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 capital, have higher growth, innovation, and productivity and the rest of the economy, especially in industries that rely and are not differentially affected by exogenous competitive on a skilled labor force, raising concerns about their market shocks. Their pricing power supports their high intangi- power. This paper shows that the divergence is explained by ble capital investment. Some exceptional firms may pose the mismeasurement of intangible capital. Compared with concerns due to their potential to foreclose competition other firms, star firms produce more per dollar of invested 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 ∗, Asli Demirguc-Kunt † and Vojislav Maksimovic‡ ∗ 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: vmaksi- movic@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.1 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 firms2 and their industries. We examine the role of market power, intangi- ble 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.3 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- 1 Several 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]). 2 Following 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. 3 Our implementation of operating markups, which follows the work of Foster et al. [2008], De Loecker et al. [2018], and Traina [2018] is discussed below. 1 cratic firm growth strategies4 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.5 Over the period 1965-2015, the ratio of the 90th 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 4 Successful idiosyncratic growth strategies may also be due to successful innovation or superior management practices (e.g. Bloom and Van Reenen [2007]). 5 We 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 firms. 2 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.6 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 6 The relation between other measures of market power such as size and market share is economically weaker than markups. 3 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.7 In addition, in sensitivity tests we also find that our results are robust 7 It 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. 4 to varying the fraction of intangible capital that is used to correct the ROIC measures.8 All our results hold with these alternative definitions. 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 90th 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 errez and Philippon [2017] and in markups and market power (De Loecker the last two decades (Guti´ and Eeckhout [2017]; Barkai [2016]).9 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 8 We follow Peters and Taylor [2017] in constructing our measure of intangible capital to include knowledge cap- ital (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. 9 For a strong dissenting view arguing that the observed changes in concentration computed at the national level are economically immaterial, see Shapiro [2018]. 5 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.10 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 cor- porate 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. 6 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. 7 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: unadj EBITit + AMit ROICit = unadj (1) Invested Capitalit −1 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: unadj Invested Capitalit = P P EN Tit + ACTit + IN T ANit − LCTit − GDW Lit − max(CHE − 0.02 × SALE, 0) (2) 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.11 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 11 We replace missing values of AM and GDWL with 0. 8 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.12 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 ROICunadj Star as a dummy variable that takes the value 1 if the firm’s ROIC is above the 90th 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 90th 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.13 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 12 In 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. 13 In 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. 9 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 informa- tion 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, Induc- tive 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 Dex- terity. 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 IN T ANit in equation (2), with the new definition of intangible capital from Peters and Taylor [2017], ICAPit . Invested Capitalit = P P EN Tit + ACTit + ICAPit − LCTit − GDW Lit − max(CHEit − 0.02 × SALEit , 0) (3) 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 int know and organization capital K int 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.14 The estimates of ICAP, K int know, and K int 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: ADJP Rit ROICit = (4) Invested Capitalit−1 where ADJP Rit = EBITit + AMit + XRDit + 0.3 × SGAit − δRD × K int knowit − δSGA × K int orgit (5) 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 in- creases 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 14 Since 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. 12 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 )15 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 90th 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 non- routine 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 15 We 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. 13 concentration is measured as: N HHI = s2 i (6) i=1 SALE where si is market share of firm i given by and N is total number of firms in industry j j SALE 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* : OP EX ∗ = OP EX − XRD − RDIP − 0.3 × SGA (7) 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 trans- parent 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 errez and Philippon the literature as a measure of market power (see e.g. Grullon et al. [2017], Guti´ [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 (M arkups) and 1.221 using the production function approach (M arkups prodf n). 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. 15 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, M arkups, in the US economy over our sample period. We see an upward trend only for the 90th 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: P erf ormanceijt = α0 + β1 × Log (Assets)it−5 + β2 × Log (Age)it−5 + β3 × Starit−5 (orROICit−5 ) + φj + γt + ijt (8) 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 ): Vit Qit = (9) T OT CAPit 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 90th 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 + β1 × Log (Assets)it−1 + β2 × Log (Age)it−1 + β3 × M arketShareit−1 + β4 × M arkupsit−1 + β5 × HHIjt + φj + γt + ijt (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 M arkups 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]). 18 The main coefficient of interest in the above specification is β4 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, M arkups prodf n 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 = α0 + β1 × Log (Assets)it + β2 × Log (Age)it + β3 × M arket Shareit + β4 × M arkupsit + β5 × Log (Assets)it × Industry Skillj + β6 × M arket Shareit × IndustrySkillj + β7 × M arkupsit × Industry Skillj + τjt + ijt (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.16 In Figure 7, we explore the sensitivity of ROIC to markups over time by plotting the regression coefficients from the following regression: Starijt = a + β1 × Log (Assets)it−1 + β2 × Log (Age)it−1 + β3 × M arkupsit−1 + β4 × M arkupsit−1 × Y earDummies + φj + γt + ijt (12) The coefficient of interest is β4 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 16 Blonigen 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. 21 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 for- mally 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.17 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, M arkups prodf n 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.18 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. 17 Figure A8 in the Internet Appendix shows the binscatter regression plots when we define ROIC stars three and five years back. 18 Note 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. 23 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.19 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., ImportsU SA, by Chinese imports into eight other developed economies, ImportsOT H . Our iden- tification 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 supply- driven 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 Chi- nese 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 compar- ative 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 poten- tially 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- 19 Market power can arise because firms have differentiated brands and products, unique products, control of distribution channels, network externalities, regulatory capture, among other reasons. 24 ing import competition with star status. To mitigate reverse causality, we measure star status as of two years prior. We instrument ImportsU SA and ImportsU SA x ROICStarStatusijt−2 two years prior with ImportsOT H and ImportsOT H x ROICStarijt−2 .20 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 errez and Philippon [2017] firms to market power is modest. Moreover, the concern raised by Guti´ 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 90th percentile of markups in our sample for most of the sample period.21 Therefore, surely a small number of superstar firms are truly diverging from the rest and 20 The results are unaffected when we measure star status in the current year or three years prior. 21 Figure A9 in the Internet Appendix reproduces this figure using Markups estimated by the production function approach and finds similar results. 25 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 short- run 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)22 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 22 See 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 26 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. 27 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 CASH Invested Capitalit = P P EN Tit + ACTit + ICAPit − LCT it − GDW Lit (13) Analogously we define ROIC with this new adjustment as: CASH ADJP Rit ROICit = CASH (14) Invested Capitalit 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 CASH , in columns 4-6, we consider excess cash to be any cash over 1% of sales, ROIC 1per , and in columns 7-9 we consider excess cash to be any cash over 10% of sales, ROIC 10per . 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 organiza- tional 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. Further- more, 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 publicly- listed 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. 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Detailed variable definitions are in the Appendix. 37 Figure 2: Differences in Human Capital This figure plots the 25th , 50th , 75th , and 90th percentile of Return on Invested Capital (ROIC) (unadjusted for intangible capital) in each year in low and high routine manual (RMAN) manu- facturing industries in the first figure and in industries with low and high intangible capital/assets ratio in the second figure. Detailed variable definitions are in the Appendix. 38 Figure 3: Rise in Star Firms - correcting for intangible capital This figure plots the 25th , 50th , 75th , and 90th percentile of Return on Invested Capital (ROIC ) in each year across all public firms in the US economy. ROIC includes the Peters and Taylor [2017] adjustment for intangible capital. Detailed variable definitions are in the Appendix. 39 Figure 4: Differences in Human Capital - correcting for intangible capital This figure plots the 25th , 50th , 75th , and 90th percentile of Return on Invested Capital (ROIC ) in each year in low and high routine manual (RMAN) manufacturing industries in the first figure and in industries with low and high intangible capital(ICAP )/assets ratio in the second figure. ROIC and ICAP includes the Peters and Taylor [2017] adjustment for intangible capital. Detailed variable definitions are in the Appendix. 40 Figure 5: Markups in the US Economy This figure plots the 25th , 50th , 75th , and 90th percentile of M arkups in each year across all public firms in the US economy. M arkups are defined as Sales/Variable Cost where we use operating expenses with intangible capital adjustments, OPEX*, as a measure of variable cost. Detailed variable definitions are in the Appendix. 41 Figure 6: Markups in the US Economy - Differences in Human Capital This figure plots the 25th , 50th , 75th , and 90th percentile of M arkups in each year in low and high routine manual (RMAN) manufacturing industries and in industries with low and high intangible capital(ICAP )/assets ratio. M arkups are defined as Sales/Variable Cost where we use operating expenses with intangible capital adjustments, OPEX*, as a measure of variable cost in estimation of markups. Detailed variable definitions are in the Appendix. 42 Figure 7: ROIC and Markups over time This figure plots the interaction coefficient of markups and year dummies from a regression of ROIC on Markups, Log(Assets), Log(Age), Markups x Year, industry and year fixed effects. ROIC includes the Peters and Taylor [2017] adjustment for intangible capital. M arkups are defined as Sales/Variable Cost where we use operating expenses with intangible capital adjustments, OPEX*, as a measure of variable cost in estimation of markups. Detailed variable definitions are in the Appendix. 43 Figure 8: ROIC and Markups over time - Differences in Human Capital This figure plots the interaction coefficient of markups and year dummies from a regression of ROIC on Markups, Log(Assets), Log(Age), Markups x Year, industry and year fixed effects. ROIC includes the Peters and Taylor [2017] adjustment for intangible capital. M arkups are defined as Sales/Variable Cost where we use operating expenses with intangible capital adjustments, OPEX*, as a measure of variable cost in estimation of markups. The first figure shows ROIC-markup sensitivity across low and high routine manual (RMAN) industries and the second figure shows sensitivity across industries with low and high intangible capital(ICAP )/assets ratios. Detailed variable definitions are in the Appendix. 44 Figure 9: Distribution of Markups across Star firms and all other firms This figure plots the histogram of M arkups for ROIC Stars and all other firms. The figure also shows the smoothed values of a kernel-weighted local polynomial regression of ROIC on M arkups. ROIC stars are firms that are in the top 10% of ROIC in a particular year. ROIC includes the Peters and Taylor [2017] adjustment for intangible capital. M arkups are defined as Sales/Variable Cost where we use operating expenses with intangible capital adjustments, OPEX*, as a measure of variable cost in estimation of markups. Detailed variable definitions are in the Appendix. 45 Figure 10: Output and Markups over time This figure plots the binned scatterplots with robust pointwise confidence intervals and uniform confidence bands of Sales/Invested Capital on M arkups for ROIC stars and all other firms. The first figure does not control for covariates whereas the second figure controls for Firm size, age, industry and year fixed effects.ROIC stars are firms that are in the top 10% of ROIC in a particular year. ROIC includes the Peters and Taylor [2017] adjustment for intangible capital. M arkups are defined as Sales/Variable Cost where we use operating expenses with intangible capital adjustments, OPEX*, as a measure of variable cost in estimation of markups. Detailed variable definitions are in the Appendix. 46 Figure 11: ROIC of Elite Firms (Apple, Facebook, Amazon, Microsoft, Google) This figure plots the 90th percentile of Return on Invested Capital (ROIC ) in each year across all public firms in the US economy as well as the ROIC for five firms referred to as superstars anec- dotally. ROIC includes the Peters and Taylor [2017] adjustment for intangible capital. Detailed variable definitions are in the Appendix. 47 Figure 12: Markups of Elite Firms (Apple, Facebook, Amazon, Microsoft, Google) This figure plots the 90th percentile of M arkups in each year across all public firms in the US economy as well as the M arkups for five firms referred to as superstars anecdotally. M arkups are defined as Sales/Variable Cost where we use operating expenses with intangible capital adjustments, OPEX*, as a measure of variable cost in estimation of markups. Detailed variable definitions are in the Appendix. 48 Table 1: Are Star Firms Persistent Performers? This table reports estimates from the following panel regression model: P erf ormanceijt = α0 + β1 × Log (Assets)ijt−5 + β2 × Log (Age)ijt−5 + β3 × ROICijt−5 + β4 × Starijt−5 + φjt + εijt Performance is Sales growth/Employment growth (each defined as the 5-year log difference in sales or employment respectively divided by 5), Labor Productivity, or ROIC averaged over 5 years. Log(Assets) is the 5-year lagged value of the logarithm of total assets. Log(Age) is the 5-year lagged value of the firm age. M arkups is the 5-year lagged value of Markups computed using operating expenses as a variable input of production and includes correction for intangible capital. Star is a dummy variable that takes the value 1 if firm i s 5-year lagged ROIC was above the 90th percentile of ROIC respectively across all firms 5 years back and 0 otherwise. The regressions are 5-year stacked panel regressions: 1990-1995, 1995-2000, 2000-2005, 2005-2010, and 2010-2015 and include industry x year fixed effects with standard errors clustered at the firm level. Detailed variable definitions are in the Appendix. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively. (1) (2) (3) (4) (5) (6) (7) (8) ROIC ROIC Sales Sales Emp Emp Labor Labor Growth Growth Growth Growth Productiv- Productiv- ity ity L5.Log(Assets) 3.359*** 0.972*** -0.005*** -0.005*** -0.005*** -0.008*** 36.204*** 30.382*** (0.092) (0.059) (0.001) (0.001) (0.001) (0.001) (2.003) (2.053) 49 L5.Log(Age) 0.068 0.492*** -0.032*** -0.034*** -0.023*** -0.022*** -35.186*** -33.991*** (0.226) (0.139) (0.002) (0.002) (0.002) (0.002) (4.404) (4.350) L5.ROIC Star 34.310*** 0.033*** 0.053*** 85.715*** (0.623) (0.005) (0.005) (8.850) L5.ROIC 0.639*** 0.000 0.001*** 1.596*** (0.007) (0.000) (0.000) (0.107) N 18224 18224 11867 11867 11422 11422 17768 17768 Adj. R-sq 0.396 0.733 0.083 0.080 0.077 0.085 0.405 0.413 Fixed Effects — ————————————————- Industry x Year ——————————————–— Table 2: Who are America’s Stars? Correcting for intangible capital This table reports estimates from the following regression model in panel A: Starijt = α0 + β1 × Log (Assets)ijt−1 + β2 × Log (Age)ijt−1 + β3 × HHIjt−1 + β4 × M arket shareijt−1 + β5 × M arkupsijt−1 + φj + γt + εijt Star is a dummy variable that takes the value 1 if the firm i ’s ROIC is above the 90th percentile of ROIC respectively across all firms in a particular year and 0 otherwise. Log(Assets) is the logarithm of total assets and Log(Age) is logarithm of firm age. HHI is Herfindahl Index of market share in each industry in each year. M arkups are estimated using operating expenses as a variable input of production and includes correction for intangible capital. Market Share is the ratio of firm i ’s sales to total industry j s sales in a particular year. Panel A presents results using industry and year fixed effects and panel B reports results using industry x year fixed effects. In both panels, the large firm sample is identified by firms with Real value of assets ≥ USD 200Mil and the young firm sample is determined by firms of Age ≤ 5years. All regressions in both panels are estimated using ordinary least squares with standard errors clustered at the firm level. Detailed variable definitions are in the Appendix. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively. (1) (2) (3) (4) (5) (6) (7) (8) ROIC Star ROIC Star ROIC Star ROIC Star ROIC Star ROIC Star ROIC Star ROIC Star Sample Full Full Full Full Full Manuf Large Young L.Log(Assets) 0.017*** 0.017*** 0.006*** 0.016*** 0.009*** 0.006*** -0.011*** -0.012*** 50 (0.001) (0.001) (0.001) (0.001) (0.002) (0.003) (0.001) (0.004) L.Log(Age) -0.065*** -0.064*** -0.064*** -0.066*** -0.063*** -0.057*** -0.061*** -0.151*** (0.003) (0.003) (0.003) (0.003) (0.004) (0.004) (0.003) (0.019) L.HHI -0.032 -0.050 -0.007 -0.032 -0.031 (0.041) (0.042) (0.114) (0.055) (0.144) L.Market Share 0.108 0.323*** 0.099 0.595*** 0.565* (0.085) (0.083) (0.106) (0.100) (0.337) L.Markups 0.145*** 0.146*** 0.143*** 0.170*** 0.205*** (0.007) (0.007) (0.010) (0.011) (0.012) N 83120 81762 81612 81631 79550 41221 44782 8514 adj. R-sq 0.074 0.074 0.074 0.107 0.108 0.080 0.142 0.128 Fixed Effects — ——————————————————————- Industry, Year —————————————————–— Table 2: Who are America’s Stars? Correcting for intangible capital (Continued...) (1) (2) (3) (4) (5) (6) (7) ROIC Star ROIC Star ROIC Star ROIC Star ROIC Star ROIC Star ROIC Star Sample Full Full Full Full Manuf Large Young L.Log(Assets) 0.017*** 0.017*** 0.007*** 0.010*** 0.007*** -0.012*** -0.009** (0.001) (0.001) (0.001) (0.001) (0.002) (0.003) (0.004) L.Log(Age) -0.066*** -0.067*** -0.065*** -0.063***-0.057*** -0.061*** -0.144*** (0.003) (0.003) (0.003) (0.003) (0.004) (0.004) (0.022) L.Market Share 0.095 0.315*** 0.089 0.658*** 0.656 (0.089) (0.086) (0.111) (0.110) (0.438) L.Markups 0.144*** 0.148*** 0.145*** 0.175*** 0.196*** (0.007) (0.007) (0.010) (0.011) (0.013) N 83014 81522 81525 80615 41702 45128 8339 adj. R-sq 0.079 0.080 0.112 0.114 0.086 0.146 0.136 Fixed Effects — ———————————————————— Industry x Year ————————————————-— 51 Table 3: Skill, Intangible Capital and Star Status This table reports estimates from the following panel regression model: Starijt = α0 + β1 × Log (Assets)ijt−1 + β2 × Log (Age)ijt−1 + β3 × ICAP/T otalAssetsijt−1 + β4 × M arketShareijt + β5 × M arkupsijt−1 + β6 × LogAssetsijt−1 × RM ANj or ICAP/T otalAssetsijt−1 + β7 × M arketShareijt−1 × RM ANj or ICAP/T otalAssetsijt−1 +β8 × M arkupsijt−1 × RM ANj or ICAP/T otalAssetsijt−1 + φjt + εijt Star is a dummy variable that takes the value 1 if the firm i ’s ROIC is above the 90th percentile of ROIC respectively across all firms in a particular year and 0 otherwise. Log(Assets) is the logarithm of total assets and Log(Age) is the logarithm of firm age. Markups are estimated using operating expenses as a variable input of production and includes correction for intangible capital. Market Share is the ratio of the firm i s sales to total industry j s sales in a particular year. ICAP/Total Assets is the ratio of intangible capital to total assets. RMAN is industry-level measure of routine manual skills employed by the workforce. All regressions include industry x year fixed effects and are estimated using ordinary least squares with standard errors clustered at the firm level. Detailed variable definitions are in the Appendix. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively. (1) (2) (3) (4) (5) (6) ROIC Star ROIC Star ROIC Star ROIC Star ROIC Star ROIC Star L.Log(Assets) 0.009*** 0.007*** 0.007*** 0.006*** -0.000 -0.001 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) L.Log(Age) -0.057*** -0.057*** -0.057*** -0.058*** -0.059*** -0.058*** (0.004) (0.004) (0.004) (0.003) (0.003) (0.003) 52 L.Market Share 0.134 0.240 0.100 0.397*** 0.356*** 0.446*** (0.116) (0.226) (0.111) (0.088) (0.114) (0.086) L.Markups 0.143*** 0.144*** 0.144*** 0.155*** 0.153*** 0.202*** (0.010) (0.010) (0.010) (0.007) (0.007) (0.010) L.ICAP/Assets -0.036*** -0.076*** 0.010 (0.007) (0.004) (0.007) L.Log(Assets) x Skill -0.006** (0.003) L.Market Share x Skill -0.236 (0.251) L.Markups x Skill 0.002 (0.014) L.Log(Assets) x L.ICAP/Assets -0.010*** (0.002) L.Market Share x L.ICAP/Assets 0.179 (0.217) L.Markups x L.ICAP/Assets -0.068*** (0.006) N 41289 41289 41289 79304 79304 79304 Adj. R-sq 0.087 0.087 0.086 0.129 0.128 0.132 Fixed Effects — ————————————— Industry x Year —————————————–— Table 4: Output and Investment in Star Firms This table reports estimates from the following panel regression model: Output or Investmentijt = α0 + β1 × LogAssetsijt−1 + β2 × LogAgeijt−1 + β3 × Starijt−1 + φjt + εijt The dependent variable is Output (Sales/Invested Capital) or Investment which is measured by CAPEX/Invested Capital or R&D Expenses/Invested Capital. Star is a dummy variable that takes the value 1 if the firm i ’s ROIC or Tobins Q is above the 90th percentile of ROIC or Tobins Q respectively across all firms in a particular year and 0 otherwise. Log(Assets) is the logarithm of total assets and Log(Age) is the logarithm of firm age. All regressions include industry x year fixed effects and are estimated using ordinary least squares with standard errors clustered at the firm level. Detailed variable definitions are in the Appendix. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively. (1) (2) (3) (4) (5) (6) Output Output Investment Investment R&D R&D L.Log(Assets) 0.031*** 0.044*** 0.004*** 0.005*** 0.000 0.000 (0.006) (0.006) (0.000) (0.000) (0.000) (0.000) L.Log(Age) 0.002 -0.039*** -0.013*** -0.013*** -0.015*** -0.014*** (0.013) (0.014) (0.001) (0.001) (0.001) (0.001) 53 L.ROIC Star 0.632*** 0.030*** 0.008*** (0.026) (0.002) (0.001) L.Q Star 0.130*** 0.035*** 0.029*** (0.025) (0.002) (0.002) N 80805 76530 80618 76279 81929 77571 Adj. R-sq 0.306 0.277 0.345 0.353 0.416 0.423 Fixed Effects — ————————————— Industry x Year —————————————–— Table 5: Who are America’s Stars? Role of Productivity This table reports estimates from the following regression model in panel A: Starijt = α0 + β1 × Log (Assets)ijt−1 + β2 × Log (Age)it−1 + β3 × P roductivityijt−1 + β4 × M arket shareijt−1 + β5 × M arkupsijt−1 + φjt + εijt Star is a dummy variable that takes the value 1 if the firm i ’s ROIC or (Tobin’s Q) is above the 90th percentile of ROIC (or Tobin’s Q) respectively across all firms in a particular year and 0 otherwise. Log(Assets) is the logarithm of total assets and Log(Age) is logarithm of firm age. Markups are estimated using operating expenses as a variable input of production and includes correction for intangible capital. Market Share is the ratio of firm i ’s sales to total industry j s sales in a particular year. Columns (1) and (5) include the full sample; columns (2) and (6) is manufacturing sub-sample, columns (3) and (7) is large firm sample (Real value of assets is ≥ USD 200Mil) and columns (4) and (8) is young firm sample (Age ≤ 5years). All regressions iare estimated using ordinary least squares with industry x year fixed effects and standard errors clustered at the firm level. Detailed variable definitions are in the Appendix. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively. (1) (2) (3) (4) (5) (6) (7) (8) ROIC Star ROIC Star ROIC Star ROIC Star Q Star Q Star Q Star Q Star Sample Full Manuf Large Young Full Manuf Large Young L.Log(Assets) 0.006*** 0.007*** -0.010*** -0.003 -0.006*** -0.007*** -0.028*** -0.022*** 54 (0.001) (0.002) (0.003) (0.005) (0.001) (0.002) (0.003) (0.005) L.Log(Age) -0.057*** -0.048*** -0.060*** -0.106** -0.052*** -0.047*** -0.043*** 0.025 (0.003) (0.004) (0.004) (0.043) (0.003) (0.004) (0.004) (0.045) L.Market Share 0.260*** -0.028 0.550*** 0.545 0.371*** 0.268*** 0.713*** 0.059 (0.089) (0.084) (0.115) (0.550) (0.087) (0.088) (0.109) (0.345) L.Markups 0.113*** 0.101*** 0.121*** 0.149*** 0.067*** 0.088*** 0.091*** 0.038** (0.007) (0.009) (0.011) (0.016) (0.007) (0.011) (0.010) (0.018) L.Productivity 0.107*** 0.110*** 0.205*** 0.158*** 0.114*** 0.148*** 0.265*** 0.176*** (0.010) (0.015) (0.021) (0.020) (0.012) (0.019) (0.022) (0.027) N 72690 38278 40911 5140 70376 37275 39597 4982 Adj. R-sq 0.115 0.086 0.156 0.141 0.068 0.079 0.127 0.068 Fixed Effects — ————————————————- Industry x Year ——————————————–— Table 6: Who are America’s Stars? Role of Import Competition This table reports estimates from the following instrumental variable regression model: Yijt = α0 + β1 × Log (Assets)ijt−1 + β2 × Log (Age)ijt−1 + β3 × Importsjt−1 + β4 × Starijt−2 + β5 × Starijt−2 × Importsjt−1 + φjt + εijt Y is one of the following variables: Markups, ROIC, Output (Sales/Invested Capital) or Investment which is measured by CAPEX/Invested Capital or R&D Expenses/Invested Capital. Star is a dummy variable that takes the value 1 if the firm i ’s ROIC is above the 90th percentile of ROIC respectively across all firms in a particular year and 0 otherwise. Log(Assets) is the logarithm of total assets and Log(Age) is logarithm of firm age. Markups are estimated using operating expenses as a variable input of production and includes correction for intangible capital. Imports is the value of Chinese Imports in each industry in the US scaled by initial absorption in that industry in 2005, instrumented by the value of Chinese imports in each industry in eight other developed countries scaled by initial absorption in that industry in 2000. Initial Industry Absorption is defined as Shipments + Imports Exports. Panel A shows results without interaction terms and panel B reports results including the interaction of Imports and past ROIC star status. Both the main effect of Imports and the interaction terms are instrumented in panel B. In panel A, we report the first stage F-statistic and in panel B we report the Weak ID test, which is the Stock-Yogo weak identification test with critical values: 10% maximal IV size=7.03 15%=4.58 20%=3.95 25%=3.63. All regressions are estimated using industry and year fixed effects and standard errors clustered at the industry level. Detailed variable definitions are in the Appendix. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively. Panel A: (1) (2) (3) (4) (5) 55 Markups ROIC Output Investment R&D L.Log(Assets) 0.071*** 4.050*** 0.052*** 0.003*** 0.002** (0.020) (0.347) (0.014) (0.001) (0.001) L.Log(Age) 0.000 -1.763* -0.034 -0.013*** -0.019*** (0.015) (0.983) (0.031) (0.002) (0.006) L.Imports -0.841** -74.215** -1.637** 0.061 0.041 (0.351) (32.801) (0.814) (0.067) (0.093) N 12576 12805 12666 12758 12777 Adj. R-sq 0.102 0.114 0.015 0.030 0.052 First Stage F-Test 56.96 56.33 56.62 56.03 56.62 Fixed Effects ——————————- Industry, Year ————————————–— Table 6: Who are America’s Stars? Role of Import Competition (Continued...) Panel B: (1) (2) (3) (4) (5) Markups ROIC Output Investment R&D L.Log(Assets) 0.066*** 3.418*** 0.037*** 0.003*** 0.002** (0.020) (0.271) (0.012) (0.001) (0.001) L.Log(Age) 0.018 0.946 0.016 -0.009*** -0.019*** (0.019) (0.887) (0.033) (0.002) (0.005) L.Imports -0.528* -78.062** -1.323 0.092* 0.024 (0.267) (36.147) (0.948) (0.053) (0.068) L.Imports x L2.ROIC Star 0.070 -0.216 -0.670 -0.003 0.011 (0.320) (22.339) (0.669) (0.045) (0.054) L2. ROIC Star 0.222*** 24.747*** 0.463*** 0.020*** 0.002 56 (0.049) (2.322) (0.094) (0.005) (0.011) N 10403 10595 10486 10540 10570 adj. R-sq 0.123 0.238 0.037 0.038 0.048 Weak ID Test 17.037 16.939 16.966 17.006 16.962 Fixed Effects ——————————- Industry, Year ————————————–— Table 7: Skill, Intangible Capital and Star Status: Measurement of Excess Cash This table reports estimates from the following panel regression model: Starijt = α0 + β1 × LogAssetsijt−1 + β2 × LogAgeijt + β3 × ICAP/T otalAssetsijt−1 + β4 × M arketShareijt + β5 × M arkupsijt−1 + β6 × LogAssetsijt−1 × ICAP/T otalAssetsijt−1 + β7 × M arketShareijt−1 × ICAP/T otalAssetsijt−1 +β8 × M arkupsijt−1 orICAP/T otalAssetsijt−1 + φjt + εijt Star is a dummy variable that takes the value 1 if the firm i ’s ROIC is above the 90th percentile of ROIC respectively across all firms in a particular year and 0 otherwise. In columns 1-3, we use the firm’s total cash holdings in computing ROIC, ROIC CASH , in columns 4-6, we consider excess cash to be any cash over 1% of sales in computing ROIC, ROIC 1per and in columns 7-9 we consider excess cash to be any cash over 10% of sales in computing ROIC, ROIC 10per . Log(Assets) is the logarithm of total assets and Log(Age) is the logarithm of firm age. Markups are estimated using operating expenses as a variable input of production and includes correction for intangible capital. Market Share is the ratio of the firm i s sales to total industry j s sales in a particular year. ICAP/Total Assets is the ratio of intangible capital to total assets. RMAN is industry-level measure of routine manual skills employed by the workforce. All regressions include industry x year fixed effects and are estimated using ordinary least squares with standard errors clustered at the firm level. Detailed variable definitions are in the Appendix. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively. (1) (2) (3) (4) (5) (6) (7) (8) (9) ROICCASH ROICCASH ROICCASH ROIC1per ROIC1per ROIC1per ROIC10per ROIC10per ROIC10per Star Star Star Star Star Star Star Star Star L.Log(Assets) 0.006*** 0.003* 0.003* 0.003 -0.002 -0.003* 0.002 -0.002 -0.002 57 (0.002) (0.001) (0.001) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) L.Log(Age) -0.054*** -0.055*** -0.054*** -0.067*** -0.068*** -0.067*** -0.069*** -0.069*** -0.069*** (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) L.ICAP/Assets -0.034*** -0.053*** -0.003 -0.048*** -0.082*** -0.001 -0.051*** -0.080*** 0.002 (0.007) (0.003) (0.006) (0.007) (0.004) (0.007) (0.007) (0.004) (0.007) L.Market Share 0.437*** 0.399*** 0.461*** 0.485*** 0.440*** 0.529*** 0.458*** 0.387*** 0.497*** (0.088) (0.115) (0.087) (0.089) (0.115) (0.087) (0.082) (0.107) (0.081) L.Markups 0.117*** 0.116*** 0.145*** 0.157*** 0.156*** 0.202*** 0.161*** 0.160*** 0.207*** (0.007) (0.007) (0.009) (0.007) (0.007) (0.010) (0.007) (0.007) (0.010) L.Log(Assets) x L.ICAP/Assets -0.005** -0.008*** -0.007*** (0.002) (0.002) (0.002) L.Market Share x L.ICAP/Assets 0.127 0.175 0.222 (0.225) (0.215) (0.201) L.Markups x L.ICAP/Assets -0.040*** -0.064*** -0.065*** (0.005) (0.006) (0.006) N 79710 79710 79710 82346 82346 82346 82503 82503 82503 Adj. R-sq 0.096 0.096 0.098 0.132 0.132 0.135 0.132 0.132 0.135 Fixed Effects — ————————————————————— Industry x Year ——————————————————————-— Table A1: Summary Statistics This table reports the summary statistics of the key variables used in our analysis. All variable definitions are in the Appendix. Variable Obs Mean Std. Dev. Min Max ROIC Star 83,120 0.1 0.285 0 1 ROIC 83,120 12.798 24.988 -129.511 150.069 Log(Assets) 83,120 5.578 1.975 -6.908 12.906 Log(Age) 83,120 2.746 0.700 1.386 4.205 HHI 81,804 0.093 0.082 0.028 0.596 Market Share 81,634 0.015 0.036 0.000 0.316 Markups 81,694 1.315 0.395 0.006 3.628 Markups prodfn 78,225 1.221 0.278 0.204 2.627 ICAP/Assets 81,551 0.647 0.547 0.000 4.049 Industry-level Variables Skill (CPS) 254 0.003 0.441 -1.159 1.228 Skill (NRCOG) 254 -0.352 0.347 -1.321 0.628 Skill (RMAN) 254 0.219 0.430 -0.938 1.432 ImportsUSA 808 0.071 0.138 5.88E-05 0.92799 ImportsOTH 808 0.060 0.103 0.000208 0.809136 58 Table A2: Variable Definitions Variables Definition unadj Invested Capital Invested Capital = PPENT + ACT + INTAN - LCT - GDWL - max(CHE-0.02 x SALE, 0) where PPENT is Net Property, Plant, and Equipment, ACT is Current Assets, IN- TAN 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/turnover. This definition does not include the Peters and Taylor [2017] correction for intangible capital. ROICunadj (EBITt +AMt )/Invested Capitalunadj t-1 where EBIT is Earnings before Interest and Taxes and AM is Amortization of Intangibles. This definition does not include the Peters and Taylor [2017] correction for intangible capital. ROIC Starunadj Dummy variable that takes the value 1 if the firms ROICunadj is above the 90th percentile of ROICunadj across all firms in the US economy in a particular year and 0 otherwise. This definition does not include the Peters and Taylor [2017] correction for intangible capital. Invested Capital Invested Capital = PPENT + ACT + ICAP - LCT - GDWL - max(CHE-0.02 x SALE, 0) where PPENT is Net Property, Plant, and Equipment, ACT is Current Assets. ICAP is defined as the sum of externally purchased intangible capital (INTAN) and inter- nally purchased intangible capital, both measured at replacement cost. Internally purchased intangible capital is in turn measured as the sum of knowledge capi- tal (K int know) and organization capital (K int org). LCT is Current Liabilities, GDWL is Goodwill that represents the excess cost over equity of an acquired com- pany, CHE is Cash and Short-term Investments, and SALE is net sales/turnover. ROIC ROIC = (EBIT + AM + XRD + 0.3 x SGA - δRD x K int know - δSGA x K int org)/Invested Capitalt-1 where EBIT is Earnings before Interest and Taxes, AM is Amortization of Intan- gibles, XRD is Research and Development Expense, SGA is Selling, General, and Administrative Expense defined below, 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, Kadyrzhanova, and Sim (2013) and Peters and Taylor (2017). K int know and K int org are the firms intangible capital replacement cost and organization capital replacement cost respectively from Peters and Taylor [2017] SGA SGA= XSGA-XRD-RDIP where XRD is Research and Development Expense, RDIP is in-process R&D expense, XSGA is Selling, General, and Administrative Expense. This definition of SGA follows Peters and Taylor [2017]. ROIC Star Dummy variable that takes the value 1 if the firms ROIC is above the 90th percentile of ROIC across all firms in the US economy in a particular year and 0 otherwise. OPEX* Operating expenses adjusted for intangible capital given by OP EX ∗ = OP EX − XRD − RDIP − 0.3 × SGA where OPEX is Total Operating Expenses, XRD is Re- search and Development Expense, RDIP is in-process R&D expense, SGA is Selling, General, and Administrative Expense Markups Markups following the cost share approach = Sales/Variable Input where Operating Expenses* (OPEX*) is used as a variable input. Markups prodfn Markups following the estimation in De Loecker and Eeckhout (2017) using Operating Expenses* (OPEX*) as a variable input. COGS Markups Markups following the estimation in De Loecker and Eeckhout (2017) using Cost of Goods Sold (COGS) as a variable input. 59 Table A2: Variable Definitions Variables Definition Log(Assets) Logarithm of total assets. Log(Age) Log(1+Firm Age) where Firm Age is the number of years the firm has appeared in Compustat. Market share Ratio of firm i’s sales to total industry j’s sales in a particular year. HHI Herfindahl-Hirschman Index defined as the sum of squares of the market shares of the firms within each 3-digit NAICS industry. Output Sales/Invested Capital. Investment Capital Expenditures/Invested Capital. R&D R&D Expenses/Invested Capital. Tobin’s Q Q = V/TOTCAP 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 F) plus the book value of debt (Compustat items DLTT + DLC) minus the firms current assets (Compus- tat item ACT) which includes cash, inventory, and marketable securities. TOTCAP is sum of Property, Plant and Equipment (Compustat item PPENT) and Intangi- ble Capital (ICAP). ICAP is defined as the sum of externally purchased intangible capital (INTAN) and internally purchased intangible capital, both measured at re- placement cost. Internally purchased intangible capital is in turn measured as the sum of knowledge capital (K int know) and organization capital (K int org). Q is provided by Peters and Taylor [2017]. Skill(CPS) Identifying complex problems and reviewing related information to develop and eval- uate options and implement solutions.Source: O*NET Skill(NRCOG) Mathematical Reasoning + Inductive Reasoning + Developing Objectives and Strate- gies + Making Decisions and Solving Problems. Source: O*NET Skill(RMAN) Spend time making repetitive motions + Pace Determined by Speed of Equipment + Manual Dexterity + Finger Dexterity. Source: O*NET ImportsUSA Total value of Chinese imports into the US in each 4-digit NAICS industry j scaled by initial absorption in that industry measured as total industry shipments, Yj,2005 plus total imports, Mj,2005 minus total exports, Ej,2005 in that industry in 2005. Source: US Census Bureau ImportsOTH Total value of Chinese imports into 8 other developed economies in each 4-digit NAICS industry j scaled by initial absorption in that industry measured as total industry shipments, Yj,2005 plus total imports, Mj,2005 minus total exports, Ej,2005 in that industry in 2005. Source: UN Comtrade Database 60