Eponymous Entrepreneurs⇤

Sharon Belenzon† Aaron K. Chatterji‡ Brendan Daley§

Fuqua School of Business Duke University

November 11, 2014

Abstract We demonstrate that firm eponymy—the familiar convention of firms being named after their owners—is linked to superior performance. We propose a novel explanation, referred to as “utility amplification,” and develop a corresponding signaling model. The model generates three main empirical predictions: (1) The incidence of eponymy will be low; (2) Eponymous firms will outperform other firms; (3) These e↵ects will be intensified when the entrepreneur’s name is rare. Using unique data on over 485,000 firms from Europe and the United States, we find support for all of these predictions. Several extensions and robustness checks are considered.

Keywords: Entrepreneurship, Signaling, Firm Names

⇤The authors thank seminar participants at Harvard, MIT, Stanford, Northwestern, NBER, UCLA, and UVA for their useful comments and suggestions. †[email protected] ‡[email protected] §[email protected] 1 Introduction Many firms are eponymously named; that is, they bear the name(s) of their founding owner(s). Leveraging a unique dataset, this paper demonstrates that eponymy is linked to superior firm performance. For instance, controlling for other characteristics, eponymous ventures generate, on average, a 3.2 percentage-point higher return on assets (ROA), which is approximately one-third the magnitude of the sample mean ROA. Further, and perhaps counterintuitively, we propose that non-pecuniary considerations may be a large driver of the eponymy-performance relationship. Succinctly put, we propose that eponymy creates a stronger association between the entrepreneur and her firm that amplifies the utility or disutility of favorable or unfavorable market outcomes, respectively. We refer to this phenomenon as “utility amplification,” and intend it to capture any non- pecuniary benefits (or costs) to the entrepreneur of having a favorable (or unfavorable) impression of her firm tied more closely to herself.1 Consequently, high-ability entrepreneurs are more drawn to eponymy than are low-ability ones. While we believe this explanation to be novel, it clearly fits in a recent literature arguing that non-pecuniary considerations likely play a significant role in entrepreneurship. Hamilton (2000) and Moskowitz and Vissing-Jørgensen (2002) propose that there are likely substantial non-pecuniary benefits to entrepreneurship, given the large wage di↵erential between self- employment and paid employment and that returns to private equity (where entrepreneurs typically invest their private holdings in a single company) are no greater than public equity. Further, Pugsley and Hurst (2012) report survey evidence that non-pecuniary motivations are major drivers in entrepreneurial decision-making and could account for why the majority of small businesses do not grow. In a similar vein as our results, they find that entrepreneurs who report non-pecuniary motives have slightly higher survival rates than those who report pecuniary motivations. To formalize this explanation, we introduce a model, characterize its equilibrium, and derive its testable implications. We then demonstrate that the evidence is consistent with the predictions of the theory, using novel data on over 420,000 companies and 5.5 million individuals from Europe and a smaller dataset of over 60,000 firms from the United States.

Eponymous Firms Selecting the name of their ﬁrm is an important and highly visible choice all new-business owners must make. As a Wall Street Journal article memorably stated, “For entrepreneurs,

1While our primary interpretation is that these benefits/costs are non-pecuniary, more generally they could be any benefits/costs distinct from firm-performance measures. For example, future business or employment opportunities for the entrepreneur may be influenced by this impression (see Section 2).

1 the importance of picking the right name for a company may rank second only to naming a child. (And it’s a lot more expensive to change.)”2 Not only is there a proliferation of practitioner guides for choosing a business name, an entire industry of naming consultants exists solely for this purpose. Interestingly, one point of seeming consensus among them is that naming the firm after the owner is not advisable because doing so indicates a lack of creativity and reduces resale value.3 Nevertheless, eponymy is certainly a familiar naming strategy. Well-known examples include Dow Chemical, Gucci, Guinness, Hewlett Packard, Hess, Johnson & Johnson, Kroger, Porsche, Proctor & Gamble, Ryanair, Walgreens, and many others. In our dataset, approximately 13% of firms bear the name of their majority owner. Although not all entrepreneurs think carefully about the name of the firm (Hewlett and Packard reportedly flipped a coin to see whose name would come first4), other naming stories indicate that names, particularly eponymous names, matter. Marvin Bower of McKinsey & Company explicitly chose not to put his own name on the firm after the death of Mr. McKinsey. He noted, “I didn’t want anybody dictating to me how I was going to spend my time. So I had no interest in calling it Bower & Co., or even McKinsey-Bower. I wanted my freedom.”5 To our knowledge, previous academic research has not addressed this well-known naming strategy.

Eponymy, Utility Amplification, and Firm Performance Our model builds on a traditional signaling framework with some important variations. The entrepreneur can engage in activities, eponymy being the prime example, that a↵ect the degree to which the firm is associated with the entrepreneur herself. Our key, novel assumption is that higher levels of signaling activity (i.e., a stronger association between the firm and the entrepreneur herself) are not directly costly, but instead “amplify” the utility or disutility of the favorable or unfavorable market outcomes, respectively. In Section 2 we further discuss the interpretation of this assumption as well as its support in the prior literature. Intuitively, this amplification e↵ect makes eponymy a more attractive strategy for high-ability entrepreneurs, who expect better market outcomes, than for low-ability ones. We demonstrate that our model has a unique stable equilibrium, and that it is partial

2Bounds, Wendy. “How to Choose a Company Name: A 12-Point Test,” Wall Street Journal, June 5, 2008 (http://blogs.wsj.com/independentstreet/2008/06/05/how-to-choose-a-company-name-a-12-point-test). 3For example, please see http://www.businessnamingbasics.com/namedevelopment/naming-business- oneself-easy/ (last accessed November 11, 2014). 4Burrows, Peter. “Hewlett & Packard: Architects of the Info Age,” BusinessWeek, March 28, 2004 (http://www.businessweek.com/stories/2004-03-28/hewlett-and-packard-architects-of-the-info-age) 5Huey, John. “How McKinsey Does It,” Fortune, November 1, 1993.

2 pooling when high-ability entrepreneurs are relatively rare (as appears to be the case in our data). In terms of eponymy, high-ability entrepreneurs engage in it, while low-ability ones mix between doing so and not. In equilibrium, low types trade o↵the boost in perception that eponymy (i.e., pooling with high types) brings, with the amplified disutility in the (likely) event of unfavorable market outcomes. The model then makes three main empirical predictions: (1) The incidence of eponymy will be low; (2) Eponymous firms will perform better than other firms; and (3) These e↵ects will be intensified when the entrepreneur’s name is rare. We believe (3) to be a particularly discriminating test of our model: for entrepreneurs with rarer names, the link between eponymy and performance will be stronger, but they will be less likely to engage in eponymy. While other ex-ante plausible explanations for a link between eponymy and performance exist, explaining this potentially counterintuitive pattern is a more dicult task. We find empirical support for all three predictions. First, we confirm that eponymy is indeed rare in our large sample of public and private firms across several nations. Sec- ond, we empirically document an association between eponymy and higher profitability and higher returns on assets. Third, we find the relationship between eponymy and performance is strongest in rare names (e↵ectively disappearing in common names), but also that the incidence of eponymy is lowest with rare names, exactly as our signaling model predicts. Furthermore, we conduct several additional analyses designed to reveal whether the mechanism we propose is plausible. First, since our argument is based on eponymy signaling privately known ability, it is encouraging that we find our empirical results to be strongest for young firms, of which likely little is known. Second, our theory predicts that the eponymy-performance relationship should be strongest in industries with greater performance dispersion and in industries where market information more accurately reflects the entrepreneur’s skills, both of which we find empirical support for. Finally, we check the sensitivity of our main results with numerous robustness checks, including accounting for di↵erences in ownership structure, name switching, ownership changes, serial entrepreneurs, and the ethnic background of business owners.

Broader Implications for the Study of Entrepreneurship Beyond the specifics of our findings, this work may have broader implications for the study of entrepreneurship. New and small firms are often closely held and rely primarily on the contributions of their founders. Yet founders vary in their underlying ability as well as in their commitment to the enterprise. These attributes constitute important information for customers, investors, and suppliers, but are generally dicult to observe. Our model

3 formalizes a connection between firms and founders and explains how firm names can operate as a credible signal of ability (though not a perfectly revealing one). Future work could build on this model to better understand how other indicators may shed light on the quality and commitment of entrepreneurs. Additionally, while other characteristics associated with superior entrepreneurial performance have been documented, they rarely represent choices available to all entrepreneurs.6 Hsu and Ziedonis (2012) point out that despite a growing literature on entrepreneurship, there is scarce evidence on strategic actions new firms can actually undertake to gain a foothold in the market. This work, however, explores a choice that, presumably, every entrepreneur faces: selecting the name of the firm. Not only is the naming choice universal, it is arguably a decision the owner is most likely to make on her own (as opposed to later decisions in which investors and employees may be involved). Finally, the naming decision is also unlikely to directly influence the underlying ability of the entrepreneur and the quality of her idea. These characteristics make the name of the firm particularly interesting for the study of entrepreneurship.

The remainder of the paper is organized as follows. The next subsection discusses the related literature. Section 2 presents and analyzes our signaling model (proofs are located in Appendix A). Section 3 provides an overview our data and the econometric speciﬁcation. Section 4 tests the predictions of the model. Section 5 contains robustness checks. Section 6concludes.

1.1 Related Literature This paper contributes to three areas of the literature: entrepreneurship, signaling, and names (and their intersections). Scholars of entrepreneurship have observed that information asymmetries are especially pernicious for new business ventures. For example, an entrepreneur’s new business will likely have diculty attracting investment and customers if no mechanism is available by which she can transmit information about her ability/quality (Shapiro, 1983; Amit, Glosten, and Muller 1990; Shane and Cable 2002). In this paper, we propose that eponymy acts a strategic choice of the entrepreneur that ameliorates some of this informational asymmetry. Speciﬁcally, eponymy functions as a signal (a la Spence 1973, 1974; Milgrom and Roberts 1986; Bagwell and Riordan 1991) of the entrepreneur’s quality or skill. In terms of technique,

6Examples include prior industry experience (e.g., Chatterji 2009) and prominent aliations (Stuart, Hoang, and Hybels 1999).

4 the model builds on tools developed in Daley and Green (2014). Within the signaling literature, our theory is distinguished by the manner in which the signal (i.e., eponymy) a↵ects the payo↵s of the sender (i.e., the “utility-amplification” assumption). There is an academic literature on names across several di↵erent disciplines (Bertrand and Mullainathan 2004; Fryer Jr. and Levitt 2004; Simcoe and Waguespack 2011), and on firm names in particular (Ingram 1996; Tadelis 1999, 2002; Lee 2001; Glynn and Abzug 2002). In common with the present paper, some of this work also explores how variation in naming strategies is related to firm performance. For example, Ingram (1996) argues that hotel chains that name all of their properties after the corporate parent (e.g., Marriott) do so to establish a credible commitment to quality service, and finds that firms using this naming strategy survive longer. In perhaps the most related paper to ours, McDevitt (2013) builds and tests a model in which plumbers name their firms to signal quality. However, the preferences of both senders and receivers leading to his results are quite di↵erent from ours. Specifically, low-quality plumbers use names that appear early in the alphabetized list to attract customers who are unwilling to invest much time in searching for the right contractor, whereas high-quality plumbers rely on repeat business and word-of-mouth. In addition, McDevitt (2013) uses all (approximately 2,300) plumbing firms in Illinois as of 2008 for its main analysis. In contrast, we employ data on over 485,000 firms from Europe and the U.S. across a wide variety of industries. In a paper on individual names, Fryer Jr. and Levitt (2004) endeavor to explain striking empirical patterns in distinctively black names in the United States. They consider whether signaling theory could be applied. For example, they posit that giving your child a distinctively black name could potentially be a costly signal to others about your anity for the black community. They argue that the empirical results are more consistent with other explanations, such as those related to social identity. A significant portion of the literature concerns itself with name changes and the market for names. Tadelis (1999, 2002) describes how the market for names arises, sustains, and provides long-term incentives throughout the business owner’s career, a fundamental issue in the literature on career concerns (e.g., Fama 1980; Kreps 1990; Holmström 1999). The logic in these models relies on the key assumption that the firm’s name and the owner’s identity can be separated. In our study, some owners choose to explicitly attach their identity to the firm’s name, which would likely have new implications in Tadelis’s models, a topic we leave for future work. Other papers have documented strategic reasons for name changes. McDevitt (2011) finds that low-performing plumbing firms are more likely to change their names, whereas a

5 set of papers in the marketing and finance literatures explore whether firm name changes create value (for a summary, see Cooper, Dimitrov, and Rau 2001). Some evidence suggests names can influence stock prices (Horsky 1987; Cooper et al. 2001; Lee 2001) and mutual fund inflows (Cooper, Gullen, and Rau 2005). In sum, we aim to add to these literatures by both building a novel model and leveraging a unique dataset to explain and measure the incidence of eponymy, and its link to firm performance, across hundreds of thousands of public and private firms.

2 A Signaling Model of Eponymy as Utility Ampliﬁcation

In our signaling model, the sender is an entrepreneur with type ✓,whichiseitherH or L (mnemonic for high or low). To fix language, we refer to ✓ as the ability of the entrepreneur and also the quality of her firm. For specificity, the receivers are potential customers, though one could consider the larger set of market participants that form an impression about the firm’s quality, including suppliers, lenders, competitors, regulators, etc.7 We use the term the market synonymously with receivers.Theentrepreneurprivatelyknows✓ and receivers share a commonly known prior µ =Pr(✓ = H) (0, 1). 0 2 We first describe the sequence of the game in full and then remark about our assumptions and their interpretations. The entrepreneur engages in a signaling activity, s, that she selects from the interval [s, s], where 1 s < s.Themarketobservesthechoiceofs,as  well as an additional indicator of the firm’s quality, g.Thisindicatorisarandomvariable taking values in l, h according to Pr(g = h ✓ = H)=Pr(g = l ✓ = L)=p ( 1 , 1). { } | | 2 2 Based on its observation of both s and g,themarketformsitsoverallimpressionofthe firm, ↵ =Pr(✓ = H s, g). The entrepreneur’s utility will depend on this impression. Let | V (↵)=↵ (1 ↵), that is, the ↵-weighted convex combination of 1 and 1. Our main, and novel, assumption is that the entrepreneur’s utility scales with her level of signaling: U(s, ↵)=sV (↵). In other words, the choice of higher s increases both the utility of favorable impressions and the disutility of negative ones. We now discuss the modeling choices.8

7For example, an entrepreneur whose ﬁrm sells wares intended for conspicuous consumption cares not only about its impression with potential customers, but also about how it is perceived by individuals unable to purchase the good but who are among the “audience” of its consumers. 8For signaling to even be possible, the entrepreneur must have some private information to convey. For both simplicity and keeping with the signaling literature, we refer to ✓ as the entrepreneur’s ability. However, it can be less stark than this. Qualitatively, nothing is altered if the entrepreneur has only an imperfect, but meaningful, initial assessment of her ability—including the possibility that the entrepreneur may be “over-conﬁdent” (see Camerer and Lovallo (1999) for evidence and Santos-Pinto (2012) for a model that accommodates this feature).

6 The market impression: That the entrepreneur cares directly about the market impression, ↵,isareduced-formassumptioncommoninthesignalingliteraturestartingwithMailath (1987). We assume that for any chosen signaling level, the entrepreneur prefers a more favorable impression for two reasons. First, the prices the firm can charge, the volume it can expect to sell, and ultimately its profitability and performance are increasing in the market impression. Second (and more novel), there are additional factors, beyond firm performance, for which the sender’s utility is increasing in ↵.Weprimarilyinterpretthesefactorsasnon- pecuniary considerations, as we discuss below, and their impact is accentuated by higher levels of the signal.

Eponymy as utility amplification:Ourmainassumptionisthatthesignalingactivity,s, “amplifies” the utility or disutility of a market impression. The interpretation is that s corresponds to the degree to which the entrepreneur ties the identification of the firm (and its market impression) to her person. One way to unpack the sender’s utility function is:9

U(s, ↵)=V (↵) +(s 1) V (↵) (1) firm level of non-pecuniary performance association factors | {z } | {z } | {z } A simple example of the non-pecuniary factors could be that the founder feels pride/shame if the market believes her (as opposed to an unnamed owner of the firm) to be of high/low ability; according to the adages, an eponymous entrepreneur may “bring honor to her name,” but also risks “besmirching” it.10 More generally, utility amplification is the assumption that the owner of a firm with a favorable market impression would prefer to be more closely associated with the firm, and, conversely, that an owner of a of a firm with an unfavorable market impression would prefer to be less closely associated with the firm. While non- pecuniary factors seem prime candidates for generating such a preference, more market-based explanations include the possibility that the impression of the firm may lead to better or

9The precise functional form of V is chosen for tractability. For a simple example that generates such a V , suppose the firm faces the demand curve Q(P ↵)=ap↵ bP , has a fixed cost of 1, and has zero marginal a2↵ | 2 costs. Then firm profits are 4b 1, which is V when a =8b. Formally, though, results are unchanged for @U any linear V , so long as @s is negative when ↵ is below a threshold and positive when ↵ is above it. For example, nothing is altered by adding a constant to the sender’s utility function if dealing only with positive utility values is preferable. Finally, results would be qualitatively similar if both appearances of V in (1) were replaced with (possibly distinct) bounded, strictly increasing, and di↵erentiable functions. 10There is ample evidence that individuals care directly about others’ impressions, even if these impressions are (arguably) only tenuously linked to back to them. From Leary and Kowalski’s (1990) extensive survey: “However, the fact that people often are concerned with how others perceive them, even when no immediate or future outcomes depend on the impressions they make [emphasis added], suggests that other factors may motivate impression management.” See also Andreoni and Petrie (2004), Bohnet and Frey (1999), Frey (1978), Ho↵man et al. (1994), Ho↵man et al. (1996), and Rege and Telle (2004).

7 worse future business or employment opportunities for the eponymous entrepreneur.11 We have modeled the set of possible signals as an interval, [s, s], with the interpretation that there can be varying degrees to which the impression of the firm is identified with the entrepreneur’s person.12 However, as Proposition 1 formalizes, in equilibrium, the sender will always select either s or s,andthatasimplermodelinwhichsignalingwereabinarychoice, with the clear interpretations of “non-eponymy” or “eponymy,” would yield precisely the same predictions. We feel that, in addition to accommodating more real-world possibilities, allowing for the richer set of signals actually clarifies the model’s properties (see Section 2.1 below) and puts more distance between its assumptions and conclusions.

The additional indicator:Theadditionalindictor,g,standsinforanyadditionalinformation regarding the firm’s quality besides the entrepreneur’s choice of s.Forinstance,inthecase of a new restaurant, this information would include its review in the local newspaper. Of course, this additional information likely does not all appear in one-shot—for example, con- sumer experiences accumulate and word-of-mouth spreads over time. In reality then, market information and firm performance evolve jointly throughout the life of the firm. However, the works of Alos-Ferrer and Prat (2012) and Daley and Green (2014) show that a fully dynamic, infinite-horizon modeling of this phenomenon, while more analytically complex, does not meaningfully alter the incentives for the initial signaling decision. One final feature to note is that in contrast to standard signaling models, the utility of the entrepreneur depends only on s and ↵,andnoton✓.Crucially,however,becausethe market’s impression of the firm depends on g, in addition to s,theentrepreneur’sexpected utility prior to the realization of g does depend on her ability. This fact becomes apparent as we analyze the game below.

2.1 Preliminary Analysis Our solution concept is Perfect Bayesian Equilibrium, henceforth equilibrium.13 In any equilibrium, after s is chosen, but before g is realized, the market updates from its prior,

11The implicit assumption in this argument is that receivers can tell when a firm is eponymously named. Of course, if a firm name is potentially eponymous (i.e., appears to be a personal name), perhaps not all receivers would know whether it is actually eponymous or not. However, we argue that many consumers will know, and more are likely to find out over time. We therefore operate under the assumption that eponymy is identifiable to receivers. Consistent with this, empirically, we find that eponymous firms perform better than those bearing the names of non-owners (see Section 5.1). 12In light of (1), one can interpret s = 1 as no association between the identity of the entrepreneur and the firm (in which case her utility is based only on firm performance), and s as the minimum association between the entrepreneur and the firm. Clearly then, s 1, but they can be distinct if some degree of association is unavoidable. 13Our notion of Perfect Bayesian Equilibrium is that of Fudenberg and Tirole (1991, pp. 331-333).

8 1 1 2 1 2 2 1

1 0 1

1 0 1 1 Α 2 Μ 2 Μ 2

!1 0 !1

!1 !2 !2 2 0 0 0 ! s s s s s s s s s (a) The (s, ↵)-indi↵erence (b) The (s, µ)-indi↵erence (c) The (s, µ)-indi↵erence curves of the entrepreneur. curves of a low-type entrepreneur. curves of a high-type entrepreneur.

Figure 1: Plots of indi↵erence curves for utility/expected-utility levels 2, 1, 0, 1, 2 using 3 3 { } parameters p = 4 ,s= 2 , and s =7.

µ ,toaninterim belief, denoted µ(s)=Pr(✓ = H s). Then, upon observing g,themarket 0 | updates its belief from µ(s)to↵(µ(s),g). Notice that, given any interim belief µ,thesecond update is merely a straightforward application of Bayes rule:

µp µ(1 p) ↵(µ, h)= and ↵(µ, l)= . (2) µp +(1 µ)(1 p) µ(1 p)+(1 µ)p It follows that, given arbitrary values for s and µ(s), the entrepreneur’s expected utility is

u✓(s, µ(s)) = Eg[sV (↵(µ(s),g)) ✓]. |

As is typical in signaling models, analysis and intuition are aided by studying the indif- ference curves of the sender. Given the analysis above, the indi↵erence curves of interest are the level sets of the entrepreneur’s expected utility function, u✓(s, µ). Figure 1 illustrates the key features of the entrepreneur’s indi↵erence curves. Panel (a) shows the entrepreneur’s indi↵erence curves over (s, ↵), which, recall, are independent of type. For any fixed s,utilityisincreasingin↵. However, for fixed ↵, utility is increasing in 1 s if and only if ↵> 2 .Thisisthemanifestationoftheassumptionthatalargers amplifies the utility (or disutility) of a market impression. In the extremes, the best outcome for the entrepreneur is to have chosen the maximal s coupled with the market being convinced she is high ability, whereas the worst outcome also involves having chosen the maximal s but coupled with the market being convinced she is low ability. Panels (b) and (c) depict the indi↵erence curves over (s, µ)forthelow-andhigh-ability

9 entrepreneur, respectively. Because ↵ is derived from µ via Bayes rule, these panels retain the features just described, but with two key di↵erences. First, there is a level e↵ect: relative to panel (a), the indi↵erence curves are shifted up for the low type (panel (b)) and shifted down for the high type (panel (c)). This is due to their respective expectations about the realization of g. Fixing a belief level b (0, 1), be it interim or ﬁnal, because the realization 2 of g is informative, uL(s, b)

Fact 1 The (s, µ)-indi↵erence curves satisfy the single-crossing property.

2.2 Equilibrium and Empirical Properties As is common in signaling models, the game has many equilibria. It may be worth noting, however, that none of them are separating.Thisfollowsfromtheatypicalstructureof the sender’s utility function. In a typical signaling model, separation occurs in equilibrium because the high type chooses a signaling level great enough that the low type finds it too costly to imitate, even though doing so would “fool” the receivers. In our model, selecting alarges is “costly” to the low type only if there is sucient chance that the market will ultimately settle upon an unfavorable impression of the firm. If an equilibrium called for types to separate, imitating the high-ability entrepreneur would therefore be costless, because doing so would completely convince the market of the entrepreneur’s high ability. Hence, deviation by the low-ability entrepreneur would be profitable, contradicting the existence of a separating equilibrium. In the theory of signaling games, there is a long tradition of using refinements to narrow the set of equilibria. The most well-known and commonly used of these refinements are those related to the concept of strategic stability (Kohlberg and Mertens 1986), which focus

10 on eliminating o↵-path beliefs thought to be unreasonable.14 In keeping with this tradition, we focus on equilibria that are stable in that they satisfy the D1 criterion. Intuitively, the refinement can be interpreted as requiring that, following an o↵-path (i.e., unexpected) choice of s by the entrepreneur, the market believes the deviating entrepreneur to be of whichever type is more willing to undertake this deviation.15 As we have just seen, the model’s single-crossing property means the high-ability entrepreneur is more willing than the low to associate the firm with herself by choosing higher signaling levels. In our model then, the refinement will eliminate equilibria in which the high type selects a signaling level s 0.

~~Proposition 1 For any µ0, the game has a unique stable equilibrium. In it, the high-ability entrepreneur selects s = s. The strategy of the low-ability entrepreneur depends on µ0:~~

~~If µ µ⇤, the low-ability entrepreneur pools with the high-ability entrepreneur, select- • 0 ing s = s.~~

If µ <µ⇤, the low-ability entrepreneur partially pools by playing a mixed strategy. • 0 µ0(1 µ ) Speciﬁcally, she selects s = s with probability ⇤ and s = s with the complementary µ (1 µ0) ⇤ probability.

17, Therefore, in the stable equilibrium, µ(s)=0and µ(s) = max µ ,µ⇤ . { 0 } In the equilibrium, the entrepreneur always chooses either s or s.Infact,itisnotdicult to show that the proposition remains valid if the entrepreneur’s choice of s were restricted to s, s .18 Hence, nothing hinges on whether we consider the signaling choice to be from a { } continuous interval or just that interval’s endpoints. 14These include the Intuitive Criterion, D1, Divinity, and Never-a-weak-best-response (Cho and Kreps 1987; Banks and Sobel 1987). While the Intuitive Criterion may be best known, it is straightforward to demonstrate that it has no reﬁning power in our game. 15The formal details of the reﬁnement are given in Appendix A. 16If µ = 0, the realization of g is irrelevant because the market is already thoroughly convinced that ✓ = L. 17The interim belief following any s that is o↵the equilibrium path is µ(s) = 0. 18While perhaps only of theoretical interest, the analogous statement is not true of the canonical signaling model of Spence (1973), where the unique stable equilibrium is the least-cost-separating one. That is, even though intermediate signaling levels are never chosen in the least-cost-separating equilibrium, removing them leads to some full-pooling equilibria also being stable for some priors.

11 An intuition for the equilibrium is as follows. First, the high-ability entrepreneur, ex- pecting a favorable impression for her firm, seeks to amplify her utility by choosing s. As discussed above, the high-ability entrepreneur cannot be fully separating from the low-ability entrepreneur by choosing s. Hence, the low-ability entrepreneur must also be selecting s with positive probability—but with how much? She faces the following tradeo↵. If she selects any other s = s,shewillrevealherselftobelowability,µ(s)=0.Inthiscaseitisoptimal 6 to choose s = s, leading to utility u (s, 0) = U(s, 0) = s. If she imitates the high-ability L entrepreneur, her expected utility is uL(s, µ(s)). Of course, in equilibrium, µ(s)mustbe correct. Recall that, by definition, if µ µ⇤,thenu (s, µ ) s; so the low-ability en- 0 L 0 trepreneur then finds it optimal to fully pool (which, of course, is what leads to µ(s)=µ0).

Alternatively, if µ <µ⇤,thenu (s, µ ) < s;sothelowtypewillnotwanttofullypool. 0 L 0 Hence, she must be mixing between s and s,whichrequiresindi↵erence:u (s, µ(s)) = s. L By definition, this occurs when µ(s)=µ⇤.Thisbeliefmustbecorrectinequilibrium, requiring the mixing probabilities given in Proposition 1. To understand the uniqueness claim, first notice that the reasoning in the paragraph above shows that, for each µ0,theequilibriuminthepropositionistheonlyoneinwhich the high type selects signaling level s.Second,suppose(forthepurposeofcontradiction) that the high type is meant to choose a signaling level s other than s in equilibrium. For this to be the case, it must be that deviating to s0 >sis not profitable. However, now recall two points. One, the refinement implies that the market should ascribe deviations to the type that is more willing to select it. Two, the model’s single-crossing property implies that the high type is more willing to deviate to higher signaling levels. Hence, the deviation would, in fact, lead the market to believe the entrepreneur was high ability, and therefore be profitable. This logic eliminates all other equilibria. Following the inverse of this logic, we can see that the equilibrium in the proposition does satisfy the refinement.

~~Empirical Predictions We conclude this section by recording the empirical predictions that can be ascertained from the stable equilibrium of the model.~~

~~Prediction 1 Eponymy is rare.~~

In our dataset covering over 485,000 public and private firms in Europe and the United States, approximately 13% of all firms bear the name of the majority owner. This, of course, is not a sharp prediction of the model; for any proportion of eponymous firms, say q,there exists a prior belief µ0 such that the model predicts eponymy with probability q. However,

12 according to the model, low incidence of eponymy corresponds with the prior belief being low. That is, under the assumption that high-ability entrepreneurs are relatively rare, the model predicts a low incidence of eponymy. Given the high failure rates of new businesses in the United States and Europe, the assumption that highly skilled entrepreneurs are rare seems plausible. Hence, we focus on the model under a low prior and the corresponding partial-pooling equilibrium from Proposition 1 hereafter.

~~Prediction 2 Eponymous ﬁrms perform better.~~

In our empirical analysis, we ﬁnd that eponymy is associated with greater proﬁts and greater return on assets. The correspondence with the model is straightforward. Eponymous entrepreneurs (those selecting s)areofhigheraverageabilitythannon-eponymous(those selecting s), meaning eponymy provides meaningful information to both the market and the econometrician.

Prediction 3 For rarer entrepreneur names, (a) the incidence of eponymy is lower, (b) the performance of eponymous ﬁrms is higher, (c) there is no e↵ect on the performance of non-eponymous ﬁrms; (d) therefore, the performance di↵erence stated in Prediction 2 is higher.

We interpret (a)-(d) above as interrelated comparative statics results. Recall that our main assumption is that eponymy amplifies the value of the firm’s market impression for the reasons discussed above. Under these interpretations, it is natural that the rarer is an entrepreneur’s name, the higher is the corresponding s.Thatis,thereisgreaterscopefor amplification with a rarer name because the entrepreneur is that much more identifiable. Within the model, the comparative statics go hand-in-hand and are easily derived. As s increases, so too does µ⇤. In equilibrium, the average ability of eponymous entrepreneurs,

µ(s), equals µ⇤. As µ⇤ increases then, fewer low-ability entrepreneurs select eponymy, s,and the di↵erence between the eponymous and non-eponymous ﬁrms increases. In addition to Predictions 1-3, which we refer to throughout as the model’s main predictions for empirical testing, taking a comparative static on p (the accuracy of additional market information, g)yieldsthefollowingadditionalproperties.

~~Prediction 4 As the accuracy of market information increases, (a) the performance di↵erence stated in Prediction 2 increases. (b) performance outcomes become more dispersed.~~

13 These are both straightforward comparative statics. The intuition is that if the indictor is more accurate, then low-ability entrepreneurs will find eponymy less attractive because unfavorable outcomes are now more likely for them. Hence, the performance di↵erence between eponymous and non-eponymous firms will grow, and the distribution of performance outcomes will more accurately reflect underlying di↵erences in ability. However, we regard Prediction 4 as a secondary prediction because our data do not allow a direct measure of the accuracy of market information. In Section 4.4, we will argue that industry variations in the eponymy-performance relationship and performance dispersion are consistent with this explanation—that is, plausibly attributable to di↵erences in market information.

3 Data and Econometric Specification We conduct our primary empirical tests of the model’s predictions using data from Amadeus, a database maintained by Bureau van Dijk (BvD), which contains ownership, management, and financial information for European firms. BvD obtains its data from regulatory filings, third-party vendors, and its own proprietary sources. Amadeus includes both private and public firms in its data collection, allowing for an in-depth examination of new firms. It also contains detailed ownership information, including the names of each shareholder, the number and type of shares held, and information on the board of directors and management of each firm. We build our sample from firms located in Western European countries. In our sample, 31% of firms are from France, 17% from Great Britain, and 33% from Spain. We exclude German firms because small German firms are not required to disclose balance sheet information, making the calculation of financial performance outcomes such as return on assets (ROA) impossible.19 We retain only those firms for which we have ownership information; we exclude firms for which we are unable to identify at least 90% of reported shareholders and those whose annual sales are not reported. Our final estimation sample includes 426,498 firms and 686,685 firm-year observations for the years 2007 and 2010.20 In our sample, 258,078 firms appear in both 2007 and 2010. To track individual owners, we assigned a unique identifier to each owner within and across years, using a multi-step approach to narrow down each individual’s identity. We used direct and fuzzy matching techniques to account for name-spelling errors and recording variations in determining whether

19If we examine profit margin (profits over sales) instead of ROA, our results hold even when including German firms. However, because ROA is a more widely used performance measure in the literature, we choose to exclude German firms from the estimation sample. 20The dataset contains firm and performance information for the intervening years, 2008 and 2009, but data on firm names was only updated in 2007 and 2010. Our results are robust to the inclusion of these observations.

14 any two records are of the same person. This allows us to track within-firm changes in eponymy and identify serial entrepreneurs. We supplemented this matching by utilizing detailed physical address information, which allowed us to further discriminate identities of individuals with the same name, and we used identities of their business partners to gain further insight into whether two records represented the same person. Using this process, we matched over 5.5 million individual owner records associated with our sample firms and assigned unique identification both within and across years, which allowed us to track owners to determine the number of businesses they own simultaneously and how ownership changes over time.

3.1 Coding Eponymy and Name Changes The main variable of interest in this paper is whether an entrepreneur names the firm after herself. To code this variable, we need to check for matches in our dataset between the name of the firm’s owners and the name of the firm. For each firm, we consider only the majority owners as indicated by equity shares. To determine whether it is an eponymous venture, we use a string-matching algorithm that matches the last name of the majority owner to the firm name. The automated process compares both names, assigns a matching score, and identifies matches if the matching score meets a certain threshold.21 To refine our match, we compared the normalized matching scores across di↵erent thresh- olds. After extensive manual checks and iteration, we chose the optimal threshold of 0.68, as it produced the most accurate matches (see Appendix B for more detail). This matching process goes beyond simple direct matching and can identify last names that are used in combination with other words or that are partially embedded with other words. For instance, the algorithm we employ would classify a match whether the last name “Johnson” appears in “Johnson Consulting” or “Johnsontown.” We create a dummy for owner name that receives the value of 1 for firms whose names include the name of owner (eponymous ventures), and 0 for all other firms. In our discussion of the results, we refer to these firms as eponymous ventures, and the coecient of interest is labeled “eponymous.” In our Amadeus sample, 13.8% of the firms share their name with their owner. We observe that 2,837 firms change from being eponymous in 2007 to non-eponymous in 2010 (7.8% of all eponymous firms in 2007), but no eponymous firm changes its name between our two sample periods. In addition, no firms in our sample switch from being non- eponymous in 2007 to being eponymous in 2010. Therefore, all the within-firm variation in

21We obtain the matching score by ﬁrst calculating the Levenshtein distance score between two strings or vectors of strings for a given pair of names. The distance score is deﬁned as the minimum number of insertions, deletions, and substitutions necessary to change one string into the other.

15 eponymy is attributed to ownership changes. In these instances, the name of the firm remains the same, but the owner has changed, leading to a non-eponymous classification of the firm in 2010. This feature of the data suggests that the traditional firm-fixed-e↵ects analysis would be capturing the e↵ect of ownership changes on firm performance. Because we do not have insight into what drives ownership changes, it is dicult to interpret these results. We address this shortcoming in two ways. First, we do an owner-fixed-e↵ects analysis to exploit variation within owner (Section 4.2). Second, we explore changes in ownership over time, leveraging the panel structure of the data (Section 5.5). Together, these analyses generate further insight into the relationship between the owner and firm performance, which is tied directly to the predictions of the model.

3.2 Descriptive Statistics Table 1 presents summary statistics for the main variables in our sample. The average firm has an ROA (profits over assets) of 0.09 (a median of 0.04) and profit margin (profits over sales) of 0.05 (a median of 0.03), grows at an annual rate of 13% (a median of 18%), generates $4.8 million in annual sales (a median of $1.1 million), holds $5.3 million in assets (a median of $0.8 million), is 15 years old (a median of 13), and has 16 employees (a median of 5). Table 2 presents mean comparison tests for di↵erences in main characteristics between eponymous ventures and other companies. Eponymous ventures appear to have higher returns on assets (0.13 vs. 0.09) and are more profitable (0.08 vs. 0.05). Although eponymous firms are on average about two years older than other firms, they hold fewer assets ($4.2 million vs. $5.5 million). We find no substantial di↵erences in sales and number of employees between eponymous and non-eponymous firms. Firms in our sample are drawn from a wide industry distribution. For ease of presentation, we aggregate the three-digit SIC codes to broad industry-level categories. Details on the classification of SIC codes to main industry categories are available upon request. Table 3 presents the distribution of firms by industry. The most represented industries in our sample are construction contracting (74,896 firms), professional and personal services (47,324 firms), and food stores and restaurants (37,292 firms). Other common industries include general retail (17,135 firms) and metal (14,777 firms). We find that 23% of the firms operate in the services industries, and the remaining firms operate in manufacturing. The share of eponymous firms varies across industries from a high of 23.6% in construction contractors to a low of 4.3% in the professional equipment industry.

16 3.3 Econometric Specifications We are primarily interested in the relationship between eponymous ventures and firm performance. We measure performance using return on assets (ROA), but all of our results are robust to using alternatives measures, such as profit margin, and these results are available on request. We estimate the following specification for the relationship between eponymous ventures and the performance outcome (i indexes firms and t index years):

ROAit = ↵1 ln (Salesit 1)+↵2 ln (Ageit)+↵3Eponymousit + ⌘i + ⌧t + cc + ✏it, (3) where t denotes one of two periods: 2007 and 2010. For every period, we have information on financial accounts, ownership, and names. Age is years from date of incorporation, and we include it to control for firm-life-cycle e↵ects. The terms ⌘, ⌧,andc are complete sets of three-digit SIC codes, country, and year dummies, and ✏ is an i.i.d. error term. Standard errors are clustered by firms. Our main interest is the coecient ↵3,whereweexpect↵3 > 0. Finally, before presenting our estimation results in detail, we note that the main relationship we document in this paper can be seen non-parametrically. Plotting the cumulativeb distributions of ROA separately for eponymous and non-eponymous firms in our sample, we find that the distribution of ROA for eponymous firms (weakly) first-order stochastically dominates the distribution for non-eponymous firms (Appendix Figure C1). That is, there is no ROA value for which the share of non-eponymous firms with higher values is greater than the corresponding share of eponymous firms. Further, the finding is strengthened when the owner’s name is rare (Appendix Figure C1(c)).

4 Testing the Predictions of the Model In this section, we test the predictions of the model. After testing the main predictions directly, we conduct several other analyses to shed more light on our proposed mechanism. In Section 4.4, we exploit di↵erences across industries to ascertain whether the relationship between eponymy and performance is consistent with Prediction 4. In Section 5, we demonstrate the generalizability of our core results in a smaller dataset of American ﬁrms, conduct robustness checks related to alternative explanations, and explore other dimensions of the data, such as the relationship between eponymy and ownership changes.

4.1 Prediction 1: The Rarity of Eponymy Clearly, the prediction that the incidence of eponymy will be low is the easiest to evaluate. Within our sample, 13.8% of ﬁrms are eponymous. It is worth noting, however, that in the

~~17 model this prediction is intimately tied with Predictions 2 and 3. Hence, it is important to verify Prediction 1, given that we substantiate Predictions 2 and 3.~~

4.2 Prediction 2: The Eponymy-Performance Relationship Table 4 presents the estimation results for the relationship between eponymy and ROA. The general pattern of results shows that eponymous firms have better performance as indicated by higher ROA. Eponymous ventures generate on average 0.032 higher ROA, which accounts for about one-third of the sample mean (Column 1). Column 2 presents a slightly larger estimate of the eponymous coecient in a cross-sectional regression. In Column 3, we estimate our specification for owners of at least two firms. This allows us to control for owner fixed e↵ects and to study the relationship between eponymy and outcomes by comparing eponymous firms to non-eponymous firms owned by the same owner. For this analysis, we develop new data that assigns a unique identification number for each owner based on the owner’s first and last names and address. The identification number allows us to identify individuals that own multiple firms in the cross-section of 2007 and over time in 2010 (see Appendix B for more detail). For each multiple-firm owner, we estimate owner fixed e↵ects while controlling for observables. Based on our unique identification- number-assignment procedure, we have 422,104 distinct individual owners, out of which about 7% owned at least two businesses.22 The vast majority of owners with multiple businesses focus on the same industry both within a given year and over time. The within- owner estimate of the coecient on eponymy is positive and significant, but is about one third of the size of the estimate from the pooled regression. These results suggest that both owner and venture quality are important drivers of the relationship between eponymy and performance. Multi-firm owners are likely to be di↵erent from single-firm owners. To check that our results on the eponymy-performance relationship are not driven solely by multi-firm owners, Column 4 restricts the sample to single-firm owners. The coecient estimate on the eponymous dummy remains large and statistically significant. Column 5 restricts the sample to firms with ages of five years or less. Since our argument is based on eponymy signaling privately known ability, we would expect our results to be strongest for young firms of which little is known.23 As expected, the estimated coecient on eponymous firms is larger for the young-firm subsample (0.092).

22In 2007, the average number of firms per owner is 2.5 and the median is 1, and in 2010, the average number of firms per owner is 1.4 and the median is 1. 23A straightforward variant of the model in Section 2 in which information about firm quality is revealed gradually to the market (i.e., g becomes a stochastic process instead of a random variable) would yield this prediction.

18 In sum, our initial results indicate that eponymous firms are more profitable, and this relationship is present even when exploiting variation across ventures of the same owner. Because our theory pertains most closely to new businesses, the finding that these empirical patterns are most apparent early in the firm life-cycle is encouraging.

4.3 Prediction 3: The E↵ect of Name Rarity/Commonality Having shown that the evidence supports Predictions 1 and 2, we turn to Prediction 3: for entrepreneurs with rarer names, the link between eponymy and performance will be stronger, but they will be less likely to engage in eponymy (i.e., both Predictions 1 and 2willbestrengthenedwhentheentrepreneurhasararername).Weviewthistobea particularly discriminating test of our model. While other ex-ante plausible explanations for alinkbetweeneponymyandperformanceexist,explainingthispotentiallycounterintuitive pattern is a more dicult task. Our empirical approach is to measure the frequency of each owner’s last name in the population of managers and owners in the same city. Note that this measure captures the “commonality” of an owner’s name, which is simply the inverse of its “rarity.” We started with a list of all manager and owner names for all of the firms in our sample countries, which totals over 13 million names. After standardizing all the names within country according to the processes we outline in Appendix B, we counted the number of times each unique name appears overall and calculated the share of a particular name within the entire population of names in the country, region, and city, respectively. On average, we found over 90,000 di↵erent last names in a country and 16,000 in a city. We use the city-based measure in our results going forward, though our results are robust to whichever geographic unit we choose (e.g., region, country). Table 5 presents the estimation results of how the eponymy- performance relationship varies by owner-name commonality. First, consistent with our model (Prediction 3(a)), the share of eponymous firms de- creases with owner-name rarity (i.e., increases with name commonality). The distribution of eponymy by quartiles of commonality (higher quartiles imply more common names) is 13.1%, 13.9%, 14.5%, and 19.5%. Columns 1-2 show that this pattern is robust also when controlling for firm characteristics. In these columns, we estimate a linear probability model in which the dependent variable is a dummy for eponymous firms. The coecient estimate on owner-name commonality is 0.268, which indicates that a two-standard-deviation increase in owner-name commonality is associated with a 3.5 percentage point increase in the incidence of eponymy (or 22% of the sample average eponymy incidence). Column 2 shows a similar pattern when including quartile dummies for owner-name commonality.

19 Next, we explore whether the relationship between eponymy and performance changes as the name becomes more common (Prediction 3(d)).24 Columns 3-7 present results from an analysis that categorizes eponymous owner names by their commonality. The results are consistent with our model’s prediction. The relationship between eponymy and performance declines as the owner name becomes more common: the coecient estimate on the interaction term between eponymy and owner-name commonality (-0.281) is negative and significant (for profit margins, it is -0.156, not reported in the table). These estimates imply there is a large variation in the eponymy-performance relationship depending on owner-name commonality. For example, moving from the 10th percentile to the 90th percentile of owner- name commonality reduces the coecient estimate on eponymy by about 30%. Moving from the 10th percentile to the 95th percentile lowers this estimate by 60%. Column 4 includes interaction terms between eponymy and dummies for di↵erent quartiles of owner-name commonality. As expected, the coecients on the interaction terms are negative and highly significant, and they grow in magnitude with an increase in owner-name commonality, thus o↵setting the relationship with eponymy.25 The coecient estimate on the fourth quartile of owner-name commonality is statistically di↵erent from the estimates on the third and second quartiles (p-value<0.01 for both tests). In unreported regressions, we estimate the relationship between eponymy and firm outcomes for the lowest and highest deciles of owner-name commonality. We observe striking di↵erences in the magnitudes of eponymy. For example, for ROA, in the lowest decile (very rare names), the coecient estimate on eponymy is 0.085 (highly significant). This coecient is close to zero (0.009) for the highest decile of owner-name commonality. Columns 5-6 show that the results also hold with alternative measures of name commonality: when using country- and NUTS-level frequency of names, the estimated coecient on the interaction between the eponymous dummy variable and frequency of names remain robust. Finally, in Column 7 we calculate owner-name commonality at the level of city- industry pair. That is, for each owner name, we calculate its frequency at the level of the city where the firm is located and the 3-digit industry where the firm operates. The results remain robust. 24Prediction 3(c) states that the e↵ect of owner-name rarity on performance (ROA) should be zero for non-eponymous firms. This prediction strongly holds in our data. For the sample of non-eponymous firms, the coecient estimate on owner-name commonality (city-level) in the ROA specification is 0.002 (standard error of 0.006). Prediction 3(b) is then a direct implication. 25We also experiment with including a complete set of city dummies to mitigate the concern that owner- name commonality may be capturing the size of the city where the firm operates. The results continue to hold with the city e↵ects. Further, to ensure we are not picking up di↵erences between large and small regions in the pooled regressions, we repeat the above estimations separately for regions with high and low GDP, population, and size, and find the same pattern of results.

20 4.4 Industry Characteristics and Prediction 4 Our data allows us to investigate whether, and how, the relationship between firm performance and eponymy varies, depending on industry characteristics. We find that the relationship is stronger for i)industrieswithgreaterperformancedispersion;ii)serviceindustries (compared to manufacturing); and iii)industriescharacterizedasmore“artisanal.” In light of Prediction 4, one explanation of these empirical results is that the market’s information about firm quality (i.e., the indicator, g)moreaccuratelyreflectsthe entrepreneur’s ability in industries with these characteristics. Regarding (i), this merely gives an intuitive explanation for the covariance in performance dispersion and the strength of the eponymy-performance relationship: if the market has relatively accurate information in an industry, it leads to both greater performance dispersion and a stronger link between eponymy and performance. Regarding (ii)and(iii), we find the assumption plausible; in service or artisanal industries, there is a more direct connection between the skills of the purveyor (who is often the entrepreneur herself given that most of the firms in the dataset are small) and the realized product. Of course, this assumption need not be universally true, but must only hold in aggregate to explain the data. Table 6 shows the relationship between eponymy and ROA by industry characteristics in our data.

Performance Dispersion. We follow Acemoglu et al. (2007) and measure an industry’s dispersion of performance as the di↵erence between the highest and lowest performing ﬁrms.26 We use the complete Amadeus database over the years 1996–2006 to compute the di↵erence between the 95th and 5th percentiles of ROA. Column 2 presents the estimation results for industry performance dispersion. Consistent with our prediction, the coecient estimate on the interaction between industry dispersion and eponymy is positive and highly signiﬁcant. Moving from the 25th to the 75th percentile of dispersion raises the coecient estimate on eponymy from 0 to 0.15.

Services vs. Manufacturing. We manually classify industries into services or manufacturing based on their SIC code description. Column 3 includes an interaction between a dummy variable that receives the value of 1 for manufacturing and 0 for services. The coef- ﬁcient estimate on this interaction is negative, highly signiﬁcant, and very large, indicating the eponymy-performance relationship is stronger in service industries.

26Acemoglu et al. (2007) study the dispersion of labor-productivity growth. We focus on ROA as our main performance measure throughout, but our results are robust to using this alternative measure of ﬁrm performance. See Column 1.

21 Artisanship. Column 4 adds an interaction between eponymy and one measure of the level of “artisanship” in the industry.27 The results are consistent with our theory. The eponymy-peformance relationship increases in the industry’s level of artisanship. Moving from the 25th to the 75th percentile of industry artisanship raises the coecient estimate on eponymy by 30%. For robustness, we include measures of how the eponymy-performance relationship varies with Tobin’s Q and R&D intensity.28 Like artisanship, these measures also capture features of industries in which “intangibles” play a greater role, which may be related to a greater importance of owner skills. Columns 4 and 5 are consistent with our predictions. Moving from the 25th to the 75th percentile of Tobin’s Q almost doubles the coecient estimate on eponymy (Column 5). For R&D intensity, as expected, the coecient estimate on this interaction is positive and highly signiﬁcant. The magnitude of the e↵ect is very large as well (Column 6).

5 Robustness Checks and Extensions 5.1 Ownership Structure and Management Some of the relationships this paper documents could be related to variation in the ownership structure of firms, and in family versus professional management. We examine these concerns using the rich and detailed information in our data on firm ownership and management. Table 7 presents the estimation results. We begin by comparing single-owner to multiple-owner firms. Firms with single owners might be more likely to be eponymous than firms with multiple owners (e.g., if agreeing on an eponymous name is more dicult when the firm has more than one owner), and ownership structure might a↵ect performance. The estimation results do not suggest substantial di↵erences in the role of eponymy between the two sets of firms. The coecient estimates on eponymy are the same for the two subsamples (0.012 as shown in Columns 1 and 2). Similarly, eponymy is likely to be more common in firms that are owned by related owners, compared to firms that are owned by unrelated owners (agreeing on an eponymous name for a single family name should be easier than for multiple family names). If family-owned

27We follow Costinot, Oldenski, and Rauch (2011) and rank industries according to their level of task “routineness,” using data from the U.S. Department of Labor’s Occupational Information Network (O*NET) and measuring the level of task routineness by the extent to which the task involves “making decisions and solving problems.” Our measure of artisanship is the reverse of their measure of routineness. 28Tobin’s Q is the ratio between firm value and the book value of assets. We calculate firm value as the sum of the values of common stock, preferred stock, and total debt net of current assets. The book value of capital includes net plant, property, and equipment, inventories, investments in unconsolidated subsidiaries, and intangibles other than R&D. We calculate Tobin’s Q using American Compustat firms over the period 1980–96 (which is prior to our estimation sample). R&D intensity is spending over sales. For each industry, we compute the average ratio of R&D expenditures over sales from Compustat firms over the period 1980-96.

22 firms di↵er from non-family firms across the dimensions documented here, we may incorrectly attribute an ownership e↵ect to eponymy. We distinguished between firms that are owned by family-related individuals and those that are not. Splitting the sample between family and non-family firms allows us to mitigate the concern that eponymy picks up some underlying mis-measured di↵erences between family and non-family firms. We follow Belenzon and Zarutskie (2012) and classify firms as family firms if the firm’s majority shareholders have the same last name, and classify firms as non-family if no single family name has majority stakes in the firm. We find a larger coecient estimate on eponymy for the subsample of family firms (Columns 3-4), suggesting that unobserved di↵erences related to family businesses are not driving our results. Because greater involvement of owners in the management of the firm may also influence firm outcomes, we examine the management structure of firms as well. We also examine how the eponymy-outcomes relationship varies by firms that are owner managed (in which the leading owner is also the CEO or in an equivalent leadership position in the firm) and non-owner-managed firms. The coecient estimates on eponymy are similar for the two subsamples (Columns 5-6). Further, we checked whether our analysis has confounding e↵ects associated with firms being named after individuals that are unrelated to the owner of the firm. That is, within the sample of firms that are named after any individual, we ask whether having the same name as the firm owner a↵ects outcomes. We determine whether a firm’s name is an individual name by matching all firm names to the population of first and last names in the Amadeus database. We start with the population of all owner names in our sample countries (1.5 million names) and match our sample firm names to this population of individual names. We utilize both direct and approximate matching to account for misspellings and variations in firm names that incorporate individual names. For example, “AlderBrook Asso- ciates,” “AlderCabs,” and “Alders Air Conditioning” all matched to last names “Alder” and “Alders.” We carefully reviewed matched results to determine the appropriate threshold for the matching score. Using this process, we determine that 36.6% of all firms in our sample feature individual names either in full or as part of their company names. Column 7 presents the estimation results. We find a strong relationship between eponymy and performance also for the subsample of firms that include the last name of individuals in the complete Amadeus database.29 29One remaining concern is that our results may be picking up varying degrees of family involvement in the business even within the subsample of firms that are family owned or managed by the owner. In unreported specifications, by matching last names and using age information when available, we try to ascertain whether owners’ children are managers in the firm. According to this method, 15.9% of our sample firms have managers who share last names with the owner and are of a reasonable age to be a child. We

23 5.2 Alternative Naming Strategies Although we focus on one binary naming strategy (eponymous or not), other naming strategies could also operate as signals. We concentrate on names that might be related to high quality or low cost. The most common quality-related terms in firm names include “luxury,” “innovation,” “best,” “quality,” “top,” “premier,” and “superior.” For low cost, the most common terms in firm names include “cheap,” “budget,” “economy,” “value,” “discount,” and “price.” We translated these terms into the native languages of each country in our data and cross-checked the list against every firm name in our data. We found 9,768 firms that include a quality-related term in their name and 1,234 firms that include a low-cost- related term in their name. Table 8 presents results from estimating the relationship between eponymy and outcomes for these firms. The results strongly show that these firms, even those that include quality-related terms in their name, appear to be less profitable. We find such firms have lower ROA and profit margins compared with eponymous firms. In Columns 1-3, we examine the relationship between these naming conventions and ROA, and find a strong negative association (Column 1). When we add the eponymy dummy, the negative coecient on quality-cost naming remains the same (Column 2). In Column 3, we separate the names into quality and low- cost types by including a dummy for each, and find the negative coecient remains for both types, with a larger estimated coecient on the low-cost dummy. Consistent with our theory, these findings suggest that eponymy has unique attributes as a signaling mechanism. An additional concern is that the coecient on eponymy is picking up the broader e↵ect of having a rare firm name. That is, if eponymous firm names are rarer than non-eponymous ones, and if, for some reason, rare firm names are positively related to performance, our estimate of the eponymy-performance relationship will be biased. To address this concern, for each firm (both eponymous and non-eponymous) in our sample, we calculate the frequency of every word in its name in the complete Amadeus database at the country level. We then calculate the average word frequency for each firm name in our sample and include this variable in our baseline model. Column 4 presents the estimation results. Importantly, the coecient estimates for the eponymous dummy remain unchanged, lending support to our key arguments. estimate our baseline specification separately for firms with the owner’s children as managers and for firms without owner’s children as managers. The results (available upon request) show the eponymy-performance relationship is largely driven by firms in which the owners’ children are not involved as managers. The coecient estimates on the eponymy dummy in the subsample of firms without children as managers are similar to those in the main specification on the entire sample: 0.040 (standard error of 0.002).

24 5.3 Evidence from Dun and Bradstreet To generalize our results beyond our European database, we also test our predictions using a large dataset of American firms. We utilize Dun and Bradstreet (D&B) data on credit ratings and financial risk for 60,853 American firms. The D&B rating measures provide composite appraisals of firm creditworthiness based on firms’ financial accounts, payment history, and third-party evaluations of risk ratings. Firms are ranked along these measures to allow for direct comparisons. For each firm, we also observe the individual listed as the key contact person. Our conversations with D&B indicate that individual is typically the owner or CEO. Note that for the D&B sample of firms, we do not have the rich information on ownership as in Amadeus. Moreover, the data are cross-sectional, and thus we cannot perform the same detailed analysis on ownership changes and within-firm variation in eponymy as we did for the main sample. The D&B data do provide the advantage of having several measures of performance related to creditworthiness that are especially important for small firms. In addition, the dataset allows us to corroborate our earlier results with a completely di↵erent dataset with firms located in a di↵erent part of the world. With these considerations in mind, we follow the same procedure as we employed in the Amadeus sample to determine eponymy by comparing the name of the firm to the last name of the main contact person. Interestingly, we find a similar percentage of eponymous firms in the D&B sample compared with the Amadeus sample: 12.5% of firms in the D&B sample are classified as eponymous, compared to 13.8% of firms in our main European sample. We proceed to examine the relationship between eponymy and a wide set of financial strength indicators provided by D&B. Note that after several correspondences with D&B, we have confirmed that in computing their scores, they do not take eponymy into consideration, nor any other similar measure of owner skills. Table 9 presents the estimation results for the conditional correlation between eponymy and D&B financial indicators. We begin by examining the relationship between eponymy and credit score. A firm’s credit-score percentile is an outcome variable, which ranges from 1 to 100, where 1 is assigned to firms with the highest probability of severe delinquency in paying its bills and 100 represents firms with the lowest risk of payment delinquency, based on an overall assessment of a firm’s creditworthiness or ability to take on additional debt. Based on the credit-score distribution, we generate a dummy variable that receives the value of 1 for firms in the highest-score quartile, and use this variable as our dependent variable. Column 1 presents the estimation results of a Probit model for the relationship between the high-credit-score dummy and eponymy. We find a positive and significant relationship between eponymy and credit score.

25 In Column 2, we examine the relationship between eponymy and financial stress. The financial-stress score is an indicator of the business’s potential of failure compared to the national average and the focal industry. The score is based on a multitude of demographic and financial information, credit history, and public filings. The variable takes on the value of 1 if a firm has the highest probability of financial stress and a value of 100 with the lowest risk of business failure. We construct a dummy variable that equals 1 if the score falls into the fourth quartile of the score distribution. We estimate a Probit model using this indicator as the dependent variable. The coecient estimate on the eponymy dummy is also positive and significant, implying a lower risk of financial stress and failure for eponymous firms. Next, we examine the likelihood of supplier failure for eponymous firms. The Supplier Evaluation Risk Rating (SIR) ranks firms according to their probability of obtaining legal relief from creditors or ceasing operations without paying their creditors in full in the next 12 months. The rating ranges from 1 to 9, with 1 representing firms that have the lowest probability of supplier failure, and 9 for firms with the highest probability of supplier failure. Our dependent variable is a dummy variable that receives the value of 1 for firms with very low supplier risk—an SIR score of 1 or 2. This score indicates a failure risk of less than 0.16% (6% of the population of firms has such low risk). Our results are robust to alternative risk cuto↵s. The estimated coecient is positive and significant for eponymous firms, again indicating a lower risk of failure (Column 3). In Columns 4 and 5, we use records on the timeliness of payments by the firm as outcome variables. First, we explore how the probability of on-time payment varies with eponymy. We construct a dummy variable for on-time payment that equals 1 if a firm has been prompt in making its payments to creditors—their D&B Paydex score is at 80 and above (the range is from 20 to 100 and indicates prompt payments at 80 and above). Column 4 reports a positive and significant estimate on the eponymy dummy. In Column 5, we use a count of past-due and delinquent payments in the past 12 months for each firm as a measure of slow payments or non-payments. We estimate this specification using a Negative Binomial count model. The estimated coecient on the eponymy dummy is negative and significant, which is consistent with the overall pattern of greater financial performance of eponymous firms. Next, we employ two additional tests by utilizing data on legal and collection recourse actions initiated against firms. In Column 6, we estimate a negative binomial model for the number of liens for each company as a measure of the potential impact of legal action on a company’s financial stability. The number-of-liens variable counts the number of claims against firm property held by creditors as security for the satisfaction of debt. The coecient estimate on the eponymy dummy is negative and significant and thus indicates fewer liens held for eponymous firms than for non-eponymous firms. In Column 7, we use a dummy for a

26 collections indicator, which equals 1 if a firm has been sent a collections notice on an unpaid obligation (which happens for about 5% of our firm sample). The estimated coecient on the eponymy dummy is negative and significant, as expected. Finally, in Column 8, we use a three-year sales growth variable to replicate a result from the main European sample: eponymous firms grow substantially more slowly than non-eponymous firms (Appendix Table C2). Overall, the results from the D&B sample of firms provide additional independent confirmation of our main findings on eponymy and its relationship to financial performance.

5.4 Other Robustness Checks We perform additional robustness checks as follows (full results are not reported and are available upon request). Length of Names. An additional robustness check relates to the length of firm name and how this characteristic might be interacting with eponymy and performance. Perhaps eponymous firm names are systematically longer or shorter and the length of names is related to performance. To test the robustness of our results to firm name length, we counted the number of words that are included in the name of each firm. This number ranges from one to seven, with a median of three and an average of 3.1 words. The results do not appear to vary by firm name length. For firms whose names include three or fewer words, the coecient estimate on eponymy is 0.014 (a standard error of 0.001). For firms whose names include more than three words, the coecient estimate is 0.020 (a standard error of 0.003). We also checked for di↵erences in the number of letters in owner names. Owners of eponymous and non-eponymous firms both have the same average number of letters in their last names (6.3. letters). In unreported regressions, we remove very long names from our analysis, including the top 5% (9 letters or more) or the top quartile (7 letters or more), and our results are robust. Non-Parametric Estimation. We performed other robustness checks to comprehensively address alternative explanations, but do not report the complete results in this manuscript (tables are available upon request). We conducted a two-stage propensity-score-matching estimation. The estimation is cross-sectional and includes the most recent year information available for each firm. In the first stage, the dependent variable is a dummy for eponymous venture. The first-stage specification includes sales, age, and a complete set of dummies for three-digit SIC industry code and country. The non-parametric estimates are higher (in absolute value) than the parametric estimates. The non-parametric estimate of the relationship between names and ROA is 0.132, compared to the parametric estimate of 0.032 (Table 4, Column 1). We also examine how the non-parametric estimates vary by

27 name rarity/commonality and industry characteristics, and find the same pattern of results as in the parametric specifications. Owner Ethnicity. An important concern in our analysis is that minority owners are likely to have relatively rare names in their region, almost by definition. Thus, minority business patterns might confound our results exploiting variation in name rarity/commonality. To test this concern, we collect new data on owners’ ethnicity by matching owners’ last names to a specialized database owned by OriginsInfo (a subsidiary of Experian). OriginsInfo relies on a database that can identify the likely cultural origin of over 1,800,000 family names and 700,000 personal names. Using this data, we attach a unique ethnic background to each owner in our sample based on owners’ last names. We then classify owners as ethnic minorities in three di↵erent ways: (1) if their ethnicity is di↵erent from the country where the firm is incorporated (e.g., an Italian owner in France); (2) if their ethnicity is not Western European; and (3) if the owner is South Asian or Chinese. Our results are not sensitive to excluding minority owners, because in all cases the same pattern of results continues to hold. Atablewiththeseresultsisavailableuponrequest.

5.5 Ownership Changes As one important extension, we also explore variation in the number and nature of ownership changes across eponymous and non-eponymous firms. Owners who choose to name firms after themselves may be less willing to sell or may simply have a more dicult time doing so. Our unique data allow us to explore this question, because we observe the complete ownership structure for firms in two separate periods; thus we can document detailed changes in ownership for the firms in our sample. Comparing firm ownership between 2007 and 2010, we find that 16% of firms change their majority owner (39,784 firms). We distinguish between inter-family and intra-family ownership changes. A change is classified as inter-family if the last name of the new leading owner is di↵erent from the last name of the previous leading owner. A change is classified as intra-family if the last name of the new and old owners is the same, but their first names are di↵erent. Two-thirds of the ownership changes we observe are inter-family. We turn next to an econometric analysis of the relationship between ownership changes and eponymy. In this analysis, the dependent variable is a dummy for ownership change. All specifications are estimated using a Probit model, and marginal e↵ects are always reported. Our results point to a strong negative relationship between inter-family owner change and eponymy. Table 10 presents the estimation results. The estimation is cross sectional and includes 247,435 firms for which we have information for both 2007 and 2010. Column 1 shows an overall negative relationship between eponymy and ownership changes

28 pooling together inter- and intra-family changes. The coecient estimate in this specification is -0.026, which is large relative to the sample share of ownership changes (0.16). Columns 2and3separatelyestimatetherelationshipbetweeneponymyandownershipchangesfor inter- and intra-family changes. A strong negative relationship exists between eponymy and inter-family ownership change (Column 2). In this specification, the coecient estimate on eponymy is -0.040, relative to a sample share of inter-family ownership changes of 0.11. Column 3 shows an opposite pattern when estimating the relationship between eponymy and intra-family ownership change (0.008 relative to a sample share of intra-family changes of 0.05). Column 4 includes only firms that changed ownership, and reveals, as expected, that for this set of firms, an ownership change is much more likely to be intra-family for eponymous firms. Columns 5-10 show that the negative relationship between eponymy and inter-family ownership changes is robust. Di↵erent forces could be driving these intriguing results. On one hand, some owners who seek to exert tighter control over their firm may choose an eponymous name and may also be the same owners who are least likely to sell. Alternatively, founding owners may use eponymy to signal high skills, but this benefit is also accompanied by a cost later on in the life of the firm, when selling to new owners is more dicult. Disentangling these forces is beyond the scope of this paper but is a promising topic for future research.

6 Conclusion Aburgeoningliteratureisdevelopingfocusedonentrepreneurship.Priorworkhasfoundthat new-venture performance is hamstrung by information asymmetries. In these environments, we propose the naming of the firm, specifically eponymy, as one mechanism entrepreneurs may use to signal unique skills or high ability. Using a signaling framework, we model eponymy as a form of utility amplification that is likely driven in large part by non-pecuniary considerations, and generate three main empirical implications. Using a dataset on over 485,000 firms and 5.5 million owners from Europe and the United States, we find support for these predictions. The model also has a number of ancillary implications, which are supported by the data. In addition to our specific findings, we believe that several elements of this paper may be useful for future research. First, because new firms are often closely held and rely heavily on the founder’s own human capital, there is an interesting connection between the individual and the organization that is very relevant in entrepreneurship research. Our model provides a tractable framework to analyze variation on this dimension, which could be useful in future work. Interestingly, other work has documented that as firms evolve, founding owners are often replaced, and has suggested the “horse” (the underlying business) is more important to

29 long-term performance than the “jockey” (founder) (Kaplan, Sensoy, and Stromberg 2009). To the extent that founding owners try to attach themselves to the firm, whether to signal skills or even increase their bargaining power with outside investors, additional frictions could be introduced into the financing market for firms. Second, eponymy may prove a useful variable to consider in future studies. The decision of what to name the firm is one of the few business decisions every owner has to make, presumably independent of many of the later influences that complicate firm strategy. In addition, the choice of name is unlikely to directly influence the underlying quality of the firm. The presence of clear benefits and costs to eponymy as it possibly relates to signaling unique skills, establishing control rights, or reinforcing a social identity could play part in a wide variety of research questions.30 For example, we find eponymous firms are far less likely to have ownership changes. This finding could be driven by characteristics of eponymous owners or could be a consequence of the eponymy decision. Consider an entrepreneur who seeks to exert greater control over her business from the outset, perhaps because she has strong beliefs about how to run the business and is concerned later investors may take majority control and remove her. Axing her name to the firm may be one mechanism for preserving control over the firm as it grows, an explanation with significant implications for the literature on the property rights theory of the firm (Alchian and Demsetz 1972; Grossman and Hart 1986; Hart and Moore 1990). This sort of owner could be less likely to grow and less motivated to sell the business. Interestingly, the development of financial markets could also influence these dynamics. Future research could shed light on this explanation by looking at the availability of external finance for owners (Rajan and Zingales 1998) across time periods, industries, or regions and how it relates to eponymy. Alternatively, the eponymy decision itself could have consequences for growth and the likelihood of selling the firm. For example, if eponymous ventures are particularly tied to the owner, especially in industries or competitive positions with more complex or artisanal production processes, delegation to employees could be more dicult. This scenario might also make hiring talented employees and scaling production harder. Finally, for eponymous firms looking to exit, potential buyers may be less interested because the business will be closely attached to the previous owner through the name of the firm. We leave it for future research to untangle which factor is driving the relationship between eponymy and fewer ownership changes. Explaining this result is particularly important given that existing explanations

30Third, our use of name rarity/commonality can also be applied to future research. Geographic di↵erences in the commonality of ethnic names could be an interesting source of variation to use in future work. Developing more ﬁne-grained measures of the familiarity of names, based on spelling conventions and sounds, may also be possible.

30 of the welfare benefits of a market for firm reputations hinge on the ability to separate the owner’s identity from the firm name (Tadelis 2002), while a significant percentage of owners around the world essentially meld them together. Regarding heterogeneity in the goals of di↵erent firms, our large dataset covers a wide variety of industries, some of which are likely characterized by more traditional “mom-and- pop” businesses and others that contain “high-growth” entrepreneurs. While some additional analysis suggests that eponymous firms grow more slowly (See Appendix Table C2), future research could more definitively address whether eponymous ventures are more likely to be “lifestyle” businesses. Further, whereas we use di↵erences in firm age to focus our arguments on eponymous entrepreneurs,futureresearchcouldhavemuchtoo↵erregardingeponymous ventures as they grow older. Finally, interesting trends in naming conventions could evolve over time (e.g., Glynn and Abzug 2002), particularly those related to eponymy. Whereas eponymous firms such as Dis- ney and Ford loomed large in an earlier golden era of entrepreneurship, today’s high-growth startups anecdotally appear much less likely to be named after the founding owner. Whether this di↵erence reflects changes in the financial market that make personal reputations less important, shifting cultural attitudes, or the increasing importance of the “non-human” assets of firms as opposed to human capital (Hart 1995; Kaplan et al. 2009) is a topic worthy of future research.

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~~34 Table 1. Summary Statistics Distribution Variable Obs. Firms Mean Std. Dev. 10th 50th 90th~~

~~Dummy for eponymous 686,685 426,498 0.157 0.363 0 0 1~~

~~Returns on Assets 686,685 426,498 0.093 0.373 -0.113 0.042 0.333~~

~~Profit margin 686,685 426,498 0.053 0.351 -0.097 0.028 0.283~~

~~Sales growth 686,511 426,438 0.128 0.506 -0.386 0.183 0.561~~

~~Sales ($,'000) 686,685 426,498 4,856 11,333 109 1,082 11,325~~

~~Assets ($,'000) 686,685 426,498 5,299 53,454 68 815 9,527~~

~~EBITDA ($,'000) 686,685 426,498 206 3,752 -69 30 522~~

~~Firm age 686,685 426,498 15 11.3 5 13 28~~

~~Number of employees 686,685 426,498 16 61 0 5 34~~

~~Owner name density, City-level 686,685 426,498 0.021 0.065 0.0004 0.006 0.04~~

~~Owner name density, NUTS-level 686,685 426,498 0.004 0.006 0.0001 0.002 0.012~~

~~Owner name density, Country-level 686,685 426,498 0.004 0.005 0.0001 0.002 0.011~~

Owner name density, City-3-Digit SIC 686,685 426,498 0.379 0.364 0.010 0.25 0.50 Notes: This table provides summary statistics for the main firm-level variables used in the econometric analysis. Return on Assets is EBITDA over Assets ; Profit Margin is EBITDA over Sales . Firm age is years from date of incorporation.

Table 2. Eponymous vs. Non-Eponymous Firm Characteristics Eponymous Non-Eponymous Eponymous - non- Eponymous Variable Obs. Mean Median Std. Dev. Obs. Mean Median Std. Dev. Returns on Assets 0.049** 107,483 0.134 0.059 0.393 579,202 0.085 0.039 0.369 Profit margin 0.035** 107,483 0.082 0.035 0.320 579,202 0.047 0.026 0.356 Sales growth 0.010** 107,460 0.137 0.189 0.454 579,051 0.127 0.182 0.515 Extreme drop in sales -0.016** 92,905 0.064 0 0.244 490,082 0.080 0 0.272 Extreme rise in sales -0.002 92,905 0.108 0 0.311 490,082 0.110 0 0.313 Firm age 1.8** 107,483 16.7 14 12.710 579,202 14.9 13 11 Sales ($,'000) -64.1 107,483 4801.5 990.3 11631.4 579,202 4865.6 1101.0 11276.5 Assets ($,'000) -1,265.3** 107,483 4232.0 641.5 24482.5 579,202 5497.3 851.9 57236.7 Number of employees -0.5* 73,982 15.4 4 75.1 429,993 15.8 5 58.4 Notes: This table reports mean comparison tests for eponymous and non-eponymous firms. ** denotes that the difference in means is significant at the 1 percent level. Table 3. Eponymy Incidence by Main Industries

~~Industry % Eponymous firms Standard deviation Number of firms~~

Agriculture 22.8 0.4 4,308 Amusement and museums 11.0 0.3 4,777 Auto leasing and parking 11.3 0.3 724 Banks, lenders, and insurance 15.0 0.4 4,796 Building materials and home furniture 14.3 0.4 3,592 Car dealers 18.1 0.4 3,181 Chemicals 10.2 0.3 9,182 Collection, employment, building 14.0 0.3 15,584 Communications 7.9 0.3 1,612 Construction contractors 23.6 0.4 74,896 Education and social services 8.2 0.3 3,828 Electric and electronic non-computer 6.4 0.2 5,147 Electric, gas, sanitary 15.0 0.4 1,710 Food stores and restaurants 13.1 0.3 37,292 Food and tobacco 16.7 0.4 7,267 General retail and apparel 14.7 0.4 17,135 Groceries, grain, livestock, farm 16.6 0.4 11,609 Hardware, plumbing, heating equipment 11.6 0.3 15,004 Health services 21.2 0.4 4,612 Heavy construction engineering 22.7 0.4 2,512 Holding companies inc. non profit 15.9 0.4 4,413 Hotels 6.1 0.2 4,404 Industrial machines 12.8 0.3 6,494 Mailing, copying, and graphics 10.9 0.3 5,211 Measuring and analyzing 10.7 0.3 1,306 Metal 13.7 0.3 14,777 Mining and oil 14.5 0.4 1,293 Paper lumber and furniture 17.9 0.4 8,039 Paper, drugs, apparel 10.9 0.3 5,318 Personal services 10.4 0.3 22,795 Printing and publishing 7.8 0.3 7,401 Professional equipment inc. medical 4.3 0.2 2,252 Professional services 15.6 0.4 24,529 Real estate 13.8 0.3 23,180 Repair shops and services 18.5 0.4 10,306 Textile 12.5 0.3 4,430 Transportation services 19.5 0.4 18,972 Vehicles, furniture, construction 16.1 0.4 16,518 Wholesale trade-non-durable goods 12.6 0.3 2,410 Other 13.5 0.3 13,682 Total 13.8 0.3 426,498 Table 4. Eponymy and Firm Performance

~~Dependent variable: Return on Assets (Profits/Assets) (1) (2) (3) (4) (5) Cross- sectional Within- Single-firm Firm Pooled 2007 Owner owners Age≤5~~

~~Dummy for eponymous 0.032** 0.038** 0.012** 0.039** 0.092** (0.001) (0.002) (0.003) (0.002) (0.005)~~

~~ln(Sales )t-1 -0.007** -0.012** 0.012** -0.014** -0.033** (0.000) (0.001) (0.001) (0.001) (0.001) ln(Firm age ) -0.027** -0.027** -0.003 -0.040** -0.041** (0.001) (0.001) (0.002) (0.001) (0.007)~~

~~Owner fixed-effects No No Yes No No Three-digit SIC dummies Yes Yes Yes Yes Yes Country dummies Yes Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Yes~~

~~Average sample value: 0.092 0.111 0.049 0.128 0.211~~

Observations 685,161 414,770 106,191 578,970 93,960 Firms 425,410 414,770 77,927 347,348 86,095 R-squared 0.15 0.17 0.39 0.20 0.24 Notes: This table reports OLS estimation of the relationship between eponymy and firm outcomes. The model in column 3 limits the sample to firms with owners who own more than one business and include owner fixed effects. Standard errors (in parentheses) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. * and ** indicate statistical significance at the 5% and 1% level, respectively. Table 5. Eponymy and Name Commonality (1) (2) (3) (4) (5) (6) (7) Dummy for Dependent variable: eponymous Return on Assets Name commonality is computed at the level of: City City City City NUTS Country City-SIC

~~Dummy for eponymous 0.036** 0.063** 0.038** 0.039** 0.057** (0.002) (0.004) (0.002) (0.002) (0.003)~~

~~Dummy for eponymous × Name commonality -0.281** -0.965** -1.042** -0.039** (0.027) (0.174) (0.233) (0.005)~~

~~Dummy for eponymous, interacted with dummy for:~~

~~2nd quartile of name commonality -0.032** (0.005)~~

~~3rd quartile of name commonality -0.041** (0.005)~~

~~4th quartile of name commonality -0.052** (0.005)~~

~~Owner Name commonality 0.268** 0.136** 0.350** 0.400** 0.002 (0.011) (0.021) (0.063) (0.089) (0.002)~~

~~Dummy for name commonality, 2nd quartile 0.004** -0.013** (0.002) (0.002)~~

~~Dummy for name commonality, 3nd quartile 0.016** 0.003 (0.002) (0.002)~~

~~Dummy for name commonality, 4th quartile 0.053** 0.004* (0.002) (0.002)~~

ln(Sales )t-1 -0.008** -0.008** -0.007** -0.007** -0.007** -0.008** -0.015** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) ln(Firm age) 0.040** 0.040** -0.029** -0.029** -0.029** -0.030** -0.038** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

~~Three-digit SIC dummies Yes Yes Yes Yes Yes Yes Yes Country dummies Yes Yes Yes Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Yes Yes Yes~~

~~Average sample value: 0.157 0.157 0.089 0.089 0.089 0.089 0.089~~

Observations 649,716 649,716 649,716 649,716 649,716 649,716 649,716 Firms 405,803 405,803 405,803 405,803 405,803 405,803 405,803 R-squared 0.19 0.07 0.19 0.19 0.15 0.16 0.17 Notes: This table reports OLS estimation of how the relationships between eponymy and ROA vary by owner name commonality. Dummies for name commonality equals one if owner name commonality is at the respective quartile of the name commonality distribution. Standard errors (in parentheses) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. * and ** indicate statistical significance at the 5% and 1% level, respectively. Table 6. Variation by Industry Characteristics Dependent variable: Return on Assets (1) (2) (3) (4) (5) (6)

~~Dummy for eponymous -0.015** -0.082** 0.043** -0.369** 0.005 -0.013 (0.004) (0.004) (0.002) (0.032) (0.004) (0.011)~~

~~Dummy for eponymous × Industry growth dispersion 0.035** (0.003)~~

Dummy for eponymous × Industry ROA dispersion 0.252** (0.011) Dummy for eponymous × Dummy for Manufacturing -0.035** (0.003) Dummy for eponymous × Industry artisanship 0.658** (0.053) Dummy for eponymous × Industry Tobin's Q 0.090** (0.013) Dummy for eponymous × Industry R&D intensity 0.006** (0.001)

ln(Sales )t-1 -0.007** -0.008** -0.007** -0.007** -0.009** -0.010** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) ln(Firm age ) -0.027** -0.029** -0.030** -0.027** -0.026** -0.030** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

~~Three-digit SIC dummies Yes Yes Yes Yes Yes Yes Country dummies Yes Yes Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Yes Yes~~

~~Average sample value: 0.093 0.160 0.092 0.093 0.096 0.096~~

Observations 669,882 669,882 685,161 672,922 519,246 501,602 Firms 415,995 415,995 425,410 418,011 320,958 312,011 R-squared 0.15 0.16 0.16 0.15 0.16 0.16 Notes: This table explores how the relationship between eponymy and firm outcomes varies by industry characteristics. Standard errors (in parentheses) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ** significant at 1%; * significant at 5%. Table 7. Robustness Checks: Ownership and Management Dependent variable: Return on Assets (1) (2) (3) (4) (5) (6) (7)

~~Firms Single- Multi- Family Non-family Owner- Non-owner named after Variable owner firm owners firm firms firms managed manager individuals~~

~~Dummy for eponymous 0.012** 0.012** 0.046** 0.012** 0.034** 0.030** 0.038** (0.002) (0.001) (0.002) (0.001) (0.002) (0.002) (0.003)~~

ln(Sales )t-1 0.013** 0.008** -0.019** 0.011** -0.013** -0.002** -0.018** (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) (0.001) ln(Firm age ) -0.006** -0.021** -0.052** 0.001 -0.026** -0.024** -0.046** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.002)

~~Three-digit SIC dummies Yes Yes Yes Yes Yes Yes Yes Country dummies Yes Yes Yes Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Yes Yes Yes~~

~~Average sample value: 0.050 0.064 0.154 0.038 0.122 0.064 0.129~~

Observations 215,816 280,902 322,374 362,787 336,545 348,616 170,961 Firms 132,233 175,589 217,975 235,609 335,697 297,067 106,202 R-squared 0.06 0.08 0.20 0.04 0.18 0.12 0.18 Notes: This table reports OLS estimation of the robustness of the relationship between eponymy and ROA to different firm ownership structures. We classify multi-owner firms as family and non-family based on whether the leading shareholders have the same last name. In Column 7 we include only firms that are named after an individual. Our universe of individual last names includes the complete set of individual shareholders of all firms in the Amadeus database. Standard errors (in parentheses) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. * and ** indicate statistical significance at the 5% and 1% level, respectively. Table 8. Alternative Naming Strategies: Names Referencing High Quality/Low Cost (1) (2) (3) (4) Dependent variable: Return on Assets

~~Dummy for eponymous 0.032** 0.032** 0.032** (0.001) (0.001) (0.001) Dummy for quality or low cost naming -0.022** -0.020** (0.004) (0.004)~~

~~Dummy for quality terms -0.017** (0.004)~~

~~Dummy for low cost terms -0.043** (0.011) ln(Firm name frequency ) 0.001** (0.0002)~~

~~ln(Sales )t-1 -0.007** -0.007** -0.007** -0.007** (0.001) (0.001) (0.001) (0.001) ln(Firm age ) -0.026** -0.027** -0.027** -0.031** (0.001) (0.001) (0.001) (0.001)~~

~~Three-digit SIC dummies Yes Yes Yes Yes Country dummies Yes Yes Yes Yes Year dummies Yes Yes Yes Yes~~

~~Average sample value: 0.092 0.092 0.093 0.093~~

Observations 685,161 685,161 683,245 683,245 Firms 425,410 425,410 424,389 424,389 R-squared 0.15 0.15 0.15 0.15 Notes: This table reports OLS estimation results for naming strategies that include quality- and low cost-related terms as part of the name of the firm. Dummy for quality- low cost naming is a dummy variable that receives the value of one for firms whose name includes terms that are associated with low cost or quality, and zero for all other names. Dummy for quality terms is a dummy variable that includes the value of one for firms whose name includes terms that are related to quality (such as "best", "original", "superior"), and zero for all other firms. Dummy for low cost terms is a dummy variable that receives the value of one for firms whose name includes cost-related terms (such as "cheap", "budget", "bargain"), and zero for all other firms. In Column 4, firm name frequency counts the average number of times each word in firm's name appear in the name of other firms in the same country using the complete Amadeus sample. Standard errors (in parentheses) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. * and ** indicate statistical significance at the 5% and 1% level, respectively. Table 9. Evidence from Dun and Bradstreet (1) (2) (3) (4) (5) (6) (7) (8) Dummy for Dummy for Dummy for high Dummy for low low supplier Dummy for on- Number of past- Number of collection Three-year Dependent variable: credit score financial stress risk time payment due bills legal actions notice sales growth

~~Dummy for eponymous 0.012** 0.025** 0.007** 0.015** -0.518** -0.213** -0.005* -0.066* (0.004) (0.005) (0.002) (0.004) (0.072) (0.087) (0.002) (0.034)~~

ln(Sales )t-1 0.008** 0.008** 0.011* -0.014** 0.386** -0.010 0.005** 0.073** (0.001) (0.001) (0.001) (0.001) (0.017) (0.010) (0.001) (0.010) ln(Firm age ) 0.059** 0.110** 0.041** -0.091** 1.193** -0.033 0.005** -0.012 (0.001) (0.002) (0.001) (0.003) (0.036) (0.029) (0.001) (0.015)

~~Three-digit SIC dummies Yes Yes Yes Yes Yes Yes Yes Yes State dummies Yes Yes Yes Yes Yes Yes Yes Yes~~

Average sample value: 0.12 0.21 0.06 0.12 3.69 1.47 0.04 0.28 Firms 60,565 60,631 59,171 60,456 60,853 7,375 59,189 60,853 R-squared/Log likelihood 0.12 0.15 0.23 0.15 -117,897 -11,596 0.04 0.01 Notes: This table presents the estimation results of the relationship between eponymy and a set of financial risk and stability indicators from Dun and Bradstreet. The share of eponymous firms in this sample is 12.5 (as compared to 13.8 in the Amadeus sample). Columns 1-4 and 7 are estimated using a Probit model. Columns 5 and 6 present the estimation results of negative binomial count models. For the Probit and Negative Binomial specifications marginal effects are reported. All other specifications are estimated using OLS. The estimation is cross-sectional for 2012 American firms. Robust standard errors are in parentheses. * and ** indicate statistical significance at the 5% and 1% level, respectively. Table 10. Eponymy and Ownership Changes (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Ownership Inter-family Intra-family Inter-family Inter-family Inter-family Inter-family Inter-family Inter-family Inter-family Dependent variable: change change change change change change change change change change Changed High-artisan Low-Artisan Common- Sample: 2007 firms 2007 firms 2007 firms ownership industries industries name Rare-name Young firms Mature firms

~~Dummy for eponymous -0.026** -0.040** 0.008** -0.074** -0.046** -0.034** -0.044** -0.015** -0.054** -0.034* (0.002) (0.002) (0.001) (0.004) (0.003) (0.003) (0.004) (0.003) (0.004) (0.004)~~

ln(Sales )t-1 0.021** 0.023** -0.002** 0.027** 0.024** 0.024** 0.023** 0.015** 0.023** 0.024** (0.001) (0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) ln(Firm age ) 0.020** 0.014** 0.005** -0.004 0.014** 0.013** 0.020 0.008** 0.001 0.051** (0.001) (0.001) (0.001) (0.002) (0.001) (0.002) (0.002) (0.002) (0.004) (0.005)

~~Three-digit SIC dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes~~

Average sample value: 0.16 0.11 0.05 0.69 0.11 0.11 0.11 0.08 0.09 0.15 Firms 247,435 247,435 247,435 39,531 124,611 122,824 63,209 57,820 60,803 60,692 R-squared 0.09 0.04 0.36 0.48 0.045 0.033 0.05 0.03 0.05 0.04 Notes: This table presents the estimation results of Probit models for the relationship between eponymy and ownership changes. Marginal effects are reported. We classify a firm as experiencing an ownership change if the leading shareholder in 2007 is different from the leading shareholder in 2010. A leading shareholder is the shareholder that owns the majority of equity in the firm (at the first and last name level). Columns 2 and 3 distinguish between inter-family and intra-family ownership changes. An inter-family ownership change is the case where the new leading shareholder has a different last name than the previous leading shareholder and an intra-family change is the case where the new leading shareholder has the same last name (but a different first name) as the previous leading shareholder. Column 4 includes only firms with ownership change. In columns 5 and 6 we classify industries to artisan and non-artisan based on median value of industry task routine. In Columns 7 and 8 we classify owner name to common and rare based on the forth and first quartile value of name frequency in the city where the firm is located. In Columns 7 and 8 we classify firms as young or mature based on the first and forth quartile values of firm age distribution as of 2007. All Columns are estimated using a Probit model and marginal effects are reported. The estimation is cross-sectional for 2007. Robust standard errors are in parentheses. * and ** indicate statistical significance at the 5% and 1% level, respectively. A Proofs for Section 2

~~Useful Deﬁnitions and Properties.~~

Sender Expected Utility: We note three properties of the function u✓:i)u✓ is di↵erentiable in both of its arguments; ii) @u✓ > 0; iii) u (s, µ) u (s, µ) for any (s, µ), and the inequality @µ H L is strict if and only if µ (0, 1). Property (i) is immediate. To see (ii), note that u (s, µ)= 2 H s pV (↵(µ, h)) + (1 p)V (↵(µ, l)) ,so @u @V @↵(µ, h) @V @↵(µ, l) H = s p +(1 p) . @µ @↵ @µ @↵ @µ ✓ ◆ Further, using the expressions in (2), we get

@V @↵(µ, h) (1 p)p @↵(µ, l) (1 p)p =2, = > 0, and = > 0, @↵ @µ (1 p µ +2pµ)2 @µ (p + µ 2pµ)2 which imply the result when ✓ = H. A symmetric argument holds for ✓ = L. For (iii), see that

2(1 2p)2s(µ 1)µ 2(1 2p)2s(µ 1)µ u (s, µ) u (s, µ)= = . H L ( µ + p( 1+2µ))(1 µ + p( 1+2µ)) (µ2 µ)+(p2 p)(2µ 1)2 It is clear that the numerator is negative for all µ(0, 1) and zero when µ =0, 1. Because p ( 1 , 1), 2 2 the detonator is negative for all µ [0, 1], giving the result. 2 Sender (s, µ)-Indi↵erence Curves and Single-Crossing:Defineb (s uˆ)tobetheuniqueinterim ✓ | belief level satisfying u (s, b (s uˆ)) =u ˆ. That is, b ( uˆ)representsthetype-✓ sender’s (s, µ)- ✓ ✓ | ✓ ·| indi↵erence curve as a function from signaling level to interim belief, holding fixed the expected utility levelu ˆ. Notice that since u✓ is di↵erentiable in both arguments (see above), b✓ is di↵erentiable as well. We now establish the single-crossing property of (s, µ)-indi↵erence curves. Define v✓(µ)=Eg[V (↵(µ, g)) ✓]. Fix an expected utility levelu ˆ and let the function µ(s)=b✓(s uˆ). | | Then, by definition, u✓(s, µ(s)) =u ˆ. Total di↵erentiation of both sides with respect to s gives @u✓ @u✓ @µ @s + @µ @s = 0. Hence, @µ @b @u @u v (µ) = ✓ = ✓ ✓ = ✓ . @s @s @s @µ sv✓0 (µ) vL(µ) vH (µ) vH (µ) vL(µ) For single-crossing, we need to show that sv (µ) > sv (µ) , equivalently sv (µ) sv (µ) > 0, for L0 H0 H0 L0 all (s, µ). Using the expressions in (2), one obtains

v (µ) ( µ + p( 1+2µ))(1 µ + p( 1+2µ)) ( 1+µ)µ + p 1+2µ 4µ2 + p2 1 2µ +4µ2 H = sv (µ) 2( 1+p)ps (( 1+µ)2 + p ( 3+8µ 4µ2)+p2 (3 8µ +4µ2)) H0 v (µ) ( µ + p( 1+2µ))(1 µ + p( 1+2µ)) ( 1+µ)µ + p 1+6µ 4µ2 + p2 1 6µ +4µ2 L = . sv (µ) 2( 1+p)ps (p + µ2 4pµ2 + p2 ( 1+4µ2)) L0

~~And then~~

A.1 v (µ) v (µ) H L = svH0 (µ) svL0 (µ) A B (1 2p)2(p + µ 2pµ)2(1 µ + p( 1+2µ))2 1 2µ +2µ2 . 2(1 p)ps p + µ2z 4pµ2 + p2 1+4µ}|2 ( 1+µ)2 + p 3+8{ z µ }|4µ2 +{p2 3 8µ +4µ2 C D E We| now{z verify} | that each{z of the ﬁve terms,} |A-E, are positive for all{z (s, µ). A is the product} of 1 squared terms, and therefore positive. B is a second-order polynomial, with minimum 2 obtained at µ = 1 . C is positive since p ( 1 , 1) and s>0. To see that D is positive, note that when µ = 0, 2 2 2 D = p(1 p) and when µ = 1, D =1 3p +3p2, both of which are positive for any p ( 1 , 1). In 2 2 addition, D is monotonic since dD = 2(1 2p)2µ 0, implying that D is positive. An analogous dµ argument demonstrates that E is positive as well, which completes the proof.

The D1 Refinement: In our model, the D1 refinement can be stated as follows. Fix an equilibrium endowing expected utilities u ,u . Consider a signal s that is not in the support of either type’s { L⇤ H⇤ } strategy. Define B (s, u ) µ : u (s, µ) >u .IfB (s, u ) B (s, u ), then D1 requires ✓ ✓⇤ ⌘{ ✓ ✓⇤} L L⇤ ⇢ H H⇤ that µ(s)=1(where denotes strict inclusion). If B (s, u ) B (s, u ), then D1 requires that ⇢ H H⇤ ⇢ L L⇤ µ(s) = 0. The interpretation of the refinement is that if, for a deviant s,type✓ has a strictly larger

~~B✓ (in sense of set inclusion) than type ✓0, then receivers should believe that the deviator is of type ✓.31~~

Proof of Proposition 1. The proof consists of three parts: i) verifying that the proposed proﬁles are equilibria; ii) verifying that they satisfy D1; and iii) demonstrating that no other equilibria satisfy D1.

Part (i): For the first case, fix µ µ . The proposed strategies are full pooling on s.The 0 ⇤ interim belief µ(s)=µ0 is therefore consistent with the strategy profile. If the sender deviates to s [s, s), the interim belief is µ(s) = 0. Recall that u is increasing in µ and nondecreasing 2 ✓ in ✓, and that u (s, µ )= s. Therefore, deviating to s = s leads to u (s, 0) = s s = L ⇤ 6 ✓  u (s, µ ) u (s, µ ) u (s, µ ), where the last two terms are the low- and high-type equilibrium L ⇤  L 0  H 0 expected utility levels, respectively. Hence, there is no incentive to deviate, establishing that this is an equilibrium. For the second case, fix µ0 <µ⇤. The proposed strategies are partial pooling with the high type selecting s and the low type mixing between s and s,selectings with probability µ0(1 µ ) ⇤ . From Bayes rule, the on-path interim beliefs µ(0) = 0 and µ (1 µ0) ⇤

µ0 µ(s)= = µ⇤ µ0(1 µ ) µ +(1 µ ) ⇤ 0 0 µ (1 µ0) ⇤ 31See Daley and Green (2014) for further discussion regarding its motivation and the equivalence, in our model, between this deﬁnition and D1’s original deﬁnition (Banks and Sobel, 1987; Cho and Kreps, 1987).

A.2 are consistent with the strategies. By deﬁnition of µ⇤, uL(s, 0) = uL(s, µ⇤), so the low type is indeed indi↵erent between s and s as required by her mixing. Further, µ(s) = 0 for all s = s. Hence, just as 6 above, any deviation to a signal s leads to a payo↵of u (s, 0) = s s = u (s, µ ) u (s, µ ), ✓  L ⇤  H ⇤ where the last two terms are the low- and high-type equilibrium expected utility levels respectively. Hence, there is no incentive to deviate, establishing that this is an equilibrium.

Part (ii): To see that the equilibrium satisfies D1 in both of the above cases, for either case, let u ,u be the equilibrium expected utilities. Notice that b (s u )=b (s u ). Therefore, single- { L⇤ H⇤ } L | L⇤ H | H⇤ crossing implies that b (s u ) b (s u ) . Therefore, b (s u ) s, the low type would L⇤ L gain by selecting s instead, so it must be that s = s, establishing the result. Third claim: in any D1 equilibrium, S = s . For the purpose of contradiction, suppose H { } not, and that S = s ,wheres = s.Thenu = u (s, µ(s)) and, because the low type has H { } 6 H⇤ H the option of selecting s as well, u u (s, µ(s)). Therefore, b (s u ) b (s u ). But then, by L⇤ L H | H⇤  L | L⇤ single-crossing, for ✏>0 small enough, b (s + ✏ u ) u (s, µ(s)) for any µ(s) [0, 1]. Because u = u (s, µ(s)), the deviation is H H 2 H⇤ H profitable, producing the contradiction. Finally, from our second and third claims, in any D1 equilibrium, the low type’s strategy can be summarized by the probability with which she selects s, denoted by (as she plays s with the complementary probability 1 ). We now return to our two cases regarding µ . For the first 0 case, fix µ µ .If<1, then by Bayes rule, it must be that µ(s) = 0 and µ(s) >µ µ . 0 ⇤ 0 ⇤ By definition of µ⇤ and uL increasing in µ,wehaveuL(s, µ(s)) >uL(s, µ⇤)=uL(s, 0), meaning the low type strictly prefers s to s, contradicting <1. Hence, = 1, and the equilibrium is full pooling as described in the proposition. For the second case, fix µ0 <µ⇤.If = 1, then by

~~Bayes rule, it must be that µ(s)=µ0 <µ⇤. By deﬁnition of µ⇤ and uL increasing in µ,wehave~~

A.3 u (s, µ(s)) µ⇤, which means the low type would prefer s to s for the reason given in the first case above and generating a contradiction. Hence, (0, 1), meaning the low type is strictly 2 mixing and must therefore be indi↵erent between s and s.ByBayesrule,µ(s) = 0, so indi↵erence requires that uL(s, µ(s)) = uL(s, 0). By definition, this is only satisfied when µ(s)=µ⇤. For this µ0(1 µ ) belief to be consistent with Bayes rule, it must be that = ⇤ , as described in the proposition. µ (1 µ0) ⇤

Comparative Statics on µ⇤: In Predictions 3 and 4, it is claimed that µ⇤ is increasing in s and p, and that the dispersion of performance (measured by ↵) is increasing in p. In the partial-pooling equilibrium, only three ↵-values are reached with positive probability: 0 <↵(µ⇤,l) <↵(µ⇤,h). Hence, a simple measure of dispersion is the di↵erence between the highest and the lowest of these, 32 which is just ↵(µ⇤,h). Using that µ⇤ solves uL(s, µ⇤)=s and (2), we can solve for both µ⇤ and s ↵(µ⇤,h) in closed-form. Letting r = s ,

p(1 p)(4 6r)+r 1+X p p(1 p)(4 6r)+r 1+X µ⇤ = , and ↵(µ⇤,h)= , 2(1 2p)2(r 1) 2p 1 1 2p r +2p2r + X where X = (r 1)2 +4p3(p 2)r(5r 4) 4p (1 3r +2r2)+4p2 (1 7r +7r2). It is mun- dane, but tediouslyp lengthy, to show that µ⇤ is increasing in r (equivalently, s) and p, and ↵(µ⇤,h) is increasing in p.

~~B Data Construction~~

~~This Appendix details the processes by which ﬁrm and owner names were matched, and steps we took to link individual owner records across ﬁrms and years.~~

~~B.1 Matching Firm and Owner Names~~

The first task in matching firm and owner last names is to standardize the names. We use first names to help identify unique owners. Firm names and individual owner names in the Bureau van Dijk database can display varying patterns and include descriptive components that are not central to the name, such as “Mr.,” “Dr.,” and “Mlle” in individual names and “Limited Partnership,” “Corporation,” and “Services” in corporate names. Because the matching algorithm we use is sensitive to the string length in calculating matching scores, standardizing the names and reducing them to key components is important. Due to varying naming patterns in each country, we manually determined and removed common individual-name and firm-name descriptive components. For

~~32It can also be shown that the result holds if dispersion is measured by the variance of the distribution of ↵.~~

A.4 instance, “Mr. John Smith” would become “John” for the first-names list and “Smith” for the last-names list, and “Nellemann International Holding APS” would become simply “Nellemann” in the firm-names list. To determine whether owners share their names with firms they own, we used direct and indirect string-matching algorithms. Direct string matching looks for exact matches between two pairs of string entries. To account for spelling variations, embedded names, and typographical errors, we also employed two indirect matching algorithms. The first is similar to direct matching, as it looks for direct matches embedded within strings. For example, the algorithm identifies a match for the last name “Wilke” as a substring within the firm name “Wilhelm Wilke Spedition.” Even though the company was founded in 1817 by Friedrich Wilhelm Wilke and named after him, the current owners are his descendants (also last name Wilke). Thus the algorithm is able to match names embedded in company names. The second indirect matching algorithm utilizes a bigram matching procedure that calculates a probability score that the two strings are the same, by comparing the two using all combinations of two consecutive characters within each string. This comparison function calculates a score between 0 and 1, which divides the total number of common bigrams between the strings by the average number of bigrams in two strings. In our case, the manual checking of matched results indicated an optimal threshold score of 0.68. This matching algorithm looks for intentionally and unintentionally (spelling mistakes or imprecise translation of special characters) transformed names. For example, “Ambridge” is the last name of one of the owners of “Ambro Sports and Events,” for which the owner’s last name was transformed and incorporated into a firm name, and “Unterschuetz” is the last name of one of the owners of a firm listed as “Unterschütz Sondermaschinenbau,” for which direct matching would classify a mismatch due to the special character in the firm name. By utilizing these di↵erent matching techniques, we were able to compare firm and owner first and last names to determine whether firms share names with their owners.

~~B.2 Owner-Unique ID~~

The Bureau van Dijk database does not provide unique identifiers for individual managers and owners as it does for firms. Individuals are observed by their names and associations with their respective firms through employment or ownership status in each year. To identify owners that own multiple firms in each year and to track ownership changes over time, we must match and link individual records across firms and over multiple years. The two main challenges are in determining whether two distinct records are the same individual, given variations in the spelling of their names, and once matched, to ensure the individuals with the same or similar names are indeed the same person. To match and link individual records, we utilized a multi-step process to assign a unique identification number to each individual owner, as detailed below.

~~A.5 B.3 Standardizing the Owner Names~~

The Bureau van Dijk data can have varying patterns in recording individual names, for example, “Mr. Ronald Duncan,” “Ron Duncan,” “Ron W. Duncan,” and “Duncan, R. W.” These variations can also be due to country-specific name-recording customs, titles, and translations of special characters. To start, we standardized these variations by removing preceding titles, such as “Mr.,” “Mlle,” and “Mme,” and by replacing all the special characters by uniform English-language sub- stitutes, for example, “Æ” with “AE” and “ô”with “OE,” and by listing first names before last names. For instance, all the name variations of the aforementioned Mr. Duncan would become “Ronald W. Duncan.” We took care to preserve middle names and initials as much as possible, because these became invaluable at a later stage in the process in distinguishing between multiple individuals named Ronald Duncan. Once the names were standardized, we used direct and fuzzy name-matching algorithms on standardized full names to link individual records. The direct matching algorithm looks for exact matches, whereas the fuzzy matching algorithm searches for similar names to account for spelling variations and typographical errors. The fuzzy matching algorithm we used compares two names, assigns a matching score, and identifies matches if the matching score meets a certain threshold (after manual checking of matched results, the optimal threshold score is 0.68). We obtained the matching score by utilizing a bigram algorithm, which compares two strings using all combinations of two consecutive characters within each string. Once we matched all individuals in our data by their names, we utilized several filtering algorithms to distinguish unique individuals with the same name. Especially for common and popular names, we had numerous possible matches for a single individual. The various filtering algorithms assigned scores based on the likelihood that two records are the same, using geographic and co-owner information. We utilized geographic filters first, which used address information to limit the matches initially by country, then by region, city, and zip code, in that particular order. For example, “Steven Collins” and “Steven R. Collins” were determined to be the same person owning multiple real estate and hotel businesses in London, UK: “City and General Estate Company,” “Marlow Hotel Company,” “Johnson Collins Limited,” and “Ravenscourt Properties Limited,” but di↵erent from “Steven Collins” in Buckinghamshire, UK, who founded and owns a composite design and engineering company named “C-Tech Composites Limited.” Thus this filtering exercise significantly reduced the number of incorrect matches. For the remaining multiple matches, we utilized information on co-owners to create collaboration networks to further di↵erentiate among individuals. For example, if Flynn James and Ronald Duncan own a business together in Reading, UK, then a business owned by Ronald Duncan in London is likely to be owned by the same Ronald in Reading if one of the other owners in London is named Flynn James. In sum, for each individual owner in our data, we assigned a unique identification number across all firms and years by utilizing information on their names, addresses, common co-owners, and collaboration networks. As a result of this iterative process, we identified 422,104 unique individuals within our sample across two years. Although most owners hold a single business, about 7% (28,762 individuals) own more than one business in a given year.

~~A.6 Appendix Table C1. Mean Comparison for Owner Fixed-Effects (1) (2) (3) (4) (5) (6) (7) (8) (9)~~

Owner Fixed-Effect Computed from ROA equation Computed from profit margins equation Computed from growth equation Diff: Diff: Diff: Eponymous - Eponymous - Eponymous - Non- Non- Non- Non- Non- Non- eponymous Eponymous eponymous eponymous Eponymous eponymous eponymous Eponymous eponymous

~~All firms 0.037** 0.031 -0.006 0.035** 0.029 -0.006 -0.036** -0.030 0.006~~

~~Firm characteristics:~~

~~Young firms 0.142** 0.121 -0.021 0.074** 0.063 -0.011 -0.217** -0.185 0.032~~

~~Mature firms 0.014** 0.011 -0.003 0.021** 0.017 -0.004 -0.049** -0.040 0.009~~

~~Industry characteristics:~~

~~Non-durable 0.006** 0.005 -0.001 0.008** 0.007 -0.001 0.009* 0.008 -0.001~~

~~Durable 0.039** 0.032 -0.007 0.038** 0.031 -0.007 -0.074** -0.060 0.014~~

~~Services 0.056** 0.048 -0.008 0.058** 0.049 -0.009 -0.154** -0.131 0.023~~

Artisanal industries 0.066** 0.058 -0.008 0.057** 0.050 -0.007 -0.192** -0.168 0.024 Non-artisanal industries 0.025** 0.020 -0.005 0.035** 0.028 -0.007 -0.025** -0.020 0.005 Notes: This table reports the estimated owner fixed effects computed from main estimations for the relationship of eponymy with ROA. The regressions include log of lagged sales, firm age and complete sets of dummies for three-digit industry SIC codes and countries. Young and mature firms are at the first and fourth quartile of firm age distribution, respectively. The owner name commonality is determined by comparing owner names to the population of individual names and common and rare names are distinguished at first and fourth quartile of name commonality distribution, respectively. * and ** indicate statistical significance at the 5% and 1% level, respectively. Appendix Table C2. Eponymy and Firm Growth (1) (2) (3) (4) (5) Owner fixed- Owner name effects commonality

~~Sales Extreme Extreme rise Sales Dependent variable: growth drop in sales in sales growth Sales growth~~

~~Dummy for eponymous -0.009** -0.007** -0.011** 0.001 -0.010 (0.001) (0.001) (0.001) (0.005) (0.002)~~

~~Dummy for eponymous × Owner name commonality 0.136** (0.036)~~

ln(Sales )t-1 -0.047** -0.007** -0.019** -0.060** -0.049** (0.001) (0.000) (0.000) (0.002) (0.001) ln(Firm age ) -0.011** -0.011** -0.007** -0.014** -0.009** (0.001) (0.001) (0.001) (0.003) (0.001) Owner name commonality 0.085* (0.039)

~~Owner fixed-effects No No No Yes No Three-digit SIC dummies Yes Yes Yes Yes Yes Country dummies Yes Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Yes~~

~~Average sample value: 0.128 0.077 0.110 0.156 0.121~~

Observations 684,991 582,097 582,097 106,159 649,562 Firms 425,453 322,810 322,810 77,909 405,753 R-squared 0.15 0.05 0.20 0.40 0.18 Notes: This table reports OLS estimation of the relationship between eponymy and sales growth. In Columns 2-3, extreme drop (rise) in sales is a dummy variable that receives the value of 1 for firm-year observations where sales drop (rise) is at least 30%. Column 4 limits the sample to owners of at least two firms, and includes owner fixed-effects. Standard errors (in parentheses) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. * and ** indicate statistical significance at the 5% and 1% level, respectively. Appendix'Figure'C1.''Cumula=ve'Distribu=on'of'Firm'Performance'For'Eponymous'vs.'NonI C1(b).'Return'on'Assets,'Common'Owner'Names'(4

~~0 .2 .4 .6 .8 1 -2 '' -1 Eponymous ''''Eponymous'Firms'by'Owner'Name'Commonality' roa 0~~

0 .2 .4 .6 .8 1 Non-Eponymous -2 1 C1(a).'Return'on'Assets,'All'Firms' th 'quar=le)' -1 Eponymous 2 roa 0 C1(c).'Return'on'Assets,'Rare'Owner'Names'(1 Non-Eponymous 0 .2 .4 .6 .8 1 -2 1 -1 Eponymous 2 roa 0 Non-Eponymous 1 st 'quar=le)' 2