firms.
Because the census-based HHI is not available for non-manufacturing industries, we also look at the share of the top four firms in terms of sales in each NAICS three-digit industry, which is also census based. The advantage of this measure is three-fold. First, it covers almost all US industries, including manufacturing, retail, financial, and service sectors.7 Second, it is based on both public and private firms’ information, thus extending beyond the Compustat universe. Third, the share of top four firms can be calculated out of total sales of the entire industry; therefore, the scope of the measure is not limited to the top 50 firms, as occurs with the census-based HHI.
Figure 2 panel C shows the distribution of percentage changes in the share of the top four firms in each industry between 1997 and 2012. The distribution is heavily skewed to the right, demonstrating a greater number of industries in which the share of the largest firms has increased compared with industries in which the largest four firms became diluted by smaller peers. Moreover, a large proportion of the positive changes were extreme in magnitude: In twenty-one out of sixty-five industries the increase has exceeded 40%. Among Furniture and Home Furnishings retailers (NAICS 442), for example, the share of the four largest firms went up from 6.5% in 1997 to 19.4% in 2012, which is equivalent to an almost 200% increase. Another example is Food and Beverage retail (NAICS 445). As early as 2000, the USDA Economic Research Service published a special report pointing to an unprecedented consolidation of supermarkets that created a small group of de facto nationwide food retailers by bringing together regional chains.8 Together, the evidence indicates that the consolidation trend has continued over the last 20 years: While the revenues of the top four firms have increased from 18.3% in 1997 to 26.9% in 2012, the industry has lost over two-thirds of its publicly traded firms, and its HHI has more than tripled.
Finally, to examine whether public firms have remained dominant in the overall economy despite their dwindling numbers, we calculate the share of sales by public firms out of the total sales by public and private firms (see Online Appendix, Figure O-A.2).9 We find that the share of public sales in the total revenues of US business enterprises has remained stable. To focus on the potential role of large firms, we repeat our analysis for the subsample of firms with sales over $100 million, which is the largest firm-size category classified in Statistics of US Businesses report, and find a similar trend.10 Therefore, even though the number of private firms increased and the number of public firms decreased, the share of private firms’ sales relative to those of public firms did not increase.
Our final measure of concentration examines the change in the number of publicly traded firms across industries. Figure 2 panel D shows that the number of publicly traded firms has significantly declined in most industries. Out of seventy-one industries, sixty-six have experienced a negative change between 1997 and 2014. Moreover, the largest portion of the distribution is concentrated in the extreme range, indicating that 73% of the industries have lost over 40% of their publicly traded peers.11
We also decompose the changes in the number of public firms by sources of entry and exit to address the possibility that the increase in industry concentration could be driven by industries shrinking due to declining demand, which, in turn, leads to fewer participants in the market. We find that a decrease in the number of IPOs and an increase in M&A activity are two key mechanisms responsible for the decline in the number of public firms (Online Appendix, Figure O-A.3). Firms do not usually exit public markets due to liquidation or involuntary delisting. Instead, our results show that the remaining firms are not only thriving but also expanding at a positive and persistent rate.
Overall, the results consistently point to an increase in product market concentration over the past two decades. The pattern is economically large, robust to different measures of product market concentration and different industry classifications, and prevalent across the majority of US industries.
3. The Economic Implications of the Increase in Concentration Levels
Although existing literature in economics has devoted much attention to the question whether concentration is associated with profitability, researchers have not yet been able to detect a significant relationship between these two variables (Domowitz, Hubbard, and Petersen, 1986a, 1986b, 1987; Schmalensee, 1989). Given the change in the nature of product markets over the past two decades, we reexamine this important question by analyzing the relation between profitability and changes in industry concentration in a panel-data setting, while controlling for other factors that can influence firms’ profitability levels.
3.1 Industry Concentration Levels and Profitability
If markets are contestable, that is, few barriers to entry, then even firms operating in highly concentrated industries should behave as if they had many competitors (Baumol, 1982). Consequently, profitability should not be affected by changes in industry concentration levels because the threat of potential entrants would keep markets competitive.12 Furthermore, Sutton (1991) goes a step further to show that the presence of sunk costs such as advertising and R&D may result in declining industry profitability as concentration levels increase. More recently, Autor et al. (2017) present a model in which a higher degree of competition helps the most productive “superstar firms” capture market share, thus increasing industry concentration. Taken together, this strand of economic literature posits that intense quality competition may increase the total costs of operating in a particular industry, which, in turn, will lead to concentrated markets, as low price-cost margins reduce the number of market participants.
Alternatively, if barriers to entry, including economies of scale, technological barriers, and large capital requirements, become more salient, then firms operating in increasingly concentrated industries may generate larger profits by exercising market power, and/or becoming more efficient. Note that under both scenarios, firms’ profitability levels should be positively correlated with industry concentration levels. Nevertheless, the market power hypothesis predicts that this positive relation will be driven primarily by increasing profit margins. The efficiency hypothesis predicts that the increased profitability will be driven primarily by improvements in operational efficiency, and in the absence of competition, at least part of this surplus will result in increased profitability. We test these predictions in Section 3.2.
ROA ijt = α i + α t + β 1 log ( Assets it ) + β 2 log ( Age it ) + β 3 log ( Concentration Level jt ) + ε ijt , (1) i is a firm-fixed effect, α t is a year-fixed effect, Assets is the book value of total assets, Age is the time in years from the firm’s CRSP listing date, and Concentration Level jt is a proxy for the level of product market concentration in industry j at time t. Our main proxies for concentration are: (i) the HHI at the NAICS three-digit level using sales from Compustat (HHI); (ii) the total number of public firms in an industry (Number of firms); and (iii) a cross-sectional ranking of the previous two measures that is equal to the sum of the annual rank of the HHI combined with the annual inverse rank of the total number of industry incumbents (Concentration Index). Note that by construction this index increases as the level of industry concentration increases. We start by examining the relation between changes in profitability and changes in industry concentration levels. Specifically, we estimate the parameters of the following regression model:where ROA is the operating income before depreciation (Compustat item OIBDP) scaled by the book value of assets (item AT), αis a firm-fixed effect, αis a year-fixed effect, Assets is the book value of total assets, Age is the time in years from the firm’s CRSP listing date, and Concentration Levelis a proxy for the level of product market concentration in industry j at time t. Our main proxies for concentration are: (i) the HHI at the NAICS three-digit level using sales from Compustat (HHI); (ii) the total number of public firms in an industry (Number of firms); and (iii) a cross-sectional ranking of the previous two measures that is equal to the sum of the annual rank of the HHI combined with the annual inverse rank of the total number of industry incumbents (Concentration Index). Note that by construction this index increases as the level of industry concentration increases.
To control for potential time-series dependence in the residuals, we cluster the standard errors at the firm level. Since we include firm-fixed effects, and firms rarely switch industries, the proxies for industry concentration can be interpreted as the changes in concentration relative to the industry mean. The inclusion of firm-fixed effects addresses several alternative explanations, in addition to several potential endogeneity concerns. For example, if profitable firms systematically acquire the nonprofitable ones, this matching can lead to a mechanical relation between concentration levels and profitability. The inclusion of firm-fixed effects addresses this concern by focusing the analysis on the within-firm variation in profitability over time.
We use ROA as a proxy for profitability because this metric is not affected by changes in capital structure nor by the presence of unusual and nonrecurring items. Additionally, simulation evidence (Barber and Lyon, 1996) indicates that ROA is superior to other measures of profitability in detecting abnormal operating performance. Finally, ROA is calculated net of organizational capital expenses (SG&A), including R&D and advertising, therefore ROA mitigates concerns that the relationship between concentration and profitability is driven by those industries in which the role of intangible capital has increased over time (Bessen, 2016).13 Following Bertrand and Mullainathan (2003) and Giroud and Mueller (2010), we include firm size and age in all our regressions. In addition to firm-fixed effects, we also include year-fixed effects to control for unobserved time-specific shocks affecting all firms. Finally, to mitigate the impact of extreme ratio values, we exclude firms with assets or sales less than $5 million, that is, microcaps.14
Figure 3 plots the dynamics of aggregate ROA over time. Aggregate ROA is calculated as the aggregate operating income before depreciation scaled by the aggregate book value of assets. Panel A shows that aggregate ROA has declined over approximately the past four decades from 11% in 1972 to almost 5% in 2014. Although this evidence implies that aggregate profitability and aggregate concentration levels are moving in the opposite direction, additional analysis reveals that this is not the case. When we split the sample into nonfinancial and financial firms (NAICS two-digit sector code 52), in Figure 3 panels B and C, respectively, both groups exhibit reasonably stable trends in profitability, thus enabling us to posit that the negative aggregate trend in ROA is driven primarily by the increasing importance to the economy of financial firms, which tend to have lower ROA.15 These findings highlight the importance of controlling for other factors when examining the relation between profitability and concentration levels. To ensure that our results are not driven by the change in the mix of financial versus nonfinancial firms, we exclude financial firms from the main analysis. We also exclude utilities (NAICS two-digit sector code 22) because these firms were highly regulated during part of our sample period.16
Figure 3. Trends in aggregate ROA. Panel A depicts the aggregate ROA for all the firms that appear in the CRSP-Compustat merged dataset (see Sections 2.1 and 3.1 for details) over the period 1972–2014. Aggregate ROA is equal to the aggregate operating income before depreciation scaled by the aggregate book value of assets. Panels B and C depict aggregate ROA for nonfinancial and financial firms (NAICS two-digit sector code 52), respectively. Open in new tabDownload slide
Table I panel A reports the coefficients of Equation (1) estimated over the period 1972–2014. We find that ROA is positively related to both the HHI and the Concentration Index, and negatively related to the Number of Firms. This result shows that firms tend to generate significantly higher profits when their industries become more concentrated. The results also reaffirm our earlier findings that the increase in concentration levels is not due to firms’ leaving unprofitable industries. Note that profitability is positively correlated with changes in firm size, indicating that economies of scale are an important determinant of firms’ profitability during the sample period.
Table I. Open in new tab Change in the level of product market concentration and profitability This table reports coefficients from regressions of firm profitability on several proxies for the level of product market concentration and other control variables. ROA is the operating income before depreciation scaled by the book value of assets. Assets is the book value of total assets. Age is the time (in years) from the firm’s CRSP listing date. HHI is the Herfindahl–Hirschman index based on sales data from Compustat. Number of Firms is the total number of public firms in an industry. Concentration Index is the sum of the annual rank of the HHI and the annual inverse rank of the total number of industry incumbents. See Sections 2.1 and 3.1 for dataset description, and Appendix A for details of variables construction. Industry is defined using a firm’s three-digit NAICS code. Standard errors (reported in parentheses) are clustered at the firm level. Symbols a, b, and c indicate significance at 1%, 5%, and 10%, respectively. Panel A: Entire sample . . Dependent variable: ROA . Variable . 1972–2014 . Constant 0.0696a 0.1092a 0.0854a (0.0107) (0.0067) (0.0044) Log(Assets) 0.0169a 0.0171a 0.0169a (0.0012) (0.0012) (0.0012) Log(Age) –0.0178a –0.0177a –0.0178a (0.0013) (0.0013) (0.0013) Log(HHI) 0.0027c (0.0014) Log(Number of Firms) –0.0056a (0.0014) Concentration Index 0.0014b (0.0007) N 143,602 143,602 143,602 Adjusted R2 57.21% 57.22% 57.21% Year-fixed effects Yes Yes Yes Firm-fixed effects Yes Yes Yes Clustering at firm level Yes Yes Yes Panel A: Entire sample . . Dependent variable: ROA . Variable . 1972–2014 . Constant 0.0696a 0.1092a 0.0854a (0.0107) (0.0067) (0.0044) Log(Assets) 0.0169a 0.0171a 0.0169a (0.0012) (0.0012) (0.0012) Log(Age) –0.0178a –0.0177a –0.0178a (0.0013) (0.0013) (0.0013) Log(HHI) 0.0027c (0.0014) Log(Number of Firms) –0.0056a (0.0014) Concentration Index 0.0014b (0.0007) N 143,602 143,602 143,602 Adjusted R2 57.21% 57.22% 57.21% Year-fixed effects Yes Yes Yes Firm-fixed effects Yes Yes Yes Clustering at firm level Yes Yes Yes Panel B: Subperiods . . Dependent variable: ROA . Variable . 1972–86 . 1987–2000 . 2001–14 . Constant 0.1914a 0.1644a 0.1708a 0.0743a 0.0751a 0.0770a –0.2444a –0.0665a –0.1740a (0.0175) (0.0107) (0.0079) (0.0191) (0.0148) (0.0092) (0.0320) (0.0243) (0.0202) Log(Assets) 0.0011 0.0011 0.0009 0.0198a 0.0198a 0.0198a 0.0349a 0.0351a 0.0353a (0.0021) (0.0021) (0.0021) (0.0021) (0.0021) (0.0021) (0.0030) (0.0030) (0.0030) Log(Age) –0.0200a –0.0199a –0.0199a –0.0327a –0.0327a –0.0326a 0.0097a 0.0100a 0.0090a (0.0020) (0.0020) (0.0020) (0.0024) (0.0024) (0.0024) (0.0036) (0.0036) (0.0036) Log(HHI) –0.0038c 0.0007 0.0168a (0.0023) (0.0024) (0.0040) Log(Number of Firms) 0.0002 0.0008 –0.0140a (0.0022) (0.0027) (0.0037) Concentration Index –0.0026b 0.0006 0.0095a (0.0012) (0.0010) (0.0019) N 44,622 44,622 44,622 54,883 54,833 54,833 44,147 44,147 44,147 Adjusted R2 53.06% 53.05% 53.06% 59.29% 59.29% 59.29% 67.08% 67.08% 67.11% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes Panel B: Subperiods . . Dependent variable: ROA . Variable . 1972–86 . 1987–2000 . 2001–14 . Constant 0.1914a 0.1644a 0.1708a 0.0743a 0.0751a 0.0770a –0.2444a –0.0665a –0.1740a (0.0175) (0.0107) (0.0079) (0.0191) (0.0148) (0.0092) (0.0320) (0.0243) (0.0202) Log(Assets) 0.0011 0.0011 0.0009 0.0198a 0.0198a 0.0198a 0.0349a 0.0351a 0.0353a (0.0021) (0.0021) (0.0021) (0.0021) (0.0021) (0.0021) (0.0030) (0.0030) (0.0030) Log(Age) –0.0200a –0.0199a –0.0199a –0.0327a –0.0327a –0.0326a 0.0097a 0.0100a 0.0090a (0.0020) (0.0020) (0.0020) (0.0024) (0.0024) (0.0024) (0.0036) (0.0036) (0.0036) Log(HHI) –0.0038c 0.0007 0.0168a (0.0023) (0.0024) (0.0040) Log(Number of Firms) 0.0002 0.0008 –0.0140a (0.0022) (0.0027) (0.0037) Concentration Index –0.0026b 0.0006 0.0095a (0.0012) (0.0010) (0.0019) N 44,622 44,622 44,622 54,883 54,833 54,833 44,147 44,147 44,147 Adjusted R2 53.06% 53.05% 53.06% 59.29% 59.29% 59.29% 67.08% 67.08% 67.11% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes
Table I. Open in new tab Change in the level of product market concentration and profitability This table reports coefficients from regressions of firm profitability on several proxies for the level of product market concentration and other control variables. ROA is the operating income before depreciation scaled by the book value of assets. Assets is the book value of total assets. Age is the time (in years) from the firm’s CRSP listing date. HHI is the Herfindahl–Hirschman index based on sales data from Compustat. Number of Firms is the total number of public firms in an industry. Concentration Index is the sum of the annual rank of the HHI and the annual inverse rank of the total number of industry incumbents. See Sections 2.1 and 3.1 for dataset description, and Appendix A for details of variables construction. Industry is defined using a firm’s three-digit NAICS code. Standard errors (reported in parentheses) are clustered at the firm level. Symbols a, b, and c indicate significance at 1%, 5%, and 10%, respectively. Panel A: Entire sample . . Dependent variable: ROA . Variable . 1972–2014 . Constant 0.0696a 0.1092a 0.0854a (0.0107) (0.0067) (0.0044) Log(Assets) 0.0169a 0.0171a 0.0169a (0.0012) (0.0012) (0.0012) Log(Age) –0.0178a –0.0177a –0.0178a (0.0013) (0.0013) (0.0013) Log(HHI) 0.0027c (0.0014) Log(Number of Firms) –0.0056a (0.0014) Concentration Index 0.0014b (0.0007) N 143,602 143,602 143,602 Adjusted R2 57.21% 57.22% 57.21% Year-fixed effects Yes Yes Yes Firm-fixed effects Yes Yes Yes Clustering at firm level Yes Yes Yes Panel A: Entire sample . . Dependent variable: ROA . Variable . 1972–2014 . Constant 0.0696a 0.1092a 0.0854a (0.0107) (0.0067) (0.0044) Log(Assets) 0.0169a 0.0171a 0.0169a (0.0012) (0.0012) (0.0012) Log(Age) –0.0178a –0.0177a –0.0178a (0.0013) (0.0013) (0.0013) Log(HHI) 0.0027c (0.0014) Log(Number of Firms) –0.0056a (0.0014) Concentration Index 0.0014b (0.0007) N 143,602 143,602 143,602 Adjusted R2 57.21% 57.22% 57.21% Year-fixed effects Yes Yes Yes Firm-fixed effects Yes Yes Yes Clustering at firm level Yes Yes Yes Panel B: Subperiods . . Dependent variable: ROA . Variable . 1972–86 . 1987–2000 . 2001–14 . Constant 0.1914a 0.1644a 0.1708a 0.0743a 0.0751a 0.0770a –0.2444a –0.0665a –0.1740a (0.0175) (0.0107) (0.0079) (0.0191) (0.0148) (0.0092) (0.0320) (0.0243) (0.0202) Log(Assets) 0.0011 0.0011 0.0009 0.0198a 0.0198a 0.0198a 0.0349a 0.0351a 0.0353a (0.0021) (0.0021) (0.0021) (0.0021) (0.0021) (0.0021) (0.0030) (0.0030) (0.0030) Log(Age) –0.0200a –0.0199a –0.0199a –0.0327a –0.0327a –0.0326a 0.0097a 0.0100a 0.0090a (0.0020) (0.0020) (0.0020) (0.0024) (0.0024) (0.0024) (0.0036) (0.0036) (0.0036) Log(HHI) –0.0038c 0.0007 0.0168a (0.0023) (0.0024) (0.0040) Log(Number of Firms) 0.0002 0.0008 –0.0140a (0.0022) (0.0027) (0.0037) Concentration Index –0.0026b 0.0006 0.0095a (0.0012) (0.0010) (0.0019) N 44,622 44,622 44,622 54,883 54,833 54,833 44,147 44,147 44,147 Adjusted R2 53.06% 53.05% 53.06% 59.29% 59.29% 59.29% 67.08% 67.08% 67.11% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes Panel B: Subperiods . . Dependent variable: ROA . Variable . 1972–86 . 1987–2000 . 2001–14 . Constant 0.1914a 0.1644a 0.1708a 0.0743a 0.0751a 0.0770a –0.2444a –0.0665a –0.1740a (0.0175) (0.0107) (0.0079) (0.0191) (0.0148) (0.0092) (0.0320) (0.0243) (0.0202) Log(Assets) 0.0011 0.0011 0.0009 0.0198a 0.0198a 0.0198a 0.0349a 0.0351a 0.0353a (0.0021) (0.0021) (0.0021) (0.0021) (0.0021) (0.0021) (0.0030) (0.0030) (0.0030) Log(Age) –0.0200a –0.0199a –0.0199a –0.0327a –0.0327a –0.0326a 0.0097a 0.0100a 0.0090a (0.0020) (0.0020) (0.0020) (0.0024) (0.0024) (0.0024) (0.0036) (0.0036) (0.0036) Log(HHI) –0.0038c 0.0007 0.0168a (0.0023) (0.0024) (0.0040) Log(Number of Firms) 0.0002 0.0008 –0.0140a (0.0022) (0.0027) (0.0037) Concentration Index –0.0026b 0.0006 0.0095a (0.0012) (0.0010) (0.0019) N 44,622 44,622 44,622 54,883 54,833 54,833 44,147 44,147 44,147 Adjusted R2 53.06% 53.05% 53.06% 59.29% 59.29% 59.29% 67.08% 67.08% 67.11% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes
Since most of the increase in industry concentration levels occurs in the latter part of our sample, we test for change in the empirical relation between profitability and concentration levels over that time period. To perform this analysis, we estimate the regression parameters of Equation (1) over three different subperiods: 1972–86; 1987–2000; and 2001–14. The rationale behind our choice of subperiods is as follows: Our univariate analysis collectively indicates that the recent increase in concentration levels started between 1996 and 2001. To determine the split accurately, we divide the overall sample into subsamples of equal length, that is, 1972–86 covers 15 years of data; 1987–2000 covers 14 years of data; and 2001–14 covers 14 years of data. We also conduct a Wald test of a structural break at an unknown break date in the time-series of the aggregate HHI and find a statistically significant structural break in the trend coefficient around the year 2000.17 Separating the sample into alternative subperiods does not qualitatively affect any of our main results.
Table I panel B reports the results from this analysis. Similar to Domowitz, Hubbard, and Petersen (1986a, 1986b, 1987) and Schmalensee (1989), who have studied the intra-industry relation between industry-level price-cost margins and concentration levels over the 1958–81 period, we do not find a strong relation between ROA and measures of concentration during the earlier part of our sample. In fact, evidence exists for the correlation between these two variables being negative over the period 1972–86. The relation between ROA and our proxies for industry concentration levels is only positive and statistically significant across all measures during the later subperiod, 2001–14. In terms of economic significance, the coefficient of Concentration Index estimated over this period indicates that a change in concentration from the 25th to the 75th percentile leads to an increase in ROA of about 32.3% relative to its median. We find similar magnitudes when we use HHI and the number of firms as alternative measures of concentration. Consequently, this analysis points to a significant structural shift, beginning at the turn of the 21st century, in the economic relation between industry structure and firms’ profitability.
3.2 The Sources of Abnormal Profits
A potential explanation for the increase in profitability in more concentrated industries is the decrease in contestability over time resulting from increasing barriers to entry. Thus, lack of competition may allow remaining industry incumbents to gain wider profit margins by setting higher prices relative to production costs. Consistent with this explanation, Barkai (2016) uses a general equilibrium model to demonstrate that increase in markups is the only factor able to explain the increase in profit share in the US nonfinancial sector in the past 30 years. Alternatively, some analysts argue that given the changing nature of US industries, the consolidation of firms within an industry may increase operational efficiency. For example, a large firm may enhance flexibility by reallocating its existing resources to extract the highest productivity from any unit of capital, consequently increasing firm profitability. To this end, we examine whether the empirical relation between profitability and change in industry concentration levels stems from higher profit margins, higher operational efficiency, or both.
We start by decomposing return on assets into two components: the Lerner Index and the Asset Utilization ratio. The Lerner Index measures the extent to which prices exceed marginal costs (price-cost margins), while the Asset Utilization ratio measures how efficiently firms manage their assets to generate sales. Following Aghion et al. (2005), we define the Lerner Index as operating income before depreciation (Compustat item OIBDP) minus depreciation (item DP), all scaled by total sales (item SALE). We subtract depreciation from operating income to take into account the cost of physical capital (Hall and Jorgenson, 1967). Asset Utilization is defined as total sales scaled by total assets.
Figure 4 plots the dynamics of the aggregate Lerner Index and the aggregate Asset Utilization over the period 1972–2014. This figure demonstrates that while the aggregate Lerner Index experienced a positive shift in the early 2000s, aggregate Asset Utilization has declined over time. This pattern suggests that the positive link between concentration and ROA is potentially driven by higher profit margins rather than by higher operational efficiency.
Figure 4. Trends in the aggregate Lerner index and the aggregate asset utilization: non-financial firms. Panel A depicts the aggregate Lerner Index and Panel B depicts Asset Utilization for all non-financial firms (excluding firms with the NAICS two-digit sector code 52) that appear in the CRSP-Compustat merged dataset (see Sections 2.1 and 3.1 for details) over the period 1972–2014. The aggregate Lerner Index is defined as the aggregate operating income after depreciation scaled by aggregate sales, while the aggregate Asset Utilization is defined as aggregate sales scaled by the aggregate book value of assets. Open in new tabDownload slide
Using the same specification we employed in Equation (1), in Table II we estimate the coefficients of the model using the Lerner Index and the Asset Utilization ratio as dependent variables. Table II panel A shows a strong relation between the Lerner Index and concentration measures during the whole sample period (1972–2014): the Lerner Index is positively correlated with both the HHI and the Concentration Index, and negatively correlated with the Number of Firms. On the other hand, Table II panel B shows a negative correlation between Asset Utilization and concentration measures over the same time period.
Table II. Open in new tab Change in the level of product market concentration, profit margins, and efficiency This table reports coefficients from regressions of profit margins and efficiency measures on several proxies for the level of product market concentration and other control variables. Lerner Index is the operating income before depreciation minus depreciation, all scaled by total sales. Asset Utilization is defined as total sales scaled by total assets. Assets is the book value of total assets. Age is the time (in years) from the firm’s CRSP listing date. HHI is the Herfindahl–Hirschman index based on sales data from Compustat. Number of Firms is the total number of public firms in an industry. Concentration Index is the sum of the annual rank of the HHI and the annual inverse rank of the total number of industry incumbents. See Sections 2.1 and 3.1 for dataset description, and Appendix A for details of variable construction. Industry is defined using a firm’s three-digit NAICS code. Standard errors (reported in parentheses) are clustered at the firm level. Symbols a, b, and c indicate significance at 1%, 5%, and 10%, respectively. Panel A: Concentration and Lerner index—entire sample . . Dependent variable: Lerner Index . Variable . 1972–2014 . Constant –0.1251a 0.0287c –0.0369a (0.0255) (0.0173) (0.0118) Log(Assets) 0.0155a 0.0159a 0.0157a (0.0037) (0.0037) (0.0037) Log(Age) –0.0012 –0.0011 –0.0013 (0.0039) (0.0039) (0.0039) Log(HHI) 0.0147a (0.0036) Log(Number of Firms) –0.0142a (0.0033) Concentration Index 0.0066a (0.0017) N 143,230 143,230 143,230 Adjusted R2 57.31% 57.31% 57.31% Year-fixed effects Yes Yes Yes Firm-fixed effects Yes Yes Yes Clustering at firm level Yes Yes Yes Panel A: Concentration and Lerner index—entire sample . . Dependent variable: Lerner Index . Variable . 1972–2014 . Constant –0.1251a 0.0287c –0.0369a (0.0255) (0.0173) (0.0118) Log(Assets) 0.0155a 0.0159a 0.0157a (0.0037) (0.0037) (0.0037) Log(Age) –0.0012 –0.0011 –0.0013 (0.0039) (0.0039) (0.0039) Log(HHI) 0.0147a (0.0036) Log(Number of Firms) –0.0142a (0.0033) Concentration Index 0.0066a (0.0017) N 143,230 143,230 143,230 Adjusted R2 57.31% 57.31% 57.31% Year-fixed effects Yes Yes Yes Firm-fixed effects Yes Yes Yes Clustering at firm level Yes Yes Yes Panel B: Concentration and asset utilization—entire sample . . Dependent variable: Asset Utilization . Variable . 1972–2014 . Constant 2.0301a 1.8617a 1.8961a (0.0521) (0.0377) (0.0226) Log(Assets) –0.1991a –0.1990a –0.1993a (0.0061) (0.0061) (0.0061) Log(Age) 0.1051a 0.1051a 0.1053a (0.0066) (0.0067) (0.0067) Log(HHI) –0.0222a (0.0070) Log(Number of Firms) 0.0041 (0.0080) Concentration Index –0.0098a (0.0033) N 143,807 143,807 143,807 Adjusted R2 83.47% 83.46% 83.47% Year-fixed effects Yes Yes Yes Firm-fixed effects Yes Yes Yes Clustering at firm level Yes Yes Yes Panel B: Concentration and asset utilization—entire sample . . Dependent variable: Asset Utilization . Variable . 1972–2014 . Constant 2.0301a 1.8617a 1.8961a (0.0521) (0.0377) (0.0226) Log(Assets) –0.1991a –0.1990a –0.1993a (0.0061) (0.0061) (0.0061) Log(Age) 0.1051a 0.1051a 0.1053a (0.0066) (0.0067) (0.0067) Log(HHI) –0.0222a (0.0070) Log(Number of Firms) 0.0041 (0.0080) Concentration Index –0.0098a (0.0033) N 143,807 143,807 143,807 Adjusted R2 83.47% 83.46% 83.47% Year-fixed effects Yes Yes Yes Firm-fixed effects Yes Yes Yes Clustering at firm level Yes Yes Yes Panel C: Concentration and Lerner index—subperiods . . Dependent variable: Lerner Index . Variable . 1972–1986 . 1987–2000 . 2001–14 . Constant 0.1088a 0.0715a 0.0689a –0.0592 –0.0272 –0.0343 –0.8904a –0.1008 –0.5419a (0.0198) (0.0120) (0.0083) (0.0361) (0.0327) (0.0217) (0.1348) (0.1065) (0.0833) Log(Assets) 0.0150a 0.0153a 0.0149a 0.0241a 0.0241a 0.0241a –0.0109 –0.0103 –0.0089 (0.0023) (0.0023) (0.0023) (0.0056) (0.0056) (0.0056) (0.0148) (0.0149) (0.0147) Log(Age) –0.0208a –0.0205a –0.0207a –0.0346a –0.0347a –0.0347a 0.1268a 0.1290a 0.1233a (0.0023) (0.0023) (0.0023) (0.0048) (0.0048) (0.0048) (0.0191) (0.0192) (0.0191) Log(HHI) –0.0068b 0.0040 0.0835a (0.0027) (0.0044) (0.0188) Log(Number of Firms) –0.0029 –0.0010 –0.0503a (0.0024) (0.0052) (0.0146) Concentration Index –0.0034a 0.0008 0.0471a (0.0013) (0.0017) (0.0085) N 44,260 44,260 44,260 54,832 54,832 54,832 44,138 44,138 44,138 Adjusted R2 56.58% 56.56% 56.57% 68.30% 68.30% 68.30% 58.65% 58.62% 58.69% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes Panel C: Concentration and Lerner index—subperiods . . Dependent variable: Lerner Index . Variable . 1972–1986 . 1987–2000 . 2001–14 . Constant 0.1088a 0.0715a 0.0689a –0.0592 –0.0272 –0.0343 –0.8904a –0.1008 –0.5419a (0.0198) (0.0120) (0.0083) (0.0361) (0.0327) (0.0217) (0.1348) (0.1065) (0.0833) Log(Assets) 0.0150a 0.0153a 0.0149a 0.0241a 0.0241a 0.0241a –0.0109 –0.0103 –0.0089 (0.0023) (0.0023) (0.0023) (0.0056) (0.0056) (0.0056) (0.0148) (0.0149) (0.0147) Log(Age) –0.0208a –0.0205a –0.0207a –0.0346a –0.0347a –0.0347a 0.1268a 0.1290a 0.1233a (0.0023) (0.0023) (0.0023) (0.0048) (0.0048) (0.0048) (0.0191) (0.0192) (0.0191) Log(HHI) –0.0068b 0.0040 0.0835a (0.0027) (0.0044) (0.0188) Log(Number of Firms) –0.0029 –0.0010 –0.0503a (0.0024) (0.0052) (0.0146) Concentration Index –0.0034a 0.0008 0.0471a (0.0013) (0.0017) (0.0085) N 44,260 44,260 44,260 54,832 54,832 54,832 44,138 44,138 44,138 Adjusted R2 56.58% 56.56% 56.57% 68.30% 68.30% 68.30% 58.65% 58.62% 58.69% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes Panel D: Concentration and asset utilization—subperiods . . Dependent variable: Asset Utilization . Variable . 1972–86 . 1987–2000 . 2001–14 . Constant 2.1227a 2.0700a 2.1095a 2.0253a 2.227a 2.1137a 2.3305a 2.5846a 2.4732a (0.0814) (0.0553) (0.0439) (0.0808) (0.0691) (0.0408) (0.1016) (0.0833) (0.0619) Log(Assets) –0.2056a –0.2062a –0.2057a –0.2393a –0.2387a –0.2393a –0.2831a –0.2830a –0.2826a (0.0113) (0.0114) (0.0113) (0.0093) (0.0093) (0.0093) (0.0094) (0.0095) (0.0094) Log(Age) 0.0262b 0.0256b 0.0262b 0.1211a 0.1224a 0.1214a 0.0926a 0.0936a 0.0921a (0.0114) (0.0114) (0.0114) (0.0097) (0.0096) (0.0097) (0.0113) (0.0113) (0.0113) Log(HHI) –0.0027 –0.0213b 0.0294b (0.0101) (0.0104) (0.0131) Log(Number of Firms) 0.0099 –0.0267b –0.0126 (0.0118) (0.0137) (0.0124) Concentration Index –0.0025 –0.0023 0.0120b (0.0057) (0.0043) (0.0058) N 44,683 44,683 44,683 54,931 54,931 54,931 44,193 44,193 44,193 Adjusted R2 89.95% 89.95% 89.95% 85.81% 85.81% 85.80% 87.96% 87.95% 87.95% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes Panel D: Concentration and asset utilization—subperiods . . Dependent variable: Asset Utilization . Variable . 1972–86 . 1987–2000 . 2001–14 . Constant 2.1227a 2.0700a 2.1095a 2.0253a 2.227a 2.1137a 2.3305a 2.5846a 2.4732a (0.0814) (0.0553) (0.0439) (0.0808) (0.0691) (0.0408) (0.1016) (0.0833) (0.0619) Log(Assets) –0.2056a –0.2062a –0.2057a –0.2393a –0.2387a –0.2393a –0.2831a –0.2830a –0.2826a (0.0113) (0.0114) (0.0113) (0.0093) (0.0093) (0.0093) (0.0094) (0.0095) (0.0094) Log(Age) 0.0262b 0.0256b 0.0262b 0.1211a 0.1224a 0.1214a 0.0926a 0.0936a 0.0921a (0.0114) (0.0114) (0.0114) (0.0097) (0.0096) (0.0097) (0.0113) (0.0113) (0.0113) Log(HHI) –0.0027 –0.0213b 0.0294b (0.0101) (0.0104) (0.0131) Log(Number of Firms) 0.0099 –0.0267b –0.0126 (0.0118) (0.0137) (0.0124) Concentration Index –0.0025 –0.0023 0.0120b (0.0057) (0.0043) (0.0058) N 44,683 44,683 44,683 54,931 54,931 54,931 44,193 44,193 44,193 Adjusted R2 89.95% 89.95% 89.95% 85.81% 85.81% 85.80% 87.96% 87.95% 87.95% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes
Table II. Open in new tab Change in the level of product market concentration, profit margins, and efficiency This table reports coefficients from regressions of profit margins and efficiency measures on several proxies for the level of product market concentration and other control variables. Lerner Index is the operating income before depreciation minus depreciation, all scaled by total sales. Asset Utilization is defined as total sales scaled by total assets. Assets is the book value of total assets. Age is the time (in years) from the firm’s CRSP listing date. HHI is the Herfindahl–Hirschman index based on sales data from Compustat. Number of Firms is the total number of public firms in an industry. Concentration Index is the sum of the annual rank of the HHI and the annual inverse rank of the total number of industry incumbents. See Sections 2.1 and 3.1 for dataset description, and Appendix A for details of variable construction. Industry is defined using a firm’s three-digit NAICS code. Standard errors (reported in parentheses) are clustered at the firm level. Symbols a, b, and c indicate significance at 1%, 5%, and 10%, respectively. Panel A: Concentration and Lerner index—entire sample . . Dependent variable: Lerner Index . Variable . 1972–2014 . Constant –0.1251a 0.0287c –0.0369a (0.0255) (0.0173) (0.0118) Log(Assets) 0.0155a 0.0159a 0.0157a (0.0037) (0.0037) (0.0037) Log(Age) –0.0012 –0.0011 –0.0013 (0.0039) (0.0039) (0.0039) Log(HHI) 0.0147a (0.0036) Log(Number of Firms) –0.0142a (0.0033) Concentration Index 0.0066a (0.0017) N 143,230 143,230 143,230 Adjusted R2 57.31% 57.31% 57.31% Year-fixed effects Yes Yes Yes Firm-fixed effects Yes Yes Yes Clustering at firm level Yes Yes Yes Panel A: Concentration and Lerner index—entire sample . . Dependent variable: Lerner Index . Variable . 1972–2014 . Constant –0.1251a 0.0287c –0.0369a (0.0255) (0.0173) (0.0118) Log(Assets) 0.0155a 0.0159a 0.0157a (0.0037) (0.0037) (0.0037) Log(Age) –0.0012 –0.0011 –0.0013 (0.0039) (0.0039) (0.0039) Log(HHI) 0.0147a (0.0036) Log(Number of Firms) –0.0142a (0.0033) Concentration Index 0.0066a (0.0017) N 143,230 143,230 143,230 Adjusted R2 57.31% 57.31% 57.31% Year-fixed effects Yes Yes Yes Firm-fixed effects Yes Yes Yes Clustering at firm level Yes Yes Yes Panel B: Concentration and asset utilization—entire sample . . Dependent variable: Asset Utilization . Variable . 1972–2014 . Constant 2.0301a 1.8617a 1.8961a (0.0521) (0.0377) (0.0226) Log(Assets) –0.1991a –0.1990a –0.1993a (0.0061) (0.0061) (0.0061) Log(Age) 0.1051a 0.1051a 0.1053a (0.0066) (0.0067) (0.0067) Log(HHI) –0.0222a (0.0070) Log(Number of Firms) 0.0041 (0.0080) Concentration Index –0.0098a (0.0033) N 143,807 143,807 143,807 Adjusted R2 83.47% 83.46% 83.47% Year-fixed effects Yes Yes Yes Firm-fixed effects Yes Yes Yes Clustering at firm level Yes Yes Yes Panel B: Concentration and asset utilization—entire sample . . Dependent variable: Asset Utilization . Variable . 1972–2014 . Constant 2.0301a 1.8617a 1.8961a (0.0521) (0.0377) (0.0226) Log(Assets) –0.1991a –0.1990a –0.1993a (0.0061) (0.0061) (0.0061) Log(Age) 0.1051a 0.1051a 0.1053a (0.0066) (0.0067) (0.0067) Log(HHI) –0.0222a (0.0070) Log(Number of Firms) 0.0041 (0.0080) Concentration Index –0.0098a (0.0033) N 143,807 143,807 143,807 Adjusted R2 83.47% 83.46% 83.47% Year-fixed effects Yes Yes Yes Firm-fixed effects Yes Yes Yes Clustering at firm level Yes Yes Yes Panel C: Concentration and Lerner index—subperiods . . Dependent variable: Lerner Index . Variable . 1972–1986 . 1987–2000 . 2001–14 . Constant 0.1088a 0.0715a 0.0689a –0.0592 –0.0272 –0.0343 –0.8904a –0.1008 –0.5419a (0.0198) (0.0120) (0.0083) (0.0361) (0.0327) (0.0217) (0.1348) (0.1065) (0.0833) Log(Assets) 0.0150a 0.0153a 0.0149a 0.0241a 0.0241a 0.0241a –0.0109 –0.0103 –0.0089 (0.0023) (0.0023) (0.0023) (0.0056) (0.0056) (0.0056) (0.0148) (0.0149) (0.0147) Log(Age) –0.0208a –0.0205a –0.0207a –0.0346a –0.0347a –0.0347a 0.1268a 0.1290a 0.1233a (0.0023) (0.0023) (0.0023) (0.0048) (0.0048) (0.0048) (0.0191) (0.0192) (0.0191) Log(HHI) –0.0068b 0.0040 0.0835a (0.0027) (0.0044) (0.0188) Log(Number of Firms) –0.0029 –0.0010 –0.0503a (0.0024) (0.0052) (0.0146) Concentration Index –0.0034a 0.0008 0.0471a (0.0013) (0.0017) (0.0085) N 44,260 44,260 44,260 54,832 54,832 54,832 44,138 44,138 44,138 Adjusted R2 56.58% 56.56% 56.57% 68.30% 68.30% 68.30% 58.65% 58.62% 58.69% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes Panel C: Concentration and Lerner index—subperiods . . Dependent variable: Lerner Index . Variable . 1972–1986 . 1987–2000 . 2001–14 . Constant 0.1088a 0.0715a 0.0689a –0.0592 –0.0272 –0.0343 –0.8904a –0.1008 –0.5419a (0.0198) (0.0120) (0.0083) (0.0361) (0.0327) (0.0217) (0.1348) (0.1065) (0.0833) Log(Assets) 0.0150a 0.0153a 0.0149a 0.0241a 0.0241a 0.0241a –0.0109 –0.0103 –0.0089 (0.0023) (0.0023) (0.0023) (0.0056) (0.0056) (0.0056) (0.0148) (0.0149) (0.0147) Log(Age) –0.0208a –0.0205a –0.0207a –0.0346a –0.0347a –0.0347a 0.1268a 0.1290a 0.1233a (0.0023) (0.0023) (0.0023) (0.0048) (0.0048) (0.0048) (0.0191) (0.0192) (0.0191) Log(HHI) –0.0068b 0.0040 0.0835a (0.0027) (0.0044) (0.0188) Log(Number of Firms) –0.0029 –0.0010 –0.0503a (0.0024) (0.0052) (0.0146) Concentration Index –0.0034a 0.0008 0.0471a (0.0013) (0.0017) (0.0085) N 44,260 44,260 44,260 54,832 54,832 54,832 44,138 44,138 44,138 Adjusted R2 56.58% 56.56% 56.57% 68.30% 68.30% 68.30% 58.65% 58.62% 58.69% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes Panel D: Concentration and asset utilization—subperiods . . Dependent variable: Asset Utilization . Variable . 1972–86 . 1987–2000 . 2001–14 . Constant 2.1227a 2.0700a 2.1095a 2.0253a 2.227a 2.1137a 2.3305a 2.5846a 2.4732a (0.0814) (0.0553) (0.0439) (0.0808) (0.0691) (0.0408) (0.1016) (0.0833) (0.0619) Log(Assets) –0.2056a –0.2062a –0.2057a –0.2393a –0.2387a –0.2393a –0.2831a –0.2830a –0.2826a (0.0113) (0.0114) (0.0113) (0.0093) (0.0093) (0.0093) (0.0094) (0.0095) (0.0094) Log(Age) 0.0262b 0.0256b 0.0262b 0.1211a 0.1224a 0.1214a 0.0926a 0.0936a 0.0921a (0.0114) (0.0114) (0.0114) (0.0097) (0.0096) (0.0097) (0.0113) (0.0113) (0.0113) Log(HHI) –0.0027 –0.0213b 0.0294b (0.0101) (0.0104) (0.0131) Log(Number of Firms) 0.0099 –0.0267b –0.0126 (0.0118) (0.0137) (0.0124) Concentration Index –0.0025 –0.0023 0.0120b (0.0057) (0.0043) (0.0058) N 44,683 44,683 44,683 54,931 54,931 54,931 44,193 44,193 44,193 Adjusted R2 89.95% 89.95% 89.95% 85.81% 85.81% 85.80% 87.96% 87.95% 87.95% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes Panel D: Concentration and asset utilization—subperiods . . Dependent variable: Asset Utilization . Variable . 1972–86 . 1987–2000 . 2001–14 . Constant 2.1227a 2.0700a 2.1095a 2.0253a 2.227a 2.1137a 2.3305a 2.5846a 2.4732a (0.0814) (0.0553) (0.0439) (0.0808) (0.0691) (0.0408) (0.1016) (0.0833) (0.0619) Log(Assets) –0.2056a –0.2062a –0.2057a –0.2393a –0.2387a –0.2393a –0.2831a –0.2830a –0.2826a (0.0113) (0.0114) (0.0113) (0.0093) (0.0093) (0.0093) (0.0094) (0.0095) (0.0094) Log(Age) 0.0262b 0.0256b 0.0262b 0.1211a 0.1224a 0.1214a 0.0926a 0.0936a 0.0921a (0.0114) (0.0114) (0.0114) (0.0097) (0.0096) (0.0097) (0.0113) (0.0113) (0.0113) Log(HHI) –0.0027 –0.0213b 0.0294b (0.0101) (0.0104) (0.0131) Log(Number of Firms) 0.0099 –0.0267b –0.0126 (0.0118) (0.0137) (0.0124) Concentration Index –0.0025 –0.0023 0.0120b (0.0057) (0.0043) (0.0058) N 44,683 44,683 44,683 54,931 54,931 54,931 44,193 44,193 44,193 Adjusted R2 89.95% 89.95% 89.95% 85.81% 85.81% 85.80% 87.96% 87.95% 87.95% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes
Consistent with our previous findings, the average within-firm relation between profitability measures and proxies for industry concentration levels is stronger over the subperiod 2001–14. In this subperiod, both the Lerner Index and the Asset Utilization ratio increase as industries become more concentrated (Table II panels C and D, respectively). These results indicate that firms operating in increasingly concentrated industries are able to generate abnormal profits by boosting their profit margins and, to a lesser extent, by enhancing the efficiency of their existing assets. The economic significance of the profit-margin impact is in fact considerably greater than the efficiency effect. While a change in the Concentration Index from the 25th to the 75th percentile leads to an increase in the Lerner Index of about 142% relative to its median, a similar change in the Concentration Index only leads to an increase in Asset Utilization of about 6% relative to its median. These results indicate relations between profitability (ROA) and the changes in concentration levels (Table I) are driven primarily by the positive effect of product market concentration on profit margins, and not by efficiency gains.
After establishing the main results, we perform a battery of robustness tests to ensure that our findings are not sensitive to the choice of industry definition. Section 1.A of the Online Appendix discusses the differences in industry definitions based on three versus four-digit NAICS code, and shows that the profitability results are robust to the use of four-digit NAICS as a more granular industry definition. In Section 1.B of the Online Appendix, we consider the changing landscape of industry structure, as well as potential role of multisegment firms, and re-estimate the results using text-based industry classification, described in Section 2.2 of the paper. Our main results are robust to these alternative specifications.
The accumulated evidence demonstrates that market power is likely playing an important role in explaining the increased profitability in many industries. One possible explanation is that higher barriers to entry have increased firms’ abilities to generate higher profit margins by fending off potential competitors. Alternatively, the possibility exists that firms have become more efficient due to declines in their marginal cost of production. However, our measure of efficiency, widely used in the literature (see, e.g., Nohel and Tarhan, 1998; Ang, Cole, and Lin, 2000; Patatoukas, 2012; Irvine, Park, and Yildizhan, 2016), is not significantly higher in more concentrated markets. This evidence, combined with the documented decline in capital and labor share (Barkai, 2016; Gutiérrez and Philippon, 2017), indicates that neither capital nor labor is the source of the efficiency gains that can explain the increased profitability. However, we cannot rule out other possible gains in efficiency that might have contributed to the gains in profitability.
Finally, since we do not have data on consumer prices, we cannot determine whether there is also a positive relation between concentration and consumer prices, which would substantially determine the need for antitrust intervention. However, our analysis effectively rules out the explanation that, in competitive markets, changes in the optimal distribution of firm size can lead to increases in concentration levels without affecting profit margins. Furthermore, our analysis rules out the possibility that improvements in efficiency are derived from better asset utilization or improvements in TFP (as will be described in Section 6.2).
3.3 Accounting Profits versus Economic Profits
Our previous analysis used accounting profits to measure firms’ profitability. However, because no market transactions are recorded for capital services, the profits we measure from the accounting statements can differ from the true economic profits as a result of industry variations in the price and the use of capital. In Section 3 of the Online Appendix, we show that the wedge between accounting and economic profits is driven by the two determinants of capital payments: the price of capital and the capital share. Therefore, if the payment for capital is higher in concentrated industries, ignoring these two factors can lead to a spurious correlation between accounting profits and concentration levels.
To address this econometric concern, we gather data on the price of capital and the capital share at the three-digit NAICS industry level from the KLEMS Multifactor Productivity Tables produced by the BLS. Price of Capital is defined as the capital payments scaled by the stock of assets, while Capital Share is defined as the capital payments scaled by the total value of production. Capital payments are equal to the flow of services from the stock of assets, which include equipment, structures, intellectual property products, inventories, and land. The BLS aggregates the stock of assets using weights based on the implicit prices these assets would generate in a rental market.18 These variables have been available on an annual basis from 1987.
Table III reports the results from regressions of ROA, Lerner Index, and Asset Utilization on the Concentration Index controlling for the Price of Capital and the Capital Share. Consistent with our previous findings, the results show that the ROA and the Lerner Index are positively related to concentration levels over the period 2001–13. The results further indicate that the industry cross-sectional variation in the use and cost of capital does not drive our main findings. Moreover, Table III shows that Asset Utilization is negatively correlated with the Capital Share and positively correlated with the Price of Capital. This evidence implies that the marginal productivity of capital declines as the capital share increases, which is consistent with diminishing returns on capital. Further, this evidence posits that the productivity of capital is reflected in the price of capital. Table III also shows that the positive within-firm correlation between Asset Utilization and concentration levels documented in Section 3.2 disappears after controlling for these two factors. Overall, these results strengthen the claim that the higher profits earned by firms in increasingly concentrated industries result from markups, and not from the use of more capital or from better utilization of the given firms’ assets.
Table III. Open in new tab Change in the level of product market concentration and profitability—controlling for the use and cost of capital This table reports coefficients from regressions of ROA, profit margins, and efficiency measures on the industry concentration index (defined as the sum of the annual rank of the HHI and the annual inverse rank of the total number of industry incumbents), controlling for the use and cost of capital and other variables. ROA is the operating income before depreciation scaled by the book value of assets. Lerner index is the operating income before depreciation minus depreciation, all scaled by total sales. Asset Utilization is defined as total sales scaled by total assets. Price of Capital is equal to the industry-level capital payments scaled by the stock of assets. Capital Share is equal to the industry-level capital payments scaled by the total value of production. See Sections 2.1 and 3.1 for dataset description, and Appendix A for details of variables construction. Industry is defined using a firm’s three-digit NAICS code. Standard errors (reported in parentheses) are clustered at the firm level. Symbols a, b, and c indicate significance at 1%, 5%, and 10%, respectively. . 1987–2013 . 1987–2000 . 2001–13 . Variable . ROA . Lerner Index . Asset Utilization . ROA . Lerner Index . Asset Utilization . ROA . Lerner Index . Asset Utilization . Constant –0.0743a –0.2082a 0.9870a 0.0487 –0.2458a 1.4727a –0.1735a –0.2041 1.3002a (0.0242) (0.0550) (0.1172) (0.035) (0.0781) (0.1704) (0.0423) (0.1314) (0.1370) Log(Assets) 0.0274a 0.0277a –0.2212a 0.0224a 0.0361a –0.2444a 0.0387a 0.0065 –0.2797a (0.0019) (0.0065) (0.0069) (0.0026) (0.0056) (0.0098) (0.0034) (0.0167) (0.0097) Log(Age) –0.0204a –0.0042 0.1137a –0.0371a –0.0482a 0.1138a 0.0051 0.1099a 0.0913a (0.0020) (0.0066) (0.0078) (0.0027) (0.0049) (0.0101) (0.0044) (0.0217) (0.0117) Concentration Index 0.0037a 0.0055c –0.0105b 0.0011 –0.0004 –0.0064 0.0144a 0.0420a 0.0053 (0.0013) (0.0034) (0.0054) (0.0015) (0.0023) (0.0060) (0.0030) (0.0131) (0.0093) Log(Price of Capital) 0.0190a 0.0218a 0.1932a 0.0087 0.0384a 0.0787a –0.0020 –0.0454a 0.1768a (0.0037) (0.0075) (0.0192) (0.0055) (0.0124) (0.0263) (0.0065) (0.0175) (0.0215) Log(Capital Share) –0.0023 0.0186 –0.1414a 0.0063 –0.0146 –0.1271a 0.0001 0.0539b –0.1650a (0.0050) (0.0117) (0.0209) (0.0072) (0.0157) (0.0316) (0.0069) (0.0224) (0.0231) N 74,586 74,586 74,682 41,645 41,645 41,702 32,941 32,941 32,980 Adjusted R2 59.01% 56.13% 80.20% 58.95% 62.03% 82.45% 67.59% 59.38% 85.97% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes . 1987–2013 . 1987–2000 . 2001–13 . Variable . ROA . Lerner Index . Asset Utilization . ROA . Lerner Index . Asset Utilization . ROA . Lerner Index . Asset Utilization . Constant –0.0743a –0.2082a 0.9870a 0.0487 –0.2458a 1.4727a –0.1735a –0.2041 1.3002a (0.0242) (0.0550) (0.1172) (0.035) (0.0781) (0.1704) (0.0423) (0.1314) (0.1370) Log(Assets) 0.0274a 0.0277a –0.2212a 0.0224a 0.0361a –0.2444a 0.0387a 0.0065 –0.2797a (0.0019) (0.0065) (0.0069) (0.0026) (0.0056) (0.0098) (0.0034) (0.0167) (0.0097) Log(Age) –0.0204a –0.0042 0.1137a –0.0371a –0.0482a 0.1138a 0.0051 0.1099a 0.0913a (0.0020) (0.0066) (0.0078) (0.0027) (0.0049) (0.0101) (0.0044) (0.0217) (0.0117) Concentration Index 0.0037a 0.0055c –0.0105b 0.0011 –0.0004 –0.0064 0.0144a 0.0420a 0.0053 (0.0013) (0.0034) (0.0054) (0.0015) (0.0023) (0.0060) (0.0030) (0.0131) (0.0093) Log(Price of Capital) 0.0190a 0.0218a 0.1932a 0.0087 0.0384a 0.0787a –0.0020 –0.0454a 0.1768a (0.0037) (0.0075) (0.0192) (0.0055) (0.0124) (0.0263) (0.0065) (0.0175) (0.0215) Log(Capital Share) –0.0023 0.0186 –0.1414a 0.0063 –0.0146 –0.1271a 0.0001 0.0539b –0.1650a (0.0050) (0.0117) (0.0209) (0.0072) (0.0157) (0.0316) (0.0069) (0.0224) (0.0231) N 74,586 74,586 74,682 41,645 41,645 41,702 32,941 32,941 32,980 Adjusted R2 59.01% 56.13% 80.20% 58.95% 62.03% 82.45% 67.59% 59.38% 85.97% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes
Table III. Open in new tab Change in the level of product market concentration and profitability—controlling for the use and cost of capital This table reports coefficients from regressions of ROA, profit margins, and efficiency measures on the industry concentration index (defined as the sum of the annual rank of the HHI and the annual inverse rank of the total number of industry incumbents), controlling for the use and cost of capital and other variables. ROA is the operating income before depreciation scaled by the book value of assets. Lerner index is the operating income before depreciation minus depreciation, all scaled by total sales. Asset Utilization is defined as total sales scaled by total assets. Price of Capital is equal to the industry-level capital payments scaled by the stock of assets. Capital Share is equal to the industry-level capital payments scaled by the total value of production. See Sections 2.1 and 3.1 for dataset description, and Appendix A for details of variables construction. Industry is defined using a firm’s three-digit NAICS code. Standard errors (reported in parentheses) are clustered at the firm level. Symbols a, b, and c indicate significance at 1%, 5%, and 10%, respectively. . 1987–2013 . 1987–2000 . 2001–13 . Variable . ROA . Lerner Index . Asset Utilization . ROA . Lerner Index . Asset Utilization . ROA . Lerner Index . Asset Utilization . Constant –0.0743a –0.2082a 0.9870a 0.0487 –0.2458a 1.4727a –0.1735a –0.2041 1.3002a (0.0242) (0.0550) (0.1172) (0.035) (0.0781) (0.1704) (0.0423) (0.1314) (0.1370) Log(Assets) 0.0274a 0.0277a –0.2212a 0.0224a 0.0361a –0.2444a 0.0387a 0.0065 –0.2797a (0.0019) (0.0065) (0.0069) (0.0026) (0.0056) (0.0098) (0.0034) (0.0167) (0.0097) Log(Age) –0.0204a –0.0042 0.1137a –0.0371a –0.0482a 0.1138a 0.0051 0.1099a 0.0913a (0.0020) (0.0066) (0.0078) (0.0027) (0.0049) (0.0101) (0.0044) (0.0217) (0.0117) Concentration Index 0.0037a 0.0055c –0.0105b 0.0011 –0.0004 –0.0064 0.0144a 0.0420a 0.0053 (0.0013) (0.0034) (0.0054) (0.0015) (0.0023) (0.0060) (0.0030) (0.0131) (0.0093) Log(Price of Capital) 0.0190a 0.0218a 0.1932a 0.0087 0.0384a 0.0787a –0.0020 –0.0454a 0.1768a (0.0037) (0.0075) (0.0192) (0.0055) (0.0124) (0.0263) (0.0065) (0.0175) (0.0215) Log(Capital Share) –0.0023 0.0186 –0.1414a 0.0063 –0.0146 –0.1271a 0.0001 0.0539b –0.1650a (0.0050) (0.0117) (0.0209) (0.0072) (0.0157) (0.0316) (0.0069) (0.0224) (0.0231) N 74,586 74,586 74,682 41,645 41,645 41,702 32,941 32,941 32,980 Adjusted R2 59.01% 56.13% 80.20% 58.95% 62.03% 82.45% 67.59% 59.38% 85.97% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes . 1987–2013 . 1987–2000 . 2001–13 . Variable . ROA . Lerner Index . Asset Utilization . ROA . Lerner Index . Asset Utilization . ROA . Lerner Index . Asset Utilization . Constant –0.0743a –0.2082a 0.9870a 0.0487 –0.2458a 1.4727a –0.1735a –0.2041 1.3002a (0.0242) (0.0550) (0.1172) (0.035) (0.0781) (0.1704) (0.0423) (0.1314) (0.1370) Log(Assets) 0.0274a 0.0277a –0.2212a 0.0224a 0.0361a –0.2444a 0.0387a 0.0065 –0.2797a (0.0019) (0.0065) (0.0069) (0.0026) (0.0056) (0.0098) (0.0034) (0.0167) (0.0097) Log(Age) –0.0204a –0.0042 0.1137a –0.0371a –0.0482a 0.1138a 0.0051 0.1099a 0.0913a (0.0020) (0.0066) (0.0078) (0.0027) (0.0049) (0.0101) (0.0044) (0.0217) (0.0117) Concentration Index 0.0037a 0.0055c –0.0105b 0.0011 –0.0004 –0.0064 0.0144a 0.0420a 0.0053 (0.0013) (0.0034) (0.0054) (0.0015) (0.0023) (0.0060) (0.0030) (0.0131) (0.0093) Log(Price of Capital) 0.0190a 0.0218a 0.1932a 0.0087 0.0384a 0.0787a –0.0020 –0.0454a 0.1768a (0.0037) (0.0075) (0.0192) (0.0055) (0.0124) (0.0263) (0.0065) (0.0175) (0.0215) Log(Capital Share) –0.0023 0.0186 –0.1414a 0.0063 –0.0146 –0.1271a 0.0001 0.0539b –0.1650a (0.0050) (0.0117) (0.0209) (0.0072) (0.0157) (0.0316) (0.0069) (0.0224) (0.0231) N 74,586 74,586 74,682 41,645 41,645 41,702 32,941 32,941 32,980 Adjusted R2 59.01% 56.13% 80.20% 58.95% 62.03% 82.45% 67.59% 59.38% 85.97% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at firm level Yes Yes Yes Yes Yes Yes Yes Yes Yes
4. Changes in Industry Concentration and the Value of Mergers
From a theoretical perspective, mergers can create value by improving efficiency, including economies of scale and scope, synergies, and elimination of duplicate functions; mergers also create value through increasing market power. The latter effect should become more dominant as competition declines. Therefore, examining the relation between M&A announcement returns and concentration levels should enable further insight into what drives the relationship between increased concentration and profitability. If investors perceive the wealth effects in mergers as partially due to increases in market power, then the market reaction to these corporate events should be stronger in industries with increased concentration, especially in related mergers. The rationale for this conclusion is that, with all else constant, mergers in concentrated markets are more likely than mergers in competitive markets to further reduce competition. This argument is consistent with the antitrust polices of the Federal Trade Commission and the Department of Justice, in which horizontal mergers in highly concentrated markets are predominantly investigated and/or blocked.
We gather data from the Securities Data Company’s (SDC) M&A database. Our sample consists of M&A transactions over the period 1980–2014 that meet the following conditions: (i) percent of ownership by acquirer prior to event is lower than 50%; (ii) percent of ownership by acquirer after event is higher than 50%; (iii) both acquirer and target are identified as public firms; (iv) acquirer and target firm have different identifiers; (v) the transaction is friendly; (vi) return data around the announcement date are available on CRSP; and (vii) the method of payment is known. We also exclude financials and utilities from our sample because these firms face more regulatory uncertainty during the merger process.
Combined CAR d = MV A , t + 1 + MV T , t + 1 MV A , t - 1 + MV T , t - 1 - 1 - r CRSP , t - 1 , t + 1 , (2) A (MV T ) is the market value of equity of the acquiring (target) firm, and r CRSP , t - 1 , t + 1 is the cumulative return on the CRSP value-weighted market portfolio from t - 1 to t + 1. Deals in our sample generate an average combined CAR of 1.15%. The aggregate dollar value of the estimated combined CAR across all transactions is approximately $245.4 billion. We focus on the change in the combined value of the target and the acquiring firms to gauge the magnitude of the total wealth creation around the merger announcement. To capture this effect, we calculate the cumulative abnormal return (CAR) of the combined firm over a 3-day event window [−1, 1] around the announcement of merger d:where t is the announcement date of the transaction, MV(MV) is the market value of equity of the acquiring (target) firm, andis the cumulative return on the CRSP value-weighted market portfolio from t1 to t + 1. Deals in our sample generate an average combined CAR of 1.15%. The aggregate dollar value of the estimated combined CAR across all transactions is approximately $245.4 billion.
CAR d = α t + α j + β 1 B / M T , t − 1 + β 2 B / M A , t − 1 + β 3 log ( MV T , t − 1 ) + β 4 log ( MV A , t − 1 ) + β 5 DumCash d + β 6 DumStock d + β 7 log ( Concentration Level j , t − 1 ) + β 8 Related d + β 9 Related d × log ( Concentration Level j , t − 1 ) + ε d . (3) To investigate the effect of market power considerations on M&A transactions, we test whether the effect of the changes in concentration levels on announcement returns is stronger when the target and the acquirer belong to the same industry (related mergers) than the effect is when they belong to different industries (unrelated mergers). If the impact of the change in concentration levels on expected synergies is primarily driven by the impact of the merger on the competitive landscape of the given industry, then the effect should be stronger for related mergers. To test this hypothesis, we estimate the parameters of the following model:where T and A denote target and acquirer, respectively; t is the year of the merger; j is the NAICS three-digit code industry of the acquiring firm; and d is the deal indicator.
The main variable of interest is the effect of concentration levels on related mergers. Therefore, we include a dummy variable (Related) equal to 1 if the target and the acquiring firm are in the same NAICS three-digit code industry, and an interaction variable equal to the product of Related and Concentration Level. We also include year-fixed effects (α t ) to control for the impact of merger waves and macroeconomic conditions on announcement returns and an industry-fixed effects based on the acquirer’s industry (α j ) to control for time-invariant industry factors. To control for deal characteristics, we include the book-to-market ratios of the target (B/M T ) and the acquiring firm (B/M A ) as control variables to capture the effect of investment opportunities (Jovanovic and Rousseau, 2002) and/or potential misvaluation (Shleifer and Vishny, 2003) on the wealth effects of mergers.19 We also include the market values of the target (MV T ) and the acquirer (MV A ) as proxies for firm size to control for the potential economies of scales generated by the merger (see, e.g., Asquith, Bruner, and Mullins, 1983). Further, we include dummies for both pure cash transactions (DumCash d ) and pure stock transactions (DumStock d ) to control for the established empirical fact that stock-financed transactions generate lower CARs than cash-financed transactions (see, e.g., Andrade, Mitchell, and Stafford, 2001).
If investors expect market power considerations to be an important part of the anticipated synergies from a merger, then we should observe a positive coefficient on the interaction variable. Table IV reports the estimated coefficients from this regression. The regression results show that the market reaction around M&A announcements is stronger for related mergers occurring in highly concentrated industries.20 Consistent with our profitability results, we find that this effect is much stronger during the post-2000 period. While the middle panel (1980–2000) shows that the interaction variable is insignificant for all measures of concentration, the right panel (2001–2014) shows the effect of concentration levels on Combined CARs tending to be much stronger during related mergers. The relationship is also economically significant. A one-standard-deviation increase in the Concentration Index increases the CAR of a related merger by approximately 104 basis points. This effect is large, considering that the average CAR in our sample is 114 basis points. Moreover, if the merger results are driven by efficiencies of horizontal mergers, rather than by market power, our size controls should capture some of this effect (see, e.g., Asquith, Bruner, and Mullins, 1983).
Table IV. Open in new tab Change in the level of product market concentration and M&A returns The table presents results of regressing CARs around merger announcements on several proxies for the level of product market concentration and other control variables. The sample consists of M&A transactions over the period 1980–2014. CAR is the cumulative abnormal return of the combined firm (acquirer plus target) over a 3-day event window [−1, 1] around the merger announcement (see Equation (2)). Related is a dummy variable that takes on a value of 1 if the acquirer and the target belong to the same NAICS three-digit industry, and zero otherwise. Industry is defined using the acquirer’s three-digit NAICS code. We control for deal characteristics by including the market values and book-to-market ratios of the target and acquiring firms, and dummies for pure cash transactions and pure stock transactions. See Section 4 for dataset description and full specification, and Appendix A for details of variable construction. Symbols a, b, and c indicate significance at 1%, 5%, and 10%, respectively. Variable . 1980–2014 . 1980–2000 . 2001–14 . Constant 0.0514c 0.0477a 0.0400a 0.0671c 0.0577b 0.0439a 0.0022 0.0621 0.0301b (0.0279) (0.0189) (0.0154) (0.0371) (0.0295) (0.0147) (0.0424) (0.0431) (0.0142) Log(HHI) –0.0012 –0.0034 0.0044 (0.0034) (0.0050) (0.0054) Log(Number of Firm) –0.0011 –0.0029 –0.0059 (0.0027) (0.0051) (0.0077) Concentration Index 0.0057 0.0159 –0.0079 (0.0164) (0.0196) (0.0288) Related –0.0296 0.0271a 0.0066c –0.0059 0.0073 0.0003 –0.0566 0.0549a 0.0176a (0.0261) (0.0075) (0.0035) (0.0308) (0.0086) (0.0045) (0.0371) (0.0117) (0.0060) Proxy for Concentration× Related 0.0046 –0.0053a 0.0206a 0.0005 –0.0019 0.0085 0.0094c –0.0098a 0.0531a (0.0039) (0.0014) (0.0081) (0.0047) (0.0016) (0.0107) (0.0054) (0.0023) (0.0198) N 5,281 5,281 5,281 3,340 3,340 3,340 1,941 1,941 1,941 Adjusted R2 3.88% 4.04% 3.97% 3.42% 3.44% 3.50% 6.33% 6.82% 6.39% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Acquirer’s industry-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Deal characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at industry level Yes Yes Yes Yes Yes Yes Yes Yes Yes Variable . 1980–2014 . 1980–2000 . 2001–14 . Constant 0.0514c 0.0477a 0.0400a 0.0671c 0.0577b 0.0439a 0.0022 0.0621 0.0301b (0.0279) (0.0189) (0.0154) (0.0371) (0.0295) (0.0147) (0.0424) (0.0431) (0.0142) Log(HHI) –0.0012 –0.0034 0.0044 (0.0034) (0.0050) (0.0054) Log(Number of Firm) –0.0011 –0.0029 –0.0059 (0.0027) (0.0051) (0.0077) Concentration Index 0.0057 0.0159 –0.0079 (0.0164) (0.0196) (0.0288) Related –0.0296 0.0271a 0.0066c –0.0059 0.0073 0.0003 –0.0566 0.0549a 0.0176a (0.0261) (0.0075) (0.0035) (0.0308) (0.0086) (0.0045) (0.0371) (0.0117) (0.0060) Proxy for Concentration× Related 0.0046 –0.0053a 0.0206a 0.0005 –0.0019 0.0085 0.0094c –0.0098a 0.0531a (0.0039) (0.0014) (0.0081) (0.0047) (0.0016) (0.0107) (0.0054) (0.0023) (0.0198) N 5,281 5,281 5,281 3,340 3,340 3,340 1,941 1,941 1,941 Adjusted R2 3.88% 4.04% 3.97% 3.42% 3.44% 3.50% 6.33% 6.82% 6.39% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Acquirer’s industry-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Deal characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at industry level Yes Yes Yes Yes Yes Yes Yes Yes Yes
Table IV. Open in new tab Change in the level of product market concentration and M&A returns The table presents results of regressing CARs around merger announcements on several proxies for the level of product market concentration and other control variables. The sample consists of M&A transactions over the period 1980–2014. CAR is the cumulative abnormal return of the combined firm (acquirer plus target) over a 3-day event window [−1, 1] around the merger announcement (see Equation (2)). Related is a dummy variable that takes on a value of 1 if the acquirer and the target belong to the same NAICS three-digit industry, and zero otherwise. Industry is defined using the acquirer’s three-digit NAICS code. We control for deal characteristics by including the market values and book-to-market ratios of the target and acquiring firms, and dummies for pure cash transactions and pure stock transactions. See Section 4 for dataset description and full specification, and Appendix A for details of variable construction. Symbols a, b, and c indicate significance at 1%, 5%, and 10%, respectively. Variable . 1980–2014 . 1980–2000 . 2001–14 . Constant 0.0514c 0.0477a 0.0400a 0.0671c 0.0577b 0.0439a 0.0022 0.0621 0.0301b (0.0279) (0.0189) (0.0154) (0.0371) (0.0295) (0.0147) (0.0424) (0.0431) (0.0142) Log(HHI) –0.0012 –0.0034 0.0044 (0.0034) (0.0050) (0.0054) Log(Number of Firm) –0.0011 –0.0029 –0.0059 (0.0027) (0.0051) (0.0077) Concentration Index 0.0057 0.0159 –0.0079 (0.0164) (0.0196) (0.0288) Related –0.0296 0.0271a 0.0066c –0.0059 0.0073 0.0003 –0.0566 0.0549a 0.0176a (0.0261) (0.0075) (0.0035) (0.0308) (0.0086) (0.0045) (0.0371) (0.0117) (0.0060) Proxy for Concentration× Related 0.0046 –0.0053a 0.0206a 0.0005 –0.0019 0.0085 0.0094c –0.0098a 0.0531a (0.0039) (0.0014) (0.0081) (0.0047) (0.0016) (0.0107) (0.0054) (0.0023) (0.0198) N 5,281 5,281 5,281 3,340 3,340 3,340 1,941 1,941 1,941 Adjusted R2 3.88% 4.04% 3.97% 3.42% 3.44% 3.50% 6.33% 6.82% 6.39% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Acquirer’s industry-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Deal characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at industry level Yes Yes Yes Yes Yes Yes Yes Yes Yes Variable . 1980–2014 . 1980–2000 . 2001–14 . Constant 0.0514c 0.0477a 0.0400a 0.0671c 0.0577b 0.0439a 0.0022 0.0621 0.0301b (0.0279) (0.0189) (0.0154) (0.0371) (0.0295) (0.0147) (0.0424) (0.0431) (0.0142) Log(HHI) –0.0012 –0.0034 0.0044 (0.0034) (0.0050) (0.0054) Log(Number of Firm) –0.0011 –0.0029 –0.0059 (0.0027) (0.0051) (0.0077) Concentration Index 0.0057 0.0159 –0.0079 (0.0164) (0.0196) (0.0288) Related –0.0296 0.0271a 0.0066c –0.0059 0.0073 0.0003 –0.0566 0.0549a 0.0176a (0.0261) (0.0075) (0.0035) (0.0308) (0.0086) (0.0045) (0.0371) (0.0117) (0.0060) Proxy for Concentration× Related 0.0046 –0.0053a 0.0206a 0.0005 –0.0019 0.0085 0.0094c –0.0098a 0.0531a (0.0039) (0.0014) (0.0081) (0.0047) (0.0016) (0.0107) (0.0054) (0.0023) (0.0198) N 5,281 5,281 5,281 3,340 3,340 3,340 1,941 1,941 1,941 Adjusted R2 3.88% 4.04% 3.97% 3.42% 3.44% 3.50% 6.33% 6.82% 6.39% Year-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Acquirer’s industry-fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Deal characteristics Yes Yes Yes Yes Yes Yes Yes Yes Yes Clustering at industry level Yes Yes Yes Yes Yes Yes Yes Yes Yes
Similar to the analysis of profitability, we verify the robustness of our M&A results to alternative industry definitions, and find that our conclusions remain unaffected (the results based on NAICS four-digit industry definition, as well as industry classification based on text-based analysis, are tabulated in the Online Appendix). Overall, we conclude that market power considerations appear to be an important source of value during M&A transactions. These findings strengthen the claim that increased market power affects profit margins for firms in industries with increased concentration.
5. Substitution by Foreign Firms
So far, we have used industry measures to capture the overall product market environment. Industries and product markets are not, however, identical concepts. While domestic production is crucial in supplying final goods to consumers in the USA, imports remain a significant component of overall product market activity. If foreign firms have been filling the gap left by the disappearing US public firms, then the level of product market competition in US industries may not have been adversely affected by the increased concentration of domestic firms over the last two decades.
Foreign competition takes two main forms. Foreign firms can ship their products into the USA in the form of imports, or operate and sell directly out of the USA. The latter, despite being a different form of competition, is considered part of US domestic production. Operations of foreign firms in the USA are not captured by Compustat, but the census-based calculation of industry concentration does account for these operations. In addition to including sales of both public and private firms, the economic census tabulates the data of business establishments physically located in the USA regardless of their ownership, domestic, or foreign, and thus captures the operations of foreign competitors. Moreover, the census-based measures exclude the activity of foreign subsidiaries of US firms, which is also significant.
To ensure that our results using Compustat-based measures of concentration are also robust to the presence of foreign competition, thereby successfully describing the product market space rather than the portion of domestic production, we augment our main regressions of profitability and efficiency, as well as the regression of M&A announcement returns, with two proxies for foreign competition. Each proxy corresponds to a different form of foreign competition, as outlined above.
We first control for import penetration, which is one of the most common measures of foreign competition (e.g., Katics and Petersen, 1994; Borjas and Ramey, 1995; Cuñat and Guadalupe, 2009; Irvine and Pontiff, 2009; Autor, Dorn, and Hanson, 2013; Acemoglu et al., 2016). We use the total value of import activity at the NAICS three-digit industry level (Import), obtained from the US Census Bureau, as a proxy for international competition.21
To control for international competition in the form of foreign operations on US territory, we also include the scope of operations by foreign multinationals. Adding a control variable for sales by foreign multinationals captures the degree of foreign competition in industries not significantly affected by imports, such as transportation, accommodation services, or entertainment. To construct the proxy, we consider the activities of US affiliates of foreign multinational enterprises. We obtain information on total sales of majority-owned foreign affiliates by industry for the period of 2002–13 from the Bureau of Economic Analysis (BEA), and include the total sales figures in the USA (variable Intersales), converted into logs, in the main regressions of profitability and M&A returns.
Table V reports the results from the profitability regressions controlling for log(1 + Import) and log(1 + Intersales). We find that the positive and significant relation between concentration levels and firm profitability remains unaffected. For example, the middle panel of Table V indicates that the coefficient of log(HHI) in the estimation of the Lerner Index is 0.093, compared with the coefficient of 0.084 in the main analysis of Table II panel C, and both coefficients are significant at a 1% level. The coefficients on the number of firms and the concentration index are also close in magnitude to their values in the original specification, ta
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