metadata: authors: 0: author: Ayyagari,Meghana
1: author: Demirguc-Kunt,Asli
2: author: Maksimovic,Vojislav
count: World
docna: 0: docna: The Rise of Star Firms : Intangible Capital
            and Competition
docty: Policy Research Working Paper
subtopic: Labor Markets,Plastics & Rubber Industry,Business Cycles and Stabilization Policies,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Food & Beverage Industry,Common Carriers Industry,Construction Industry,General Manufacturing,Employment and Unemployment
teratopic: Industry,Social Protections and Labor,Macroeconomics and Economic Growth
abstracts: cdata!: There is a divergence in the returns of
            top-performing firms and the rest of the economy, especially
            in industries that rely on a skilled labor force, raising
            concerns about their market power. This paper shows that the
            divergence is explained by the mismeasurement of intangible
            capital. Compared with other firms, star firms produce more
            per dollar of invested capital, have higher growth,
            innovation, and productivity and are not differentially
            affected by exogenous competitive shocks. Their pricing
            power supports their high intangible capital investment.
            Some exceptional firms may pose concerns due to their
            potential to foreclose competition in the future.
repnme: repnme: The Rise of Star Firms : Intangible Capital
            and Competition
display_title: The Rise of Star Firms : Intangible
            Capital and Competition
listing_relative_url: /research/2019/04/31015787/rise-star-firms-intangible-capital-competition
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Manufacturing Sectors m1326521 1281,Jobs m1503931 2670,Labor Productivity m1504060 2756,Census Data m1504073 2763
publishtoextweb_dt: 2019-04-29T00:00:00Z
historic_topic: Industry,Social Protections and Labor,Macroeconomics and Economic Growth
no_of_pages: 63
author_keys: 0: author_key: 606806
1: author_key: 94122
2: author_key: 430603
model_extractions: Policy Research Working Paper 8832 The Rise of Star Firms Intangible Capital and Competition _Meghana Ayyagari Asli Demirguc-Kunt Vojislav Maksimovic_ Europe and Central Asia Region Office of the Chief Economist April 2019
Policy Research Working Paper 8832

# **Abstract**

There is a divergence in the returns of top-performing firms and the rest of the economy, especially in industries that rely on a skilled labor force, raising concerns about their market power. This paper shows that the divergence is explained by the mismeasurement of intangible capital. Compared with other firms, star firms produce more per dollar of invested capital, have higher growth, innovation, and productivity and are not differentially affected by exogenous competitive shocks. Their pricing power supports their high intangible capital investment. Some exceptional firms may pose concerns due to their potential to foreclose competition in the future.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ayyagari@gwu.edu, ademirguckunt@worldbank.org, and vmaksimovic@rhsmith.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Rise of Star Firms: Intangible Capital and Competition**

_Meghana Ayyagari_<sup>_∗_</sup> , _Asli Demirguc-Kunt_<sup>_†_</sup> and _Vojislav Maksimovic_<sup>_‡_</sup>

> _∗_ School of Business, George Washington University, Ph: 202-994-1292; Email: ayyagari@gwu.edu

> _†_ The World Bank, Ph: 202-473-7479; Email: Ademirguckunt@worldbank.org

> _‡_ Robert H. Smith School of Business at the University of Maryland, Ph: 301-405-2125; Email: vmaksimovic@rhsmith.umd.edu

The authors thank Paulo Bastos, Miriam Bruhn, Michael Faulkender, Francisco Ferreira, Murray Frank, E. Han Kim, David McKenzie, Terrance Odean, Bob Rijkers, Rene Stulz, Luke Taylor, and seminar participants at the Mitsui Symposium on Comparative Corporate Governance and Globalization at the University of Michigan, 2019 Annual Meetings of the Mid-west Finance Association, University of Maryland, George Mason University, and the SEC for helpful comments and suggestions, and Elliot Oh for excellent research assistance. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. An earlier draft of this paper was circulated under the title ”Who are America’s Star Firms?”
# **Introduction**

A great deal of attention has been paid to two trends in the US economy: (1) the emergence of star firms that have pulled away from the rest of the economy (Furman and Orszag [2015], Koller, Goedhart, and Wessels [2017], Autor, Dorn, Katz, Patterson, and Van Reenen [2017]) and (2) the introduction of new technologies together with a fundamental structural change towards a more intangible intensive economy (Corrado and Hulten [2010]) with corresponding implications for corporate investment and the overall economy.<sup>1</sup> However, the two trends are typically analyzed separately, and the rise of star firms has been linked to a decline in market competition. In fact, we have little systematic evidence on the characteristics of these star firms. Is the rise of star firms the result of increasing market power and increasing concentration in the U.S.? What is the role of intangible capital? What is the relation between productivity and returns on invested capital and market value?

In this paper, we use a dataset of publicly listed firms in the United States from the Compustat database to identify star firms<sup>2</sup> and their industries. We examine the role of market power, intangible capital, technological change at the industry level (dependence on routine manual tasks vs high cognitive skills), and productivity in influencing star firm status. We use three measures of market power - a firm-level measure of operating markups, firm-level market share, and a raw measure of firm size.<sup>3</sup> Finally, we examine the persistence of the star firms’ performance and whether they differ in their investment and output per unit of capital compared to other firms in the economy.

Determining whether the performance gap between star firms and other firms is the result of luck, market imperfections, measurement of intangible capital, or a reflection of successful idiosyn-

> 1Several papers have explored the implications of the rise in intangible assets and knowledge capital on corporate investment (e.g. Peters and Taylor [2017], Falato et al. [2013]) and other macroeconomic impacts (e.g. Atkeson and Kehoe [2005], McGrattan and Prescott [2010], Eisfeldt and Papanikolaou [2014] and Caggese and Perez-Orive [2017]).

> 2Following Furman and Orszag [2015], we define star firms as firms in the top 10% of Return on Invested Capital (ROIC), calculated pre-tax, in the US in a particular year. ROIC is an important profitability metric in corporate finance measuring how efficiently a company can allocate its capital to profitable investment and has been widely used in the literature (e.g. Ben-David, Graham, and Harvey [2013]) and by practitioners (e.g. Koller [1994], Koller et al. [2017]). For instance, David Benoit writing for the Wall Street Journal argued that General Motors placated activist investors with the help of higher return on invested capital (ROIC). See _The Hottest Metric in Finance: ROIC_ , Wall Street Journal (2016). However, in a parallel treatment we obtain similar results when we use Tobin’s Q to define star firms.

> 3Our implementation of operating markups, which follows the work of Foster et al. [2008], De Loecker et al. [2018], and Traina [2018] is discussed below.

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cratic firm growth strategies<sup>4</sup> is a public policy priority that will shape policies that promote or regulate high-value firms. The dominant concern is that these firms are gaining their distinction through market power by restricting competition which enables them to charge high prices without investing much.

Using conventional ROIC measures, in Figure 1, we find that there has indeed been a run-up in the ROIC of the top decile of large, non-financial sector, publicly-listed U.S. firms.<sup>5</sup> Over the period 1965-2015, the ratio of the 90<sup>_th_</sup> percentile ROIC firm to the median ROIC firm has increased by over 69%. Importantly, we find that the star firms whose returns are diverging from the rest of the firms are in industries that require high cognitive skills or that rely on high levels of intangible capital and that in these industries average returns are higher. In industries where the tasks involve routine manual skills and which score low on non-routine cognitive and complex problem solving skills, we see lower returns and do not see the star firms pulling away from the rest of the economy.

However, conventional return metrics do not capitalize research and development, brand capital, or other forms of organizational capital. The consequences of not measuring intangible capital are far-reaching because they affect measures of firms’ earnings, identification of variable costs, capital investment and estimates of pricing power, outcomes which are subject to controversy. For instance, De Loecker and Eeckhout [2017] show that there is a dramatic rise in firm pricing power in the U.S. using Cost of Goods Sold (COGS) as a measure of variable cost, Traina [2018] argues that once we include Selling, General, and Administrative Expenses (SGA) which are an increasingly vital share of variable costs for firms and accounts for intangible organization/management capital, there is no rise in markups. To address these issues, we adjust the conventional ROIC measures, measures of capital stock, variable costs and measures of pricing power to take into account investment in intangible capital.

Once we re-compute the ROIC calculations to factor in estimates of intangible capital from the

> 4Successful idiosyncratic growth strategies may also be due to successful innovation or superior management practices (e.g. Bloom and Van Reenen [2007]).

> 5We restrict our sample to large firms (defined as firms with assets more than $200 million in 2009 dollars, adjusted for inflation) to replicate the equivalent figure in the previous studies. However, the evidence in Council of Economic Advisors [2016], Furman and Orszag [2015], and Koller et al. [2017] is based on a proprietary dataset of US firms from McKinsey & Co. whereas Figure 1 is based on publicly available Compustat data. If we were to use the full sample of Compustat firms without restricting to large firms, we get much higher increases in ROIC for the top decile of

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finance literature (see Peters and Taylor [2017] and the references therein), we find that both the run-up by top decile of firms and the much higher mean returns in the cognitively skilled industries disappear. Thus, the differences that we found earlier in firm differentiation between industries are likely attributable, in great part, to not accounting for intangible capital consistently. Industries that rely heavily on complex cognitive skills are likely to have higher amounts of intellectual and organizational capital, which is not measured by ROIC prepared according to generally accepted accounting principles. We find similar results using Tobin’s Q adjusted for intangible capital in place of ROIC.

Once we adjust the markups based on operating expenses for intangible capital, there is only a modest rise in markups over time, and most of this increase is in the top 10% of firms in high skilled industries. Moreover, the relation between ROIC and markups is weaker in industries that rely on intangible capital, and has been growing weaker over time across all industries.<sup>6</sup> When we investigate the link between markups and star firm status, we do see that markups are positively related to high profits and greater probability of being a star. This is a potential concern, as high markups, as well as high market share, are commonly interpreted as evidence of welfare reducing market power. However, the existence of higher markups is not sufficient evidence of market imperfections. If star firms have lower costs, produce higher quality products, or are sole producers of innovative products, they may realize higher markups even in the absence of anticompetitive behavior or restrictions on output that reduce customer welfare.

We next investigate whether there is evidence that star firms, especially those with high markups, restrict output and invest less than other firms. First, we find that at every level of markup, the star firms have higher Output (sales/invested capital), Capex, and R&D investment compared to other firms. Second, a large fraction of star firms have relatively low markups. Third, high productivity predicts star status and is comparable to markups in predicting profits and market value.

Finally, we test the effect of an exogenous increase in market competition on market power and star status directly. If the main driver of high returns is high market power, then star firms will be

> 6The relation between other measures of market power such as size and market share is economically weaker than markups.

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differentially less exposed to increases in competition. We measure increased competition in U.S. manufacturing by penetration of Chinese imports. To address endogeneity issues, we instrument Chinese imports into the U.S. by Chinese imports into eight other developed economies. While we find that an exogenous shock to competition (increase in Chinese imports to the US) affects ROIC, output, and markups of all firms negatively, we find no evidence that star firms are differentially affected by import competition compared to other firms in the economy.

Overall, once intangible capital is taken into account, there is not a growing polarization in the economy between firms with high returns and other firms. While there is positive relation between star firm status and pricing power, star firms produce more and invest more at every level of pricing power. Taken together, these results suggest that the star firms are not behaving like true monopolists i.e. raising prices and reducing output and instead seem to be using the pricing power to acquire intangible assets. Thus, the conventional focus on market power that does not take into account intangible capital has the potential of penalizing highly skilled and productive firms, with adverse effects on the economy.

Our results are robust to a number of checks and alternate specifications. We find all our conclusions above to hold even when we tighten the requirement for star status down to the top 100 or 150 firms (when ranked by ROIC) each year. There is no run-up over time of the top 100 or 150 firms once we correct for intangible capital. We do find that the effects of star status are persistent. Five years later, star firms have higher ROIC, sales growth, and Tobin’s Q suggesting that our results are not driven by firms that have randomly realized high returns in specific years. We also find similar results when we use an alternative definition of star status which categorizes star firms as those in the top decile of market value (Tobin’s Q), taking into account the adjustment for the value of intangible capital.

To account for the fact that cash holdings at some of the technology companies are substantial, we use yet another definition of star status where we consider only non-cash working capital in our definition of ROIC.<sup>7</sup> In addition, in sensitivity tests we also find that our results are robust

> 7It is not clear how we should treat firms’ holdings of cash and near-cash securities. At one extreme, they are required precautionary balances, part of the firm’s invested capital. At the other extreme, they mostly consist of excess cash retained by the firm’s managers and should not be used in evaluating the economic value of the firm’s business.

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to varying the fraction of intangible capital that is used to correct the ROIC measures.<sup>8</sup> All our results hold with these alternative A policy implication of our analysis is that there is little evidence that extraordinary returns are being realized as a result of high industry concentration or high market share. To look at possible disruptive and system wide effects of star firms, we need to focus our search on a very small number of firms. The analysis of these firms is not straightforward, both because of their small numbers and their adoption of pricing policies that reduce current returns in expectation of higher subsequent returns.

A very small number of firms are often cited in the press as disrupting conventional business models, Amazon, Facebook, Google, Apple, and Microsoft (AFGAM), and we do see that these firms (especially Apple) have supernormal returns to capital. However, some of their markups, such as that of Apple and Amazon are not necessarily much larger than those of the 90<sup>_th_</sup> percentile firm over the sample period. As we discuss below, these firms may have more market power than is even evidenced by their markups. In particular, they may be following strategies that emphasize holding markups and profits below their short run optimal values and growing quickly as a means of dominating their industries in the long run. Such strategies pose complex public policy challenges.

Our paper is related to the growing literature on the rise in concentration in U.S. industries in the last two decades (Guti ́errez and Philippon [2017] and in markups and market power (De Loecker and Eeckhout [2017]; Barkai [2016]).<sup>9</sup> Grullon et al. [2017] find that firms in industries that are more concentrated enjoy higher profit margins and positive abnormal stock returns. They proxy price-cost margins by the Lerner Index (operating income after depreciation scaled by total sales) and returns by Return on Assets (ROA) and make no adjustments for intangible capital in their calculations. The focus in their paper is on increase in industry concentration over time rather than on identifying characteristics of star firms. Baker and Salop [2015] directly link a decline in the enforcement of anti-trust statutes to increases in concentration and rising inequality in the U.S. Kurz

> [2017] argues that modern developments in information technology have created higher barriers to

> 8We follow Peters and Taylor [2017] in constructing our measure of intangible capital to include knowledge capital (R&D expenses) and organization capital (SG&A expenses). We obtain similar results when we include only knowledge capital instead of organization capital in our definition of intangible capital.

> 9For a strong dissenting view arguing that the observed changes in concentration computed at the national level are economically immaterial, see Shapiro [2018].

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entry leading to a rise in market concentration and increasing monopoly power of firms. Alexander and Eberly [2018] and Crouzet and Eberly [2018] argue that the rise in intangible investment in retail trade can account for the increase in concentration and decreased investment. By contrast, the focus in our paper is on the characteristics of star firms and providing a link to the rise of intangible capital, increases in market power and industry concentration. We find little evidence for the hypothesis that star firms are exercising market power in traditional ways by restricting investment.

Our paper also contributes to the recent literature on star firms. Several researchers have used alternative definitions of star firms, and have focused on the consequences of the rise in industry concentration. Autor et al. [2017] use a definition of star firms based on productivity and market share and argue that star firms contribute to inequality within the US because they are more profitable and have lower wage to sales ratios, despite paying higher unit wages. Hall [2018] studies mega firms, defined as firms with more than 10,000 workers and finds no evidence that industries with high proportion of these firms have high markups but does find that markups increase in sectors with rising share of mega firms.<sup>10</sup> There is also concern that star firms may be creating systemic problems and disruptions through the economy. For instance, Van Reenen and Patterson [2017] suggest that the rise of star firms will lead to a fall in economic dynamism and productivity with declining pay and job opportunities for the average worker. In contrast to these papers, we use a market based measure of returns on invested capital to characterize star firms, which is also the definition that studies highlighting the emergence of star firms have focused on such as Furman and Orszag [2015], Koller et al. [2017], Council of Economic Advisors [2016].

More generally, our paper points to the importance of adjusting for intangible capital in corporate finance research. Differences in intangible capital across firms and over time not only affect ROIC and our evaluation of investment and market power, but most likely will effect optimal capital structures, governance, and firms’ cash policies.

> 10 There is also an international literature where researchers have looked at export markets or labor productivity to document the rise in market power. De Loecker et al. [2016] examine the effect of tariff reductions on competition and markups. Freund and Pierola [2015] show that much of the exports of many countries can be attributed to a small number of firms which they refer to as export superstars. Andrews et al. [2015] highlight the notion of frontier firms, a small number of firms that are much more efficient than the bulk of their competitors. None of these papers look at returns or the role played by intangible capital.

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# **1 Identifying Star Firms**

We use data from Compustat that provides detailed financial information on publicly traded firms in the US over an extended period of time. We drop cross listed ADRs and restrict the sample to firms incorporated in the US. We also drop firms in Utilities (SIC 49), Finance, Insurance and Real estate (SIC 60-69) and Public Administration (SIC 90-99), observations with missing SIC codes, negative values for employees, sales, total assets, current assets and current liabilities, fixed assets, cash, and goodwill and missing total assets or sales.

The advantage of using Compustat is that we have detailed balance sheet information that allows us to compute intangible capital. The caveat however, is that there are firm selection issues. First, it may be that listed firms, as a class, might not consistently represent star firms. Doidge, Kahle, Karolyi, and Stulz [2018] and Kahle and Stulz [2017] show that there are fewer US listed corporations today than 40 years ago. However, Grullon et al. [2017] argue that the void left by listed firms has not been filled by an increase in the number of private unlisted businesses. Using US Census data that includes both private and public firms, they show that even though more private firms have entered the economy, their marginal contribution to the aggregate product market activity has been relatively small. Public firms also account for one third of total US employment (Davis, Haltiwanger, Jarmin, Miranda, Foote, and Nagypal [2006]) and about 41% sales (Asker, Farre-Mensa, and Ljungqvist [2014]). Also using U.S. Census data, Maksimovic, Phillips, and Yang [2017] show that high initial firm quality at birth predicts subsequent listing decision. These findings suggest that while our sample will not be picking up small and young potential star firms in their private stages, we are targeting the sample of firms among which economically significant stars are highly likely to arise.

The second, and potentially more important issue, as pointed out by Doidge et al. [2018], is that small, young, high-technology firms may benefit from private status where specific financial institutions, such as venture capital partnerships and private equity firms better meet their financing needs than public capital markets. Thus, such firms may be underrepresented in our sample of star firms. To the extent that this listing gap has emerged only since 1999 (see Doidge, Karolyi, and Stulz [2017]), the early part of our sample period is immune to this.

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We define star firms as firms that realize high returns for their investors. We begin by using a standard definition of Return on Invested Capital (ROIC) as our measure of returns, where ROIC for firm _i_ in year _t_ is defined as: where _EBIT_ is Earnings before Interest and Taxes (Compustat item EBIT) and _AM_ is Amortization of Intangible Assets (Compustat item AM). ROIC, as used in the Council of Economic Advisors [2016] report and Ben-David et al. [2013], among many others, computes the earnings that a corporation realizes over a period, as a fraction of capital that investors have invested into the corporation. The advantage of ROIC is that it measures investment capital as more than physical capital (fixed asset investment) which Doidge et al. [2018] show to be a declining portion of total assets over time in the US.

We adopt a relatively conservative definition for Invested Capital as the amount of net assets a company needs to run its business: where _PPENT_ is Net Property, Plant, and Equipment, _ACT_ is Current Assets, _INTAN_ is Total Intangible Assets, _LCT_ is Current Liabilities, _GDWL_ is Goodwill that represents the excess cost over equity of an acquired company, _CHE_ is Cash and Short-term Investments, and _SALE_ is net sales. All these variable labels are the corresponding items in Compustat.<sup>11</sup> The intangible assets as registered in Compustat, _INTAN_ , include externally purchased assets like blueprints, copyrights, patents, licenses etc. and goodwill but do not include internal intangible assets like R&D and SG&A. We exclude Goodwill, which are the intangible assets arising out of M&A transactions when one company acquires another for a premium over fair market value, in the computation of invested capital in equation 2. Thus, our measure is not distorted by price premiums paid for in acquisitions, allowing for an even comparison of operating performance across

> 11We replace missing values of AM and GDWL with 0.

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companies. As a result, ROIC measures the return that an investment generates for the providers of capital and reflects management’s ability to turn capital into profits.<sup>12</sup> In calculating ROIC, we also subtract cash stocks in excess of those estimated required for transactions purposes. Following Koller et al. [2017], we treat cash above 2% of sales as excess cash and subtract it from the firm’s invested capital. In section 4.1 we undertake robustness tests allowing for varying percentages. Our estimates are not affected by firms’ decisions on whether to stockpile cash in low-tax jurisdictions in order to manage their tax liabilities, as is the case of many large U.S. multinationals.

We define _ROIC_<sup>_unadj_</sup> _Star_ as a dummy variable that takes the value 1 if the firm’s ROIC is above the 90<sup>_th_</sup> percentile of ROIC across all firms in the US economy in a particular year and 0 otherwise. To replicate the figure in previous studies such as Furman and Orszag [2015] and Koller et al. [2017], we restrict our sample to large firms (defined as firms with assets more than $200 Million in 2009 dollars, adjusted for inflation) and drop firms with negative invested capital. As noted before, Figure 1 shows that there is a large rise in capital returns over the past three decades where the ratio of the 90<sup>_th_</sup> percentile ROIC firm to the median ROIC firm has increased by over 69%. We also see that the divergence of the top decile of firms from the rest of the economy really takes off in the 1990s.<sup>13</sup> These results are qualitatively consistent with Furman and Orszag [2015], Koller et al. [2017] and the Council of Economic Advisors [2016], all of which were produced using a proprietary dataset of US firms from McKinsey & Co.

# **1.1 Role of Human Capital**

Firms differ in the complexity of tasks that they perform and the product market may reward certain capabilities more than others. To address this issue, we construct industry-level indices of the composition of tasks firms perform and assess how they affect the likelihood of a firm from that industry becoming a star firm. In creating these indices, we draw on a large labor market

> 12In particular, if we do not subtract _GDWL_ from _INTAN_ we would run the risk of capitalizing future monopoly rents reflected in high acquisition premiums, thereby incorrectly attenuating the relation between ROIC and pricing power when one firms buys another.

> 13In an early version of the paper, we find much higher increases in ROIC (over 190%) for the 90th percentile without the sample restrictions to large firms and firms with positive invested capital.

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literature in economics. Autor et al. [2003], Costinot et al. [2011] and Acemoglu and Autor [2011], have argued that globalization and advances in technology and computerization have increased the comparative advantage of individuals who perform non-routine tasks requiring problem solving, intuition, persuasion, and creativity.

To obtain measures of human capital, following Ayyagari and Maksimovic [2017] we use O*NET, a database maintained by the U.S. Department of Labor that provides data on occupation-specific descriptors that define the key features of an occupation such as worker abilities, technical skills, job output, work activities, etc. We focus on the following three measures of human capital: _CPS_ (Complex Problem Solving) which is identifying complex problems and reviewing related information to develop and evaluate options and implement solutions; _NRCOG_ (Non-routine Cognitive Analytical skills) from Keller and Utar [2016] which is the sum of Mathematical Reasoning, Inductive Reasoning, Developing Objectives and Strategies, and Making Decisions and Solving Problems; and _RMAN_ (Routine Manual) from Keller and Utar [2016] which is the sum of Spend time making repetitive motions, Pace Determined by Speed of Equipment, Manual Dexterity, and Finger Dexterity. We merge the occupation-level scores with the Occupational Employment Statistics (OES), a US establishment level dataset from the Bureau of Labor Statistics, where workers are classified into occupations on the basis of the work they perform and skills required in each occupation. We compute a weighted average across occupations in each firm weighting by the number of employees in each occupation to obtain a score for each establishment. We then take weighted averages across all establishments in an industry to compute industry-level skill scores.

We separate our sample into high and low skill industries based on the _CPS_ , _NRCOG_ , and _RMAN_ scores where high skill is defined as greater than or equal to the median value for each of the skill measures and low skill is defined as less than the median value for each of the skill measures. In Figure 2, we identify star firms in each of these sub-samples as firms in the top 10% of ROIC in that sample in a particular year. We again focus on large firms to be consistent with the sample in Figure 1. Figure 2 shows that the ROIC and the run-up for star firms is higher in industries with high skill as measured by low RMAN. We find similar increases in high skill industries as measured by high CPS and high NRCOG as seen in Figure A1 of the Internet Appendix. If this finding is correct, it would imply that firms employing a high skill labor force are also more likely to earn 10
higher returns and that there is a growing divergence between the most profitable of those firms and the other high-skill firms. We also see a large divergence between the ROIC in high skilled versus low skilled industries when we split industries by RMAN, CPS, or NRCOG. However, a concern with these estimates is that in high skilled industries, intangible capital is being mis-measured so as to reduce total invested capital, thereby inflating ROIC numbers. Indeed when we split industries by their Intangible Capital/Assets ratio into Low ICAP industries (Intangible capital/Assets ratio is less than median) and High ICAP industries(Intangible capital/Assets ratio is greater than or equal to median), we see the divergence to exist mainly in High ICAP industries. A detailed definition of ICAP is provided in the following section.

# **1.2 Mis-measurement of Intangible Capital**

One of the concerns with the above definition of star firms is that financial statements do not measure intangible assets accurately and the consequent underestimation of intangible capital is likely to be more important in high skilled industries. This would lead to overestimation of ROIC and biased regression estimates. The concern that conventional measures of invested capital do not properly capitalize the value of intangibles is a long standing one. Earlier attempts to address it include Peles [1971], Hirschey [1982], and Falato et al. [2013]. More recently, Peters and Taylor [2017] have produced firm-level estimates of intangible capital and shown that including intangible capital in the definition of Tobin’s q produces a superior proxy for investment opportunities. They also show that their adjustments are not sensitive to specific assumptions on the depreciation of intellectual capital. Thus, while these measures are, by construction, approximations, they are arguably the best available.

Hence, as an alternate definition of invested capital, we replace the _INTANit_ in equation (2), with the new definition of intangible capital from Peters and Taylor [2017], _ICAPit_ .

where _ICAPit_ , is defined as the sum of externally purchased intangible capital (Compustat item 11
_INTAN_ ) and internally purchased intangible capital, both measured at replacement cost. Internally purchased intangible capital is in turn measured as the sum of knowledge capital _K_ _~~i~~ nt know_ and organization capital _K_ _~~i~~ nt org_ . The perpetual-inventory method is applied to a firm’s past research and development expenses (Compustat item _XRD_ ) to measure the replacement cost of its knowledge capital. Similarly, a fraction (0.3) of past selling, general, and administrative ( _SGA_ ) spending is used as an investment in organization capital, which includes human capital, brand, customer relationships, and distribution systems.<sup>14</sup> The estimates of _ICAP_ , _K int_ _~~k~~ now_ , and _K_ _~~i~~ nt org_ have been made publicly available by Peters and Taylor [2017].

Correspondingly, we also adjust the profits in the numerator to account for the use of intangible capital in computing invested capital. Thus, the new ROIC is given by: where _ADJPRit_ = _EBITit_ + _AMit_ + _XRDit_ + 0 _._ 3 _× SGAit_ where _δRD_ is the depreciation rate associated with knowledge capital and is set to 15% following Peters and Taylor [2017] and _δSGA_ is the depreciation rate associated with organization capital and is set to 20% following Falato et al. [2013].

Note that using an adjustment for intangible capital affects ROIC in two ways. First, it increases the denominator by the amount of the adjustment for intangible capital. Second, R&D and a portion of SGA expenditure, which would previously have been expensed are now treated as additions to capital stock. Thus, it is not subtracted from the firm’s conventionally calculated earnings (EBIT) to obtain the adjusted earnings. However, since the stock of intangible capital is now treated as an asset, an additional depreciation expense is now deducted from EBIT. This second adjustment either increases or decreases the numerator of ROIC, depending on the level of

> 14Since Compustat item _XSGA_ is the sum of SG&A and R&D, we follow the procedure in Peters and Taylor [2017] to isolate SGA as XSGA-XRD-RDIP where RDIP is In-Process R&D. We replace missing values of XSGA, XRD, and RDIP with 0.

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current R&D and SG&A expenditures compared to the stock of intangible capital.

After dropping firms with negative invested capital, missing or negative book value of assets or sales, and firms with less than $5 million in physical capital (Compustat variable _PPEGT_ )<sup>15</sup> and top and bottom 1% outliers in _ROIC_ , we define _ROIC Star_ as a dummy variable that takes the value 1 if the firm’s _ROIC_ is above the 90<sup>_th_</sup> percentile of _ROIC_ across all firms in the US economy in a particular year and 0 otherwise. We also focus on the years 1990-2015 for all the figures and tables henceforth since the high run-up in ROIC in Figures 1 and 2 starts around 1990.

When we correct invested capital to include intangible capital, we see no run-up in _ROIC_ for the top 10% of firms in Figure 3. These results are robust to a number of checks: As shown in Figure A2 of the Internet Appendix, we obtain a similar picture when we restrict the sample to large firms, and extend the time period to 1965 to be consistent with the sample in Figure 1. In Figure A3 of the Internet Appendix, we obtain a similar picture if we were to NOT subtract goodwill from our estimates of invested capital. Finally, in Figure A4 of the Internet Appendix, we narrow our definition of star firms and plot the mean ROIC for the Top 100 and Top 150 firms each year. Once again we find no run-up in ROIC over time for even the top 100 or 150 firms.

In Figure 4, we present estimates for high skilled versus low skilled industries. The run-up we saw in Figure 2 in high skilled (Low RMAN) industries and high ICAP industries disappears once we adjust for intangible capital. Figure A5 of the Internet Appendix shows that we obtain a similar picture if we were to define skill in terms of complex problem solving skills (CPS) or nonroutine cognitive analytical skills (NRCOG). In section 4 of the paper, we discuss various robustness tests and alternate definitions of intangible capital to address concerns with the definition of cash holdings.

# **2 Estimating Concentration and Market Power**

As a measure of competition, we define the Herfindahl Index (HHI) of market share in each 3-digit NAICS industry in each year. Specifically, in each year _t_ for each 3-digit NAICS industry _j_ , industry

> 15We apply the PPEGT filter since Peters and Taylor [2017] recommend that the intangible capital adjustment is not appropriate for firms with less than $5 million in physical capital.

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concentration is measured as: where _si_ is market share of firm _i_ given by <u>�</u> _<u>SALEj</u>_<sup>_SALE_and</sup><sup>_N_istotalnumberoffirmsinindustry</sup><sup>_j_</sup> in year _t_ . A higher HHI implies weaker competition.

While HHI measures industry concentration, it treats all firms in an industry identically. Thus, HHI ignores potential firm-specific indicators of market power such as firm size and market share. We use _Log(Assets)_ as a measure of firm size where assets are the Compustat item _AT_ . _Market Share_ is the ratio of firm _i_ ’s sales to total industry _j_ ’s sales in a particular year, to allow for the possibility that large market share firms in a concentrated industry realize different returns compared to low market share firms.

We also use firms’ markup of price over marginal cost, _Markups_ , as a firm-level measure of the firm’s pricing power. De Loecker and Eeckhout [2017] show that average markups have increased from 18% above marginal costs (where variable costs are measured by Cost of Goods Sold) in 1980 to 67% above marginal cost by 2014. Recent work by Traina [2018] however argues that _COGS_ grossly underestimate firms’ variable costs. Other expenses, such as _SGA_ are increasingly a lion’s share of variable costs for US firms. Traina shows that once we include _SGA_ in the calculation of marginal costs, there is no increase in public firm markups.

Consistent with Traina [2018], we base our measure of variable inputs on Operating expenses (Compustat item _OPEX_ ) rather than Cost of Goods Sold (Compustat _COGS_ ) as in De Loecker and Eeckhout [2017]. _OPEX_ includes SGA expenses whereas _COGS_ only includes costs of production such as material, labor, and overhead and does not include SGA expenses. COGS has been a declining share of variable costs for US firms as shown in Figure A6 of the Internet Appendix. We differ from Traina [2018] in our adjustment of operating expenses to include the correction for intangible capital. Specifically, following Peters and Taylor [2017], we treat research expenditures as an intangible investment and a portion of the SGA as an organizational investment. Thus, intangible investments such as R&D and a portion of SGA are subtracted from _OPEX_ in order to 14
obtain our measure of variable costs, _OPEX*_ : The standard definitions of markups rely on the cost shares framework by Foster et al. [2008] or the production framework by De Loecker and Warzynski [2012] and De Loecker and Eeckhout [2017]. To estimate markups, we primarily rely on the cost shares approach where markups can be computed directly from the data (Markups is simply Sales/Variable Cost, that is _Sales/OPEX*_ ), as discussed in Foster et al. [2008]. While requiring constant returns to scale, the derivation is transparent and is not subject to econometric and optimization challenges faced by alternative methods that rely on explicit estimates of productivity using the control function approach Rovigatti and Mollisi [2018]. Furthermore, this is close to the Lerner Index (measured by the difference between the output price of a firm and the marginal cost divided by the output price) that is widely used in the literature as a measure of market power (see e.g. Grullon et al. [2017], Guti ́errez and Philippon [2017]). However, for consistency with the preceding literature we also detail our estimation of markups using the production function approach in the Internet Appendix. The latter is based on the cost minimization of a variable input of production, without additional assumptions on firm demand or competition. Heuristically, this measure takes the firm’s capital stock as given, and estimates the markup that the firm can charge customers over its variable costs.

Table A1 of the Appendix presents summary statistics of the main variables in our analysis. We drop top and bottom 1% outliers in constructing all our firm-level variables. In addition to the variables discussed above we also use a proxy for firm age which is defined as the number of years since the firm first appears in Compustat following Giroud and Mueller [2010].

The mean _ROIC_ in our sample once we adjust for intangible capital is 13%. By definition, 10% of our sample is classified as star firms. Once we take into account intangible capital, the average markup is 1.31 using the cost shares approach ( _Markups_ ) and 1.221 using the production function approach ( _Markups_ _~~p~~ rodfn_ ). The latter has fewer observations because they are first estimated within each industry necessitating a minimum number of firms in that industry. The average Herfindahl industry concentration is 0.09 and the average firm market share is 0.015.

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# **3 Empirical Findings**

## **3.1 Is there a rise in Markups?**

Using marginal costs measured by COGS, De Loecker and Eeckhout [2017] document a stunning rise in markups in the US over the past three decades. Traina [2018] however argues that COGS are a declining share of firm costs and once we use operating expenses that includes COGS and SGA, there has been no rise in firm markups. This is an important policy question as it also speaks to the discussion on the rise in industrial concentration and decline in labor share (see Grullon et al. [2017] and Autor et al. [2017]). Once we do take into account intangible capital, how have markups evolved over this period?

In Figure 5, we estimate the evolution of markups using capital adjustments, _Markups_ , in the US economy over our sample period. We see an upward trend only for the 90<sup>_th_</sup> percentile firms. To see if there is dispersion in markups by industry skill, we look at industries that rely heavily on routine manual tasks versus those that do not rely heavily on routine manual tasks and industries that have high vs low ratio of intangible capital/assets in Figure 6. We find that markups are higher in high skilled industries (low RMAN) than low skilled industries and in high ICAP industries than low ICAP industries for the top 10% of firms.

Overall, we see that there has indeed been a rise in markups once we adjust operating expenses for investment in intangible capital. While there is just a modest divergence between the top 10% of firms with the highest markups and the rest of the economy, we see these differences amplified in high skilled industries which use more intangible capital.

## **3.2 Future Performance of Stars**

We define a firm as a star firm in a given year if its return on invested capital is in the top 10% of firms in that year. It could be the case that there is a lot of churning in this top 10% of firms each year with different sets of firms randomly realizing high returns each year. In the first part of this sub-section we explore if these high returns are persistent and if being a superstar is associated with superior performance. To this end, we construct five non-overlapping panels: 1990-1995, 1995-2000, 16
2000-2005, 2005-2010, and 2010-2015 and examine if being a star is associated with higher average performance in the subsequent five year period. Specifically, for firm _i_ in industry _j_ in year _t_ , the regression we estimate is as follows: _Performanceijt_ = _α_ 0 + _β_ 1 _× Log_ ( _Assets_ ) _it−_ 5 + _β_ 2 _× Log_ ( _Age_ ) _it−_ 5 + _β_ 3 _× Starit−_ 5 We look at the following four performance measures: 5-year average _ROIC_ , _Sales growth_ computed as the five year log difference in sales divided by 5, _Employment growth_ computed as the five year log difference in employment divided by 5, and 5-year average _Labor Productivity_ . Using stacked panel regressions, we examine the association between each of these measures and star firms identified at the beginning of each panel. We also control for size and age at the beginning of each panel. All regressions also include industry and year fixed effects.

Columns 1 and 2 of Table 1 shows that both star status and high _ROIC_ are on average positively associated with higher average _ROIC_ in the subsequent five year period. The predicted value of average 5-year _ROIC_ for firms that were superstars five years ago is 44.01 compared to 7.48 for firms that were not superstars five years ago. Columns 3-8 show that prior star status is also associated with higher sales growth, employment growth, and labor productivity. Replacing _ROIC_ Star by _ROIC_ yields very similar results except for sales growth where it is not significant.

While the above results rely on defining star firms based on returns to equity, as an alternate definition, we define stars in terms of Tobin’s Q. Again following Peters and Taylor [2017], we define _Q_ as the ratio of Firm value to _TOTCAP_ which is the sum of physical ( _PPENT_ ) and intangible capital ( _ICAP_ ): where _V_ is the market value of the firm defined as the market value of equity (=total number of common shares outstanding (Compustat item _CSHO_ ) times closing stock price at the end of the fiscal year (Compustat item _PRCC_ ) plus the book value of debt (sum of Compustat items _DLTT_ and _DLC_ ) minus the firm s current assets (Compustat item _ACT_ ) which includes cash, inventory, and marketable securities. After dropping top and bottom 1% outliers in _Q_ , we define _Q star_ as a 17
dummy variable that takes the value 1 if the firm’s _Q_ is above the 90<sup>_th_</sup> percentile of _Q_ across all firms in the US economy in a particular year and 0 otherwise.

While _Q_ has the advantage of using a market valuation of the firm’s prospects, the measure is prospective in that it captures the value of the firm’s investment opportunities given the market’s view of its investment plans (e.g. Novy-Marx [2007]) and thus, it may not measure current product market performance. In Internet Appendix Table A1, we find that Q stars are also associated with higher Tobin’s Q, sales growth, employment growth, and labor productivity in the subsequent five year period. We find similar results replacing _Q_ Star by _Q_ .

Overall these results suggest that star status is associated with higher future performance and there is a fair degree of persistence in star status as firms that were stars five years ago have higher average returns, valuation, growth, and productivity over the subsequent five-year period than firms that were not stars.

# **3.3 Explaining the Rise of Star Firms**

To explore the incidence of star firms, for firm _i_ in industry _j_ in year _t_ , we estimate the following regression: _Starijt_ = _a_ + _β_ 1 _×Log_ ( _Assets_ ) _it−_ 1+ _β_ 2 _×Log_ ( _Age_ ) _it−_ 1+ _β_ 3 _×MarketShareit−_ 1+ _β_ 4 _×Markupsit−_ 1 + _β_ 5 _× HHIjt_ + _φj_ + _γt_ + _εijt_ (10) where _Star_ is a dummy variable that takes the value 1 if the firm is a star firm and 0 otherwise. We estimate the equation using _ROIC Star_ as the definition of a star firm and using _Markups_ that incorporate intangible capital. _Log(Assets)_ and _Log(Age)_ are measures of firm size and size, _HHI_ is Herfindahl Index measure of industry concentration, and _Market Share_ is firm level market share. All the regressions are estimated using ordinary least squares (linear probability models) but we get similar results using Logit estimation. We cluster the standard errors at the firm level to capture the lack of independence among the residuals for a given firm across years (Petersen [2009]).

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The main coefficient of interest in the above specification is _β_ 4 which shows the sensitivity of star status to firm markups. A high markup is consistent with market power, as competition would erode the firm’s ability to charge above variable costs. Importantly, since markups do not take into account the cost of tangible and intangible capital, high markups are consistent with both high and low rates of return to invested capital. Intuitively, it is natural to think of high markups as resulting from a firm’s exercise of market power by reducing sales and thereby realizing high returns on invested capital. However, it is possible for a firm to have a low markup, high sales per unit of invested capital and to be a star firm. Thus, the extent to which star status is related to high markups is an empirical question.

In Table 2, we estimate specification 10 to examine firm and industry characteristics that are associated with star status. We first estimate the equation with industry and year fixed effects in panel A so we can examine the association between star status and HHI and in column B we use industry x year fixed effects. In panel A, in columns 1-5 we focus on the full sample of firms, in column 6 we look at only manufacturing firms for which we have data on skills, in column 7 we look at large firms (defined as firms with more than $200 million in assets in real terms obtained by deflating Compustat item _AT_ by GDP deflator) and in column 8 we focus on young firms (defined as firms that are less than five years of age). We drop firms with negative invested capital in all regressions.

The results in columns 1-5 of panel A of 2 show that after correcting for intangible capital, we find evidence that high markups predict _ROIC_ . The effects are also economically significant. There is a 5 percentage point increase in the probability of being a star firm when markups go up by one standard deviation. We also see that high _ROIC_ firms are on average large and young. These results on size, age, and markups hold in the different sub-samples in columns 6-8 for manufacturing, large firms, and young firms respectively. We find limited evidence that firm level market share or industry concentration at the 3-digit NAICS level predicts star status. HHI is never significant in any of the specifications and market share comes in significant primarily for large firms. We find similar results if we use industry x year fixed effects in panel B without controlling for HHI.

Our results are robust to a number of checks as seen in Tables A2 and A3 of the Internet Appendix. In Table A2 we find our results robust to using firm fixed effects. We do not use firm 19
fixed effects in our main specification because we are interested in understanding time invariant human capital or skill characteristics that explain variation in ROIC. In the first three columns of Table A3 we explore alternate dependent variables - Q stars, continuous measures of ROIC and Tobin’s Q. We find high markups to be associated with Q star status in column 1, and high ROIC and Tobin’s Q in columns 2 and 3 respectively. In columns 4 and 5 we use the production function approach to estimate Markups, _Markups_ _~~p~~ rodfn_ and find similar association between these markups and star status. It is likely that the effect of market power indicators may vary across levels of _ROIC_ . In unreported tests, we re-estimate the full model (column 5 of Table 2) using quantile regressions. This approach also has the advantage of being directly suggested by the original star firm hypothesis, which is formulated focusing on the differences between the top 10% of firms and the rest. We use the generalized quantile regression estimator developed in Powell [2016] that allows us to estimate unconditional quantile effects in the presence of additional covariates. The results show that the profitability of firms at the top of the distribution of _ROIC_ appears more sensitive to markups than that at the bottom.

To explore the impact of human capital, we interact industry indices of human capital skill with the key variables of interest as shown in the equation below: _Starijt_ = _α_ 0 + _β_ 1 _× Log_ ( _Assets_ ) _it_ + _β_ 2 _× Log_ ( _Age_ ) _it_ + _β_ 3 _× Market Shareit_ + _β_ 4 _× Markupsit_ + _β_ 5 _× Log_ ( _Assets_ ) _it × Industry Skillj_ + _β_ 6 _× Market Shareit × IndustrySkillj_ + _β_ 7 _× Markupsit × Industry Skillj_ + _τjt_ + _εijt_ (11) In Table 3, we examine the role of industry skill and intangible capital in predicting star status controlling for industry x year fixed effects. In columns 1-3, since the industry skill, RMAN is time invariant, we do not include the main effect by itself as it is subsumed by the industry fixed effect. Columns 1-3 show that in industries that rely heavily on routine manual skills (low skilled industries), large firms are less likely to be in the top 10% of ROIC firms in a year. The interactions of markups and RMAN and market share and RMAN are not significant. When we interact each of these variables with intangible capital/asset ratios, we find that large firms with high intangible capital and firms with high markups and high intangible capital are less likely to be in the top 10% 20
of ROIC in a year. These results suggest that the markups are really used to support the high intangible capital investment in these firms. In unreported tests, we find similar results replacing the industry skill RMAN with CPS (complex problem solving skills) or NRCOG (non-routine cognitive analytical skills).

# **3.4 Markups, Output, and Investment in Star Firms**

A central policy question is whether high profits, and in particular, star firm status, are the result of monopoly power. Markups could rise due to outright monopoly or due to monopolistic competition where with high markups but also high fixed costs, firms actually earn low profits. Grullon et al. [2017] also argue that the increase in industry concentration in the US is related to high return on assets and this is mainly driven by firm’s higher profit margins rather than asset utilization.<sup>16</sup> In Figure 7, we explore the sensitivity of ROIC to markups over time by plotting the regression coefficients from the following regression: The coefficient of interest is _β_ 4 which plots the sensitivity of ROIC to markups over time. The figure shows that ROIC is less responsive to markups over time. In Figure 8 we examine splits across industries with high and low intangible capital to assets ratios and find that the declines are steeper in the industries with high intangible capital to asset ratios. These results provide suggestive evidence that pricing power increases are used for the high investment in intangible capital.

Thus, our results above on the relation between star status and markups are more nuanced. We find that a measure of pricing power, a high markup, does statistically predict star status for a firm, even when we correct for intangible capital. But, when we do correct for intangible capital

> 16Blonigen and Pierce [2016] study mergers and show that mergers are not necessarily associated with increase in efficiency but rather with an increase in market power as seen in the 15-50% increase in markups associated with mergers.

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there is only a moderate increase in markups over the past several decades unlike the stunning rise noted by De Loecker and Eeckhout [2017]. Moreover, the link between pricing power and high ROIC has attenuated, most likely because the perceived excess profits over variable costs are required to support investment into intangible capital. More fundamentally, even though high markups commonly interpreted as evidence of welfare reducing market power, their existence is not sufficient evidence of market imperfections. If star firms have lower costs, produce higher quality products, or are sole producers of innovative products, they may realize higher markups without restrictions on output that reduce customer welfare.

To gain further intuition about the role of markups in the case of star firms, we explore the relation between markups, ROIC, and a measure of output - the ratio of sales to invested capital - in non-parametric regressions for star firms and for all other U.S. public firms in general. In Figure 9 we present a histogram of markups for firms that were classified as _ROIC_ stars and for all other firms and for each of those sub-samples, a non-parametric smoothed scatter plot of _ROIC_ against markups using kernel weighted local polynomial smoothing. The figure shows that while firms are distributed across the range of markups even when we look at just the star firms, the tails are thin so there are few firms with very low markups and very high markups for both star firms and all other firms. In general, we see a monotonically increasing relationship between _ROIC_ and markups suggesting that high profits are associated with pricing power. However, for star firms the plot is quite flat, indicating that there is no visible association between markups and _ROIC_ within the sample of firms that have passed the threshold to be classified as star firms. In unreported robustness tests, when we define superstars as firms that have _ROIC_ in the top decile in 5 or more years over the period, we find the scatter plot for superstars to be similar to that of the star firms.

In Table 4, we explore the relation between star status and output and investment more formally in a multivariate regression framework controlling for _Log Assets_ , _Log Age_ , industry x year fixed effects. For output, we use _Sales/Invested Capital_ and for investment, we use both physical investment _Capex/Invested Capital_ and intangible investment ( _XRD/Invested Capital_ ). All the independent variables are lagged by one period. The table shows that both _ROIC_ and _Q_ star firms have higher Sales/Invested Capital and greater investment, both CAPEX, and Intangible (R&D) Investment compared to all other firms. We find these results to be robust to a number of checks 22
including scaling _CAPEX_ by _PPENT_ and _XRD_ by intangible capital ( _ICAP_ ).

To further examine the association between star status and output, we present a nonparametric estimator of the regression function in Figure 10 with and without covariates. Specifically following Cattaneo et al. [2019], we present binned scatterplots of Sales/Invested Capital against markups with robust confidence intervals and uniform confidence bands over the period 1990 to 2015 for all firms in the economy and for ROIC stars. We present the binscatter regressions first without controlling for covariates and next after controlling for firm size, age, industry and year fixed effects. The figure shows that there is a decline in Sales/Invested Capital with Markups for both ROIC Stars and other firms. However, for star firms, Sales/Invested Capital is higher at each level of markup than for all firms in general, suggesting that these firms are not restricting output more than other firms with the same markups. The difference is particularly high at lower markups, suggesting that low-margin star firms, in particular, are adopting a high volume marketing strategy.<sup>17</sup>

# **3.5 Productivity, Competition, and Star Firms**

The above tables do not control for the total factor productivity of firms. As detailed in the Internet Appendix, we have a measure of such productivity from the production function estimations used to derive markups, _Markups_ _~~p~~ rodfn_ that measures the productivity of firms relative to other firms in its industry. In Table 5, we introduce total factor productivity (TFP) and compare how the productivities of firms predict star status of firms and contrast that to the predictive power of markups.<sup>18</sup> With the introduction of TFP we also present specifications with Tobin’s Q stars as the principal measure of star status. Using Q as an alternate definition of star status alleviates concerns about a mechanical dependence between the return on invested capital and measures of productivity.

Our results show that both markups and productivity are positive and significant in predicting star status. The economic significance is similar for markups and productivity when we look at Q.

> 17Figure A8 in the Internet Appendix shows the binscatter regression plots when we define ROIC stars three and five years back.

> 18Note that a firm with high pricing power (high markups) may have high or low total factor productivity, depending on how much tangible and intangible capital it uses in production. Conversely, a firm with high productivity may or may not have pricing power, depending on whether or not it can maintain prices above marginal cost.

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A one standard deviation increase in productivity increases the probability of being a Q star by 2.31% whereas a one standard deviation in markups increases the probability of being a Q star by 3.4%.

In Table 6, we investigate the effect of market competition on firm star status directly by examining the effect of increased market competition on markups, ROIC, output and investment of both star and non-star firms. In general, we would expect that an increase in competitive pressure would cause a decline in ROIC, Markups, and output. However, those firms that have market power, are going be less affected than firms without such advantages.<sup>19</sup> Thus, star firms rely on market power to generate profits more than other firms, then we would expect that an exogenous increase in competitive pressure in their industry would affect them less than non-star firms. We test this below.

We measure increases in market competition by the penetration of Chinese imports at the 4- digit NAICS level. To address endogeneity issues we instrument Chinese imports into the U.S., _ImportsUSA_ , by Chinese imports into eight other developed economies, _ImportsOTH_ . Our identification strategy is derived from Autor et al. [2013] and identifies the component of US import growth that is due to Chinese productivity and trade costs. Autor et. al. identify the supplydriven component of Chinese imports by instrumenting the growth in Chinese imports to the United States using contemporaneous composition and growth of Chinese imports in eight other developed countries. The identifying assumption underlying this strategy is that the surge of Chinese exports across the world is primarily driven by China-specific events: China’s transition to a market-oriented economy and its accession to the WTO and the accompanying rise in its comparative advantage and falling trade costs explain the common within-industry component of rising Chinese imports to the United States and other high-income countries.

In panel A of Table 6, we find that, as expected, imports reduce Markups, ROIC, and Output in general. We see very little evidence of the effect of import competition on Capex or R&D potentially because firms are investing to meet the competitive challenge. In panel B, we present some descriptive tests to see if star firms are differentially affected by import competition by interact-

> 19Market power can arise because firms have differentiated brands and products, unique products, control of distribution channels, network externalities, regulatory capture, among other reasons.

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ing import competition with star status. To mitigate reverse causality, we measure star status as of two years prior. We instrument _ImportsUSA_ and _ImportsUSA_ x _ROICStarStatusijt−_ 2 two years prior with _ImportsOTH_ and _ImportsOTH_ x _ROICStarijt−_ 2.<sup>20</sup> All the interaction terms are insignificant. In particular. interactions in the markups and ROIC regressions are insignificant, suggesting that star firms do not have differentially smaller declines in markups or ROIC when faced with import competition in their industry compared to other firms in their industry. In panel B, the Cragg-Donald F statistic test (Stock and Yogo [2002]) which is a weak identification test for the excluded exogenous variables, is highly significant. This test is essential when the number of endogenous variables is more than one and the standard F-test may not truly reflect the relevance of instruments (for details see Baum et al. [2007]).

The analyses thus far jointly show that the evidence attributing high profits of ROIC star firms to market power is modest. Moreover, the concern raised by Guti ́errez and Philippon [2017] that the decline in _CAPEX_ is attributable to increasing market power is not supported. Overall, our results indicate that while markups strongly predict high profits, not all star firms have high mark-ups and star firms are not restricting output or investing less than other firms with the same markups.

The conclusion that the exercise of market power by star firms is relatively modest contrasts with the popular public policy debate in the US that has been dominated by anecdotal evidence of a few star firms - Facebook (FB), Amazon.com (AMZN), Apple (AAPL), Microsoft(MSFT) and Alphabet (GOOGL). These firms are often accused of using monopoly power as a result of proprietary technology and increasing returns to scale. To take a close look at this, we examine the returns to capital and markups of these in relation to the rest of the economy. Figure 11 shows that these firms (especially Apple) have abnormally high returns to capital which exceed even the top 10% of _ROIC_ firms. Their markups in Figure 12 show that for some of these firms like Apple and Amazon, the markups are below the 90<sup>_th_</sup> percentile of markups in our sample for most of the sample period.<sup>21</sup> Therefore, surely a small number of superstar firms are truly diverging from the rest and

> 20The results are unaffected when we measure star status in the current year or three years prior.

> 21Figure A9 in the Internet Appendix reproduces this figure using Markups estimated by the production function approach and finds similar results.

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disrupting conventional business models in the process. For these firms, their markups may be understating their market power. Indeed, in some cases these firms might be limiting their shortrun profits in the hopes of realizing future market dominance. An example of this might be Amazon. In his letter to Amazon shareholders in 1997, Jeff Bezos stated that Amazon makes decisions and weighs tradeoffs differently than most other firms: _We believe that a fundamental measure of our success will be the shareholder value we create over the long term. This value will be a direct result of our ability to extend and solidify our current market leadership position. The stronger our market leadership, the more powerful our economic model._ **_Market leadership can translate directly to_** _higher revenue, higher profitability, greater capital velocity, and correspondingly_ **_stronger returns on invested capital._** _Our decisions have consistently reflected this focus. We first measure ourselves in terms of the metrics most indicative of our market leadership: customer and revenue growth, the degree to which our customers continue to purchase from us on a repeat basis, and the strength of our brand._ **_We have invested and will continue to invest aggressively_** _to expand and leverage our customer base, brand, and infrastructure as we move to_ **_establish an enduring franchise._** (Emphasis added)<sup>22</sup> Thus, Amazon prioritized growth over profits to achieve enough scale that was central to their business model. This suggests that even for some of the most capable star firms like Amazon, metrics such as ROIC and markups may understate their potential market power. By the same token, these firms are not exercising that potential market power in ways that harm consumers in the short run. Of course, firms that follow this strategy are likely hoping that their dominant position will enable them to profit from their market dominance in the future. As seen in Figures 11 and 12, ROIC and markups of most of these elite firms seem to be reasonable initially when they are in the ”franchise” building stage and then explode for a couple of firms that have built up a large enough market, which compounds the measurement issues. Khan [2016] also argues that the current anti-trust laws and their focus on short-run consumer welfare are just not equipped to recognize the anti-competitive nature of Amazon’s predatory pricing and ability to use its dominance in one

> 22See Damodaran (2018, April 26). Amazon: Glimpses of Shoeless Joe? [Blog post]. Retrieved from http://aswathdamodaran.blogspot.com/2018/04/amazon-glimpses-of-shoeless-joe.html

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sector to gain market share in another.

Building a franchise in the expectation of future profits is not new, and these star firms of today may be likened to the superstars in the early part of the 20th century like US Steel, Standard Oil and Sears, and Roebuck and Company who have passed into history. This suggests that the critical concern for policy is not only to control the exercise of market power by these few firms, but to ensure that markets remain contestable and that entrants with new technologies are able to challenge the current market leaders. Policy measures could include limitations of acquisitions of new technologies through mergers. For instance, see Cunningham et al. [2018] for a discussion of mergers and the subsequent liquidation of new technologies by incumbent firms in order to maintain market dominance.

# **4 Robustness**

In this section, we subject our findings to a series of robustness tests. At the outset, our results are crucially dependent on the adjustment for intangible capital in the measurement of ROIC and markups. In unreported robustness tests we investigate whether our results are affected if the adjustment is partial. We vary the intangible capital adjustment from 25% to 75% of the amount recommended by Peters and Taylor [2017] and repeat the specifications in columns 4-6 of Table 3. All our results are materially similar suggesting that our results are robust to smaller adjustments to intangible capital.

## **4.1 Measurement of Excess Cash**

There is a great deal of controversy in how to treat a firm’s cash holdings in the computation of a firm’s invested capital. It is standard financial reporting practice to include a firm’s cash holdings in the definition of its invested capital. However, financial analysts routinely subtract a large fraction of cash holdings, say any cash in excess of 2% of annual revenues, from the firm’s calculated investment capital (e.g. Koller et al. [2017]). The rationale for that is that the excess cash is unnecessary to support operations and confounds valuations of product market opportunities.

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This view is also supported by a large body of academic work (e.g. Jensen [1986]; Harford et al. [2008]; Dittmar and Mahrt-Smith [2007]) which argues that large cash holdings are a reflection of agency conflicts between managers and firms shareholders, and are not relevant to the valuation of a firm’s operations.

A second reason to subtract excess cash from invested capital is to circumvent the policy of many large U.S. multinationals to stockpile cash in low-tax jurisdictions in order to manage their tax liabilities (e.g. Faulkender and Petersen [2012]; Faulkender et al. [2017]. Against that, there are numerous findings that high cash positions occur typically in R&D intensive firms, and that these cash holdings may be economically rational (see Boyle and Guthrie [2003]; Bates et al. [2009]; and Harford et al. [2014]). In particular, to the extent that R&D intensive firms face higher operational risks, and that intellectual capital cannot be easily used as collateral for bank loans, high cash positions are economically motivated. Moreover, from the perspective of the firms’ owners, the relevant returns should be calculated as a function of all the capital committed, not just the portion which would have been committed under an alternative corporate governance regime. Moreover, as Damodaran [2005] notes, the 2% ratio has been used as a rule of thumb among analysts and does not have a deep theoretical basis. This ratio can be higher or lower depending on the working capital needs of a business. In this section, we examine whether our findings are sensitive to the treatment cash holdings.

Hence as an alternate variation, we define invested capital to only include working capital and physical and intangible capital. Thus Analogously we define ROIC with this new adjustment as: In Figure A10 of the Internet Appendix, we present four _ROIC_ graphs where _ROIC_ is re-computed using cash above 1% of sales, 5% of sales, 10% of sales, and 20% of sales respectively as excess cash. Across all the figures, we see that there is no run-up in _ROIC_ for the top 10% of firms as in 28
Figure 3.

In Table 7, we repeat the interactions with Intangible Capital/Assets in Table 3 but re-estimate _ROIC_ using different treatments of cash. In columns 1-3, we use the firm’s total cash holdings in computing ROIC, _ROIC_<sup>CASH</sup> , in columns 4-6, we consider excess cash to be any cash over 1% of sales, _ROIC_<sup>1per</sup> , and in columns 7-9 we consider excess cash to be any cash over 10% of sales, _ROIC_<sup>10per</sup> . Across the columns, we obtain similar results wherein large firms with high intangible capital and firms with high markups and high intangible capital are less likely to be in the top 10% of ROIC in a year.

# **5 Conclusion**

We assess the performance of publicly-listed star firms in the United States. When we use financial statement data as conventionally presented, a small percentage of firms seem to be pulling away over time from other firms in the economy in terms of their return on capital. In particular, star firms in highly skilled industries and industries with high levels of intangible capital seem to be pulling away from the others.

However, conventional financial statements do not capitalize R&D expenditures or organizational capital. Once we adjust firms’ returns to capital to address these shortcomings, there is little evidence that the most profitable 10% of firms are pulling away from the rest of the economy, and the differences in firm returns in highly skilled and other industries shrink dramatically. Furthermore, once we adjust markups based on operating expenses for investment in intangible capital, we only find a modest increase in market power especially in high skilled industries. Star firms tend to be larger, younger, and have higher markups. While they may have more pricing power than other firms, at each level of markup star firms tend to produce more than other firms. We also find no evidence that star firms are differentially affected by import competition compared to other firms in the economy.

Overall, our results indicate that while star firms have some pricing power, this is used to support the intangible assets that are costly to acquire and so we see little evidence that these high 29
performing firms behave like true monopolies, reducing output and raising prices to achieve super normal returns. However, there is reason for concern regarding a smaller subset of elite publiclylisted firms. The usual suspects for membership in such an elite group are Apple, Facebook, Google, Amazon, and Microsoft. When we examine these firms individually, the ROIC and markups of most of these elite firms do not seem extraordinary initially and then explode but again only for a couple of firms that have built up a large enough market. Even for these firms, the critical policy concern may not only be the regulation of their use of market power today, but also the need to maintain contestable markets that allow the creation of independent technologies in the future.

Above, our work suggests that the conjecture that high performing firms are exploiting their market power needs to be reassessed once we take firms’ investment in intangible capital into account. More broadly, understanding differences in intangible capital investment across firms is likely to play a first order role in research on a wide range of corporate finance and firm governance policies.

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