Essays on the Evaluation of Novel Ideas

Jesse Michael Wahlen

B.B.A., Loyola University Chicago (2009) M.Sc., London School of Economics (2011) S.M., Massachusetts Institute of Technology (2018)

Submitted to the MIT Sloan School of Management in Partial Fulﬁllment of the Requirements for the Degree of:

Doctor of Philosophy

at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

May 2020

Author...... Department of Management April 30, 2020

Certiﬁed by...... Ezra W. Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship and Strategy Thesis Supervisor

Accepted by...... Catherine Tucker Sloan Distinguished Professor of Management Chair, MIT Sloan PhD Program Essays on the Evaluation of Novel Ideas

J. Michael Wahlen

Submitted to the Sloan School of Management on April 30, 2020, in Partial Fulﬁllment of the Requirements for the Degree of Doctor of Philosophy in Management

Abstract

In this dissertation, I examine the evaluation of novel ideas in two essays. In the first, I, along with Hong Luo and Jeffrey Macher, study a novel, low-cost approach to aggregating judgment from a large number of industry experts on ideas that they encounter in their normal course of business in the film industry, where customer appeal is difficult to predict and investment costs are high. The Black List, an annual publication, ranks unproduced scripts based on anonymous nominations from film executives. This approach entails an inherent trade-off: low participation costs enable high response rates; but nominations lack standard criteria, and which voters see which ideas is unobservable and influenced by various factors. Despite these challenges, we find that such aggregation is predictive: listed scripts are substantially more likely to be released than observably similar but unlisted scripts, and, conditional on release and investment levels, listed scripts generate higher box office revenues. We also find that this method mitigates entry barriers for less-experienced writers as: (i) their scripts are more likely to be listed and to rank higher if listed; (ii) being listed is associated with a higher release rate. Yet, the gap in release probabilities relative to experienced writers remains large, even for top-ranked scripts. In the second essay, I move from the question of the abstract value of an idea to the relative value given one’s past and resources. In markets which require architectural changes for established firms, prior research claims incumbents must best the young firms imprinted by the new technology at the dominant design. If they cannot, they should seek to avoid the market altogether. In contrast, this paper suggests that rather than compete for the dominant design, incumbents can succeed by focusing on niche segments that are proximate to their core business. This paper finds support for this strategy in console game makers’ response to the rise of mobile games. Using a novel method to measure distance from the dominant design, the analysis shows that deviating from the dominant design was associated with greater revenue for console publishers, while mobile publishers observed the opposite result.

Thesis Supervisor: Ezra W. Zuckerman Sivan Title : Alvin J. Siteman (1948) Professor of Entrepreneurship and Strategy

2 Acknowledgments

There are many people who have guided and supported me in completing this degree. My committee members — Ezra Zuckerman, Pierre Azoulay, and Hong Luo — have each invested heavily in me, generously providing their time and advice to help me develop a “taste” for academic research. Each of them was kind enough to not only invite me to work with them on a research project, but also to guide me in my own work. More generally, the BPS faculty have been hugely supportive of me and provided helpful feedback in seminars, lunches, and discussions. Catherine Turco, Emilio Castilla, Kate Kellogg, Ray Reagans, Roberto Fernandez, and Susan Silbey, among many others, have provided invaluable advice and encouragement during my time at MIT. Additionally, Hillary Ross and Davin Schnappauf have been instrumental in guiding me through the entire PhD process.

One of the greatest joys during my time at MIT has been getting to know the other students. I gained so much more from classes than the readings, lectures, and problem sets by getting to study along side Alex Kowalski, Brittany Bond, Carolyn Fu, Ethan Poskanzer, James Houghton, Jenna Myers, Heather Yang, Mahreen Kahn, Simon Friis, Summer Jackson, and Vanessa Conzon. My ofﬁce mates Ari Galper, Avi Collis, Daniel Fehder, Daniel Rock, Hagay Volvosky, and Sebastian Steffen made being physically present at MIT fun and stimulating. Finally, I owe a huge thanks to the (many) students ahead of me who took the time to point me in the right direction, including Abhishek Nagaraj, Daniel Kim, Erik Duhaime, James Riley, Joshua Krieger, Julia DiBenigno, Minjae Kim, Samantha Zyontz, and Tristan Botelho.

Finally, I owe a special thanks to my family. My wife, Megan has been at my side every step of this journey. She encouraged me to apply, helped me through the admissions process, moved with me so I could attend, and has cheered me on ever after. My parents, Jan and Kurt, made this all possible through their constant care and support. And my brother Sam and sister Hannah, who have always encouraged me and have had to put up with me their entire lives.

Thank you all.

3 Contents

1 Judgment Aggregation in Creative Production: Evidence from the Movie Industry 5 1.1 Introduction ...... 6 1.2 Background and Conceptual Framework ...... 11 1.2.1 Background ...... 11 1.2.2 Conceptual Framework ...... 12 1.3 Data ...... 19 1.3.1 Samples ...... 19 1.3.2 Variables ...... 20 1.4 Results ...... 21 1.4.1 Black List Nominations and Release Outcomes ...... 22 1.4.2 The Black List and Writers of Different Experience Levels ...... 25 1.4.3 Generalizability ...... 30 1.5 Conclusion ...... 33 1.6 References ...... 35 1.7 Tables and Figures ...... 37 1.8 Appendix ...... 45

2 Architectural Misﬁts: Console Video Game Makers and the Rise of Mobile Games 73 2.1 Introduction ...... 74 2.2 Theory ...... 76 2.3 Setting ...... 79 2.3.1 Industry Background ...... 79 2.3.2 Mobile Games’ Dominant Design ...... 81 2.3.3 The Architectural Changes Required for Mobile Games ...... 82 2.3.4 Relation to Theory ...... 84 2.4 Data ...... 84 2.4.1 Data Sources and Sampling ...... 84 2.4.2 Summary Statistics ...... 86 2.5 Results ...... 87 2.5.1 Evidence of an Architectural Problem ...... 87 2.5.2 Deviating from the Dominant Design ...... 89 2.5.3 Timing and Sustainability ...... 93 2.6 Conclusion ...... 94 2.6.1 Limitations ...... 94 2.6.2 Implications ...... 96 2.7 References ...... 98 2.8 Tables and Figures ...... 101 2.9 Appendix ...... 109

4 Chapter 1

Judgment Aggregation in Creative Production: Evidence from the Movie Industry

(With Hong Luo and Jeffrey Macher)

Abstract

We study a novel, low-cost approach to aggregating judgment from a large number of industry experts on ideas that they encounter in their normal course of business. Our context is the movie industry, in which customer appeal is difficult to predict and investment costs are high. The Black List, an annual publication, ranks unproduced scripts based on anonymous nominations from film executives. This approach entails an inherent trade-off: low participation costs enable high response rates; but nominations lack standard criteria, and which voters see which ideas is unobservable and influenced by various factors. Despite these challenges, we find that such aggregation is predictive: listed scripts are substantially more likely to be released than observably similar but unlisted scripts, and, conditional on release and investment levels, listed scripts generate higher box office revenues. We also find that this method mitigates entry barriers for less-experienced writers as: (i) their scripts are more likely to be listed and to rank higher if listed; (ii) being listed is associated with a higher release rate. Yet, the gap in release probabilities relative to experienced writers remains large, even for top-ranked scripts. These results can be explained by the premise that scripts from less-experienced writers are more visible among eligible voters than scripts from experienced writers. This highlights idea visibility as an important determinant of votes and surfaces the trade-offs, as well as potential limitations, associated with such methods.

We thank Dan Gross, Aseem Kaul, Ramana Nanda, Catherine Turco, Ezra Zuckerman, and seminar participants at Harvard Business School, Strategic Research Forum, Strategy Science Conference, University of Toronto, and MIT Economic Sociology Working Group for helpful comments. Tejas Ramdas and Esther Yan provided excellent research assistance. All errors and omis- sions are our own.

5 1.1 Introduction

Successfully evaluating and selecting early-stage ideas is immensely important and yet extremely difﬁcult.

This is especially true in domains that lack concrete and objective quality signals, such as creative industries or those with low R&D-intensity (Waldfogel, 2017; Scott et al., forthcoming). In these settings, even industry experts may be limited in their ability to predict success. Moreover, the volume of ideas is often large, making screening costly. To address these challenges, decision makers often limit their searches to ideas from creators with strong, observable track records or rely on reputable gatekeepers.

However, such remedies may lead to high entry barriers for those lacking prior experience or industry connections, as well as to an inefﬁcient allocation of resources (Caves 2000; Kaplan et al. 2009; Stuart and

Sorensen 2010).

One promising approach to addressing these challenges is the so-called ‘wisdom of crowds’

(Surowiecki 2005). Though speciﬁc applications differ, the basic idea is rooted in the statistical principle that the aggregation of noisy but relatively independent estimates systematically increases precision

(Condorcet 1785; Bates and Granger 1969). With more-precise estimates of idea quality, decision makers may ﬁnd it less necessary to rely on the reputations of the creators, which can potentially reduce entry barriers for outsiders (Mollick and Nanda 2015; Sorenson et al. 2016).

Despite this intuitive appeal, ﬁrms do not commonly use crowd-based judgment, especially in high-risk and high-cost settings. When stakes are high, decision makers—who tend to have substantial experience themselves—may naturally be skeptical of the judgment of an amateur crowd (Larrick and Soll

(2006)). Thus, to generate meaningfully better and credible predictions in such settings, a sizable number of high-quality participants appears critical. However, as Csaszar (2018) argues, this may be difﬁcult because: (i) it may be prohibitively expensive to recruit such crowds; and (ii) in innovative settings, ﬁrms may not want to broadcast their early-stage projects due to appropriation concerns (Arrow 1962).

In this paper, we study a novel approach to crowd creation that seeks to overcome the above obstacles in a high-stakes, hard-to-predict, and economically signiﬁcant setting: the movie industry.

Whether a movie will appeal to customers is notoriously difﬁcult to predict (hence award-winning screenwriter William Goldman’s famous statement: “Nobody knows anything.”), and investment costs are high (the median budget is $25m in our sample). The process is further complicated by the need to

6 convince multiple parties: the producer, having purchased or commissioned a script, needs to attract key talent and, crucially, to obtain ﬁnancing from studio executives. Our data indicate that only 15 percent of sold scripts are theatrically released.

The Black List—the judgment aggregator that we examine—is an annual, anonymous survey of

“executives at major studios, major financiers, or production companies that have had a film in major release in the past three years.” Survey participants are asked to nominate “up to ten of their favorite scripts that were written in, or are somehow uniquely associated with [a particular year], and will not be released in theaters during this calendar year.” Scripts receiving more than a threshold number of nominations are published as a list and ranked by the number of nominations received. Nearly 80 percent of listed scripts are already owned by a production company and/or a studio by the annual release of the List, but given the release rate of only 15 percent even for sold scripts, the aggregated judgment may prove valuable for studio executives in their financing decisions and in attracting talent. In his TED Talk, Franklin Leonard recounted his motivation for creating the Black List back in 2005: a junior executive at a production company at the time, he wanted to spend his time more effectively, by focusing on only high-quality scripts, and to be more inclusive by casting a wider net, thereby challenging the conventional wisdom of where scripts may be found and how they are evaluated.1

By relying on industry practitioners to nominate scripts that they became aware of in their normal course of business and by asking them only for nominations, the Black List requires minimal respondent time and effort. This enables it to achieve a nearly 50-percent response rate among busy industry insiders, resulting in a “crowd” of 250-300 credible voters. Yet such an approach also presents important tradeoffs:

(i) the nominations are not based on uniform or extensive rating criteria, which may limit the List’s predictive power;2 (ii) agents’ promotional efforts and routine social interactions may undermine the independence criteria for effective judgment aggregation; and (iii) instead of centrally allocating ideas to judges, as in NIH grant reviews or venture idea competitions, which and how many eligible voters read a given script may be inﬂuenced by various factors in the idea-circulation process and is generally unobservable. 1https://www.ted.com/talks/franklin leonard how i accidentally changed the way movies get made nov 2018?language=en. 2Unlike estimating a well-deﬁned quantity such as sales volume or stock price, voters may not have a consistent understanding of what idea potential means.

7 In light of Leonard’s motivations and the tradeoffs of the Black List’s approach, we examine two questions. First, regarding the “wisdom” of the List: Can such an aggregation system actually differentiate idea quality? Second, regarding barriers to entry: Does the List help surface ideas from less-experienced writers? And does being listed and highly ranked help close the gap in release outcomes by writer experience?

Our first set of findings is that, despite its potential flaws, the Black List appears to be predictive, in that being listed helps further differentiate the quality of scripts that are otherwise observably similar. In a regression analysis that controls for factors observable to the industry before Black List publication—including the characteristics of the script, writer, and agent, as well as the producers or studios that own the script—we find that listed scripts are 70-percent more likely than unlisted scripts to be produced. Furthermore, conditional upon being released, listed movies generate greater returns to investment than unlisted ones with the same budget, providing further evidence that listed scripts are of higher quality.

Our second set of ﬁndings contrasts scripts by writers of different experience levels. The Black List appears quite effective at highlighting scripts from relatively novice writers; their scripts are not only more likely to be listed than are those by experienced writers, but they also rank higher if listed. Among less-experienced writers, being listed is also associated with a higher release probability. Interestingly, however, the gap in the release probability relative to experienced writers does not shrink with more nominations, even for the highest-ranked scripts. This last ﬁnding is quite surprising, as the prior literature suggests that observable signals tend to be less important when idea quality is more certain; and, thus, we should expect the outcome gap by writer experience to decrease, at least for scripts on which industry experts reach relatively high levels of consensus (Simcoe and Waguespack 2011; Azoulay et al. 2013).

While several factors may likely work together to explain the patterns described above, the most compelling explanation relates to the fact that the Black List is embedded in the normal course of script sales and development, which implies that the visibility of a given script among potential buyers, talent, and

ﬁnanciers (many of whom are also Black List voters) will be a systematic determinant of the nominations.

If scripts from less-experienced writers are more visible among the voters, they will, indeed, garner more nominations. Adjusting for the wider visibility, decision makers would infer an inferior posterior quality

8 for scripts from less-experienced writers, which may explain why the gap relative to experienced writers remains large, even for the top-ranked scripts. Such an explanation seems plausible because, unlike experienced writers, who can sell their ideas without talking to many buyers, less-experienced writers may need much greater exposure in order to ﬁnd interested buyers or the best match.

Overall, our results suggest that despite its weaknesses, the Black List can help to further differentiate quality among observably similar ideas in the mainstream segment of a major creative industry. Apart from this speciﬁc context, such methods may be valuable in settings that share the following broad characteristics: (i) idea quality is difﬁcult to predict (even for experts); (ii) assembling a large number of experienced but diverse evaluators via a structured process is costly and hard to scale; and

(iii) ideas are circulated in a decentralized manner, with individuals producing judgments in their normal course of business—e.g., in industries with an active market for ideas (Arora et al. 2001; Gans and Stern

2003). Our results also suggest that such methods may help to mitigate barriers to entry for lesser-known idea providers, though with important caveats. By providing more-precise information, such a list would allow the better ideas from these providers to stand out. Furthermore, in contexts in which lesser-known providers tend to have wider visibility, casting a wide net (and treating the votes with equal weights) appears to also have some corrective effect, in that good ideas from those who find it necessary to sell their ideas harder and wider in the marketplace face greater chances to rank on top. But such methods, by design, do not change how ideas circulate in the marketplace. Thus, they are unlikely to meaningfully aid those who find it difficult to obtain an audience in the first place. Furthermore, because idea visibility is likely to be influenced by various factors and not easily observable, decision makers may make crude adjustments that further limit the intended impact.

Related Literature

This paper contributes to the body of research on the evaluation of early-stage ideas. An important topic is whether idea potential is predictable. Scott et al. (forthcoming) show that upon reading new venture summaries, experts are able to differentiate quality, but only for R&D-intensive sectors and not for consumer product, consumer web, mobile, and enterprise software sectors. Astebro and Elhedhli (2006) suggest that while expert panels are effective in forecasting the commercial success of early-stage ventures, structured processes and extensive evaluator training seem necessary. Our paper shows that aggregating

9 industry-expert opinions via a simple nomination method, but on a large scale, can predict the quality of mainstream projects in a major creative industry. A second research stream examines how reviewers’ characteristics and incentives may systematically inﬂuence their evaluations: Li (2017) shows that reviewers are both better-informed and more positively biased about projects in their own areas, while

Boudreau et al. (2016) ﬁnd that evaluators systematically give lower scores to proposals that are closer to their own expertise or highly novel. While prior research typically takes the formation of reviewer panels as given, our paper highlights that such formation can be endogenous and unobservable and that idea exposure may systematically inﬂuence aggregated votes.

Recent research applies the wisdom of crowds to prediction problems in a variety of settings, including ﬁrm performance such as sales volume and project completion dates (Cowgill and Zitzewitz

2015). Our paper provides novel evidence on the application of this principle to predicting the success of early-stage ideas. As such, our paper closely relates to a growing research stream on the evaluation of crowd-funded and crowd-sourced projects (Mollick 2014; Agrawal et al. 2015; Sorenson et al. 2016).

Mollick and Nanda (2015) ﬁnd that crowds’ evaluations, while generally coinciding with those of trained experts, may reduce the incidence of “false negatives” (that is, viable projects that experts may turn down).

Riedl et al. (2013) compare the quality of aggregated judgments when users rate crowdsourced projects under different criteria. As crowd-funded and crowd-generated projects involve mainly small budgets, our contribution to this literature is to examine a novel, low-cost approach that aims to assemble a credible, high-quality crowd and to aid in decision-making in a high-stakes setting.

Finally, our paper relates to research examining the ﬁlm industry. In contrast to studies examining

ﬁlm critics’ ability to predict the box ofﬁce success of completed movies (e.g., Eliashberg and Shugan

(1997)), our paper focuses on early-stage projects, the quality of which is much harder to discern. Another line of research focuses on the use of writers’ observable quality signals as a means of evaluating screenplays (Goetzmann et al. 2013; Luo 2014). Our paper builds on this research by examining the ability of an information aggregator to pool judgments in order to arrive at a more precise judgment beyond observable signals.

10 1.2 Background and Conceptual Framework

1.2.1 Background

Making a movie is a long, costly, and uncertain process. It begins with a script. Broadly speaking, producers acquire scripts from two sources. First, producers option or purchase finished scripts from writers. Anecdotally, thousands of new scripts enter the industry every year, but only a few hundred are acquired. Second, producers may hire writers to adapt previous works (e.g., a novel) into scripts. For simplicity, we refer to scripts acquired through both means as ‘sold.’ With a script in hand, producers are tasked with finding talent and securing financing—similar to entrepreneurs. The most important financiers are movie studios. Most producers are independent, whereas some—typically the more-established—are contractually affiliated with specific studios. Only about 15 percent of sold scripts are eventually released; and for released movies, the median time from script sale to release is 2.1 years.

Agents—intermediaries representing writers—have long been important industry gatekeepers. When selling a script, agent (and agency) reputation, experience, and connections are critical to identifying potential buyers and generating interest. Large agencies may also leverage their client portfolio by helping producers assemble a director and acting team. But obtaining reputable agent (and agency) representation is challenging; well-connected agents are busy, generally rely on referrals, and typically do not read unsolicited materials submitted by unknown writers.

As mentioned, the Black List survey is embedded in the existing sales and development process of movie projects. Potential voters (executives of production companies and studios) typically read scripts when they are being actively shopped around, either by the writer and his/her agent in the sales process, or by the producer in searching for talent and in securing ﬁnancing after the script is sold. An article in The

Atlantic noted that a script must get “into the hands of executives so that they may, in turn, like it and vote for it.”3

Because the List is published annually, close to eighty percent of the listed scripts are already sold at the time of publication. Thus, in a typical case, the potential effect of being listed is no longer about getting the script sold. But with only a 15-percent release rate even for sold scripts, the List’s stamp of approval

3“The Hollywood List Everyone Wants to Be On” by Alex Wagner, The Atlantic, March 2017.

11 could prove beneﬁcial in convincing studio executives and attracting talent. For instance, Helen Estabrook, a producer of Whiplash, said, “[T]he spot on the Black List offered a level of validation that proved, ‘Hey,

I’m not a crazy person—many other people agree with me.’” A script’s presence on the List “reassures

ﬁnanciers, executives, and producers that they are not going too far out on a limb” (The Atlantic, 2017).

Our data show that 28 percent of listed scripts are subsequently released, which is consistent with what the Black List reports. Listed movies have also achieved critical success, garnering 241 Oscar nominations and 48 wins (McGee and Mcara, HBS Case 317-027). While some observers are skeptical that such movies would not have been successful without the List (e.g., Slate’s Culture Blog, 2011), it is not surprising that the LA Times declared in 2014 that “[t]he Black List has become a Hollywood institution” and that Fast Company named Leonard as “one of Tinseltown’s youngest kingmakers.”

1.2.2 Conceptual Framework

The above context suggests that the data-generating process consists of three broad stages. In the first stage, a script may encounter potential Black List voters through the normal sales and development process. In the second stage, at the end of the year, the Black List aggregates the individual judgments produced throughout the year in the first stage and publishes the number of nominations that the scripts received. Finally, in the third stage, studio executives may update their beliefs about the quality of projects after observing the number of nominations, which, in turn, may affect their decision making about which projects to finance.

Empirically, we do not observe this whole process; rather, we observe the writer’s experience level, the outcome of the second stage of the above process (the Black List nominations), and the eventual outcome of the third stage (whether a project is produced and its box office revenues). In this section, we provide a parsimonious model that generates some basic intuitions to help structure the empirical analysis based on what we can observe in the data and surface potential mechanisms. The model focuses on (i) how the votes may be determined in the second stage; and (ii) Bayesian updating by the studio on script quality based on realized nominations in the third stage. In addition, we describe a simple, supplementary model in the Appendix that provides one possible way to micro-found how widely a script is circulated in the first stage; and we discuss how updated beliefs by the studio may or may not influence the release outcomes in the third stage.

12 Model Setup

Assume that the inherent quality of a movie script is either high or low: q ∈ {qH ,qL}. Generally speaking, quality may mean different things to different people, especially for creative projects. Given that the outcome variables that we consider are theatrical release probabilities and box office performance, we deem script ‘quality’ to be about commercial success. A voter, having read a given script, obtains a binary quality signal s ∈ {sH ,sL}, such that P(sH |qH ) = P(sL|qL) = p, and P(sL|qH ) = P(sH |qL) = 1 − p. Assume that p > 1/2; that is, signals are (positively) informative. Moreover, any two voters’ signals are conditionally independent given the true script quality. A voter will nominate a script as long as a positive signal (sH ) is received. We focus on two factors that determine the number of nominations a script receives, and both may vary with the writer’s experience level, w. The first factor is the prior belief about the probability that a script is high-quality, π(w).4 π(w) is an exogenous factor, as it reflects the probability of success understood by the industry based on past projects by writers of similar characteristics. We assume that scripts from experienced writers are more likely to be high-quality than scripts from less-experienced writers; i.e.,

Assumption 1. π0(w) > 0.

This assumption simpliﬁes the analysis greatly and is supported by empirical evidence in Section 4.2.3.

The second factor is the number of voters who have actually read a particular script, n(w). Script visibility is important because the probability of obtaining a nomination from a voter who has not yet read a script is (in principle) zero. As discussed above, script visibility among Black List voters is likely to depend on how widely the script is circulated; and the breadth of circulation is endogenous in the sense that it is affected by multiple players’ decisions that may depend on the writer’s experience level. For simplicity, we assume that visibility is a reduced-form function of w and that it is (weakly) negatively correlated with writer experience:

Assumption 2. n0(w) ≤ 0.

4Apart from writers’ track records, this belief may also be based on other observable variables before the script is read, such as genre; logline (e.g., a poor man and a rich woman fall in love on an ill-fated ship); the characteristics of the writer’s agent; among others. We control for these variables in the empirical analysis.

13 In Appendix A.1, we describe a simple model in which the writer endogenously chooses n as he/she attempts to sell a script. We assume that marketing to an additional buyer incurs an extra cost; apart from physical and time costs, industry accounts also suggest that sellers have incentives to avoid an unnecessarily wide sales scope due to concerns of information leakage. We show that the optimal number of buyers to market a script to decreases with writer experience as long as the optimal number is greater than one buyer. This result provides a micro-founded support for Assumption 2 and is quite intuitive: even though it is more costly, less-experienced writers want to approach more buyers than experienced writers because, under Assumption 1, chances of any individual buyers receiving a positive signal about their scripts are lower. It is important to emphasize that, as explained previously, multiple factors, including strategic behaviors, may affect the ultimate scope of script exposure beyond what is captured in this simple maximization problem.5 Some of these forces may push script visibility to be greater for less-experienced writers, while others may push in the opposite direction. The sign of n0(w) is, ultimately, an empirical question. As we discuss in detail later, however, what we need in order to interpret the empirical results is the possibility of n0(w) < 0. To this end, it sufﬁces to show that Assumption 2 may hold under plausible conditions (as is illustrated in Appendix A.1).

With this setup, we can write the expected number of nominations as a function of writer experience, w:

E[m|w] = π(w)n(w)p + (1 − π(w))n(w)(1 − p). (1.1)

In each state of the world (i.e., the true quality being either qH or qL), the number of nominations follows a binomial distribution. Given each state, the expected number of nominations is, thus, the number of voters who have read a given script (n(w)) multiplied by the probability that each voter draws a positive signal given this state. The above equation weighs the two states by their respective prior probabilities, π(w) and

1 − π(w). For a script that has received m nominations and is from writer of experience level w, the updated

5Apart from the likelihood of making a sale, writers of different experience levels may also differ in their opportunity costs of time; their ability to capture value if making a sale; their ability to target producers who are likely to need and like their scripts; and, the types of producers who are willing to read their scripts if presented with them. Moreover, after a script is sold, the producer may also need to shop around the script among studio executives, ﬁnanciers, and talent, which will also affect the scope of n, as all of these players may be potential Black List voters. These complex factors underlie why we do not fully model how n is determined. We also do not observe n or have means to empirically test its data-generating process.

14 belief about the probability that it is of high quality is (according to Bayes’ rule):

π(w)P(m|w,qH ) P(qH |m,w) = . (1.2) π(w)P(m|w,qH ) + (1 − π(w))P(m|w,qL) In words, this updated belief is the probability that the true quality of the script is high and that it has received m nominations, divided by the total probability that the script receives m nominations, which also includes the probability that the true quality of the script is low, and it has received m nominations.

In the following, we derive two sets of results that we take to the data. Proposition 1 suggests that the Black List is predictive, which we examine empirically in Section 4.1; and Propositions 2 to 4 relate to writer experience, which we examine in Section 4.2. See Appendix A.2 for all the proofs.

Number of Nominations and Updated Beliefs About Script Quality

Given writer experience w, the following result shows that a greater number of nominations implies a

∂P(qH |m,w) higher likelihood that the script is of high quality (that is, ∂m > 0):

Proposition 1. The posterior belief about the probability of the script’s quality being high increases with the number of nominations, m.

Note that we cannot directly test Proposition 1 because it is impossible to observe the true quality of a script. What we can do is to correlate Black List nominations with release probability. But because the

Black List may not only predict, but also inﬂuence release outcomes, there are three possible interpretations if this correlation is, indeed, positive.

The ﬁrst possibility is that the Black List is purely predictive and does not inﬂuence decision making. Consider a studio with two scripts in its portfolio: if the Black List ranks these two scripts in the same order as the studio’s internal (private) ranking, though still predictive, the List would have little impact on decision making. In other words, listed scripts will be more likely to be produced than unlisted scripts, even without the existence of the Black List.

The second possibility is that the Black List is predictive and has a causal impact on decision making. If the studio above could not further differentiate the two projects on its own, but one is

Black-Listed while the other is not, the probability of release for the listed script would be greater. Or if the studio ranks the two projects internally in the opposite order, the Black List rankings may provide a sufﬁciently strong signal to reverse the studio’s preference.

15 The third and final possibility is that the Black List does not predict script quality but somehow still influences decision making. Note that Proposition 1 is based on the assumptions that voters’ signals are informative about true script quality and that they are conditionally independent. However, as discussed in the Introduction, these assumptions are not obvious. Due to the Black List’s unstructured approach, voters may not share a consistent understanding of idea quality, and their signals may be correlated, thereby limiting the informativeness of their signals. For example, if p is, in fact, 1/2, the number of nominations would have a zero correlation with an idea’s quality. However, even if the List is not predictive, it is still possible that it influences decision making. This may happen if the studio (mistakenly) assumes that voter signals are informative of true quality, or if the Black List, by providing a focal point that attracts attention, helps to overcome any coordination failures.

As we explain in Section 4.1, we may exploit data on resource allocation and box office revenue to rule out the third possibility—that the Black List is not predictive. Specifically, suppose that a movie’s box office revenue is a positive function of three inputs (as in a typical production function): script quality; physical capital (measured by production budget); and human capital (measured by the characteristics of the director and main cast). We can test the following null hypothesis: If the Black List is not predictive, listed scripts and unlisted scripts should generate similar box office revenues, given the same physical and human capital allocated to them. If we find the opposite, we may conclude that the Black List is, indeed, predictive. To distinguish between the first and the second possibilities, however, we need to know a studio’s internal rankings of their portfolio projects and how confident it is about its own information relative to external input such as the Black List. Unfortunately, such data are impossible to obtain. As a result, we cannot pin down whether the Black List, along with being predictive, also influences decision making in a causal sense.

Nominations, Writer Experience, and Updated Beliefs About Script Quality

Because writer experience w affects our outcome variables via two different channels—(i) they have different prior probabilities of being high-quality; and (ii) they may also have different levels of visibility among Black List voters—we write all of the following propositions in two parts. The ﬁrst part focuses on the scenario in which n0(w) = 0; that is, all scripts are read by the same number of voters. This scenario provides a null hypothesis that rules out visibility as a systematic determinant of votes. In the second part,

16 we add the additional effect due to the possibility that visibility may also be different. As we discuss later, in Section 4.2, this additional channel appears to be necessary to explain our empirical results on writer experience.

The following proposition is about how the number of nominations may vary by writer experience:

Proposition 2. If n0(w) = 0, the expected number of nominations is greater for experienced writers than for less-experienced writers. If n0(w) is sufﬁciently negative, the expected number of nominations can be greater for less-experienced writers.

To see this, take the derivative of E[m|w] with respect to w:

∂E[m|w] ∂E[m|w] ∂E[m|w] = π0(w) + n0(w). (1.3) ∂w ∂π(w) ∂n(w) | {z } | {z } + + ∂E[m|w] ∂π(w) > 0 is intuitive because when voters’ signals are positively correlated with true script quality, more ∂E[m|w] positive signals will be drawn if the prior probability of being high-quality is higher. ∂n(w) > 0 is also intuitive, as scripts that have been read by more voters are likely to receive more nominations. Recall that

π0(w) > 0 under Assumption 1. Thus, when n0(w) = 0, we expect that the probability of being Black-Listed will be higher for experienced writers and that their scripts should be ranked higher. However, when scripts from less-experienced writers are read by sufﬁciently more voters (i.e., n0(w) is sufﬁciently negative), we may observe the opposite result, despite their lower prior probability of success.

For scripts that receive the same number of nominations m, the next proposition shows how updated beliefs about the script’s quality may vary by writer experience:

Proposition 3. If n0(w) = 0, for scripts with the same number of nominations, the posterior belief about the probability that the script is high-quality is greater for experienced writers than for less-experienced writers. If n0(w) < 0, this positive gap by writer experience is even larger.

This result is obtained by taking the derivative of P(qH |m,w) with respect to w:

∂P(q |m,w) ∂P(q |m,w) ∂P(q |m,w) H = H π0(w) + H n0(w) (1.4) ∂w ∂π(w) ∂n(w) | {z } | {z } + −

∂P(qH |m,w)) ∂π(w) > 0 shows that for scripts receiving the same number of nominations, one should infer a higher

∂P(qH |m,w) quality for scripts with a higher ex-ante expectation. ∂n(w) < 0 shows that the quality inferred should

17 be discounted for scripts that are believed to have been read by more voters. For instance, consider two scripts that both receive ﬁve nominations, but ten voters have read the ﬁrst and twenty voters the second.

One should infer a lower quality for the second script, as it receives more negative signals than the ﬁrst relative to the same number of positive signals. Thus, under Assumption 1 that π0(w) > 0, the ﬁrst term in the above equation is positive; and with n0(w) ≤ 0 (Assumption 2), the second term is non-negative. Thus, while w operates through two different channels, both produce the same directional predictions for

P(qH |m,w). Consequently, in contrast to Proposition 2 in which the two channels generate opposite predictions for E[m|w], Proposition 3 does not allow these two channels to be empirically distinguished.

The ﬁnal result is about how the gap in updated beliefs by writer experience in the previous

∂P(qH |m,w) proposition may change as the number of nominations increases (that is, how ∂w changes as m increases).

Proposition 4. If n0(w) = 0, there exists a unique m∗ such that the (positive) gap by writer experience ﬁrst increases with m for m < m∗ and then decreases with m for m > m∗. If n0(w) < 0, the threshold after which the gap starts to decrease is higher than m∗.

Intuitively, when the number of nominations is relatively small (m < m∗), the signal provided by the Black

List is quite weak (or even negative, as only a small number of voters having read the script received a positive signal).6 In this range, an incremental increase in m is more credible for scripts from writers with a higher prior probability of success. In other words, prior probability π and nominations m are complements in this range; and the gap by writer experience will actually increase with m. However, when the number of nominations is relatively large (m > m∗), Black List nominations provide relatively precise and positive signals. In this range, nominations and prior probability become substitute signals, and the gap by writer experience should decrease as m increases. In the case of very large n and m, we can show that the gap would converge to zero. The converging result in the m > m∗ range illustrates the intuition from the prior literature that, when uncertainty about idea quality becomes smaller, the importance of observable quality should decrease.

When n0(w) < 0, we can show that the threshold required for the gap to start to converge is higher; in

6 ∗ ∗ n Note that m is an increasing function of n, and a decreasing function of π. In particular, m = 2 when π = 0.5 (that is, if the ∗ n ∗ n idea is equally likely to be high versus low quality ex-ante); m < 2 when π > 0.5; and m > 2 when π < 0.5

18 other words, there will be a greater range of m’s in which the gap will continue to increase. This is also quite intuitive, as people expect more voters to have read scripts from less-experienced writers, further discounting the updated quality belief for less-experienced writers. This discount, due to greater visibility, helps to maintain the size of the gap even for a relatively high number of nominations.

1.3 Data

1.3.1 Samples

Our data come primarily from two sources. Our ﬁrst sample (Sample BL) uses annual Black List publications over 2007-2014 and includes 701 listed scripts. We exclude the ﬁrst two years (2005 and

2006) because these publications do not provide any buyer-side information even if they are sold (as mentioned previously, 79 percent of them are). We also cut off the data after 2014 to provide a sufﬁcient window for scripts to be produced before our ﬁnal outcome data collection date (October 2017).

Our second sample (Sample DDP) uses Done Deal Pro (DDP), an internet database that tracks script transactions on a daily basis. DDP is recognized by various industry organizations (e.g., the Writers Guild of America) as one of the leading movie project information sources. We exclude DDP records on adaptation contracts to avoid confounding different degrees of uncertainty, as we cannot systematically observe whether these scripts were later completed and were good enough to advance to the next stage (i.e., a stage comparable to that of the majority of the Black-Listed scripts). Thus, Sample DDP is more representative of scripts based on original ideas and less representative of the most expensive movies. We also exclude rewrite contracts. In addition, for a cleaner interpretation, we exclude 21 observations of

Black-Listed scripts that were sold after the List was published. We examine this small subset of scripts separately in Appendix B.4. After these exclusions, Sample DDP includes 1,566 observations over

2007-2014.

We can think of the two samples as covering different segments of the nomination distribution.

Sample BL provides a detailed picture of the top end of the distribution (above the cutoff threshold), with the key variation being the number of nominations (and ranking) that a script receives. With Sample DDP, we can compare listed to unlisted scripts, though it is still conditional on scripts being good enough to be

19 sold.7

The Black List and DDP both provide information on the writer, the representing agent and agency, and the buyers (production companies and/or movie studios that purchased or commissioned scripts). We further collect data from complementary sources, including: (i) the Internet Movie Database (IMDb), which provides writers’ industry experience; and (ii) The Numbers, which offers outcome information, including whether scripts have been theatrically released and, if so, the box-ofﬁce revenues generated.

1.3.2 Variables

Table 1.1 provides summary statistics, summarizing Sample BL in the left panel and Sample DDP on the right. In the following, we explain in detail the key variables and then brieﬂy describe the control variables.

For Black-Listed scripts, because the difference in total respondents may result in variations in nominations across years, we create a categorical variable, Black List Rank Group to indicate where listed scripts place in a survey year: 4 = top 5 (5.7 percent of all scripts in Sample BL); 3 = top 6 to top 20 (19.4 percent); 2 = top 21 to just above the cutoff number used in a given year (53.4 percent); and 1 = those at the cutoff (21.5 percent). We also check the robustness of our results to using a continuous variable, deﬁned as the number of nominations received by a script divided by the total number of respondents in that year. For scripts in Sample DDP, Black-Listed indicates listed scripts, which account for 11 percent of the sample.

Released indicates whether a script has been released in U.S. theaters and has generated positive box office revenues. For released movies, we have the movie’s worldwide (U.S. plus international) box-office revenues and, for about two thirds of them, the production budget; both variables are adjusted for inflation.

We measure writer experience by the number of writing credits the writer obtained in the previous ten years for movies distributed by the top-30 movie studios (Writer Major Credits). The maximum is taken if there is more than one writer. The ten-year restriction better captures writers’ recent industry experience and current status versus simply measuring industry tenure. The restriction to movies distributed by the top-30 studios avoids inﬂating writer experience with small-budget independent movies. Experienced

Writer indicates whether a writer has had one or more major writing credits in the previous ten years.

Control variables include the experience of the writer’s agent: Top-10 Agency indicates whether the

7Sample BL is not a subset of Sample DDP, however, because the latter is a comprehensive, but incomplete and selected, sample of all scripts that are sold and, thus, does not capture all Black-Listed scripts.

20 agency representing the writer ranks in the top ten in terms of market share, calculated using all transactions in the DDP database; Experienced Agent indicates whether the agent has more than 15 script transactions prior to a given sale. For buyers, Producer Major Credits is the number of producing credits obtained in the previous ten years for movies distributed by the top-30 studios, and Movie Studio Buyer indicates whether a movie studio is listed among the buyers. For the 21 percent of scripts in Sample BL that have not yet sold at the time of the List’s publication, the two buyer variables are replaced with zero in the regressions. Whether Reported indicates whether any articles about a given script are published in the top two trade magazines—Hollywood Reporter and Variety—according to Google search results two years before the Black List publication date in the year the script was sold. For Sample DDP, we have additional characteristics of the scripts, including whether they were based on original content (versus an adaptation); the source of content if not original (e.g., books and short stories); and genre. Three percent of observations are indicated as having been purchased through an auction process. For half of the records, some talent is committed to the project at the time of sale.

For released movies, apart from production budget, we collect other measures of investments. These include the quality or popularity of the leading cast;8 the total number of screens showing the movie during the opening weekend; and a seasonality index for weekly ﬂuctuations in movie demand.9

1.4 Results

In addressing the two questions posed in the Introduction, we present and discuss two sets of results:

Section 1.4.1 examines the relationship between nominations and release outcomes to determine whether the Black List is predictive, while Section 1.4.2, by examining the results by writer experience, explores whether the Black List reduces entry barriers. Finally, we discuss the generalizability of the Black List method in Section 1.4.3. 8These include two variables: Award-winning cast indicates whether any of the leading cast has won any award (Academy Award, Golden Globe, and BAFTA) prior to the release year of the focal movie; and Star score is an index generated by thenumbers.com, based on the total box ofﬁce revenues of movies an actor or actress has been in prior to the release year of the focal movie. 9Following Basuroy et al. (2003), the Seasonality index is based on the total number of tickets sold for all movies shown during a given weekend, averaged across years.

21 1.4.1 Black List Nominations and Release Outcomes

We ﬁrst focus on whether or not a movie is released, as this is a systematic outcome measure available for all observations. Figure 1.1a, using Sample DDP, shows that Black-Listed scripts are 10.8 percentage points more likely to be released than unlisted scripts, representing a 76-percent difference (p-value =

0.001). Using Sample BL, Figure 1.1b shows that among listed scripts, there is a monotonic increasing relationship between Black List rank group and release probability. The biggest difference is between top-ﬁve scripts (47.5 percent) and those in the three lower-ranked groups (25.0 percent on average). The differences between lower-ranked groups, though not statistically signiﬁcant at the conventional level, are economically non-trivial.

The raw data, thus, show a positive correlation between Black List nominations and release probabilities. Our goal is to understand whether the List, by aggregating voters’ judgments formed after reading a script, helps to differentiate scripts beyond what readers can glean from the scripts’ observable characteristics. For this reason, we control, to the extent possible, for factors observable to the industry prior to the List’s publication. Consider the following speciﬁcation, which uses Sample DDP as an example:

Releasei = δBlack-Listedi + βXi + c jt + εi, (1.5)

where Releasei indicates whether script i is theatrically released; Black-Listedi indicates whether script i is listed; and Xi represents the comprehensive list of observable factors that may correlate with the likelihood of release. These include the characteristics of the writers, their agents, and the scripts, as described in

Section 1.3.2; whether the article is reported (as significant publicity may result in voter signals being highly correlated); and year fixed effects. It is important to highlight that we also control for the buyer of a given script because this is generally common knowledge by the time of the Black List survey. It is, therefore, possible that voters adjust their opinions about scripts based on this information, as they may know that standards differ among buyers. We do this by controlling for producer experience and by including studio fixed effects.10 In some specifications, we also include a full set of studio× year fixed effects (c jt ). For all regressions, to be conservative, we use two-way clustered standard errors at the studio

10If no movie studios are included as a buyer in the DDP record, we pool these scripts together as a single default group.

22 and year levels to allow for free correlations between scripts developed within a studio or in the same year

(Cameron et al. (2011)).

Columns 1-3 in Table 1.2 report results using Sample DDP: Column 1 controls for year and studio

fixed effects; Column 2 uses studio×year fixed effects; and Column 3 further excludes top-20 listed scripts, under the assumption that lower-ranked scripts are more likely than top-ranked scripts to be observably similar to unlisted scripts and less likely to be subject to publicity hype. All specifications produce similar results. The most conservative estimate shows that listed scripts are 10.9 percentage points, or 72 percent, more likely to be released than unlisted scripts.

The last three columns in Table 1.2 report regression results using Sample BL. Columns 4 and 5 show that top-ﬁve scripts are 28 percentage points more likely to be released than those in the default group

(scripts receiving only the threshold number of nominations), representing a 136-percent increase. The coefﬁcient of ‘Top 6-20’ represents a 50-percent increase relative to the default group (p-value is 0.154); and the ‘Top 21-above threshold’ coefﬁcient represents a 32-percent difference (p-value is 0.208). Column

6 conﬁrms this positive correlation using the ratio between the number of nominations received by a script and the total number of respondents in this year. The coefﬁcient suggests that when this ratio increases by one standard deviation, the release likelihood increases by 6.7 percentage points.11

Box Ofﬁce Performance and Production Budget

The above results show a significant positive relationship between Black List nominations and the likelihood of release for observably similar projects by the same studio in the same year. But, as discussed in Section 1.2.2, because the Black List may influence as well as predict outcomes, we cannot yet conclude that the Black List is predictive. In this section, we provide further evidence by examining the box office performance of released movies. As discussed in Section 2.2.2, given the same investments, if listed scripts perform better than unlisted ones, we can conclude that the Black List can predict quality.

Figure 1.2a shows that for released movies in Sample DDP, the density distribution of log(Worldwide Box Ofﬁce) for listed movies is to the right of that for unlisted ones; listed movies are

11It is worth noting that the addition of the Black List variables substantially increases the amount of variation explained by these regressions. Appendix Table B1 reports the marginal increase in R2 and adjusted R2 by including the Black List variables in the regressions in Table 1.2. The percent increase ranges from 3.2% to 16.0%, depending on the speciﬁcation, and the median is 8.9%.

23 associated with both a higher mean (p-value = 0.003) and a smaller standard deviation (p-value < 0.001).

Appendix Figure B2a shows that the density distribution of log(Production Budget) is statistically similar between listed and unlisted movies, which suggests that better performance of listed scripts is not driven by greater investment. Panel (a) of Table 1.3 puts the above analysis into regression form. Column 1 confirms that released Black-Listed movies generate significantly higher box office revenues than unlisted movies do. Column 2 shows that listed scripts are not associated with a higher production budget. Similarly, regression results reported in Appendix Table B3 show that listed and unlisted movies are also statistically similar for other measures of investment level: the quality of the leading cast; the number of opening screens; and the release window. Controlling for all measures of investments, Column 3 shows that released listed movies generate significantly higher log(ROI)—where ROI (return over investment) is worldwide box office revenue divided by production budget—than unlisted ones generate. Columns 4-6 report quantile regression results for ROI, also controlling for investment. The Black List coefficients are all positive and statistically significant, confirming a statistically superior distribution for investment returns for listed movies.

Among Black-Listed scripts, the raw data show that the density distributions of log(Worldwide Box

Ofﬁce) and log(Production Budget) are statistically similar across all rank groups (see Figures 1.2b and

Appendix Figure B2b). Panel (b) of Table 1.3 shows that the coefﬁcients of all rank group dummies for regressions on ROI, though noisily estimated, are mostly positive relative to the default group of scripts receiving the threshold number of nominations.

Combining the results on release probability, investments, and box office performance, there is strong evidence that the Black List is able to separate good scripts from worse ones; among observably similar scripts, those Black-Listed are significantly more likely to be released, and, if released, generate higher investment returns given the same investment levels. Within the selective subset of listed scripts, the monotonically increasing relationship between rank and likelihood of release, coupled with the fact that conditional on release, higher-ranked scripts do not perform worse at the box office, suggests that the Black

List is able to discern levels of quality even among relatively high-quality scripts. It is, however, important to note that without knowing how studios rank projects internally prior to the List’s publication, we are unable to conclude whether the Black List, along with being predictive, also inﬂuences resource allocation

24 in a causal sense.

1.4.2 The Black List and Writers of Different Experience Levels

This section presents the results on writer experience in two stages: ﬁrst, how Black List nominations correlate with writer experience; and, second, how the relationship between nominations and release probability varies by writer experience. We discuss potential explanations and the implications after presenting the results.

Nominations and Writer Experience

The results suggest that the Black List is able to highlight ideas from writers that lack strong track records.

In particular, Figure 1.3a shows that in Sample DDP, scripts from less-experienced writers are substantially more likely to be listed than those from experienced writers (13.5 vs. 7.0 percent, p-value = 0.002);

Figure 1.3b shows that, if listed, the likelihood of achieving a top-20 ranking is also higher for less-experienced writers (27.6 vs. 14.4 percent, p-value = 0.002). Table 1.4 conﬁrms this negative correlation between Black List nominations and writer experience with OLS regression results. Columns 1 and 2, using Sample DDP, show that scripts from experienced writers are about eight percentage points less likely to be Black-Listed, representing a 67-percent difference. Columns 3 and 4 show that, among

Black-Listed scripts, those from experienced writers are 5.2 percentage points less likely to be in the top-ﬁve and 12.5 percentage points less likely to be in the top-20. Finally, Column 5 conﬁrms this negative correlation using the ratio between the number of nominations and the total respondents in a year as the dependent variable.

Nominations, Release Probability, and Writer Experience

Figure 1.4 shows the following three patterns. First, similar to the average results, the positive correlation between Black List nominations and release probability also holds when considering scripts from less-experienced and experienced writers separately. Second, scripts from less-experienced writers are uniformly less likely to be released regardless of whether or not they are listed and, if listed, their rank group. Third, the gap in the release probability by writer experience remains large for listed scripts, even for the highest-ranked ones.

These patterns are conﬁrmed by regression results presented in Table 1.5. Examining

25 less-experienced and experienced writers in Sample DDP separately, Columns 1 and 2 show that

Black-Listed scripts are signiﬁcantly more likely to be released for both types of writers. The coefﬁcient of

Black-Listed for experienced writers is 13.5 percentage points greater than that for less-experienced writers. Given the noisier estimate, this difference, though economically large, is not statistically significant.12 Columns 3 and 4, using Sample BL and including ‘nomination × survey year’ fixed effects, compare scripts that receive exactly the same number of nominations in the same year. The results show that, for scripts listed below the top 20, the gap in release likelihood between experienced and less-experienced writers is 18 percentage points, representing about a 100-percent difference. The gap by writer experience becomes economically larger for top-20 scripts (by 9.3 or 14.5 percentage points, depending on the specification), though the difference between the gaps is not statistically significant.

Potential Explanations for Results by Writer Experience

This section discusses potential explanations for the results on writer experience. Note that any explanation must explain all of the following patterns: (i) the greater likelihood of being listed and ranking high for less-experienced writers; (ii) the lower likelihood of release for less-experienced writers relative to experienced ones, given the same number of nominations; and (iii) the persistence of this gap even among top-ranked scripts. In the following, we ﬁrst propose an explanation, informed by Propositions 2-4 in

Section 1.2.2, and then describe potential alternative explanations.

Proposed Explanation: Script Visibility According to Propositions 2-4, the best explanation may be that scripts from less-experienced writers are significantly more widely circulated than scripts from experienced writers (that is, n0(w) is sufficiently negative). With significantly wider visibility, less-experienced writers would obtain more nominations despite a lower prior probability of success (the second part of Proposition 2). The wider visibility may also help to explain why the gap does not shrink for the highest-ranked scripts. The median number of nominations for top-20 ranked scripts is 20 votes.

Though impossible to know for sure, it does not seem unreasonable that such numbers are likely to fall in

12Regression results including the full set of controls, as in the sub-sample analysis, shows an economically large coefficient of the interaction term, Black-Listed × Experienced Writer, at 19.5 percentage points. The statistical significance levels differ by how we adjust the standard errors. With two-way clustering at both the studio and year levels, the p-value is 0.150, while with one-way clustering at the studio level, the interaction term becomes significant at the one-percent level. Note that the key takeaway is that the gap is not shrinking—with the most conservative standard errors, we can reject the null hypothesis that the gap is shrinking with a one-sided test at the ten-percent level.

26 the range of m > m∗ (as speciﬁed in the ﬁrst part of Proposition 4); that is, the number of nominations is likely to produce relatively precise and positive signals of idea quality.13 If so, we may expect observable quality signals to be less important and, hence, the gap by writer experience to start to shrink. Yet the data do not support this. Under the assumption that scripts from less-experienced writers are much more widely circulated, decision makers may discount the votes for less-experienced writers, which would sustain the gap even for the highest-ranked scripts.14

The simple model in Appendix A.1 shows that under plausible conditions, the writer’s optimal choice of sales scope will, indeed, decrease with his/her experience level. This is because it is harder for less-experienced writers to ﬁnd an interested buyer due to their lower (prior) probability of success.

Moreover, though relatively selective, eligible voters are still likely to be heterogeneous in their experience and status in the industry. As mentioned, less-established producers/executives may be more willing to read materials from less-experienced writers, whereas more-established producers/executives may be more strict about the number of scripts they read and from which writers. Because all votes are treated equally, the likely segmentation in the market for scripts and the different levels of stringency in the voting criteria may further contribute to the greater number of votes for less-experienced writers.

The importance of visibility in determining the Black List nominations is consistent with the industry accounts quoted previously, such as: “[F]or a script to top that list, it needs to have been read by many of those people.” It is also consistent with the results in Table 1.4, which show that the experience of the writer’s agent and the size of the agency are, overall, positively correlated with Black List nominations.

The positive association between agency size/agent experience and visibility is intuitive, as larger agencies and more-established agents are more likely to have the connections, resources, and reputation to get their client’s script “into the hands of executives so that they may, in turn, like it and vote for it.”

Of course, agent afﬁliation may correlate with other factors, as well; for example, writers associated with larger agencies are also likely to be higher-quality. To investigate this further, we conduct a split-sample analysis that ﬁxes agency size and agent experience (Table 1.6). The idea is: (i) to restrict each

13It is important to note that the typical number of Black List voters who have read any given script would be much smaller than the total number of respondents in a given year. 14Finally, Proposition 3 notes that both determinants of votes—prior probability π and script visibility n—predict that, given the same number of nominations, the likelihood of release is lower for less-experienced writers relative to experienced ones. This particular pattern is, thus, not discriminating between the two channels.

27 subsample to writers who are likely to be similar in their overall quality; and (ii) to take advantage of the cross-subsample differences in the extent of resources, connections, and experience of writers’ agents. The results reveal that the signiﬁcant negative correlation between writer experience and Black List nominations occurs only for writers represented by large agencies, and the effect is the greatest for writers also represented by experienced agents.15 Considering the baseline likelihoods (presented at the bottom of each column), writers who receive the most nominations are the least experienced but associated with experienced agents at top agencies. This cross-subsample pattern is consistent with the idea that the necessity for wider exposure needs to be complemented by the ability to reach the audience in order to achieve high visibility. Larger agencies and experienced agents can aid their less-experienced writers to obtain sufﬁcient exposure, whereas smaller agencies and less-experienced agents are less able to do so.

Alternative Explanation 1: π0(w) < 0 and Risk Aversion Note that the previous discussion was based on Assumption 1: the prior probability of success is higher for experienced writers (that is,

π0(w) > 0). Were this assumption untrue—that is, if scripts from less-experienced writers are actually more likely to be very good (π0(w) < 0)—scripts from less-experienced writers would be more likely both to be Black-Listed and to be ranked higher if listed without having wider visibility. This scenario is plausible if the quality distribution of scripts from less-experienced writers is statistically superior or has greater variance (such that the heavier right tail implies a greater likelihood that the script is exceptionally good).

This explanation alone, however, suggests that less-experienced writers would have a higher release rate relative to experienced writers for scripts receiving the same number of nominations.16 This contradicts what we observe in the data. To account for this, one may add risk aversion, which, for simplicity’s sake, we do not include in our model. Risk aversion, together with greater quality variance, would explain why, if listed, scripts from less-experienced writers are less likely to be released. Risk aversion may be important given the considerable uncertainty in the remaining process: projects associated with experienced writers represent safer bets.

This alternative combination of explanations, though plausible, does not ﬁnd strong support in the data. Using scripts from the DDP database in time periods prior to the Black List, we actually ﬁnd a monotonic increasing relationship between release likelihood and writer experience, which is consistent

15Almost all experienced agents work for a top-10 agency, so we do not split the non-top-10 agency sample by agent experience. 16 0 0 ∂P(qH |m,w) To see this, let n (w) = 0 and π (w) < 0 in equation (1.4). Thus, ∂w < 0.

28 with Assumption 1. Furthermore, quantile regressions using released movies also do not indicate a heavier right tail in the distribution of box ofﬁce revenues for scripts from less-experienced writers (see results in

Appendix Table B5).

Alternative Explanation 2: (Aesthetic) Novelty A second alternative explanation is that survey respondents nominate scripts that appeal to them for non-monetary reasons; for example, they may ﬁnd scripts from less-experienced writers more novel or interesting, which will result in the Black List overrepresenting less-experienced writers. But these scripts may be less ﬁnancially viable and, hence, less likely to be produced. We investigate this explanation in detail in the Appendix; available data and novelty measures used by prior work do not support the notion that scripts from less-experienced writers are systematically more novel.

Implications for Mitigating Entry Barriers

So, does the Black List serve its intended effect of providing more opportunity to less-experienced writers?

We believe that the results support an afﬁrmative answer, but with some important caveats.

Two key pieces of evidence support an affirmative answer. First, the finding that being listed is associated with a significantly higher release probability among less-experienced writers suggests that the

List allows better scripts by less-experienced writers to stand out. In the absence of the List, all similar scripts from less-experienced writers are likely to be pooled together, and, due to the difﬁculty of differentiating among them, decision makers are likely to allocate resources to experienced writers.

Second, the Black List does feature less-experienced writers more prominently. Given our explanation, less-established writers (albeit only those associated with large agencies) tend to have wider visibility among Black List voters. By casting a wider net and by giving equal weights to all votes, the

Black List seems to have a corrective effect, in that those who need to sell their ideas more broadly in the normal circulation process may have a greater chance of making it to the List. Even if these scripts are not produced, such attention may prove valuable in the long run (e.g., obtaining future writing assignments).

There are two important caveats, however. First, the Black List, by design, builds upon the existing channels through which ideas circulate. Scripts that have a hard time obtaining an audience in the ﬁrst place

(in particular, those without an agent, let alone a top agent at a large agency) are unlikely to be substantially helped. Second, there may be unintended consequences when it comes to making inferences. Because the

29 visibility of a speciﬁc script is not observable, decision makers may have to make crude adjustments based on coarse characteristics. Consequently, they may discount the nominations indiscriminately for less-experienced writers, even for those who have not shopped their scripts widely. Similarly, they may not discount the nominations for experienced writers, even if their scripts have had wide exposure.

To fully break down industry barriers, multiple approaches would likely need to work in combination, each addressing different segments and each with its different but inherent tradeoffs. The

Black List annual survey that we study in the paper focuses on the mainstream segment of the industry—i.e., the ideas that have already been circulating among relatively successful producers. Given the demands on ﬁlm executives’ time, it would be difﬁcult to impose a more-structured evaluation process without increasing the participation costs to a fatal extent.17 Recognizing these limitations, in 2012, the

Black List began a separate platform on which any writer can host his or her script and have it rated by a reader, both for a nominal fee. The reader rates the script based on a ten-point scale and writes reviews evaluating different aspects of the script. The average score and the number of readers are then made public. Thus, in contrast to the survey, this platform provides much greater accessibility and transparency, and the evaluation is based on extensive, uniform criteria. But, because the platform focuses on a segment far outside the mainstream—91 percent of the hosted scripts are from writers without any representation either by an agency or a manager—it does not provide useful information about the set of scripts that is immediately relevant to the studios. Consequently, industry insiders tell us that the key decision makers pay signiﬁcantly less attention to the online platform than they do to the annual survey. Examining the effect of the platform and how these different approaches could interact with each other is beyond the scope of this paper but would be a fascinating avenue for future research.

1.4.3 Generalizability

In its essence, the Black List survey aggregates, on a large scale, individual judgments about idea quality that arise during the normal sales and development process of these ideas. Though the Black List is set in a speciﬁc context, we believe that similar methods could also apply to settings that share the following three

17Film executives typically review hundreds of scripts every year; thus, while requiring potential voters to list each and every script they reviewed over the course of the year would provide an accurate measure of the visibility of a speciﬁc script, it would signiﬁcantly increase participation costs, decreasing the participation rate. It may also be possible to improve the informativeness of votes by adjusting the weights given to different voters, but to do this in a credible way is challenging and may require breaking the anonymity promise to the voters, which, in turn, may suppress their incentives to participate.

30 broad characteristics: (i) idea quality is difﬁcult to predict (even for experts) and is challenging to verify with alternative means; (ii) assembling the judgment of a sizable number of experienced, but diverse evaluators via a structured process is costly and hard to scale; and (iii) many ideas are circulated in a decentralized manner, with individuals producing judgments (albeit noisy) in their normal course of business.

The first condition is relatively general and applicable to many domains. In situations in which alternative means may produce relatively precise information (e.g., using physical experiments to test the viability of the technologies in science-based industries), the added value of such an information-aggregation approach may be limited. But the commercial viability of startups in domains such as consumer technologies or enterprise software, or even of science-based startups, often remains highly uncertain before investors make costly investments. For example, using internal data from a large, successful venture capital firm, Kerr et al. (2014) find that the correlation between the partners’ ratings of every deal at the time of the first investment and the startup’s ultimate performance was 0.1, suggesting that even successful professionals struggle to distinguish the most promising startups at the earliest stages of investment.

Condition two comes from the simple fact that people, especially successful industry insiders whose opinions are likely to be seen as credible, are busy. Thus, to gather a sizable number of experts in a structured evaluation process would be costly, and, importantly, hard to scale. Moreover, the further disclosure of ideas may be undesirable, adding to the cost of inviting a large number of evaluators. A key insight of the Black List, in our view, is that it utilizes judgment that has already been produced in the marketplace. Thus, soliciting such judgments by requesting simple nominations can lower participation costs while requiring no signiﬁcant additional exposure of ideas.

Finally, condition three—requiring the setting to have ideas in circulation upon which judgments are formed—is also relatively general. To obtain ﬁnancing, as well as business and technical advice, startup ideas are often circulated among various types of experts, including mentors at incubators and accelerators, judges in idea competitions, consultants, angel investors and executives at early-stage VC ﬁrms, crowdfunding platforms, and even social media. Similarly, within large corporations, product and project ideas may also circulate in decentralized ways and encounter a nontrivial number of people through formal

31 and informal processes. In such settings, good ideas that have been ﬂoated outside the formal funnel may get picked up via a wider survey like the Black List; and disparate judgments may be aggregated to provide valuable additional input for the few people on the project evaluation team.

In light of the above discussion, one can picture running a Black-List-like survey among a large number of participants in the venture investment community, asking them to nominate up to ten of their favorite startups that they have encountered in a given year but have not yet closed—for example, their series A round.18 As industry sources pointed out to us, an important motivation for many to nominate scripts for the Black List is to see high-quality scripts produced. Similarly, investors who do not have signiﬁcant ﬁnancial stakes—including mentors, domain experts, or those who might have passed on startups because they were poor matches or below their minimum scale—might participate in a

Black-List-style survey simply because they want to see good venture ideas advance. Yet a key concern is that voters with ﬁnancial stakes may have strategic incentives to inﬂuence votes; for example, early investors may want to promote their investments to follow-on investors to ensure additional funding and higher valuations. But similar concerns are also raised about the Black List: producers and agents may vote for their own projects and encourage friends to support their favorites. In fact, the opportunity to promote projects is, in itself, a potentially powerful incentive to participate in such surveys. How strong these strategic incentives are and how much they may threaten the usefulness of the aggregated signals are likely to be context-dependent and must be taken seriously. In our setting, at least, the List seems to able to further differentiate similar ideas despite these strategic incentives.

To a great extent, aggregation websites such as Crunchbase and AngelList are doing something similar by ranking startups based on the number and the identity of early investors and the popularity of the startups (as measured by the number of recent news articles and web searches). Thus, in a sense, these websites rank startups for potential new investors based on the “votes” of previous ones. Producthunt.com, a website backed by Y Combinator and now acquired by AngelList, is another interesting example.

Early-adopters share new products on the website, and those with the most votes rise to the top of a daily list. Cao (2019) ﬁnds a signiﬁcant relationship between rankings based on these votes on Producthunt.com

18Though we are not aware of such an institution, that does not mean that it would not be valuable. Leonard stated in his TED talk that when his ﬁrst version of the Black List caught people’s attention, he was initially afraid that he had made a terrible mistake by circumventing the conventional processes and was hesitant to repeat the survey. He was subsequently reassured six months later when an agent pitching a script to him said that “he was sure that the script would be on next year’s Black List.”

32 and follow-on ﬁnancing.

Finally, as discussed extensively in prior sections, our results suggest that visibility is a systematic determinant of votes and, in our context, seems to be wider for less-experienced providers who are also associated with a large intermediary. We emphasize that how visibility correlates with the characteristics of idea providers is, in general, an empirical question and should be understood in context. That said, recall that the simple model presented in Appendix A.1 leads to a wider choice of sales scope by lesser-known sellers under fairly intuitive assumptions. This suggests that similar dynamics could be an important force in other contexts, albeit not necessarily as dominant as in this context. For example, startups with lesser-known founders may have to enroll themselves in multiple accelerators or venture competitions or pursue a greater number of venture investors (including angel investors and executives in VC ﬁrms), with each investing a smaller amount of money than startups with better-known founders are likely to get. Thus, it seems plausible that the total count of individuals encountering (good) ideas from less-known founders may be greater and, potentially, better-represented in the voter pool with a Black-List type of aggregation approach.

1.5 Conclusion

In this paper, we study a novel, low-cost approach to solicit and aggregate opinions from a large number of industry experts on ideas that they encounter in their normal course of business. Our empirical context is the movie industry, in which investment costs are high and consumer tastes are difﬁcult to predict. The

Black List, an annual publication, ranks unproduced scripts based on the number of anonymous nominations they receive from ﬁlm executives. There are trade-offs to such an approach: on the one hand, it requires a minimal time commitment that leads to a high participation rate by knowledgable but busy voters; on the other hand, the unstructured process and lack of standard criteria, as well as the fact that one cannot observe which voters see which ideas, are risks that may reduce the power of information aggregation and make the aggregated votes hard to interpret.

We find that, despite the above challenges, the Black List helps to further differentiate quality among observably similar ideas. This finding is important, as it suggests that such methods may aid evaluation in settings that share characteristics with the film industry, as discussed in the previous section. One potential

33 limitation, revealed by our results on writer experience, is that the visibility of ideas among voters is an important determinant of votes, which may be inﬂuenced by various factors or frictions and not be easily observable. Determinants of visibility, and their consequences, are likely to be context-dependent.

Although potential limitations of this feature may be mitigated by improving the process, structure, or transparency of the aggregation methods, one needs to be mindful of the tradeoffs implied by the necessity to keep participation time- and cost-effective.

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36 1.7 Tables and Figures

Figure 1.1: Probability of Theatrical Release by Black List Nominations

(a) Sample DDP (b) Sample BL .6 .6 .5 .5 .4 .4 .3 .3 .2 .2 Probability of theatrical release of theatrical Probability release of theatrical Probability .1 .1 0 0 Unlisted Scripts Black-Listed Scripts Threshold Top 21-above threshold Top 6-Top 20 Top 5 Scripts

Note: Panel (a) uses Sample DDP and compares theatrical release probabilities for listed and unlisted scripts. Panel (b) uses Sample BL and compares theatrical release probabilities by Black List Rank Group. The ﬁgures plot the 95-percent conﬁdence intervals.

Figure 1.2: Worldwide Box Ofﬁce Revenues by Black List Nominations

(a) Sample DDP (b) Sample BL .3 .3 .2 .2 Density Density .1 .1 0 0 5 10 15 20 5 10 15 20 25 log(U.S. box ofﬁce) log(Worldwide box ofﬁce)

Unlisted Top 5 Top 6-20 Black-Listed Top 21-above threshold Threshold

Note: Panel (a) uses Sample DDP and plots the density distribution of log(Worldwide Box Ofﬁce) for released movies by whether or not the script is Black-Listed, and Panel (b) plots the same distribution for Sample BL by Black List Rank Group.

37 Figure 1.3: Probability of Being Black-Listed (or Top-20 if Listed) by Writer Experience

(a) Sample DDP (b) Sample BL .35 .35 .3 .3 .25 .25 .2 .2 .15 .15 Probability of Probability Top-20 .1 .1 Probability of Black-Listed Probability .05 .05 0 0 Less-experienced writers Experienced writers Less-experienced writers Experienced writers

Note: Panel (a) uses Sample DDP and compares the likelihood of being Black-Listed by writer experience. Panel (b) uses Sample BL and compares the likelihood of a top-20 Black List ranking by writer experience. Experienced writers are those with one or more major writing credits in the previous ten years; less-experienced writers are those with zero major writing credits in the previous ten years. The ﬁgures plot the 95-percent conﬁdence intervals.

Figure 1.4: Probability of Theatrical Release by Black List Nominations and Writer Experience

(a) Sample DDP (b) Sample BL 1 1 .8 .8 .6 .6 .4 .4 Probability of theatrical release of theatrical Probability release of theatrical Probability .2 .2 0 0 Unlisted Scripts Black-Listed Scripts Threshold Top 21-above threshold Top 6-Top 20 Top 5 Scripts

Less-experienced writers Experienced writers Less-experienced writers Experienced writers

Note: Panel (a) uses Sample DDP and compares theatrical release probabilities for listed and unlisted scripts and by writer experience. Panel (b) uses Sample BL and compares theatrical release probabilities by Black List Rank Group and by writer experience. Experienced writers are those with one or more major writing credits in the previous ten years; less-experienced writers are those with zero major writing credits in the previous ten years. The figures plot the 95-percent confidence intervals. Note that in Panel (a), the difference in the slopes by writer experience is statistically significant at the one-percent level.

38 Table 1.1: Summary Statistics

Sample BL . Sample DDP N Mean S.D. Median N Mean S.D. Median Black List Rank Group 701 2.91 0.79 3 Nomination Ratio 701 0.05 0.04 0.03 Black-Listed 1566 0.11 0.32 0 Theatrically Released 701 0.26 0.44 0 1566 0.16 0.36 0 Writer Major Credits 701 0.36 0.93 0 1566 0.62 1.12 0 Experienced Writer 701 0.19 0.39 0 1566 0.32 0.47 0 Top-10 Agency 701 0.91 0.28 1 1566 0.54 0.50 1 Experienced Agent 701 0.34 0.48 0 1566 0.16 0.37 0 Sold Prior to Publication 701 0.79 0.41 1 Producers Major Credits 701 5.11 6.21 3 1566 5.72 6.13 4 Movie Studio Buyer 701 0.37 0.48 0 1566 0.37 0.48 0 Whether Reported 701 0.09 0.28 0 1566 0.11 0.32 0 Original 1566 0.58 0.49 1 Bidding 1566 0.03 0.16 0 Talent Attached 1566 0.57 0.50 1

Released movies Worldwide Box Ofﬁce ($m) 184 89.20 121.08 40.78 243 102.81 192.79 24.66 Budget ($m) 152 33.22 34.67 23.04 163 45.59 46.94 27.21 Star Score 184 103.11 111.35 85 243 67.73 100.01 0 Open Screen 184 1654.39 1421.06 2174 243 1648.72 1534.04 2027 Award-Winning Cast 184 0.63 0.49 1 243 0.56 0.50 1 Seasonality 184 0.70 0.15 0.70 243 0.70 0.14 0.70 Franchise 184 0.07 0.26 0 243 0.12 0.33 0

Note: The left panel provides summary statistics for Sample BL; the right panel provides the summary statistics for Sample DDP. Section 1.3.2 provides a description of the variables. Dummy variables for genre and content source materials are not summarized in the table.

39 Table 1.2: Black List Nominations and Release Probability

Sample DDP Sample BL Exclude Top-20 Release Release Release Release Release Release (1) (2) (3) (4) (5) (6) Black-Listed 0.135∗∗ 0.120∗∗ 0.109∗∗ (0.047) (0.039) (0.042)

Top-5 0.295∗∗∗ 0.280∗∗∗ (0.055) (0.048)

Top 6-20 0.090 0.093 (0.072) (0.058)

Top 21-Above Threshold 0.065 0.060 (0.043) (0.044)

Nomination Ratio 1.536∗∗∗ (0.006)

Writer Major Credits 0.039 0.034 0.036 0.083∗∗∗ 0.086∗∗∗ 0.088∗∗∗ (0.022) (0.022) (0.023) (0.008) (0.010) (0.013)

Top 10 Agency 0.055∗∗ 0.060∗∗∗ 0.063∗∗∗ -0.006 0.014 0.014 (0.018) (0.016) (0.016) (0.018) (0.020) (0.018)

Experienced Agent -0.031 -0.020 -0.017 -0.033 -0.032 -0.026 (0.024) (0.023) (0.020) (0.025) (0.035) (0.034)

Producer Major Credits 0.006∗ 0.005 0.005 0.005 0.004 0.004 (0.003) (0.004) (0.003) (0.004) (0.005) (0.006)

Original 0.040 -0.112 0.031 (0.029) (0.086) (0.028)

Talent Attached 0.099∗∗∗ 0.098∗∗∗ 0.098∗∗∗ (0.018) (0.021) (0.025)

Bidding 0.039 0.016 -0.015 (0.076) (0.083) (0.104)

Whether Reported 0.045∗ 0.061∗∗ 0.061∗ -0.026 -0.090 -0.078∗ (0.020) (0.021) (0.029) (0.021) (0.057) (0.036)

Sold Prior to Publication 0.134∗∗∗ 0.140∗∗∗ 0.136∗∗ (0.023) (0.033) (0.045)

Genre FE Y Y Y Content Source FE Y Y Y Studio FE Y N N Y N N Year FE Y N N Y N N Studio×year FE N Y Y N Y Y

N 1565 1537 1473 701 659 659 R-squared 0.113 0.163 0.162 0.213 0.268 0.272 Mean(DV|Baseline) 0.143 0.141 0.142 0.205 0.190 0.190

Note: OLS regressions. The dependent variable is whether the script is theatrically released. Columns 1-3 use Sample DDP, with Column 3 excluding top-20 listed scripts. Columns 4-6 use Sample BL; the excluded group in Columns 4-5 is scripts receiving the threshold number of nominations, and Column 6 uses the ratio of nominations received by the script divided by the total number of respondents in the year. The baseline group for Columns 1-3 consists of unlisted scripts; and the baseline group for Columns 4-6 consists of scripts receiving threshold nominations. Standard errors (in parentheses) in all columns are clustered in two dimensions: year and studio. * p < 0.10, ** p < 0.05, *** p < 0.01.

40 Table 1.3: Box Ofﬁce Performance and Production Budget

(a) Sample DDP OLS regressions Quantile regressions 25th 50th 75th log(Box Ofﬁce) log(Budget) log(ROI) ROI ROI ROI (1) (2) (3) (4) (5) (6) Black-Listed 1.196** -0.160 1.275*** 0.529** 0.903* 1.961*** (0.389) (0.296) (0.192) (0.210) (0.522) (0.415)

log(Budget) 0.106 0.016 0.224 0.023 (0.127) (0.056) (0.264) (0.212)

Other controls Y Y Y Y Y Y Other investment controls N N Y Y Y Y Release Year FE Y Y Y Y Y Y Studio FE Y Y Y Y Y Y

N 239 158 153 163 163 163 R-squared 0.580 0.590 0.469 0.446 0.490 0.493

(b) Sample BL OLS regressions Quantile regressions 25th 50th 75th log(Box Ofﬁce) log(Budget) log(ROI) ROI ROI ROI (1) (2) (3) (4) (5) (6) Top 5 0.577 0.594 0.178 0.895* 0.310 0.338 (0.525) (0.399) (0.266) (0.468) (0.378) (1.082)

Top 6-20 0.515 0.010 0.440 0.200 0.093 0.660 (0.440) (0.258) (0.598) (0.500) (0.824) (1.501)

Top 21-Above Threshold 0.098 0.008 0.273 0.439 0.719* 0.616 (0.383) (0.327) (0.264) (0.374) (0.379) (0.408)

log(Budget) -0.220*** -0.297 -0.333* -0.658*** (0.046) (0.187) (0.170) (0.171)

Other controls Y Y Y Y Y Y Other investment controls N N Y Y Y Y Release Year FE Y Y Y Y Y Y Studio FE Y Y Y Y Y Y

N 177 143 143 152 152 152 R-squared 0.440 0.451 0.293 0.156 0.321 0.310

Note: Panel (a) uses released movies for Sample DDP, and Panel (b) uses released movies for Sample BL. Columns 2-6 in both panels use released movies for which production budget information is available. ROI is deﬁned as the ratio between worldwide box ofﬁce revenues and production budget. In Panel (b), the excluded group includes scripts receiving the threshold number of nominations. Other controls for panel (b) include Writer Major Credits; Top-10 Agency; Experienced Agent; Producer Major Credits; Whether Reported; Sold prior to Publication; Franchise; and Genre dummies. Other controls for panel (a) are the same as panel (b) plus Original, Talent Attached, and Bidding. Other investment controls include Star Score; Award-Winning Cast; Open Screen; and Seasonality. For both panels, standard errors in columns 1-3 are clustered in two dimensions: year and studio; and those in columns 4-6 (quantile regressions) are clustered by studio. * p < 0.10, ** p < 0.05, *** p < 0.01.

41 Table 1.4: Black List Nominations and Writer Experience

Sample DDP Sample BL Black-Listed Black-Listed Top-5 Top-20 Nomination Ratio (1) (2) (3) (4) (5) Experienced Writer -0.083** -0.087** -0.052* -0.125** -0.012*** (0.025) (0.027) (0.027) (0.037) (0.002)

Top-10 Agency 0.076** 0.074* 0.035*** 0.101* 0.009* (0.030) (0.032) (0.007) (0.048) (0.004)

Experienced Agent 0.097** 0.092** -0.007 -0.006 -0.002 (0.038) (0.036) (0.015) (0.030) (0.003)

Producer Major Credits 0.003 0.003 0.001 0.002 0.000 (0.002) (0.002) (0.002) (0.002) (0.001)

Whether Reported 0.096*** 0.094*** 0.090** 0.084 0.009 (0.026) (0.026) (0.037) (0.079) (0.007)

Original 0.108* 0.114* (0.047) (0.051)

Talent Attached -0.023 -0.02 (0.018) (0.018)

Bidding 0.236*** 0.226*** (0.052) (0.059)

Movie Studio Buyer 0.043 (0.024)

Sold Prior to Publication 0.008 0.105*** 0.009** (0.012) (0.023) (0.003)

Genre FE Y Y Content Source FE Y Y Year FE Y Y Y Y Y Studio FE N Y Y Y Y

N 1566 1565 700 700 700 R-squared 0.168 0.186 0.063 0.072 0.102 Mean(DV|Exp writer =0) 0.135 0.135 0.067 0.276 0.048

Note: OLS (linear probability) regressions. Columns 1-2 use Sample DDP; Columns 3-5 use Sample BL. The dependent variable in Columns 1 and 2 is an indicator of whether the script is Black-Listed. The dependent variables of Column 3 and 4 are, respectively, indicators of whether the script achieves top-5 or top-20 Black List ranking in the survey year; the dependent variable in Column 5 is the ratio of nominations received by the script divided by the total number of respondents in the year. Experienced writers are deﬁned as those with one or more major writing credits in the previous ten years. Standard errors (in parentheses) are clustered in two dimensions: year and studio. * p < 0.10, ** p < 0.05, *** p < 0.01.

42 Table 1.5: Black List Nominations and Release Probability, by Writer Experience

Sample DDP Sample BL Less-Experienced Writers Experienced Writers Release Release Release Release (1) (2) (3) (4) Black-Listed 0.088** 0.223* (0.037) (0.116)

Experienced Writer 0.178** 0.190*** (0.056) (0.049)

Top 20 × Experienced Writer 0.145 0.093 (0.122) (0.112)

Studio FE NY Year×Nominations FE Y Y

N 1023 459 611 608 R-squared 0.145 0.233 0.318 0.358 Mean(DV|Baseline) 0.188 0.438 0.178 0.178

Note: Columns 1 and 2 use, respectively, the subset of scripts from less-experienced writers and the subset by experienced writers in Sample DDP, and Columns 3-4 use Sample BL. The dependent variable in all columns is an indicator of whether the movie is theatrically released. Columns 1-2 include the same set of controls as in Column 2 in Table 1.2, including studio×year fixed effects; Columns 3-4 include the same set of controls as in Column 4 in Table 1.2, except for year fixed effects, as they are absorbed by Year×Nominations fixed effects. The baseline group for Columns 1 and 2 is unlisted scripts; and that for Columns 3 and 4 are less-experienced writers. Standard errors are clustered in two dimensions: year and studio. * p < 0.10, ** p < 0.05, *** p < 0.01.

43 Table 1.6: Black List Nominations and Writer Experience: Subsample by Agency Size and Agent Experi- ence

Top-10 Agency = 0 Top-10 Agency = 1 Experienced Agent = 0 Experienced Agent = 1 (1) (2) (3) Panel 1: Sample DDP Black-Listed Black-Listed Black-Listed Experienced Writer -0.019 -0.086* -0.219** (0.017) (0.041) (0.078)

N 707 601 244 R-squared 0.284 0.128 0.401 Mean (DV| Exp writer = 0) 0.09 0.22 0.45

Panel 2: Sample BL Top 20 Top 20 Top 20 Experienced Writer -0.073 -0.092 -0.165** (0.173) (0.069) (0.059)

N 62 401 224 R-squared 0.176 0.094 0.069 Mean (DV| Exp writer = 0) 1.81 2.12 2.26

Note: Panel (a) uses Sample DDP. The dependent variable indicates whether the script is Black-Listed. All regressions include the same controls as in Column 2 in Table 1.4. Panel (b) conducts the same analysis as in Panel (a), using Sample BL. The dependent variable indicates top-20 ranked scripts on the Black List. All regressions include the same controls as in Column 3 in Table 1.4. Standard errors (in parentheses) are clustered in two dimensions: year and studio. * p < 0.10, ** p < 0.05, *** p < 0.01.

44 Chapter 1 Appendix

45 Appendix A: Additional Details for the Conceptual Framework A.1 A Micro-foundation of Assumption 2 In this section, we describe a very simple writer’s maximization problem that provides one way to micro-found the assumption that the breadth of circulation may be greater for less-experienced writers. Consistent with the model setup in the paper, the inherent quality of a movie script is either high or low: q ∈ {qH ,qL}. Under Assumption 1, the prior probability of having a high-quality script is greater for experienced writers than for less-experienced writers; that is, π0(w) > 0. A potential buyer, having read a given script, obtains a binary quality signal s ∈ {sH ,sL}, such that P(sH |qH ) = P(sL|qL) = p, and

P(sL|qH ) = P(sH |qL) = 1 − p. Assume that p > 1/2; that is, signals are (positively) informative because they are more likely to reﬂect the true script quality; also assume that buyers’ signals are conditionally independent given the true script quality. The writer’s problem is to choose the number of buyers, n, to whom he/she will simultaneously market a script to in order to maximize the expected payoff. Assume that the writer will make a sale if at least one out of the n buyers gets a good signal, sH . The sale probability is, thus, n n (π(w)(1 − (1 − p) ) + (1 − (π(w))(1 − p )). In each state of the world (i.e., the true quality being either qH or qL), the probability that at least one out of n buyers draws a good signal is one minus the probability that n n all n buyers draw bad signals. This probability is (1 − (1 − p) ) when q = qH and (1 − p ) when q = qL. The probability of a sale, thus, weights the two states by their respective prior probabilities. For tractability, we assume that the writer captures a ﬁxed share (α) of qH if a script is sold. Finally, marketing to an additional buyer is costly; and the marginal cost is c. As discussed in the paper, apart from physical and time costs, industry accounts also suggest that sellers have incentives to avoid an unnecessarily wide sales scope due to concerns of information leakage.19 Given the above setup, the writer maximizes the following expected payoff:

n n maxn[π(w)(1 − (1 − p) ) + (1 − π(w))(1 − p )]αqH − nc. (1.6)

The ﬁrst derivative of the writer’s expected payoff in equation (1.6) with respect to n is

n n F(n;w) = [−π(w)(1 − p) ln(1 − p) − (1 − π(w))p ln p]αqH − c.

For a writer of experience level w, the optimal number of buyers to approach is determined by the 1 ﬁrst-order condition F(n;w) = 0. Note that when 2 < p < 1, the sign of the sum of the two terms in the brackets is positive.

19Our assumptions greatly simplify the analysis. For example, the writer’s share of the surplus does not vary by the number of buyers he/she decides to approach, n, or the writer’s experience level, w; writers face the same opportunity costs of time; and buyers are also assumed to be homogenous. Thus, the only difference writers of different experience levels face in this model is the likelihood of making a sale.

46 Notice that the second derivative of the expected payoff with respect to n is

∂F(n;w) = [−π(w)(1 − p)n(ln(1 − p))2 − (1 − π(w))pn(ln p)2]αq < 0. (1.7) ∂n H

Therefore, for a given w, there exists a unique n∗ that satisﬁes the ﬁrst-order condition of F(n∗;w) = 0. Denote n(w) as the function of the optimal number of buyers to market to for a writer of experience level w. We are interested in deriving the comparative statics of how n(w) varies with w. By the implicit function theorem, ∂F(n;w) n0(w) = − ∂w . ∂F(n;w) ∂n Equation (1.7) shows that the denominator in the above equation is negative. The derivative of F(n;w) with respect to w is:

∂F(n;w) ∂F(n;w) = π0(w) = (−(1 − p)n ln(1 − p) + pn ln p)αq π0(w) (1.8) ∂w ∂π(w) H

Under Assumption 1, π0(w) > 0. Thus, the sign of n0(w) is consistent with the sign of −(1 − p)n ln(1 − p) + pn ln p, which is negative if and only if the following condition holds:

−(1 − p)n ln(1 − p) + pn ln p < 0 ⇔ pn ln p < (1 − p)n ln(1 − p) p n ln(1−p) ⇔ ( 1−p ) > ln p p ln(1−p) ⇔ nln( 1−p ) > ln( ln p ) ln(1−p) p ⇔ n > ln( ln p )/ln( 1−p ).

p Note that we can preserve the direction of the inequality sign in the fourth line because ln( 1−p ) > 0 when 1 1 ln(1−p) p p > 2 . Numerical simulation shows that when 2 < p < 1, ln( ln p )/ln( 1−p ) < 1.5. Thus, as long as the optimal number of buyers to market to is greater than or equal to two, we have n0(w) < 0. In summary, the above model provides a simple, yet intuitive, way to derive the breadth of circulation of a script as the writer’s endogenous choice as he/she attempts to sell the script. We show that the optimal number of buyers to market to decreases with the writer’s experience level under plausible conditions. This result, thus, provides a micro-founded support for Assumption 2. It is also quite intuitive: less-experienced writers want to approach more buyers, even though it is more costly, because chances of any individual buyers receiving a positive signal about their scripts are lower.

47 A.2 Proofs of the Propositions Before presenting the proofs of the propositions, ﬁrst recall the basic setup of the model. The inherent quality of a movie script is either high or low: q ∈ {qH ,qL}. A voter, having read a given script, obtains a binary quality signal s ∈ {sH ,sL}, such that P(sH |qH ) = P(sL|qL) = p, and P(sL|qH ) = P(sH |qL) = 1 − p. p > 1/2; that is, signals are (positively) informative. Moreover, any two voters’ signals are conditionally independent given the true script quality. A voter will nominate a script as long as a positive signal (sH ) is received. For a script from a writer of experience level w, the prior probability of high quality is π(w), and the number of Black List voters that have read the script is n(w). According to Assumptions 1 and 2, π0(w) > 0 and n0(w) ≤ 0. Given this set up, the expected number of nominations for a script from a writer of experience level w is (that is, equation (1.1)):

E[m|w] = π(w)n(w)p + (1 − π(w))n(w)(1 − p).

Based on Bayes’ rule, for a script that has received m nominations and is from writer of experience level w, the updated belief about the probability that it is of high quality is (that is, equation (1.2)):

π(w)P(m|w,qH ) P(qH |m,w) = . π(w)P(m|w,qH ) + (1 − π(w))P(m|w,qL)

Note that the denominator in the above equation—the probability that a script from writer of experience level w receives m nominations—can be written as:

π(w)P(m|w,qH ) + (1 − π(w))P(m|w,qL) n(w) m (n(w)−m) n(w) m (n(w)−m) = π(w) m p (1 − p) + (1 − π(w)) m (1 − p) p .

n(w) In words, when the true script quality is qH , there are m ways to have m voters receive positive signals m (n(w)−m) n(w) sH , each with a probability of p (1 − p) . When the true script quality is qL, there are also m ways to have m voters receive positive signals, each with a probability of (1 − p)m p(n(w)−m). We weight these two states by their respective prior probabilities, π(w) and (1 − π(w)). Thus, P(qH |m,w) can be rewritten as:

π(w)pm(1 − p)(n(w)−m) P(qH |m,w) = , (1.9) π(w)pm(1 − p)(n(w)−m) + (1 − π(w))(1 − p)m p(n(w)−m)

n(w) where m is canceled out from all terms.

Proof of Proposition 1

Proof. We need to show that P(qH |m,w) is an increasing function of m, holding w ﬁxed. Taking the

48 derivative of P(qH |m,w) (in equation (1.9)) with respect to m yields:

∂P(qH |m,w) ∂m

m (n(w)−m) m (n(w)−m) ∂(π(w)p (1−p) ) (1 − π(w))(1 − p)m p(n(w)−m) − π(w)pm(1 − p)(n(w)−m) ∂((1−π(w))(1−p) p ) = ∂m ∂m [π(w)pm(1 − p)(n(w)−m) + (1 − π(w))(1 − p)m p(n(w)−m)]2 π(w)(1 − π(w))pn(w)(1 − p)n(w)2(ln p − ln(1 − p)) = > 0 [π(w)pm(1 − p)(n(w)−m) + (1 − π(w))(1 − p)m p(n(w)−m)]2

1 because ln p > ln(1 − p) when p > 2 .

Proof of Proposition 2

Proof. As is illustrated in equation (1.3), we need to show that E[m|w] is an increasing function of π(w) and also an increasing function of n(w). First,

∂E[m|w] = n(w)p − n(w)(1 − p) > 0, ∂π(w)

1 because p > 2 . Second, ∂E[m|w] = π(w)p + (1 − π(w))(1 − p) > 0. ∂n(w)

Proof of Proposition 3

Proof. As is illustrated in equation (1.4), we need to show that P(qH |m,w) is an increasing function of π(w) and also a decreasing function of n(w).

Take the derivative of P(qH |m,w) with respect to π(w), we get:

∂P(qH |m,w) ∂π(w) m (n(w)−m) m (n(w)−m) ∂(π(w)p (1−p) ) (1 − π(w))(1 − p)m p(n(w)−m) − π(w)pm(1 − p)(n(w)−m) ∂((1−π(w))(1−p) p ) = ∂π(w) ∂π(w) [π(w)pm(1 − p)(n(w)−m) + (1 − π(w))(1 − p)m p(n(w)−m)]2 pn(w)(1 − p)n(w) = > 0. [π(w)pm(1 − p)(n(w)−m) + (1 − π(w))(1 − p)m p(n(w)−m)]2 (1.10)

49 The derivative of P(qH |m,w) with respect to n(w) is:

∂P(qH |m,w) ∂n(w) m (n(w)−m) m (n(w)−m) ∂(π(w)p (1−p) ) (1 − π(w))(1 − p)m p(n(w)−m) − π(w)pm(1 − p)(n(w)−m) ∂((1−π(w))(1−p) p ) = ∂n(w) ∂n(w) [π(w)pm(1 − p)(n(w)−m) + (1 − π(w))(1 − p)m p(n(w)−m)]2 π(w)(1 − π(w))pn(w)(1 − p)n(w)(ln(1 − p) − ln p) = < 0, [π(w)pm(1 − p)(n(w)−m) + (1 − π(w))(1 − p)m p(n(w)−m)]2 (1.11) 1 because ln(1 − p) < ln p when p > 2 .

Proof of Proposition 4 ∂P(q |m,w) Proof. Taking the derivative of H with respect to m, we get ∂w

For the ﬁrst part of Proposition 4—when scripts from writers of different experience levels are read by the same number of Black List voters (i.e., n0(w) = 0)—we consider only the ﬁrst term in the above 2 0 ∂ P(qH |m,w) equation. π (w) > 0 under Assumption 1. From equation (1.10), we know that ∂π(w)∂m > 0 if the 2 ∂ P(qH |m,w) denominator of equation (1.10) decreases with m, and ∂π(w)∂m < 0 if the denominator increases with m. Note that this denominator increases with m if and only if π(w)pm(1 − p)(n(w)−m) − (1 − π(w))(1 − p)m p(n(w)−m) ≥ 0. Thus, we have the following condition:

∂2P(q |m,w) H ≤ 0 ⇔ π(w)pm(1 − p)(n(w)−m) ≥ (1 − π(w))(1 − p)m p(n(w)−m) ∂w∂m 1−p n(w)−2m 1−π(w) ⇔ ( p ) ≥ π(w) 1−p 1−π(w) ⇔ (n(w) − 2m)ln( p ) ≥ ln π(w) 1−π(w) ln π(w) ⇔ (n(w) − 2m) ≤ 1−p ln( p ) 1 (lnπ(w)−ln(1−π(w))) ⇔ m ≥ 2 (n(w) − (ln p−ln(1−p)) ).

1−p 1 The inequality sign in the fourth line reverses because ln( p ) < 0 when p > 2 . ∗ 1 (lnπ(w)−ln(1−π(w))) Letting m = 2 (n(w) − (ln p−ln(1−p)) ), we have the result for the ﬁrst part of Proposition 4:

50 ∂2P(q |m,w) ∂2P(q |m,w) when n0(w) = 0, H > 0 when m < m∗; and H < 0 when m > m∗. Furthermore, note ∂w∂m ∂w∂m ∗ ∗ n that m is an increasing function of n and a decreasing function of π. In particular, m = 2 when π = 0.5; ∗ n ∗ n m < 2 when π > 0.5; and m > 2 when π < 0.5. Now consider the second part of Proposition 4—when scripts from less-experienced writers are read by more Black List voters (i.e., n0(w) < 0). Based on equation (1.11), we can similarly show that 2 2 ∂ P(qH |m,w) ∗ 1 (lnπ(w)−ln(1−π(w))) ∂ P(qH |m,w) ∂n(w)∂m ≤ 0 ⇔ m ≥ m = 2 (n(w) − (ln p−ln(1−p)) ). Thus, qualitatively, we also have ∂w∂m > 0 2 ∗ ∂ P(qH |m,w) ∗ ∗ when m < m ; and ∂w∂m < 0 when m > m . But as stated above, m increases with n. Thus, a greater number of voters for less-experienced writers pushes the inflection point m∗ to be greater. The second part of Proposition 4 is much easier to understand with the help of a numerical example in which we use discrete values of writer experience, as well as their respective extents of script visibility, rather than considering only marginal changes around n(w). Figure 1.5 plots the updated beliefs about quality of scripts receiving a given number of nominations for experienced writers (indicated by the blue, plus signs) and less-experienced writers (indicated by the red, circle signs) separately. The figure also plots the gap in the updated beliefs between experienced and less-experienced writers (indicated by the yellow line). Figure 1.5a below presents a situation in which all writers have the same number of voters (20) who have read their scripts. Under the parameter values of this example, the gap by writer experience starts to decrease and converge to zero after 12. In Figure 1.5b, all parameters are of the same value, except that scripts from less-experienced writers are read by more Black List voters (25 instead of 20). In Figures 1.5c and 1.5d, we increase the number of voters who have read scripts from less-experienced writers to 30 and 40. As the graph shows, the inflection point m∗, after which the gap by writer experience starts to decline, gets increasingly larger as we increase the number of voters who have read scripts from less-experienced writers. In Figure 1.5d, for example, the gap continues to rise until when the number of nominations is about 18, after which it starts to decline.

51 Figure 1.5: Illustration of Proposition 4

(a) n = 20 for all writers (b) n = 25 for less-experienced writers

Note: The horizontal axis is the number of nominations, and the vertical axis is the updated belief about script quality P(qH |m,w). We plot two curves for experienced and less-experienced writers, as well as the gap between these two curves. The four ﬁgures use exactly the same parameter values, except for the number of voters who have read scripts from less-experienced writers. In particular, in (a), scripts from writers of different experience levels are read by the same number of voters (nexperienced = nless-experienced = 20). In (b), scripts from less-experienced writers are read by more people (nexperienced = 20, while nless-experienced = 25). In (c) and (d), we increased nless-experienced to 30 and 40, while keeping nless-experienced the same at 20. The other parameter values are: πexperienced = 0.3, πless-experienced = 0.1, p = 0.6.

52 Appendix B: Additional Results B.1 Samples Figure B1 provides a graphical illustration of the relationship between the two samples we use in the analysis. Consider the population of scripts that are up for sale (from the writers) or commissioned (by the producers). The solid, blue rectangle indicates Sample BL—it is a complete sample of scripts that are Black-Listed. These scripts may or may not be sold eventually (either before or after the List’s publication). The yellow, shaded rectangle indicates Sample DDP, which is a subset of the DDP database—indicated by the larger yellow-framed rectangle and itself a comprehensive but incomplete sample of all sold scripts

Figure B1: Graphical Illustration of the Relationship between Sample BL and Sample DDP

B.2 Black List Nominations and Release Outcomes Table B1 reports the marginal increase in R2 measures by including Black List variables for Table 2, the set of regressions that examines the predictiveness of Black List nominations for the probability of release. This table reproduces the results of Table 2; only Black List variables are presented, and the coefficients of other controls are omitted for ease of presentation. For each column, we reran the regression without the specific Black List variables used in this column. The bottom of the table displays both the level differences and the percent changes in two different R2 measures (unadjusted as well as adjusted by the number of predictors). The percent change is defined as (R2 measure with Black List variables - R2 measure without Black List variables)/R2 measure without BL variables. These comparisons reveal two results. First, the marginal increase in the regressions’ explanatory power is economically significant. Out of the twelve different comparisons, the percent change ranges from 3.2% to 16.0%, with the median being 8.9%. Second, these percent increases become larger when using adjusted R2 than when using unadjusted R2. Because the adjusted R2 penalizes the addition of new

53 variables, the fact that the relative increase is larger for the adjusted R2 suggests that the Black List variables provide disproportionately more explanatory power relative to the other predictors in the regressions.

Table B1: Comparisons of R-squared with and without Black List Variables for Table 2 “Black List Nomi- nations and Release Probability”

Sample DDP Sample BL Exclude Top-20 Release Release Release Release Release Release (1) (2) (3) (4) (5) (6) Black-Listed 0.135∗∗ 0.120∗∗ 0.109∗∗ (0.047) (0.039) (0.042)

Top-5 0.295∗∗∗ 0.280∗∗∗ (0.055) (0.048)

Top 6-20 0.090 0.093 (0.072) (0.058)

Top 21-Above Threshold 0.065 0.060 (0.043) (0.044)

Nomination Ratio 1.536∗∗∗ (0.006)

Other Controls Omitted

R-squared R-squared with BL variables 0.113 0.163 0.162 0.213 0.268 0.272 R-squared without BL variables 0.102 0.155 0.157 0.194 0.252 0.252 Difference 0.012 0.008 0.005 0.019 0.016 0.021 Percent change 11.3% 5.2% 3.2% 9.7% 6.5% 8.1%

Adjusted R-squared Adjusted R-squared with BL variables 0.082 0.084 0.081 0.168 0.189 0.196 Adjusted R-squared without BL variables 0.071 0.076 0.076 0.152 0.175 0.175 Difference 0.011 0.008 0.005 0.016 0.014 0.021 Percent change 16.0% 10.8% 6.3% 10.6% 7.9% 12.0%

Table B2 reports regression results analogous to those in Table 1.2 based on the coarsened exact matching (CEM) method. The regressions generate a matched sample of listed and unlisted scripts in Sample DDP that are similar to each other in pre-speciﬁed dimensions (Iacus et al., 2012). Variables used to create the matched sample are Writer Major Credits, Top-10 Agency, Experienced Agent, Producer Major Credits, Whether Studio Buyer, Original, Talent Attached, and Whether Reported. Column 2 excludes top-20 listed scripts. The CEM estimates, at about 13 to 15 percentage points, are statistically signiﬁcant and economically consistent with our baseline results reported in Table 1.2. The (unreported)

54 propensity score matching method produces an estimate of the average treatment effect at 9.8 percentage points (signiﬁcant at the ﬁve-percent level).

Table B2: Average Effects of Being Black-Listed on Release Probability (CEM)

Exclude Top-20 Release Release (1) (2) Black-Listed 0.150∗∗ 0.152∗∗ (0.051) (0.056)

Writer Major Credits 0.089∗ 0.087 (0.047) (0.062)

Top-10 Agency 0.015 0.036 (0.046) (0.060)

Experienced Agent -0.045 -0.014 (0.058) (0.047)

Producer Major Credits 0.001 0.000 (0.006) (0.007)

Original 0.101∗∗ 0.083 (0.034) (0.052)

Talent Attached 0.054 0.061 (0.045) (0.048)

Bidding -0.005 -0.057 (0.050) (0.074)

Whether Reported 0.021 0.016 (0.053) (0.063)

Genre FE Y Y Content Source FE Y Y Studio FE Y Y Year FE Y Y

N 425 287 R-squared 0.168 0.171

Note: Standard errors (in parentheses) are clustered in two dimensions: year and studio. * p < 0.10, ** p < 0.05, *** p < 0.01.

Figure B2 plots the density distributions of log(Production Budget). Panel (a) uses released movies in Sample DDP and plot log(Production Budget) by whether or not the movie is Black-Listed; and Panel (b) plots the same density for Sample BL by Black List Rank Group. As discussed in the paper, the raw data

55 show that the budget level is not statistically different between listed and unlisted scripts or by rank group.

Figure B2: Production Budget by Black List Nominations

(a) Sample DDP (b) Sample BL .4 .4 .3 .3 .2 Density .2 Density .1 .1 0

0 12 14 16 18 20 12 14 16 18 20 log(Production budget) log(Production budget) Top 5 Top 6-20 Unlisted Black-Listed Top 21-above threshold Threshold

Table B3 reports regression results in which the dependent variables are measures of investments other than production budget: Open Screen is the number of theaters screening the movie during the opening weekend; Star Score is an index generated by the-numbers.com, based on the total box ofﬁce revenues of the movies that an actor or actress has been in prior to the release year of the focal movie, averaged over all the leading cast of the movie; Award-Winning Cast is an indicator of whether any of the leading cast of the movie has won any award (Academy Award, Golden Globe, and BAFTA) prior to the release year of the focal movie; and following Basuroy et al. (2003), Seasonality is based on the total number of tickets sold for all movies shown during a given weekend, averaged across years. We use the box ofﬁce revenue data of all the movies from the-numbers.com between 2005 and 2017. Dividing the average number of tickets for the 52 weekends by the maximum sold (Christmas weekend), we generate a normalized index ranging between zero and one. As discussed in the paper, other measures of investments are also statistically similar between listed and unlisted scripts; and, for listed scripts, these measures are largely statistically similar across all rank groups.

56 Table B3: Other Measures of Investments

(a) Sample DDP Open Screen Star Score Award-Winning Cast Seasonality (1) (2) (3) (4) Black-Listed 47.661 5.775 0.043 0.044 (229.226) (29.280) (0.079) (0.035)

Other controls Y Y Y Y Release Year FE Y Y Y Y Studio FE Y Y Y Y

N 239 239 239 239 R-squared 0.619 0.312 0.153 0.234

(b) Sample BL Open Screen Star Score Award-Winning Cast Seasonality (1) (2) (3) (4) Top 5 -537.116 -18.019 0.110 0.011 (407.522) (34.322) (0.200) (0.048)

Top 6-20 -494.617 -64.982∗∗ 0.088 -0.021 (300.380) (24.775) (0.122) (0.029)

Top 21-Above Threshold -596.002∗ -17.841 0.162 0.005 (264.951) (27.679) (0.152) (0.037)

Other controls Y Y Y Y Release Year FE Y Y Y Y Studio FE Y Y Y Y

N 177 177 177 177 R-squared 0.493 0.240 0.245 0.326

Note: Panel (a) uses released movies in Sample DDP, and Panel (b) uses released movies in Sample BL. All regressions in Panel (a) include exactly the same controls as in Column 2 in Table 1.3a, and all regressions in Panel (b) include exactly the same controls as in Column 2 in Table 1.3b. For all regressions, standard errors are clustered in two dimensions: year and studio. * p < 0.10, ** p < 0.05, *** p < 0.01.

57 B.3 The Black List and Writers of Different Experience Levels Table B4 reports regression results analogous to those in Table 1.4 using different measures of writer’s experience levels. These regressions also show a signiﬁcant negative correlation between Black List nominations and writer experience. In both panels: Column 1 counts writing credits for movies distributed by top-30 distributors in the previous ten years (‘writer major credits’ deﬁned in Section 1.3.2, thus replicating Table 1.4’s results); Column 2 counts writing credits for movies distributed by top-15 distributors in the previous ten years; Column 3 counts all writing credits prior to survey (sale) year without time period or distributor size restrictions; Column 4 uses an indicator that the writer has one or more major writing credits (same as used in the paper) and includes indicators for writer other experience—e.g., any directing, producing, or acting credits for movies distributed by top-30 distributors in the previous ten years.

58 Table B4: Black List Nominations and Writer Experience: Alternative Experience Measures

(a) Sample DDP Black-Listed Black-Listed Black-Listed Black-Listed (1) (2) (3) (4) Writer’s Writing Experience -0.035** -0.031*** -0.005** -0.076** (0.011) (0.009) (0.002) (0.028)

Writer’s Directing Experience -0.030 (0.019)

Writer’s Producing Experience -0.003 (0.021)

Writer’s Acting Experience -0.027 (0.020)

N 1565 1565 1565 1565 R-squared 0.185 0.183 0.181 0.188

(b) Sample BL Top 20 Top 20 Top 20 Top 20 (1) (2) (3) (4) Writer’s Writing Experience -0.041** -0.040** -0.011** -0.093* (0.017) (0.015) (0.004) (0.041)

Writer’s Directing Experience -0.021 (0.040)

Writer’s Producing Experience -0.090** (0.036)

Writer’s Acting Experience -0.033 (0.065)

N 700 700 700 700 R-squared 0.068 0.067 0.066 0.075

Note: Panel (a) uses Sample DDP, and the dependent variable of all columns is an indicator of whether a script is Black-Listed. All regressions include the same control variables as in Column 2 in Table 1.4. Panel (b) uses Sample BL, with the dependent variable of all columns being an indicator for top-20 scripts. All regressions include the same control variables as in Column 5 in Table 1.4. Standard errors (in parentheses) are clustered in two dimensions: year and studio. * p < 0.10, ** p < 0.05, *** p < 0.01.

59 Table B5 provides a test for Assumption 1 (that is, the prior probability of being high-quality is greater for ideas from experienced writers. We also use this analysis to investigate the first alternative explanation we discuss in the paper for our results by writer experience (see Section 4.2.3). In particular, were Assumption 1 not true—that is, if scripts from less-experienced writers were actually more likely to be very good—scripts from less-experienced writers would be more likely to be Black-Listed and to rank higher if listed without having wider visibility. This explanation, together with risk aversion, would explain why, if listed, scripts from less- experienced writers are less likely to be released. To investigate this further, we use all sales records in the DDP database before 2005, the year in which the Black List was started. In Panel (a) of Table A4, the dependent variable of all columns is whether the script is theatrically released, and the default group includes writers with zero prior major experience. The results show a monotonic increasing relationship between release likelihood and writer experience, which is consistent with Assumption 1. Panel (b) uses movies that are released and for which budget information is available. Column 1 reports OLS regression results, which show that released movies from experienced writers generate either statistically similar or significantly higher box office revenues than writers with no prior major experience (the default group). Furthermore, quantile regressions (in Columns 2-5) also do not indicate a heavier right tail in the distribution of box office revenues for scripts from less-experienced writers. These results, collectively, provide supporting evidence for Assumption 1.

60 Table B5: Writer Experience and Release Outcomes (DDP database before 2005)

(a) Release Probability Release Release (1) (2) Writer Major Credits = 1 0.040 0.025 (0.024) (0.051)

Writer Major Credits = 2 0.139** 0.086* (0.063) (0.044)

Writer Major Credits = 3 0.206** 0.172** (0.093) (0.055)

Writer Major Credits ≥ 4 0.147* 0.149 (0.080) (0.085)

Other controls N Y Studio×year FE N Y Year FE Y N

N 1112 1095 R-squared 0.031 0.136

(b) Box Office Performance for Released Movies OLS Regressions Quantile Regressions 25th 50th 75th 90th log(Box Office) Box Office Box Office Box Office Box Office (1) (2) (3) (4) (5) log(Budget) 0.824*** 37.277** 43.712 67.558** 57.713*** (0.155) (18.727) (29.871) (26.397) (10.114)

Writer Major Credits = 1 0.146 27.192 62.232** 38.018 -3.707 (0.230) (131.344) (25.348) (87.878) (15.216)

Writer Major Credits = 2 -0.518 -17.476 84.220 81.846 186.484*** (0.689) (29.217) (59.655) (430.800) (46.667)

Writer Major Credits = 3 0.672** 74.349 80.002*** 48.688 229.417*** (0.279) (127.634) (30.772) (77.180) (31.332)

Writer Major Credits ≥ 4 0.288 -14.733 57.526 32.481 52.953 (0.210) (49.914) (84.900) (153.847) (36.933)

Other controls N Y Y Y Y Year FE Y Y Y Y Y Studio FE Y Y Y Y Y

N 111 115 115 115 115 R-squared 0.695 0.516 0.558 0.560 0.469

Note: Other controls include Top-10 Agency, Experienced Agent, Producer’s Major Credits, Original Idea, Talent Attached, Bidding, and Genre and Content Source ﬁxed effects. Standard errors (in parentheses) are clustered in two dimensions: year and studio. * p < 0.10, ** p < 0.05, *** p < 0.01.

61 In Table B5a, we investigate the second alternative explanation discussed in Section 4.2.3. Suppose that survey respondents nominate scripts that appeal to them for non-monetary reasons; for example, they may find scripts from less-experienced writers more novel or interesting, which will result in the Black List over-representing less- experienced writers. But these scripts may be less financially viable and, hence, less likely to be produced. This explanation seems to resonate with the following quote from an industry veteran: “[I]nnovative scripts by young writers tend to score high in the Black List, but these scripts are usually not properly ‘put together’ so when it is time to actually make a multi-million-dollar movie, there is preference for less innovative, more formulaic and better structured screenplays.”20 To investigate this explanation, we construct two types of novelty measures: one based on the genre combinations of the script and the other based on the script’s logline. The genre-based novelty measures follow the patent literature (Verhoeven et al., 2016). In particular, we create two dummy variables that indicate the least-used genre combinations accounting for ten or 25 percent of the scripts sold in a given year, with the frequency of a genre combination based on all scripts sold prior to that year. The logline-based measures follow the machine learning literature (Mikolov et al., 2013); see Appendix C.2.5 for a detailed explanation. Panel (a) in Table B6 shows that, consistent with what we would expect, Black-Listed scripts have a higher value of these measures than unlisted scripts and, for released movies, the correlations between these measures and the mean score of critic reviews are also all positive.21 Panel (b) in Appendix Table B6 shows that these measures are not significantly correlated with writer experience; the only correlation with a p-value is less than 0.10 between more-novel scripts and experienced writers. It is certainly possible that our measures are not precise enough, as novelty is a complex concept, but available data and measures used by prior work do not support the typical impression that scripts from less-experienced writers are systematically more novel.

20We thank an anonymous referee who provided us with this quote. 21Three out of the eight correlations are signiﬁcant at the ﬁve-percent or ten-percent level; and for another three, the p-value is less than 0.2. The critics’ ratings data are from metacritic.com, an aggregator that coverts movie reviews by major newspapers and magazines to a standardized score. The metacritic.com data are used in the prior literature, including Brown et al., (2013).

62 Table B6: Novelty Measures

(a) Correlations between Novelty Measures and Black-Listed and Mean Critic Reviews Logline-Based Novelty Measures Genre-Based Novelty Measures Not Weighted Weighted 10 Percent 25 Percent Black Listed 0.041 0.050 0.034 0.021 (p-value) (0.141) (0.074) (0.176) (0.402) N 1289 1289 1564 1564

Released Scripts Only Mean (critic reviews) 0.111 0.045 0.094 0.141 (p-value) (0.081) (0.481) (0.114) (0.017) N 248 248 287 287

(b) Correlations between Novelty Measures and Writer Experience Logline-Based Novelty Measures Genre-Based Novelty Measures Not weighted Weighted 10 Percent 25 Percent Experienced Writer 0.050 -0.010 0.020 0.031 (0.071) (0.713) (0.442) (0.228) 1289 1289 1564 1564

Black-Listed Scripts Only Experienced Writer -0.009 -0.002 0.012 0.037 (0.907) (0.977) (0.878) (0.626) 159 159 178 178

Note: Sample DDP. Panel (a) presents correlations between different novelty measures and whether a script is Black-Listed and the mean critical reviews for released movies. These measures are not available for all scripts in Sample DDP because we exclude observations without a logline or with a logline of fewer than six words. Panel (b) correlates these measures with writer experience, ﬁrst for all observations in Sample DDP and then for only Black-Listed scripts in this sample.

63 B.4 Black-Listed Scripts Unsold Prior to Publication Of the 145 Black-Listed scripts unsold at the time of publication, only eight (six percent) were eventually theatrically released. To further understand why so few eventually made it to the theater, we unpacked the low release rate into two components: (i) the probability of making a sale after being listed; and (ii) the probability of theatrical release conditional on being sold. To obtain a sale-rate estimate, we matched these unsold scripts to the entire DDP database. We matched 26 percent: a lower-bound estimate of the sale rate because DDP does not capture all sold scripts. By further assuming that DDP’s completeness in capturing all sold scripts is not systematically different for Black-Listed scripts that are sold and unsold before publication, we obtained a point estimate of a 40-percent sale rate.22 While not deﬁnitive, these estimates suggest that the Black List could have a sizable impact on garnering attention for unsold scripts. Using the point estimate above, we obtained a conditional release rate of 13.5 percent (8/59). This release rate (with notable small- sample caveats) is far below the (conditional) release rate for Black-Listed scripts that were already sold (32 percent) and is lower even than that for unlisted sold scripts from Sample DDP (16 percent). The above statistics indicate that unsold Black-Listed scripts have a low release rate not because they do not get sold. Instead, these scripts (once sold) have a release rate lower than scripts never listed. There are at least three possible reasons that this outcome could occur: (i) there may be a strong selection effect that results in these scripts having less-viable material than listed but sold scripts;23 (ii) relatedly, due to the fact that these scripts are not yet sold, the market may infer lower quality and, hence, hesitate to commit resources; and (iii) the Black List may carry momentum only for a short time, which suggests that, due to the time it takes for these scripts to sell, previous years’ Black List scripts may already be forgotten, thus making it difﬁcult to overcome the hurdles in the remainder of the production process. Unfortunately, the small sample size and data incompleteness restrict our ability to investigate these conjectures further.

Table B7: Sold versus Unsold Black-List Scripts

N Black List Experienced Top 10 Agent Released rank group writers agency experience Unsold 145 1.93 0.07 0.84 10.53 0.06 Sold 556 2.13 0.22 0.93 13.38 0.32 (p-value) (0.01) (0.00) (0.00) (0.02) (0.00)

Note: Raw data based on Sample BL. Sold is an indicator of whether a script is associated with a production company or studio at the time of the List’s publication.

22356 out of 556 (64 percent) of Black-Listed scripts that are sold before publication are captured by the DDP database. 23The data suggest some selection effects at least on observables. In particular, Table B7 shows that unsold scripts are ranked lower than sold scripts, are less likely to come from experienced writers, and are more likely to have representation by small agencies and less-experienced agents.

64 B.5 Voter Incentives Voters may have strategic incentives when they vote, and social stigma/reputation concerns may not be a sufficient deterrent due to the survey’s anonymity. For example, voters may want to keep “hidden gems” to themselves for scripts that they want to buy, or they may want to nominate their own scripts given the potentially large rewards from being listed. Without detailed data on who votes for which scripts, however, it is hard to tease apart these various incentives. That said, we examined the two incentives discussed above using the available data. First, among Black-Listed scripts, regression results controlling for nomination-year fixed effects (Column 1 in Table B8) show that sold scripts receive 2.60 more nominations than unsold scripts (p-value of 0.011). The result is similar when including other controls (Column 2 in Table B8). This result is consistent with the idea that voters may have incentives to keep unsold ‘hidden gems’ to themselves. However, the gap may also be due to other reasons that a given script is not sold yet—for example, it may have less-viable materials to start with (which is consistent with evidence presented in the next section on listed scripts that are not sold prior to publication). Second, we wanted to see whether scripts purchased by producers who are more likely to be eligible voters are systematically more likely to be listed and ranked higher than those purchased by producers who are less likely to be eligible voters. We defined producers as eligible voters if they had a major release (that is, a movie distributed by the top-30 studios) in the previous three years. This definition is likely to be a reasonable, though noisy, measure of the Black List’s criteria because we do not know exactly how it defines ‘major release.’ Because of potential confounding factors—e.g., larger and more-prominent producers are both more likely to be eligible voters and more likely to be matched to better scripts—we control for other factors to the extent that we can, including the producer’s experience in the previous ten years. Columns 3 and 4 in Table B8 show that, among Black-Listed scripts that are sold prior to publication, scripts associated with producers who are more likely to be eligible do not receive significantly more nominations than scripts associated with producers that are unlikely to be eligible to vote.

65 Table B8: Potential voter incentives

Dependent Variable Nominations Nominations Nominations Nominations (1) (2) (3) (4) Sold Prior to Publication 2.605∗∗ 1.984∗∗∗ (0.837) (0.611)

Eligible Voter 0.562 -0.983 (2.319) (1.345)

Other controls N Y N Y Year FE Y Y Y Y Studio FE N Y N Y

N 701 701 556 556 R-squared 0.097 0.146 0.075 0.131

Note: OLS (linear probability) regressions using Sample BL. The dependent variable is the number of nominations received by a listed script. * p < 0.10, ** p < 0.05, *** p < 0.01.

66 Appendix C: Data Appendix As Section 3.1 discusses, this study uses two different samples in its analysis. This Appendix further explains how each sample was created. C.1 Sample Creation C.1.1 Initial Data from Black List Website The Black List is published annually on a website (https://blcklst.com/lists/) and in PDF format. The information provided includes script name, writer(s), publication year, writers’ agent, agent’s agency, and the number of nominations received. Figure C.1 provides a screenshot image from 2005. Since 2007, the Black List has also included information on production companies or movie studios associated with scripts. Over 2005-2016, 1,224 scripts were listed. We use the 701 scripts listed over 2007-2014 to take advantage of buyer-side information and to provide sufﬁcient time for script release prior to our ﬁnal- outcome data collection (October 2017).

Figure C1: Image from 2005 Black List PDF

Note: Figure C.1 shows the ﬁrst six scripts appearing on the Black List in 2005.

C.1.2 Release Outcome Data To determine script release outcomes, we undertake two steps: first, using Internet Movie Database (IMDb) information, we confirm whether a script has been produced; and second, using TheNumbers.com (which records movie US box office revenues) information, we determine whether a script has been ‘theatrically released.’ Confirming whether or not a script was ultimately produced can be tricky, as script names can change during the production process. To address this issue, we used information from writers’ IMDb pages to guide our search. We began with a manual search of each writer’s IMDb page. If the writer had no film releases in or after the year the script appeared on the Black List, we coded the script as not produced. If the writer had film releases before October 2017, we coded the film as produced if a title match with the

67 Black List script occurred. This process was aided by IMDb’s “also known as” field, which often provides the original script name (e.g., at the time it was Black List eligible). If no IMDb title matched the Black List script name, but the writer had subsequent releases, we reviewed IMDb plot summaries and compared them to Black List loglines, sometimes using third parties (such as Script Shadow) if and when more-detailed summaries were required. Where sufficient plot overlap occurred, and the Black List date preceded the matching IMDb film release date, we coded the script as produced. This approach identified 222 out of 701 Black List scripts (31.6 percent) over 2007-2014 as produced. For these produced scripts, we manually searched TheNumbers.com to collect box office information. This step was relatively straightforward, as IMDb titles closely match TheNumbers titles. We ultimately identified 184 (83 percent) of the 222 produced scripts as theatrically released; the remainder were released at film festivals or through other channels such as direct to DVD. C.1.3 Writer and Producer Experience We measured writer experience by parsing writers’ IMDb pages to collect all films for which the writer obtained writing credits. Simply using all past writing credits as a writer’s experience measure is misleading, however, as the majority of IMDb films recorded are small and independently produced. To address this concern, we further collected U.S. film distributor information and used it to define major- release films as those distributed by one of the top 30 movie studios (based on market share). The number of major-release writing credits prior to a given sale represents the writer’s experience measure. When more than one writer was listed, we took the maximum of the writers’ experience as the team’s experience. We measured producer experience similarly to writer experience: parsing information from producers’ IMDb pages to collect all films for which the producer obtained producing credits. We again considered only past major-release films for which producers obtained producing credits. C.1.4 Agency Size and Agent Experience We defined agency size by leveraging the entire Deal Done Pro (DDP) database. Section C.2.5 describes this database in more detail. We calculated agency market share based on the script sales recorded by the agency relative to total industry script sales. An indicator of top-ten agency is then defined as those agencies with the ten largest market shares. We defined agent experience by leveraging the DDP database and using an approach similar to agency size. We counted the number of sales associated with agents at particular points in time. An indicator of experienced agent is then defined as those agents with more than fifteen prior sales. C.2 Sample DDP As discussed in Section 3.1, Sample DDP included only transactions for which completed scripts were in place at the time of sale. This section describes how the entire Done Deal Pro (DDP) sample was generated, including all transaction types. C.2.1 Initial Data from Done Deal Pro Done Deal Pro ((DDP) provides an online database (http://www.donedealpro.com/default.aspx) of screenplay sales since 1997.24 Each sales record includes the script title, a brief summary (called a “logline”), the writer(s), the firms and individuals involved, and a comments field (called “More”) containing additional sales information. Figure C.2 provides a sales entry example from the DDP website.

24DDP also collects adaptation-right transactions identiﬁed by information in the ‘Writer’ ﬁeld. These cases always include parentheses after the writer’s name, such as (author) or (creator). We do not consider these transactions in our analysis, however, as they do not involve a screenwriter later hired to write the screenplay.

68 Figure C2: Sample Sale Entry via Deal Done Pro Website

Note: Figure C.2 shows a DDP sales entry screen shot.

These data were used to create several key variables, including the record date (deﬁned as the sale date), writer(s), title, agent, agency, production company, movie studio, and genre. By parsing text in the “More” ﬁeld, we developed several additional variables:

• Original: We identified whether a script is original (versus an adaptation) by creating a list of terms indicating whether the idea came from a previous source, such as ‘book,’ ‘comic book,’ ‘tv series,’ or ‘short story.’ The variable “original” equals zero if such a term is identified in the “More” field and one otherwise.

• Turnaround/Rewrite: 6.55 percent of the 6,294 records over 2007-2014 are identified as a turnaround or a rewrite, defined by the “More” field containing the keywords ‘rewrite’ and/or ‘turnaround’ (an industry term for projects that were originally developed by a different studio). We excluded these cases from the main analysis, but our results are robust to including them.

• Complete: For the remaining 5,882 scripts, we identified whether a completed script exists at DDP record capture. In particular, “complete” equaled zero if the “More” field contained terms such as ‘pitch’ (an industry term for an idea sale without a completed script), ‘treatment’ (a two- to three-page outline), ‘to be based on,’ ‘to adapt,’ ‘will adapt,’ ‘to be adapted,’ ‘assignment,’ etc. This category accounted for 40 percent of the 5,882 scripts. We coded “complete” as one if the “More” field contained keywords such as ‘spec,’ ‘script,’ ‘screenplay,’ ‘based on,’ and ‘adapted from’: 27 percent of the 5,882 scripts were in this category. We coded “complete” as missing if the “More” field did not contain sufficient information to determine whether a completed script existed, and 29 percent of the 5,882 scripts were in this category (these records typically contain information only about producers managing projects). We took a conservative approach and excluded these unclear cases from the main analysis, but our results were robust to including these cases in Sample DDP.

C.2.2 Whether Black-Listed For each Black-Listed script, we manually searched for a match in the DDP database. We found matches for 59 percent of the scripts in Sample BL. There are two reasons why Black-Listed scripts do not appear on DDP: (1) these scripts may never be sold; and (2) even if these scripts are sold, DDP may not record the transaction.

69 C.2.3 Release Outcome Data We used the same two-step procedure explained in Section C.1.2 to identify whether or not a project was produced and theatrically released. Given the DDP database size, we implemented an algorithmic approach instead of a manual approach. We first cleaned DDP writer names as listed to remove any special characters and to split out multiple authors. We then created a search algorithm for writers’ IMDb pages via Google (e.g., ‘Paul Thomas Anderson’ and ‘IMDb’) to select the first IMDb link. We tested approximately 50 different writers of varying experience to confirm that this search did not produce spurious results and to verify that the site returns correct writers. We then saved writers’ html pages along with their unique IMDb identifiers. We next created a list of all unique writer-DDP sale pairs (i.e., one pair for each writer) using all DDP projects. Using the IMDb information parsed from the step above, we created a corresponding writer-IMDb film pair list for writers’ released films prior to October 2017. We then matched the writer-IMDb film pair list and the writer-DDP sale pair list by writer name and script title using the Stata function matchit. For exact matches, we recorded the DDP project as produced into this particular IMDb film. For inexact matches, we manually reviewed the closest matches (i.e., matchit function similarity scores above 0.5) and determined whether a match could be found. Separately, there are about 1,000 DDP projects containing ‘untitled’ in name (e.g., ‘Untitled Facebook Project’). Because matching based on movie titles here is unlikely to generate high similarity scores, we manually matched these titles based on plot information, using the same procedure outlined in Section C.1.2 above. Finally, we matched produced films to The Numbers database to obtain box office information and determine whether a produced film had been theatrically released. It is possible that the algorithm approach undercounted the number of listed scripts in the DDP database that were released, relative to those in Sample BL. In order to ensure that there were no systematic differences in the way that we determined listed versus unlisted script release outcomes within Sample DDP, we used the same algorithmic method for both (i.e., we did not impute the manually collected film release information from Sample BL for Black-Listed scripts). C.2.4 Other Variables We defined writer experience, producer experience, agency size, and agent experience in the same way as for Sample BL. C.2.5 Logline based novelty measure This section describes the process used to create our logline-based novelty measure of the scripts. To build our measure, we used logline data (short summaries of scripts) and textual analysis. We built our measure on the premise that a script that is dissimilar to scripts developed before it must be accompanied by a logline that is dissimilar in meaning to the loglines of the preceding scripts. To measure the semantic similarity of loglines, we employed a vector embedding approach. Similar methods have recently been used to study changes in political discourse (Ash and Labzina, 2019), judicial text (Galletta et al., 2019), and US culture (Kozlowski et al., 2018). Our data contain a sample of about 12K loglines from Sample DDP, each corresponding to a single script and containing more than six words (those with fewer than six words have been excluded from our 25 analysis). Each logline Li can be represented as a set of its constituent words wi j. Using the Word2Vec approach (Mikolov et al., 2013), each word wi j is represented as a vector Wij with dimension 1×n, where n = 300. These word vectors are built such that words that appear in the same context in our training

25As explained in the paper, not all DDP records have a complete script at the time of the record, but they all have loglines.

70 corpus are located close to each other in our vector space. These vector embeddings were obtained from the archives of the Natural Language Processing Group at the University of Oslo (Kutuzov et al., 2017). Word associations were learned from the fifth edition of the English Gigaword Corpus (Parker et al., 2011). In this vector space, words similar in meaning are represented as being closer to each other in semantic space. In addition, these vector embeddings allow for algebraic operations to be performed on words through their vector representations. For example, “Queen - Woman + Man = King,” or “Moscow - Russia + USA = Washington.” This allowed us to execute a variety of tasks involving analogies, similarities, and classification. We created two novelty measures that differ in how the ‘logline vector’ (Zi)—the vector that represents a hypothetical average word that could be used to summarize logline Li—was calculated. The first measure takes a simple average; that is, for each logline i, Zi was calculated by taking the average of Wij’s of the logline’s constituent words. We then calculated the vector Yy—taking the average of all logline vectors Zi developed before year y. This vector represents the (hypothetical) average logline of all scripts developed before year y. Our novelty measure of each movie i (developed in year y) is the cosine distance between the logline vector Zi and Yy. cosine distance = 1 − cosine similarity, where cosine similarity n ∑ AiBi A·B i=1 = cos(θ) = = r n r n . Thus, the higher the cosine distance, the more ‘novel’ the script is. kAkkBk 2 2 ∑ Ai ∑ Bi i=1 i=1 To create the second measure, we followed the same steps as above, with the following changes to the calculation of our logline vector Zi. Following Arora et al. (2017), this measure recognizes the fact that some words appear out of context, and some more-common words appear in all sentences, regardless of context. To this end, we made two adjustments. First, we weighted each word in a logline by how frequently it appears in the logline corpus (all the loglines in our database). In particular, instead of a simple average, each word vector Wij is weighted by weight ki j in calculating Zi. The weight ki j is equal to α/(α + P(w)), where α = 0.001 is a smoothing parameter following the value used in Arora et al. (2017); and P(w) is the probability of finding word wi j in the logline corpus (that is, the number of times word wi j occurred in the logline corpus divided by the total number of words in the logline corpus). Thus, words used more frequently are weighted less. Second, we boost the co-occurrence probability of words that have a high component along the common discourse of the logline corpus in order to ensure that infrequent words that are not closely related to the main theme of the loglines are deemphasized. Specifically, we T form a matrix X whose columns are weighted logline vectors Zi and let u be its first singular vector. For 0 each logline vector Zi in our matrix X, we further define Zi as the eventual logline vector to be used as T Zi − uu Zi, which intuitively removes words not closely related to the main themes of our logline corpus.

71 References

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Ash, E. and E. Labzina (2019). “Does Politicized Media Distort Political Discourse? Evidence from U. S. Cable News Channels.” Working Paper.

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Brown, A. L., C. F. Camerer, and D. Lovallo (2013). “Estimating Structural Models of Equilibrium and Cognitive Hierarchy Thinking in the Field: The Case of Withheld Movie Critic Reviews.” Management Science 59, 733-747. alletta, S., E. Ash, and D. L. Chen (2019). “ Do Judicial Sentiments Affect Social Attitudes?” Working Paper, Available at SSRN 3415393.

Iacus, S. M., G. King, and G. Porro (2012). “ Causal Inference without Balance Checking: Coarsened Exact Matching.” Political Analysis 20, 1-24.

Kozlowski, A. C., M. Taddy, and J. A. Evans (2018). “The Geometry of Culture: Analyzing Meaning through Word Embeddings.” arXiv preprint arXiv:1803.09288.

Kutuzov, A., M. Fares, S. Oepen, and E. Velldal (2017). “Word Vectors, Reuse, and Replicability: Towards a Community Repository of Large-Text Resources.” Proceedings of the 58th Conference on Simulation and Modeling 271-276. Linkoping¨ University Electronic Press.

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72 Chapter 2

Architectural Misﬁts: Console Video Game Makers and the Rise of Mobile Games

Abstract

For incumbent firms, architectural innovations are one of the most pernicious forms of technological change as they must recognize that significant organizational changes are required despite the innovation’s use of existing components. And, having done so, must then correctly determine which changes to make and incur their costs. To compete successfully, prior research claims incumbents must best the young firms imprinted by the new technology at the dominant design. If they cannot, they should seek to avoid the market altogether. In contrast, this paper suggests that rather than compete for the dominant design, incumbents can succeed by focusing on niche segments that are proximate to their core business. Doing so not only allows incumbents to leverage their existing competencies, but also limits the scale of the architectural changes required. This paper finds support for this strategy in console game makers’ response to the rise of mobile games. Using a novel method to measure distance from the dominant design, the analysis shows that deviating from the dominant design was associated with greater revenue for console publishers. But, mobile natives experienced the opposite result, with revenues declining as they moved away from the dominant design. Thus, by limiting their imitation of new firms, incumbents were able to profitably employ a niche strategy in a market that required significant architectural changes.

I am grateful to a number of people for their support on this project. Summer Jackson and Jenna Myers provided invaluable feedback in the ﬁrst stages of this idea. Carolyn Fu, Minjae Kim, Ethan Poskanzer, and Hagay Volvovsky were kind of enough to review a number of early-stage drafts and presentations. I also beneﬁted greatly from the thoughtful advice and encouragement of my committee, Ezra Zuckerman, Pierre Azoulay, and Hong Luo, as well as the MIT Economic Sociology Working Group.

73 2.1 Introduction

What prevents established ﬁrms from adopting new technologies? The past three decades have produced a dizzying array of answers to this question (for a review, see Eggers and Park 2018). But, perhaps the most pernicious obstacle remains architectural changes; i.e., the re-ordering of existing organizational processes due to changes in the relationship between product components (Gans 2016). Building on Henderson and

Clark’s (1990) landmark paper, the literature suggests that innovations which require architectural changes are particularly challenging for incumbents as they not only require extensive re-organization, but also because the reliance on existing components hides the fact that such extensive changes are even required.

To compete proﬁtably in architectural innovations, the literature claims incumbents must successfully complete a series of difﬁcult challenges. They must: (i) identify the product standards (i.e.,

“dominant design”) that will dominate the new market; (ii) realize that extensive changes will be required to make such a product; (iii) correctly determine what those changes are; and (iv) re-organize accordingly.

Firms that complete all four hurdles can be optimistic about their chances (Siggelkow 2001; Christensen

1993; Christensen et al. 1998). But, failure at any point almost certainly dooms the entire effort (Rivkin

2000; Henderson and Clark 1990).

Yet, even when these challenges are completed, the cost can outweigh the benefits. Identifying the required changes is an iterative process that can take years (e.g., Siggelkow 2001), leaving the firm vulnerable in both the new and the old markets for an extended period of time. Additionally, even if done swiftly and properly, such large-scale changes typically damage existing (often lucrative) lines of business due to changes in assets or capabilities (see Bresnahan et al. 2012), or the re-focusing away from the firm’s core customers (Christensen 1993, 1997). Thus, while the literature does find evidence that firms can make the changes required to successfully produce the dominant design within the new architecture, the overall outlook for them successfully doing so is “bleak” (Gans 2016).

This paper suggests an alternative strategy for incumbents: adopting the new technology but deviating from the dominant design in order to focus on market niches nearer their original lines of business. Rather than imitating new ﬁrms imprinted by the new technology, incumbents’ most proﬁtable strategy can be to make a version of the new technology that bears some resemblance to its current product portfolio. Doing so not only limits the extent to which architectural changes are required, but also allows

74 incumbents to leverage their strengths in existing markets. Though this segment of the market may well be smaller than the generalist segment which prefers the dominant design, having a larger piece of a smaller market may be more proﬁtable than a very small piece of a larger market (Hannan and Freeman 1977;

Carroll 1985; Porter 1996), particularly when competing in the smaller market does not require damaging other lines of business.

Empirically, this paper examines these claims in the context of the mobile gaming industry. Though virtually non-existent as late as 2007, by 2018 mobile gaming earned over $80 billion a year in revenue, more than all other forms of electronic gaming combined. Though mobile games are technologically much simpler than console games, the dominant design the industry had developed by 2008 was already very different from that of console games, and would necessitate signiﬁcant architectural changes on the part of console publishers. Consistent with the theory mentioned above, I ﬁnd that console game makers were more successful where they forwent the extensive architectural changes by creating games that were nearer console games, focusing on their existing audience and not the wider market. Thus, rather than competing head-to-head with the new entrants, they successfully carved out a niche for themselves, thereby avoiding the most extensive of architectural changes.

These ﬁndings potentially add two contributions to our understanding of incumbent adaptation. The

first is theoretical: by showing that established firms can compete in markets which require architectural changes by deviating from the dominant design, this paper provides a theoretical means of moving away from the stark binary of sitting out new innovations or completely re-organizing to adopt them. Second, methodologically, this paper deepens our understanding of the dominant design by offering a means of quantifying difference in product specifications. By suggesting a means to measure differences between products in high-dimensional space, this study offers a tool that could potentially be used in many markets, and to study questions beyond incumbent adaptation.

75 2.2 Theory

Before explaining the theory behind this alternative strategy in greater detail, this section brieﬂy reviews the literature on imprinting and dominant designs to clarify why responding to architectural change is so difﬁcult for incumbents.

Incumbents’ difﬁculty in adapting to radical technological change stems, ultimately, from the fact that ﬁrms are imprinted by a market and a technology at the time of their birth (Stinchcombe 1965). Older

ﬁrms are thus imprinted with previous market conditions and built around legacy technologies while younger ﬁrms are imprinted by the current market conditions and latest innovations. Where technological changes complement previous resources, either in terms of assets (e.g., Tripsas 1997) or capabilities (e.g.,

Tushman and Anderson 1986), incumbents have the advantage of a head-start, and are therefore likely to continue their dominance. But, where the new technology rewards new assets and capabilities, new ﬁrms built around the new market conditions are likely displace the old, resulting infamously in “creative destruction” (Schumpeter 1942).

The concept of a dominant design has then been invoked to explain the synthesis of different technologies and product elements into a common set of standards shared across the industry (see Suarez and Utterback 1995; Tushman and Murmann 1998). Though there are two competing streams of research in this area, one suggesting that the dominant design is largely driven by consumer demand (Suarez and

Utterback 1995), and the other emphasizing a more complex process affected by multiple stakeholders

(Tushman and Murmann 1998), both agree that the emergence of a dominant design reﬂects the end of a period of “ﬁrmament” in which there are many different designs each attracting different market segments

(see Christensen et al. 1998), and the beginning of a standardized period around a set of product and/or subsystem characteristics (Utterback 1994; Tushman and Murmann 1998).

Because the dominant design becomes the standard across the industry, this literature claims, ﬁrms that do not latch onto the dominant design are in a precarious position. While ﬁrms may “strategically maneuver” to attempt to affect the dominant design (Cusumano et al. 1992), once the design has been set,

“Firms that lose the dominant design ... will face severe performance decline” (Tushman and Murmann

1998). This is not to say that the dominant design is the optimal technology for all parties, or even the most technologically robust or sophisticated, only that it represents the chosen path the industry and the

76 technology will take (Suarez and Utterback 1995; Tushman and Murmann 1998). Thus, within this framework, to be successful, incumbents must: (i) identify the dominant design; (ii) recognize the changes required of them to adhere to the new design; and ﬁnally, (iii) make the changes fast enough so as to move down the learning curve ahead of the competition (Christensen 1997).

These two concepts, imprinting and the dominant design, combine to explain the difficulty that incumbents have in adapting to architectural changes. First, having been imprinted by a different technology, established firms must recognize that the new technology uses the same components as the previous technology but that the relationship between them is different (Henderson and Clark 1990). And, because each component is interrelated to each other component, it is difficult to re-organize the components in a manner that allows the firm’s culture, hierarchies, norms, processes, assets, brands, and capabilities all to “fit” together (Siggelkow 2001). Meanwhile, while the incumbents learn which changes need to be made and how to make them, new firms built around the new technology emerge (see Carroll and Hannan 2004). Though not all of these will be built around the dominant design, from the perspective of incumbents, those (perhaps few) that are have the advantage of a blank slate, and therefore, require much less sacrifice to achieve organizational fit (Carroll and Hannan 2004; Stinchcombe 1965).

This results in a strong claim by the existing literature that the path to success for incumbents is to imitate the new ﬁrms which have been imprinted by the dominant design (see Utterback 1994; Christensen

1997; Tripsas 1997). Given the premises that (a) successfully competing in a market requires focusing its dominant design, and that (b) the new, most competitive ﬁrms will have been imprinted by that design, incumbents must adopt the product and architecture a new entrant would if they are to be proﬁtable

(Christensen 1997). While the literature recognizes copying can be extremely difﬁcult when the strategy is complex (Rivkin 2000; Winter and Szulanski 2001), and that starting far away from the required design may make it impossible to ever fully adapt (Levinthal 1997), these premises leave little room for any other strategy.

This paper questions the ﬁrst premise, that incumbents should always compete for the dominant design. Such skepticism is not without basis: while empirical studies have found consistent support for imprinting (see, for example, Burton and Beckman 2007; Phillips 2002), rather than emphasizing the importance of focusing on a single design, the strategy literature has canonically emphasized the

77 importance of niche markets (see, for example, Porter 1980; Carroll and Swaminathan 2000).

While existing literature claims that the emergence of a dominant design signals the end of niche segments, customer preferences must be relatively homogeneous for this to occur. Where one design (given enough adopters) can surpass all others (Christensen 1997), or network effects push users towards a single design (e.g., David 1985), this is understandable. But, these conditions are far from universal. In contrast, markets which have signiﬁcant customer heterogeneity are likely to see many niches occur (Fullerton

1990). For instance, customer preferences vary widely for beer, giving rise to the specialist-generalist dichotomy (Carroll and Swaminathan 2000).1

Heterogeneity creates potential opportunities for established firms by allowing for the possibility of complementary market segments. First, as Porter notes (1996), different product specifications often require different capabilities. Thus, to the extent that the new product is similar to the old product, incumbents may have an advantage in being able to build on their existing architectures, assets, and capabilities (Baldwin and Clark 2006; Tripsas 1997; Tushman and Anderson 1986). Second, as the greater the number of changes incumbents need to make the greater their likelihood of failure in making them, selecting a strategy which requires fewer changes can increase the likelihood of successfully adopting the new technology at all (Rivkin 2000). Third, and finally, because fewer changes are required, established

firms are less likely to have to sacrifice existing (and profitable) assets (Teece 1986) and capabilities

(Bresnahan et al. 2012), and to have to move away from their core customers (Christensen 1993).

Consequently, targeting customer segments with preferences nearer the original technology may enable incumbents to go beyond simply mitigating their difﬁculties in the new market to possibly leveraging their existing resources to have an advantage over new ﬁrms in particular market segments.

Though these segments are likely to be (much) smaller than market segment interested in the dominant design, capturing a larger share of a smaller market may well be more proﬁtable than capturing little to no share of the dominant market (Hannan and Freeman 1977; Carroll and Swaminathan 2000) particularly once one factors in all the additional changes required (Porter 1996, 1980).

1While one could argue that absent these characteristics, markets do not have a dominant design, such a perspective signiﬁcantly limits the analytic value of the dominant design concept. If, by dominant design, the literature is referring to the “exclusive” design of a product, then in effect this literature becomes theoretically indistinguishable from the technological lock-in literature in economics (see Arthur 1998).

78 2.3 Setting

This section looks to situate this theory in the context of the mobile gaming industry by providing some back ground on electronic gaming generally before discussing the technological and architectural differences between mobile and console games.

2.3.1 Industry Background

Electronic gaming began in the 1970s and by the early 1990s had become a large, established industry characterized by large ﬁrms and incremental innovation (Schilling 2003). Mobile games began in the

1990s when mobile phones ﬁrst appeared with sufﬁcient screen size and capacity to process simple games, such as Snake. The market remained nascent, however, until 2008 when Apple and Google launched their app stores.2 Within six years, mobile games had overtaken console games in terms of global revenue. And, as Figure 2.1 shows, by 2018, mobile sales were equal to all other forms of electronic gaming combined.

Yet, despite its meteoric rise, it is important to note that the rise of mobile gaming has not dented console gaming (at least, not at the time of this writing). While one might have predicted that mobile games would slowly diminish console game sales (e.g., Christensen 1997), in fact, as the slope of the console line suggests in Figure 2.1, there has been little substitution. Though no studies of demand elasticity between the two markets are available for the global market, Yamaguchi et al. (2017) ﬁnd no evidence of substitution in the Japanese market.

Games are made by two parties: developers and publishers (see Mollick 2012). Publishers are typically large businesses of several hundred people or more that fund and manage the game-making process. In contrast, developers are typically small (often 10-50 employees) that write the games. The publisher retains final authority as project manager and owner of the project, determining the schedule, obtaining all licenses required, and providing final say on all significant decisions. The developer then physically creates the game by creating the game plot and character scripts, writing the code that will run the game, and, finally, testing the resulting product. For a AAA console game (equivalent to a tent-pole movie in the film industry), this process can cost over $100 million, last 2-3 years, and involve a couple hundred people. In contrast, for a mobile game, this process typically takes 2-3 months, requires 10-15

2For further background on the history of mobile gaming, see Appendix B.

79 people, and costs well-under $1 million.

While there are certainly differences across personal computer (PC), console, and hand-held games, and even further heterogeneity across genres, there are several key characteristics emphasized by gaming guides that comprise a dominant design during the run up to 2008. First, all three necessarily emphasize the importance of a ﬁnished, fully-completed game at launch that players pay for entirely upfront. Second, regardless of genre, games emphasized strong, recognizable characters with clear story lines and detailed plots. And, third, since Atari, game makers have recognized that games which were too easy were unsatisfying; games needed to require players to spend some initial time learning the controls, maps, and objectives of the game before being able to have some agency in how the game progressed to gain a sense of achievement upon completion.

The similarities across electronic gaming have led the industry to be relatively concentrated into only a small number of “super star” ﬁrms (Binken and Stremersch 2009) where creating and maintaining talented teams is critical (Mollick 2012; De Vaan et al. 2015). Consequently, the top console ﬁrms lead in

PC and hand-held gaming as well, with their advantage furthered by the ownership of recognizable characters which can be recycled and redeployed across multiple platforms (see Tschang 2007).

Nonetheless, mobile games differ from console games on a number of dimensions.3 First, mobile phones can only store a fraction of the memory consoles can, forcing the games to be much smaller in size and, therefore, more limited in complexity. Second, phones rely on battery power, and are consequently limited in how much the game can utilize the phone without rapidly depleting the battery. Finally, phones do not come with controllers, limiting the sophistication of the controls.

Consequently, it is not possible to simply “port” a console game directly onto a mobile phone without any changes, as a game maker might between PlayStation and Xbox, for example. While some older titles, such as Sonic the Hedgehog, could be moved over with limited changes, more contemporary games were far too complex and required too much memory for such a maneuver to be possible. Console publishers have thus been forced to create different versions of existing games, or to develop entirely new games altogether alongside their existing operations.

3For a fuller discussion of these differences, see Table B1 in Appendix B.

80 2.3.2 Mobile Games’ Dominant Design

Yet, these technological differences are only the minimum changes required to make a viable game; the dominant design emphasized by those native to the industry require additional changes. Having started their life on extremely limited platforms (see Appendix B for a fuller discussion), mobile games have always been technologically distinct from other forms of console gaming. Consequently, they evolved along different lines, the trajectories of which were in-motion before the rise of smart-phones.

First, mobile games emphasize simpler plots with a softer learning curve. Unlike console players, mobile gamers are typically not dedicated players fully-focused on the game when playing. Rather, they are often bored or multitasking, playing only for a short period. To retain these nominally interested (and focused) players, game makers have moved towards “casual” games which focus on simple plots, straightforward challenges, bright visuals, and limited controls, as opposed to large, expensive, and complex world of console games.

Second, the industry sets the price to download a mobile game much lower than the price to purchase a console game, often making it completely free. Once downloaded, players can then make

“in-app” purchases (such as characters, levels, customized avatars, etc.), which make the game proﬁtable.

Typically under $10, no one in-app purchase alone is likely to allow the game maker to recoup the “lost” revenue from a higher download price. But, by retaining players for months or years, players are likely to make several in-app purchases, thereby making up for the lost income.

Third, mobile games are typically updated extensively and indeﬁnitely after their release. Console games, by virtue of being bound to a CD or cartridge, could not be changed after release without a product recall (at least during this period). Mobile games can be quickly and cheaply updated after release by making changes on the back-end (server-side) code and data, and by pushing updated app versions to users

(client-side), however. This meant that mobile games could be regularly updated to drive additional in-app purchases for years.

Fourth, being an entertainment product in which quality is difﬁcult to predict (Yin et al. 2014), mobile games incentivize experimentation and quick iteration, as opposed to the long investment and development periods of console games (Aguiar and Waldfogel 2018). Games which receive few initial downloads are quickly abandoned, while games which are successful are frequently updated for years.

81 Having a long, protracted development cycle inhibits this strategy by making it too expensive to abandon games which were initially unsuccessful and renders the post-release updates too infrequent to maintain players’ interest.

To understand the interplay between these differences better, it is perhaps helpful to provide an example. In 2013, Rockstar released Grand Theft Auto V, which quickly became the best-selling console game to date, bringing in over $6 billion in revenue. The game took ﬁve years, and $250 million to develop. Conversely, Candy Crush Saga, released by King in 2012, cost less than $500,000 to create and earned over $7 billion in revenue. Though Grand Theft Auto V was complete upon its release, Candy Crush

Saga initially had only 65 levels; ﬁve years later, it would have over 2,000, with a new level being introduced each week.

2.3.3 The Architectural Changes Required for Mobile Games

From console publishers’ perspective, adhering to this dominant design would require two significant architectural changes. First, console publishers would have to re-order their development process to allow for ongoing updates. While previously console publishers could develop the game in the run-up to release and then re-allocate their resources once the game hit the shelves, the new design required publishers to continually allocate resources to the project. Second, the development cycle would need to be done in a fraction of the time, requiring a much less bureaucratic process. To make updates fast enough to retain players’ interest, and to launch a sufficient number of games to have a “hit,” console publishers would need to shorten their development times from years and months to weeks and days. This would require a much more agile process, a change which large firms often find quite difficult (Lindvall et al. 2004).

Critically, all four design elements were interlinked, as absent one the publisher would be unable to achieve a “fit” with the others (Siggelkow 2001). To interest the uncommitted gamer, game makers would need to eschew complex plots and controls for more casual games. But, to entice them to try the game out, a low download price was required. To have a low or free download price and still be profitable, a game must have in-app purchases. But, retaining customers long-enough for them to make a sufficient number of in-app purchases requires ongoing post-release updates. Finally, making those updates necessitated a rapid development process.

The linchpin to these architectural changes was a closer relationship between developers and

82 publishers. While developers and publishers previously functioned in a one-shot “game” in which they would collaborate on a single project and then move-on, the requirement for ongoing support meant that the relationship became a repeat game. And, as transaction-cost economics would suggest (see, for example, Williamson 1981), would therefore necessitate a closer relationship. Yet, the long-established norms and culture of the console gaming industry made this extremely challenging. Industry accounts widely agree that publishers and developers (both third-party and vertically integrated ones) have a deeply antagonistic relationship (for similar examples see Azoulay 2004; Novak and Stern 2009). Publishers, controlling the purse strings, can unilaterally change dates and requirements, thereby pressuring the developers to reach extremely difﬁcult targets. As one executive from Activision’s publishing arm explained (MacCormick and Angelo, HBS Case 9-605-020).

If the developers don’t deliver a certain list of items within the deadline they don’t get their check. That’s the leverage we have over them. Some external studios are small, and they really depend on our ﬁnds, so there’s a big incentive for them to deliver what they promise.

This has led to a “crunching culture” in which programmers would move from project to project getting “crunched” to reach unrealistic deadlines before exiting the industry to more lucrative, lower pressure programming jobs. As one gaming newsletter explained (Kotaku, September 26, 2016):

A 2014 survey by the International Game Developers Association found that 81% of polled game developers had crunched at some point over the previous two years. (50% felt crunch was expected in their workplaces and a “normal part of the job”)...Although there are no good statistics for the number of people who have left gaming for less volatile, more lucrative ﬁelds, there are plenty of stories.

Though frustrating (and perhaps unethical), this proved to be a sustainable business model in the console industry for decades. But, it presented an obstacle in attempting to adopt the rapid and ongoing development cycle for the dominant design of mobile games. New programmers were unaware of the structure of the game code and the “personality” of the characters, making it difﬁcult (and risky) to ask them to continuously add features and levels. As one senior software engineer with experience in both mobile and console games explained in a conversation with the author of this study:

...AAA [large console games] also lets you hide issue[sic] - generally speaking the code base has issues but once you ship its all over. You just need to get through submission and you are good. Mobile means constant iteration and touching many systems for many years. This

83 means that as people leave you have lost the tribal knowledge. I’ve worked on projects where I’m the third or fourth owner of a feature stack in mobile. Thats [sic] down right scary given the number of skeletons hanging out in your average code base.

2.3.4 Relation to Theory

This situation left console game makers facing the classic dilemma of architectural change. Eschewing mobile games entirely would mean missing out on an extraordinarily large and growing market, likely frustrating shareholders. But, the dominant design was both competency destroying and would require extensive architectural changes. And, as those changes would require a more understanding relationship towards mobile developers, console developers would very likely demand a similar relational contract; were mobile developers to receive better hours and similar pay, console developers would likely simply switch over (see Gibbons and Henderson 2012; Truelove and Kellogg 2016). Thus, attempting the dominant design for mobile games would likely require incumbents to make sacriﬁces in their core market of console games.

While prior research implies that incumbents would need to decide between mobile games and console games, the alternative strategy articulated above suggests a third possibility: making mobile games but not imitating mobile publishers in the dominant design. To the extent this is true, console game makers should look to leverage their existing competencies by making mobile games in familiar genres, using their previous intellectual property, and focusing on their existing customers. Consequently, to the extent that console publishers attempt this approach, and are successful, it would provide evidence for the theory above and would suggest that this niche-strategy is in fact a viable approach to architectural change.

2.4 Data

Before taking these predictions to the data, I ﬁrst outline the data sources used in this study before discussing the sampling process and providing some summary statistics.

2.4.1 Data Sources and Sampling

The first step in examining console publishers’ entry into mobile games is to identify a sample of console publishers. To do so, I use VGChartz sales data from 2005 to 2008 to identify the top 25 console firms in terms of games sales in each year, after removing three firms which also make hand-held devices

84 (Nintendo, Sony, and Microsoft), as they face a very different incentive structure in making mobile games.4

Similarly, I remove ten firms that went bankrupt in the first two years before or after the mobile gaming industry, creating a sample of 15 console game publishers.5 As displayed in Figure 2.2a, within this sample, all fifteen ultimately make a mobile game. And, all but one did so within the first three years.

Next, I then look to use a similar approach to identify “mobile natives”—firms which were born into the world of mobile games, and are therefore imprinted by it. To find these firms, I use ThinkGaming’s top sales charts to identify the top 25 mobile game firms for the end of each quarter from 2013 to 2018.6

Unfortunately, mobile sales data was not systematically collected by any major data source for the early years of the industry.7 However, using rankings from sources such as Pocket Gamer, I confirm that the leading mobile firms from the early years of the industry are represented by the ThinkGaming sample. I then rank these firms by cumulative sales over the period, and select the top 15 mobile firms.

Before examining the raw data, it is important to highlight the limitations of this sampling approach.

While the sample of console firms is, arguably, representative of the leading incumbent firms, the sample of mobile firms mechanically selects on the dependent variable by identifying only the best mobile publishers throughout the period. Consequently, in comparing the approaches of the two sets of firms, this study does not answer the question of how console publishers compared to the average mobile native, but rather, how they compared to elite mobile natives. Given the superstar nature of the industry, it is this latter question that is analytically important, however, as it is only by competing with the best firms in the industry that incumbents can hope to be profitable. Thus, while this study deliberately seeks out the most successful mobile firms as a comparison group, doing so provides a more useful benchmark for incumbents than a more representative sample.

Based on this logic, I then gather the mobile games each of the 30 publishers created as well as their performance.8 Data on which games the publishers created also comes from ThinkGaming, which further provides data on revenue, downloads, and, for the majority of games, demographic information. For even

4While console games generally were not impacted by mobile games, hand-held devices, such as Game Boy, were sharply impacted, with hand-held consoles ultimately being discontinued. Thus, the makers of hand-held devices faced a very different incentive structure than other game-makers, as they would have beneﬁted more from mobile games’ failure than success. Conse- quently, including them in the sample would complicate the interpretation of the results. 5For further discussion of this issue and the sampling method more generally, see Appendix H. 6Quarterly data was used for mobile games rather than annual data as mobile game sales can be much more ﬂeeting. 7For a discussion of how this might affect the results, as well as results for only the post-2012 period, see Appendix J. 8To see the actual publishers, see Table H1 in Appendix H.

85 greater granularity, however, I manually matched each game to MobyGames, an online database of game information. This resulted in more extensive data on the release date, developer, and facts about the game

(e.g., genre, perspective, etc.). I also manually added data from AppAnnie, which supplied information on the download price, size, number of versions released, and dates listed on the respective stores. Lastly, after comparing games across genres and ﬁnding inconsistencies in the MobyGames data, I manually re-coded each game’s genre according to MobyGames’ rubrik and created a sub-genre category using IDTech’s more comprehensive schemata.9

2.4.2 Summary Statistics

Linking these data sets results in detailed information on 934 games, 415 of which are from console publishers, and 519 from mobile publishers. Table 2.1 provides some summary statistics, which I discuss below.

The ﬁrst category presents general statistics about each game. Year is the year in which the game was initially released — on average, 2013 for mobile publishers and 2014 for console publishers. Price is the price to download the game, and Freemium is an indicator equal to one if the price is zero. Console publishers on average charge a much higher price to download the game, $2.72 versus $0.84 (p = 0.000), and were much less likely to make the game free-to-play (55% vs 74%, p = 0.000).

Next, I present measures of the games intellectual property (IP), which I manually code based on the

MobyGames data.10 Perhaps not surprisingly, console publishers are much more likely to simultaneously release titles across both mobile and console platforms (19% compared to 6%, p = 0.000), and are much more likely to use characters and IP from previous console games (54% versus 1%, p = 0.000).

Alternatively, mobile publishers are more likely to build on an existing mobile franchise (22% compared to

6% for console publishers, p = 0.000). And, both are equally likely to use non-game IP (e.g., a character from a movie), incorporating outside IP about 13% of the time.

In examining where and how the games were released, the vast majority were released on the

Appstore, both as iPhone and iPad games.11 Once placed on a platform, however, mobile publishers

9See Appendix G for further details. 10For further discussion on this process, as well as descriptions of additional IP measures used in the results section below, see Appendix I. 11This is consistent with industry accounts which suggest that Google Play has been slower to boast the number of games the Appstore does.

86 updated their games on average 29 times, compared to console publishers’ 17 (p = 0.000). Though not a perfect proxy for all the updates publishers and developers are likely to be making, industry accounts suggest that new versions correlate very highly with updates to the game as programmers seek to ensure the downloaded version of the game is compatible with the latest changes. For this reason, I use Nb. Versions as a proxy variable for the ongoing support of games described above.

Finally, the bottom of Table 2.1 shows the difference in overall performance. Total Revenue is the cumulative revenue, from all sources, earned by the game. Though the median game made less than $1 million for both console and mobile publishers, the average mobile game by a mobile native publisher made over $70 million more than those of console publishers (p = 0.003). Similarly, the median game for both achieved fewer than 500,000 downloads, but the average game by mobile publishers had nearly 19 million downloads, compared to nearly 8 million for console publishers (p = 0.000). Additionally, while games by console publishers made on average 60% of their revenue from in-app purchases, mobile publishers achieved 77% of their revenue post-download (p = 0.000). Finally, console publishers’ audience had 7% fewer women (p = 0.000).

2.5 Results

The empirical analysis of this paper examines the predictions in Section 2.3.4 in three steps. First, I examine the extent to which console publishers were able to overcome the architectural challenges.

Second, I analyze console publishers’ response to see if they moved away from the dominant design, and, if so, whether that was associated with better performance. Third, and ﬁnally, I examine the timing and sustainability of the strategy.

2.5.1 Evidence of an Architectural Problem

Figure 2.2 ﬁrst examines console publishers’ entry into the mobile games market. Figure 2.2a shows that console game makers were optimistic about their entry into the mobile market, entering swiftly. And,

Figure 2.2b shows that they were able to release a relatively similar number of mobile games per year to mobile publishers. Consistent with the difﬁculties in adopting a more agile process, console makers averaged about 1.5 fewer games per year (3.5 compared to 4.9, p = 0.032). But, this difference is not so large as to suggest that console makers were uncommitted, or that they struggled to understand the

87 technology.

In accordance with the claims regarding the architectural changes required, however, console publishers were not able to match mobile publishers in post-release updates. Figure 2.3a shows that console publishers made many fewer updates per year— on average, console publishers released 4 versions per year per game, compared to over 10 for mobile publishers (p = 0.000). Additionally, as displayed in

Figure 2.3b, console publishers earned over 17% more of their revenue from the download price compared to mobile publishers (p = 0.000).

Table 2.3 provides further evidence of these claims, presenting the results of a linear regression on console publishers and three different measures of performance. In the first two columns, the results show that console publishers were not statistically distinct from mobile publishers in terms of downloads, suggesting that attention and interest were not a significant problem. The next two columns analyze revenue from the initial download, and show a large and statistically significant coefficient, suggesting that console publishers were superior in generating revenue from the initial download. But, this result is reversed in the final two columns which reveal mobile publishers as vastly super in revenue from in-app purchases.12

Finally, Table 2.3 looks to examine the claims in Section 2.3.3 – that the need for a closer relationship between developers and publishers was the root difﬁculty of the architectural changes. The

ﬁrst three columns present a linear probability model estimating the likelihood of a repeat collaboration between developers and publishers. The results in Column 2 are precisely estimated and show that controlling for year, genre, and platform console publishers were 26% less likely to collaborate with a developer. Similarly, the next three columns examine the average number of collaborations between a given developer and publisher. Column 5 shows that console publishers had 36% fewer collaborations with their developers, and is also signiﬁcant at conventional levels. Columns 3 and 6 examine only third-party developers and reveal an even greater association. Thus, consistent with the arguments in Section 2.3.3, console publishers do appear to have been less likely to form lasting relationships.13

Overall, these results support the claim that the architectural, and not technological, changes

12As the gap in revenue from in-app purchases is larger than that of download revenue, console publishers on average earn much less revenue from their games, as shown in Table 2.1. 13Ideally, one would take these results one step further and examine turnover using the MobyGames data. However, this data is too sparse in mobile games to credibly calculate ﬁrm employment over time.

88 obstructed console publishers from adopting the dominant design. Consistent with the “perniciousness” of architectural changes, console game makers were quick to enter the market and succeeded in making games relatively rapidly. And, on average, they were able to achieve millions of downloads and charge higher prices. But, the evidence suggests that they could not compete in the post-release updates, which were the aspect of the design which required extensive architectural changes. This challenge was openly acknowledged by the incumbents. For instance, Frank Gibeau, the Executive Vice President of EA Mobile stated (Venture Beat, June 14, 2012):

When we’ve come over and started to look at how EA was going to compete and be successful there, again, you go back to a really clear-eyed competitive analysis. What are you good at? What are you the best at in the world? I would argue that we have the best portfolio of IP for the mobile business... We have talented developers. We have great tech. We have fantastic production values....If you look at App Annie, we had 600-plus million installs in the last 12 months. We’re bringing in the audience. What we’re not doing is servicing them in a live environment as effectively as our competition.

2.5.2 Deviating from the Dominant Design

But, did console publishers move away from the dominant design to pursue a more proximate market segment? Answering this question is difﬁcult as the elements of the design are each a matter of degree: lower prices, easier games, brighter colors, etc. Thus, measuring adoption of the dominant design requires comparing games across multiple features in order to understand the “latent” differences between the games (see Koren et al. 2009, for a discussion of their use of latent characteristics in creating the winning algorithm for the Netﬂix Competition in 2009)

Thankfully, the statistics literature has sought to create various distance measures that allow for the measurement of observations in high-dimensional space. While there are many possible distance measures one could use, identifying one that works in product markets is challenging as (for most products) the measure needs to be able to include both categorical and continuous variables. For this reason, I use a measure of distance originating in computational biology known as Gower distance (Gower 1971),14 which accounts for different types of variables by scaling them by a common denominator.

More speciﬁcally, Gower distance measures the dissimilarity, D, of two games, i and j, based on the

14As a robustness check, I compare the results to those based on Euclidean Distance and ﬁnd that Euclidean Distance results in, if anything, a larger, more robust result. For these results and fuller discussion around them, see Appendix D.

89 similarity, s, of each individual features, k, as:

υ ∑ ωksi jk k=1 Di j = 1 − υ ∑ ωkσi jk k=1

Where σi jk is an indicator equal to one if the games i and j can be compared based on characteristic k, and zero if not (e.g., where one observation is missing data); υ is the total number of features; and ωk is the weight assigned to feature k. Where k is a categorical variable and the two observations have the same value, si jk = 1, and, where they differ, si jk = 0. Alternatively, if k is a continuous variable, the similarity of

|xi−x j| the two games is calculated as si jk = 1 − , where Rk is the range of k. Rk This measure allows for the quantiﬁcation of console publishers’ performance relative to the dominant design by examining how much their games earned relative to their distance to the top mobile games. To do so, I ﬁrst identify the top ten games in terms of cumulative revenue across mobile natives.

Next, calculate the Gower distance of each game to every other game in the sample based on their ex-ante characteristics:15 size (in megabytes), price, color, perspective, genre, sub-genre, and seven indicator variables as to the game’s intellectual property.16 Finally, I average each game’s distance to the top 10 games that represent the “dominant design.”17

Conceptually, this results in a measure of how dissimilar each game is to the “best” games made by the ﬁrms imprinted with the new architecture. To provide further intuition, it is helpful to compare some of the lowest scoring games to the highest scoring games. For instance, Farmville Champions and Scrubby

Dubby Saga, both free-to-play puzzle games ﬁrmly in the casual genre, are in the top 10 games in terms of lowest score. Alternatively, NBA 2K19 and Football Manager Touch 2017, both sports titles closely related to console releases, are in the top 10 games furthest from the dominant design.

Figure 2.4 visually illustrates the distance measure and its relationship to revenue. Figure 2.4a shows a density plot of the logged distance relative to publisher background (mobile or console). The mean for

15I include only ex-ante characteristics, as opposed to ex-post characteristics, such as post-release updates and revenue, to prevent outcome information from leaking into the distance measure. 16 While the above distance measure includes a weight ωk, the results presented here weight each variable evenly. For robustness, I have re-run the below analysis with several different measures of ωk, presented in Appendix C. 17For further information on the variables that go into this measure, see Appendix I.

90 mobile natives is at a much lower point in the distribution (i.e., more similar to the dominant design), while console publishers’ concentration much lies further out (p = 0.000). While this would suggest console publishers concentrated on games further away from the dominant design and experienced less competition in this segment, it alone does not shed any light on revenue. Figure 2.4b displays this relationship by publisher background in a scatter plot with logged revenue on the vertical axis and logged distance on the horizontal axis. Each dot represents a game by console publishers (in orange), or mobile publishers (in gray), while the respective lines represent the least-squares line surrounded by the 95% conﬁdence interval.

Consistent with existing literature on the dominant design, moving away from the standard is associated with decreased revenue for mobile publishers. But, in accordance with the alternative strategy for incumbents articulated above, console publishers experience the opposite result: increased distance is associated with more revenue. These results imply that, in contrast to prior theory, incumbents earned more revenue the less they imitated the new ﬁrms.

To investigate this further, I use the below linear regression speciﬁcation:

Log(Revenue)it jp = β0 + β1Console j + β2Distancei + β3Console × Distancei j

+ β4Yeart + β5Publisher j + β6Platformp + εit jp

Where Log(Revenue) is the logged cumulative revenue of game i, released in year t, by publisher j, on platform p. Console is an indicator variable set equal to one if i was made by a console publisher,18

Distance is i’s distance from the dominant design as described above, Console × Distance is the interaction of Console and Distance. While the specification includes year, publisher, and platform fixed effects, additional game level controls are not included as they are incorporated into the distance measure and therefore would introduce collinearity. The key coefficient in this specification is β3, which provides the estimate of the correlation for distance and console publishers. Note too, that I remove the ten mobile native games used as the basis of the distance measure in the remainder of the analysis to avoid mechanically determining the results. Finally, the standard errors are clustered by both publisher and year to allow for correlation between games by the same publisher and in the same year (Colin et al. 2011).

The results of this analysis are presented in Table 2.4. Consistent with the theory above, as console

18As noted in corresponding tables, Console drops out of the speciﬁcation once publisher ﬁxed effects are included.

91 publishers imitate mobile firms less, i.e., as their distance to the dominant design increases, the revenue associated with their game also grows. Column 2, using publisher, year, and platform fixed effects, suggests that a 1% increase in distance is associated with a 5.24% increase in the game’s cumulative revenue for publishers. Columns 3-6 seek to shed more light on the source of the revenue boost. Column 4 shows that console firms do not gain a statistically significant increase in downloads as they move away from the type of game emphasized by mobile publishers. Rather, as is evident in Column 6, they receive more revenue per download.

Analysis of the demographic data compliments these results. As noted above, previous work suggests that incumbents need to move away from their customer base in order to adapt to the new market

(e.g., Christensen 1993, 1997). While the data do not allow me to measure if a user also owns a console, it does provide a proxy, young-men, which have traditionally been the target of console game makers (see, for example, Cross 2008). Table 2.5 measures the extent to which console publishers beneﬁt from moving away from this demographic by examining the logged revenue of the game and the percentage of the audience that is a young male, i.e., a man between the ages of 18 and 34. As displayed in Column 5, which is the most stringent speciﬁcation, controlling for year, publisher, genre, and platform, console publishers have a statistically distinguishable relationship to a young male audience from mobile natives: for each increase in the percentage of a games’ audience made up of young men, console publishers earn on average

9.42% more revenue. Note too that the correlation with this demographic for mobile publishers is negative, and that both results are signiﬁcant at conventional levels. Thus, while mobile natives’ perform more poorly when competing for young men, console publishers’ see a positive association between revenue and appealing to their traditional base.

Taken together, Tables 2.4 and 2.5 provide evidence that console publishers succeeded by identifying a proximate segment of the market. This was in fact recognized by Greg Canessa, Vice President of Mobile at Activision, who stated (Venture Beat, September 14, 2012):

I think the market is sizing up into three market types, in terms of mobile games. There’s the premium price point, which is the Inﬁnity Blade/Call of Duty type of model. There’s a big brand behind it, there’s a loyal set of customers behind it, and there’s a really high-quality, high-production-value type of value proposition behind the product. I think that’s centering in around the $6.99 price point. Maybe in some cases $5.99. I think that’s archetype A. Archetype B is the more casual, accessible game at the $.99 or $1.99 price point with some

92 microtransactions. And then the third archetype, the C archetype, would be the free-to-play model. We think that works really well for certain game genres, but not others.

While these results do not find support for three specific different markets, they do suggest there is not one monolithic market for mobile games. Rather, there are sub-segments, some of which prefer games nearer to those found on consoles. And, rather than compete head-on with the mobile firms, console game-makers sought to focus on the smaller, specialized segment which their architectures were best suited to. In practice, as displayed in Table 2.1 and Figure 2.4a, this meant charging more upfront, focusing on larger games with more complex game-play and story lines, and building off of their existing IP.

This strategy is further illustrated in Figure 2.5. This figure shows four histograms, split by publisher and distance, where those “nearest” are those in the top third of games in terms of similarity to the dominant design, while the “distant” games are the third of games furthest away. This figure highlights the difference in expected revenue based on the distance of the game. For console publishers, making a similar game is associated with significantly less revenue (the logged values are 10.598 compared to 12.256, p =

0.004). Alternatively, mobile publishers experience the opposite result, with near games earning much more revenue, on average, than distant ones, though the difference here is noisier (12.620 versus 11.941, p

= 0.212).19 Thus, while console publishers’ most distant games do not average the revenue that mobile publishers’ nearest games do, they do average more revenue than console publishers’ nearest games, and mobile publishers’ most distant games, consistent with the notion of a “second best” strategy.

2.5.3 Timing and Sustainability

This ﬁnal piece of the analysis seeks to shed light when console publishers began this strategy, and how long they employed it.

To this end, Figure 2.6 displays the average distance of games over time as well the percentage of their audience that is made up of young men. Consistent with the notion that mobile games were already on a different trajectory before 2008, Figure 2.6a shows mobile and console publishers were far apart in terms of the average distance of their games already by 2008. And, while both console and mobile publishers moved towards the dominant design from 2008-2018, console publishers grew increasingly distant from mobile natives over time. Similarly, Figure 2.6b shows that while console publishers always had a large

19To see sensitivity testing around this, please see Appendix F.

93 percentage of their downloads come from young men, this percentage grew signiﬁcantly in later years.

These results are further analyzed using linear regression in Table 2.6. Here, Column 2 shows that while the average distance from the dominant design fell slightly over time, console publishers changed slower, resulting in an increasingly large gap between them and mobile publishers. But, the growth in the difference between them is quite small at 0.07% per year. Consequently, in Column 3, which includes publisher ﬁxed effects, the coefﬁcient becomes almost exactly zero and quite noisily estimated. Similarly,

Column 5 shows that the percentage of console publishers’ audience which was comprised of young men grew at just over 2% per year faster than mobile publishers, and is very precisely estimated. But, when including publisher ﬁxed effects, this effect also becomes quite nosy and is not precisely estimated in

Column 6.

Thus, while there is some evidence that the distance and audience focus grew over time, this evidence is limited. However, what is required to support the theory above is to show that the gap did not shrink over the ten year period. And, these results do provide evidence that this was not the case.20

Furthermore, there is little evidence that mobile publishers sought to move towards console publishers.

This too is consistent with the above, as to do so they would need to the skills and IP required to successfully make a console game. Overall, these results are consistent with console publishers achieving a competitive advantage in this space.

2.6 Conclusion

This ﬁnal section discusses the limitations of this study before summarizing its main ﬁndings and their implications.

2.6.1 Limitations

There are two key limitations to the above analysis. First, though this paper seeks to make a causal claim, that the decision to away from the dominant design caused incumbent game makers to be more profitable, these results are far from being well-identified. Thus, while this paper seeks to move the literature forward by quantifying the relationship between performance and adherence to the dominant design, it stops well short of meeting the standards of causal identification. To address this, I have attempted to incorporate

20For a further discussion of this issue, see the epilogue in Appendix C.

94 qualitative evidence and included a number of different speciﬁcations and robustness checks. But, there is certainly room for future research to revisit these questions in a setting that allows for better identiﬁcation.

Second, though Section 2.2 outlined three means by which a niche strategy might be successful for incumbents, the data do not allow each individual mechanism to be isolated, tested, and compared. While the results in Section 2.5.2 provide strong evidence for the first mechanism, that incumbents will be able to create a better product where they leverage their existing competencies, the results say less about the second two mechanisms. In particular, while these results are consistent with the second mechanism, that the fewer changes required the more likely the firm will be to get those changes right, this is not tested directly. And, the third mechanism, the avoidance of cannibalization of existing lines of business, is beyond the scope of the analysis all together. Thus, while the analysis provides evidence of the viability of this strategy on the basis of one mechanism, and shows some support for the second, it is not able to confirm the third or to compare the relative strengths of these mechanisms, creating a further opportunity for additional work in this space.

But, to where else do these findings generalize? As noted in Section 2.2, industries where this strategy is likely to be viable must possess two (relatively common) characteristics. First, the market must have sufficient heterogeneity in customer preferences as to allow for different niches. Second, conditional on the market having different customer segments, some of the segments must be nearer to the firms original market than others. 21

The generality of these conditions suggest that such a strategy could be viable in many markets. As the above four characteristics correlate highly with large numbers of diverse consumers, a natural extension is to other digital markets, given their extensive reach. But, this does not imply that the strategy is only viable for digital goods. For instance, the American fast-food industry has long been stagnant, with many analysts calling for signiﬁcant changes in established companies in order for them to enter the rapidly growing market for healthier, organic, and vegetarian food. Yet, such changes would require a complete overhaul of supply chains in order to bring fresher ingredients from a large number of smaller sources.

While most established ﬁrms have avoided this altogether, Burger King recently launched the “Impossible

21Note that while this latter condition is almost tautologically true for incumbents who’s core businesses are experiencing technological change, this strategy may not apply to ﬁrms moving into wildly different business which have no overlap. For example, see Raff and Temin (1999) for a discussion of Sears and Roebuck’s move from catalog to retail stores as compared to the company’s move from retail to ﬁnancial services.

95 Whopper” made of artiﬁcial meat which allowed it to reach its core audience, fast-food consumers, but with a new product that extended its inﬂuence into younger generations and more environmentally conscience consumers. Though still early, so far the results are quite promising.

2.6.2 Implications

These findings offer two contributions to our understanding of incumbent adaptation. The first is theoretical: by putting forward a new strategy for incumbents responding to architectural changes without having to overhaul their organization, thereby avoiding jeopardizing existing sources of revenue. This contribution is significant not only academically, in suggesting a different perspective on the phenomenon of technological change, but also managerially, in providing a new approach for firms seeking to compete in markets undergoing architectural innovations.

Second, methodologically, this paper deepens our understanding of the dominant design by seeking to quantify differences in product speciﬁcations. Though the literature on technology and innovation has provided a wealth of case studies, simulations, and histories of ﬁrms attempting to anticipate and affect the dominant design, to date there has been little work to quantify this effort. By identifying and implementing

96 the use of a distance measure from the statistics literature, this paper presents a means of measuring product differences in high-dimensional space, offering a tool that could potentially be used in many markets, including those very different from mobile gaming.

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100 2.8 Tables and Figures

Figure 2.1: Growth of Mobile Gaming

Mobile Console PC 100 90 80 70 60 50 40 30 20 10

Game Sales (USD BB) 0 2011 2012 2013 2014 2015 2016 2017 2018

Note: Figure 2.1 Uses data from Statista to show the total sales in software—not including hardware—in the PC, console, and mobile gaming markets in the dashed gray, dotted black, and solid orange lines, respectively.

Figure 2.2: Console Game Makers Entry

(a) Console Publishers’ Entry (b) Games Released Per Year

100% Console Publishers Mobile Publishers 90% 40 80% 70% 30 60% 50% 40% 20 30% 20% 10 10% 0% 0 Pct. Publishers Outstanding 2008 2010 2012 2014 2016 2018 Number of Publisher−Years 0 5 10 15 20 25 30 Number of Mobile Games Released

Note: Figure 2.2a Displays a hazard graph of market entry for the 15 publishers in the paper’s sample. The orange line displays the percentage of ﬁrms which had yet to make a single mobile game from 2008-2018. Figure 2.2b presents a histogram of the number of games released by publishers each year, with console publishers in orange and mobile publishers in gray. Publishers which did not release any games at all in a given year are removed.

101 Figure 2.3: Differences in Games Post-Release

(a) Nb. Versions Released per Year (b) Pct. Rev. from In-App Purchases

Mobile Publishers Console Publishers Mobile Publishers Console Publishers 30 400

25 300 20

15 200

10 100 5 Number of Games

Publisher x Year Obs. 0 0 0 5 10 15 20 25 0% 25% 50% 75% 100% Nb. Versions Released Per Game Per Year Pct. of Revenue from In App Purchases

Note: Figure 2.3a presents a histogram of the average number of versions publishers released per game per year by publisher background. Figure 2.3b displays a histogram of the percentage of each game’s revenue that comes from in-app purchases. Note that 43 games which made no revenue at all are removed from this graph. In both graphs, observations of console publishers are in dark orange, while the light gray represents mobile publishers. The dotted orange line represents the mean for console publishers, while the dotted gray line represents the mean for mobile publishers.

Figure 2.4: Publishers Relationship to the Dominant Design

(a) Distance Density Distributions (b) Distance and Revenue

Mobile Publishers Console Publishers ● Mobile Publishers ● Console Publishers

3 25

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Note: Figure 2.4a shows the density distributions for games made by console publishers, in orange, and mobile publishers, in gray. Log(Distance) utilizes the Gower distance measure discussed in Section 2.5.2. The dotted orange line represents the mean for console publishers, while the dotted gray line represents the mean for mobile publishers. In Figure 2.4b, the logged revenue of each game is plotted on the vertical axis, while the logged Gower distance from the top ten mobile games by mobile publishers is plotted on horizontal axis. Games made by mobile publishers are represented by gray dots while games made by console publishers are represented by orange dots. The orange line represents the linear (OLS) fit for the console publisher games, while the gray line represents the linear fit for mobile publishers’ games. The light gray area around each line represents the 95% confidence interval. In both plots, the ten games used to as the standard for the subsequent distance calculation have been removed from the analysis. .

102 Figure 2.5: Distributions of Dominant Design and Revenue

Mobile Publishers Console Publishers 20

15 Near 10 5 0 20 15 Distant 10 Number of Games 5 0 $0 $5 $10 $15 $20 $0 $5 $10 $15 $20 Log(Revenue)

Note: The Figure above displays a two-by-two matrix of histograms. The left two histograms show the distribution of games by mobile publishers, in gray, while the right two histograms show the distribution of console publishers, in orange. The top two histograms reﬂect games that are near the dominant design, measured as in the bottom third of distance as described in Section 2.5, while the bottom two histograms are the top third most distant from the dominant design. The dashed gray and orange lines, for mobile and console publishers respectively, represent the mean of the corresponding distributions. The ten games used to as the standard for the subsequent distance calculation have been removed from the analysis.

Figure 2.6: Changes Over Time

(a) Distance (b) Customer Base

Mobile Publishers Console Publishers Mobile Publishers Console Publishers

3.70

45% 3.65

3.60 35% 3.55 Log(Distance) Pct. Young Men 3.50 25% 2008 2010 2012 2014 2016 2018 2008 2010 2012 2014 2016 2018

Note: Figure 2.6a shows the changes in the average distance of games from 2008-2018. Similarly, Figure 2.6b displays the percentage of downloads from men 18-34, averaged by game, over the period. In both graphs, console publishers are represented by the dark orange line, while mobile publishers are signiﬁed by the gray line.

103 Table 2.1: Mobile Game Sample Summary Statistics

Console Publishers Mobile Native Publishers Statistic N Mean Median St. Dev. Min. Max. N Mean Median St. Dev. Min. Max.

General Year 415 2014 2014 2.55 2008 2018 519 2014 2013 2.25 2008 2018 Size (in mb) 415 501.97 199 686.6 5 3,682 519 381.62 202 456.53 8 2,681 Price (USD) 415 2.72 0 4.66 0 20.99 519 0.84 0 1.79 0 9.99 Freemium 415 0.55 1 0.5 0 1 519 0.74 1 0.44 0 1

Intellectual Property Cross-Platform Release 415 0.19 0 0.39 0 1 519 0.06 0 0.24 0 1 Console Franchise 415 0.54 1 0.5 0 1 519 0.01 0 0.1 0 1 Mobile Franchise 415 0.06 0 0.23 0 1 519 0.22 0 0.41 0 1 Non-Game IP 415 0.13 0 0.34 0 1 519 0.14 0 0.35 0 1 104 Release Details Released for iPhone 415 0.9 1 0.3 0 1 519 0.88 1 0.33 0 1 Released for iPad 415 0.8 1 0.4 0 1 519 0.84 1 0.37 0 1 Released for Android 415 0.48 0 0.5 0 1 519 0.41 0 0.49 0 1 Nb. Version Updates 402 17.00 8 23.59 1 156 504 29.29 9 42.41 1 204

Performance Total Revenue (USD mm) 415 17.27 0.16 89.56 0 1,075.94 519 90.78 0.13 555.76 0 7,373.40 Total Downloads (mm) 415 7.74 0.26 25.15 0 196.91 519 18.86 0.35 53.22 0 425.31 Pct. Rev. from In-App Purchases 394 0.60 1 0.46 0 1 497 0.77 1 0.40 0 1 Pct. Female Users 323 0.42 0.38 0.14 0.2 0.85 419 0.49 0.45 0.16 0.11 0.85

Note: The above table displays the summary statistics for the mobile game data used in the analysis. For a description of the variables, see Section 2.4.2. Note that Nb. Versions Released has only 906 games, as the number of versions could not be conﬁrmed for 28. Similarly, 43 games have been removed Pct. Revenue from In-App Purchases as they made zero revenue, and therefore, the percentage of revenue from in-app purchases cannot be calculated. And, ﬁnally, Pct. Female Users has only 742 observations as demographic data was not available for 192 games. Table 2.2: Linear Regression: Performance Differences

Log(Downloads) Log(Download Revenue) Log(In-App Purchase Revenue) (1) (2) (3) (4) (5) (6) Console Publisher -0.453 -0.625 3.197∗∗ 1.522∗∗ -2.421∗ -1.814∗∗ (0.801) (0.464) (1.065) (0.547) (1.142) (0.696) Price -0.188∗∗∗ 1.099∗∗∗ -0.780∗∗∗ (0.028) (0.176) (0.106) Observations 934 934 934 934 934 934 Nb. of Publishers 30 30 30 30 30 30 Year Fixed Effects Y Y Y Y Y Y Genre Fixed Effects N Y N Y N Y Platform Fixed Effects Y Y Y Y Y Y R-Squared 0.525 0.592 0.208 0.622 0.291 0.469 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Note: The table above presents the results from a linear (OLS) speciﬁcation with standard errors (in parentheses) clustered in two dimensions: Publisher and Year. In the ﬁrst two columns, the dependent variable is logged downloads, while in columns 3 and 4 the dependent variable is revenue from the initial download. Finally, the dependent variable in columns 5 and 6 is the revenue from in-app purchases.

Table 2.3: Linear Regression: Repeat Collaborations

Pr(Repeat Collaboration) Log(Nb. Past Collaborations) (1) (2) (3) (4) (5) (6) Console Publisher -0.229∗∗∗ -0.234∗∗∗ -0.301∗∗ -0.250 -0.306∗∗ -0.496∗∗∗ (0.061) (0.055) (0.096) (0.150) (0.129) (0.150) Observations 934 934 306 934 934 306 Nb. of Publishers 30 30 24 30 30 24 Genre Fixed Effects N Y Y N Y Y Year Fixed Effects Y Y Y Y Y Y Platform Fixed Effects Y Y Y Y Y Y R-Squared 0.150 0.166 0.364 0.169 0.186 0.387 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Note: The table above presents the results from a linear (OLS) specification with standard errors (in parentheses) clustered in two dimensions: Publisher and Year. In the first three columns, the dependent variable is a binary variable equal to one if the publisher and developer had collaborated before. In the second three columns, the dependent variable is the logged number of collaborations. Columns 3 and 6 use the same specification but vertically integrated developers (those owned by the publisher) are removed from the sample.

105 Table 2.4: Linear Regression: Distance and Revenue

Log(Revenue) Log(Downloads) Log(Revenue per Download) (1) (2) (3) (4) (5) (6) Log(Distance) -2.530 -0.519 -2.869 -0.359 0.231 0.081 (1.716) (1.581) (1.960) (0.948) (0.320) (0.321) Console Publisher -26.971∗∗ -8.981 -5.685∗∗∗ (9.766) (7.463) (1.650) Console Publisher x Log(Distance) 7.386∗∗ 5.754∗ 2.451 0.556 1.609∗∗∗ 1.636∗∗∗ (2.748) (2.877) (2.239) (1.408) (0.447) (0.515) Observations 924 922 924 922 924 922 Nb. of Publishers 30 28 30 28 30 28 Year Fixed Effects Y Y Y Y Y Y Publisher Fixed Effects N Y N Y N Y Platform Fixed Effects Y Y Y Y Y Y R-Squared 0.472 0.548 0.529 0.644 0.151 0.250 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 106 Note: The table above presents the results from a linear (OLS) specification with standard errors (in parentheses) clustered in two dimensions: Publisher and Year. In the first two columns, the dependent variable is logged revenue, while in columns three to four the dependent variable is logged downloads. Finally, in columns five and six, the dependent variable is logged revenue per download. Distance is measured as Gower distance from the top ten mobile games by mobile publishers. In addition to the fixed effects noted in the table, each model includes an indicator variable equal to one if the game data on size and price was recorded from App Annie, and equal to zero if it was collected from another source. Note that Console Publisher drops out of the specification once publisher fixed effects are included. The ten games used to as the standard for the subsequent distance calculation have been removed from the analysis. Table 2.5: Linear Regression: Revenue and Audience

(1) (2) (3) (4) (5) Pct. Young Men -0.021 -0.016 -0.062 -0.063∗∗ -0.064∗∗ (0.026) (0.024) (0.036) (0.027) (0.028) Console Publisher -0.488 -3.119∗∗ (0.545) (1.004) Console Publisher x Pct. Young Men 0.139∗∗ 0.157∗∗∗ 0.154∗∗∗ (0.044) (0.032) (0.030) Observations 734 734 734 732 732 Nb. of Publishers 29 29 29 27 27 Publisher Fixed Effects N N N Y Y Genre Fixed Effects N N N N Y Year Fixed Effects Y Y Y Y Y Platform Fixed Effects Y Y Y Y Y R-Squared 0.421 0.424 0.438 0.541 0.543 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 107 Note: The table above presents the results from a linear (OLS) regression with logged revenue as the dependent variable, and standard errors (in parentheses) clustered in two dimensions: Publisher and Year. Pct. Young Male is the sum of the percentage of users listed in the “male 18-24” and “male 25-34” categories. Note that Console Publisher drops out of the speciﬁcation once publisher ﬁxed effects are included. The ten games used to as the standard for the subsequent distance calculation have been removed from the analysis. Table 2.6: Linear Regression: Changes Over Time

Log(Distance) Pct. Young Men (1) (2) (3) (4) (5) (6) Console Publisher 0.115∗∗∗ 0.065∗ 2.899∗∗ 0.270 (0.034) (0.031) (1.301) (1.108) Year -0.005∗ -0.009∗∗∗ 0.001 -0.168∗ -0.369∗∗ -0.063 (0.002) (0.002) (0.005) (0.092) (0.132) (0.108) Console Publisher x Year 0.007∗ -0.001 0.386∗∗∗ 0.155 (0.004) (0.005) (0.094) (0.136) Observations 924 924 922 734 734 732 Nb. of Publishers 30 30 28 29 29 27 Platform Fixed Effects Y Y Y Y Y Y Publisher Fixed Effects N N Y N N Y R-Squared 0.118 0.121 0.352 0.042 0.046 0.259 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

108 Note: The table above presents the results from a linear (OLS) regression in which standard errors (in parentheses) clustered in two dimensions: Publisher and Year. In the first three columns, the dependent variable is the logged distance from the top mobile games, while in the latter three columns the dependent column is the sum of the percentage of users listed in the “male 18-24” and “male 25-34” categories. Note that Console Publisher drops out of the specification once publisher fixed effects are included in columns three and six. The ten games used to as the standard for the subsequent distance calculation have been removed from the analysis. Chapter 2 Appendix Appendix A: Additional Tables and Figures

As discussed in Section 2.5, the distance measure measures the distance from the top 10 games with the most revenue made by mobile publishers. The list of 10 games is printed below in Table A2.

Table A2: Top 10 Games Used as the Dominant Design

Publisher Title Cumulative Revenue (MM USD) King Candy Crush Saga $7,373.40 Supercell Clash of Clans $6,567.72 Nantic Pokemon´ Go $4,171.82 Machine Zone Game of War: Fire Age $3,778.69 Supercell Clash Royale $3,162.46 King Candy Crush Soda Saga $3,014.52 Playtika Slotomania Vegas Casino Slots $2,121.29 Machine Zone Mobile Strike $2,035.37 Kabam Marvel Contest of Champions $1,178.18 Playrix Entertainment Homescapes $963.57

Note: The above shows the top 10 games by revenue, all of which are used as the dominant design for the distance measures in Section 2.5. Revenues are the cumulative mobile revenues from 2013-2018 in millions of dollars.

110 Appendix B: Further Details on Mobile Gaming While a complete history of mobile gaming is beyond the scope of this paper, it is worth further discussing mobile gaming’s rise and the difference between it and console gaming. As noted in Section 2.3, electronic gaming traces its past back to the 1960s, where Ralph Baer designed a “Brown Box” on which Pong could be played (Baer 1998; Cortada 2005). Console gaming continued to progress in ﬁts and starts through the 1990s and into the 2000s (Donovan 2010). Computer gaming developed in parallel at this time, with its popularity rising rapidly in the 1990s (Lowood 2009; Kent 2010). As Figure B1 shows below, the earliest mobile game was Snake, which was made popular by being hard-coded into Nokia phones during the latter half of the 1990s (Wright 2016). Though console gaming had gone “mobile” in the sense of hand-held consoles, such as the Game Boy in 1989, mobile gaming is used as a term of art to explicitly refer to games on phones. Yet, such early mobile games were very primitive, bearing a much closer resemblance to Baer’s “Brown Box” than to contemporary Nintendo and PlayStation games. Phone makers and game companies alike recognized that the very limited technology of mobile phones at the time greatly hampered gaming abilities. This led to the development of internet protocols for phones, such as WAP and J2ME both of which launched in the early 2000s, that would allow users to download new games to their phone (Wright 2016). Yet, these game still lacked color and had only primitive controls and memory. Additionally, though games could be downloaded from online stores, these were not “App Stores” in the sense that third-parties could not post games online without extensive agreements directly with the phone makers. As a result, the overall market was quite small and dominated by a few key players. Though mobile games continued to progress technologically through the 2000s, it was not until 2008, when Google and Apple launched their App Stores that mobile gaming truly took off. There are a number of reasons that mobile gaming grew so large so quickly, and indeed there exists a notable body of work analyzing the rise of mobile games (see, for example, Basole and Karla 2011; Bresnahan and Greenstein 2014; Yin et al. 2014; Kapoor and Agarwal 2017). In general, this work emphasizes four key reasons for the boom in mobile gaming: a) the tremendous increase in graphics and creation of touch screens signiﬁcantly improved the quality of games; b) internet access meant users could download games that appealed to them with ease; c) the creation online stores allowed third-party game developers to create games, thereby dramatically expanding supply; d) the ubiquity of smart phones meant that the market for mobile games was extremely large. It is important to emphasize, however, that mobile games have always been very different and distinct from console games. While the above paper emphasizes some of these differences, Table B1 displays a more comprehensive overview.

111 Table B1: Key Differences Between Mobile and Console Games

Console Games Mobile Games Development Development Cost > $10 million < $1 million Team Size (FTE) 100-300 1-10 Time Required 2-3 years 2-3 months Post-Release Updates (i.e., “LiveOps”) Rare, limited, and difﬁcult Frequent and anticipated

Technological Typical Size ˜50gb ˜400 mb Maximum Processing Load (in Watts) 110 (Xbox One), 137 (PS4) 4-5 (iPhone), 10-12 (iPad) Screen Size ˜43 inches ˜6 inches Controls Dedicated hardware Touch screen

Gameplay Most Common Game Type Serious Casual Most Common Genre Shooter Puzzle Learning Curve Steep before leveling off Slow, then rising rapidly Typical Use-Case Home On-the-go

Revenue Primary Source of Revenue Purchase Price In-Game Purchases

Note: The above table shows key differences between console games and mobile games. Note that this Table is meant to be descriptive, and therefore discusses general differences between the two types of games as a whole. For an empirical description of mobile game features, see Table 2.1.

Note that there are many more differences than are discussed above, in Section 2.3. However, the main text seeks to highlight only the most germane characteristics in an attempt to spare the reader a lengthy and unnecessary discussion.

112 Appendix C: Epilogue This paper focuses on the period of 2008-2018 for a variety of reasons, not least of which is that much of the data had to be hand-collected and these efforts began in early 2019. Thus, this paper is limited in what it can say about changes in the industry from 2018 to 2020. But, to provide further background on the industry, as well as to bolster the claim of a segmented market, it is helpful to provide a bit more information on changes in electronic gaming since 2018. In particular, this appendix speaks to three key changes: the prevalence of the free-to-play revenue model, the rise of live-services in console gaming, and the possible merging of the two markets. As Figure 2.6 and Table 2.6 show, the overall distance between mobile and console publishers did not decline over the period of 2008-2018, and if anything, increased slightly as console publishers focused more on their core market of young men. But, it is important to note that the distance measure is comprised of a variety of factors, not all of which moved in the same direction over the period. In particular, console publishers began to experience the ‘penny gap,’ which describes the fact that users are much less likely download something for even a single cent than if it is free. As a result, console publishers increasingly began to offer free-to-play games in order to compete on downloads. This alone would result in less distance to the dominant design. But, this focus occurred simultaneously with an increased focus on traditional genres, gameplay, and IP, which largely offset the change in price. Additionally, mobile publishers became increasingly laser focused on making games according to the dominant design outlined above. As a result of these two forces, console game makers’ distance relative to mobile natives was on average roughly constant, if not slightly increasing. Though Figure C1 does not display the time trends for all the variables used in the distance measure, it does provide two, free to play, in Figure C1a, and the genre “idle” in C1b. Quite clearly, the percentage of games which were free to download increases substantially over the period. But, the percentage of idle games, synonymous with casual gaming, decreases over the period as well for console gaming.

Figure C1: (Additional) Changes over Time

(a) Free-to-Play (b) “Idle” Genre

Mobile Publishers Console Publishers Mobile Publishers Console Publishers 100% 100%

75% 75%

50% 50% Pct. FTP 25% 25% Pct. Idle Games

0% 0% 2008 2010 2012 2014 2016 2018 2008 2010 2012 2014 2016 2018

Note: Figure C1a shows the changes in the percentage of games which were free-to-play (“FTP”), i.e., had a download price of zero, for console publishers, in orange, and mobile natives, in gray. Similarly, Figure C1b displays the percentage of games which were released in the “idle” genre.

113 A further nuance, which this dataset is not able to capture, is the difference in sources of revenue for free-to-play games for console and mobile publishers. As described in the main text, the primary source of revenue for free-to-play games has been consumers making in-app purchases. A further means of revenue, however, is the use of adds within the mobile game (much like commercials in a television program). While industry accounts suggest these are a relatively recent development and are not nearly as profitable as in-app purchases, unfortunately, the ThinkGaming data set does not provide this source of revenue. While industry accounts further imply that console game makers are more reliant on this source of revenue, this cannot be measured. Nonetheless, this change is consistent with the arguments put forward in this paper as in-app adds would not require further changes to the game (much like commercials do not require re-making a movie), allowing console publishers to attempt free-to-play games without the pain of the live services. The second key trend over this period which deserves further discussion is the growing use of online services in console games. The Sony PlayStation was the first console to offer online capabilities in the 1990s, but even this console still relied on physical CDs to actually play games. It was not possible to update a game in the manner of a mobile game until 2006, when Sony launched the Play Station Store. This change was promptly followed by Microsoft, and much later, Nintendo, so that all major console now allow games to be downloaded (and bought physically), and offer the possibility of ongoing updates. Yet, this has not transformed console gaming into the world of mobile gaming. Console game makers still focus on creating a finished project at launch and have resisted the creation of free games. While there are likely a number of reasons for this, a key one remains console game customers. To date, console customers have actively resisted this trend; to provide one example, when Electronic Arts attempted to defend its use of in-app purchases in the game Star Wars Battlefront in 2017 on the popular social media site Reddit, it became the most-downvoted post in Reddit’s history, earning over 667,000 downvotes.22 Though online updates do occur, they are not the regular, weekly updates of a mobile game, but distinct product launches that come with a good deal of fan-fair. In fact, sites such as MobyGames track them as distinct game releases. The fact that console game makers could not move to a free-to-play model for console games, and that there was no analogue to the live services of mobile games in console gaming is important as it underlines the extent to which console game makers could not simply merge their two businesses. Had the console gaming industry moved immediately to imitate mobile gaming, console publishers may well have overhauled their businesses in a manner consistent with Christensen (1993; 1997). But, the stable differences between the two markets made this impossible. Finally, as Figure B1 hints at, there is some question of whether or not mobile gaming and console gaming will eventually converge. Though initially impossible for a variety of technical reasons (see Table B1), the rapid increase in mobile gaming technology has recently suggested that this could be a possibility. Indeed, the popular game Fortnight already allows console players and mobile players to play against each other, which its immense profitability suggesting that others will likely follow suite. Yet, up to this point (February of 2020), the technological gap has prohibited this possibility. Additionally, the difference in customer segments and use-cases has furthered this gap. When I asked this question to one industry veteran with extensive experience developing games for both consoles and mobile phones, he replied:

There is a level of convergence. Modern phones are around PS3+ levels of performance (phones better gpus but worse cpus, more ram, more hdd space) but the controls are really the

22See https://www.reddit.com/r/StarWarsBattlefront/comments/7cff0b/seriously_i_paid_80_to_have_ vader_locked/dppum98/. Note that the Reddit algorithm breaks the one-to-one link between a comments’ points and the number of votes at high-numbers, suggesting that the post was down-voted much more than the 667,000 times its point total would suggest.

114 hard thing to get [sic]. ...Consoles and PCs will always have more processing power available. The bigger issue is how long will that matter for? 4k on a phone running 60fps will be an amazing experience. Going past that won’t gain us much. We are seeing really diminishing returns on console going from 1080 to 4k - only about 20% of the games I’ve played in 4K “feel” 4k. So the rapid advancement of hardware just means that at some point both platforms will pass some threshold and, from a user standpoint, will deliver the same experience. Controls will still be a thing but that is solvable and I expect we will see haptic gloves taking over as primary input in the next decade [sic].

Thus, while it is likely that eventually the markets will converge, this has not happened yet, and certainly not in the period this paper focuses on. And, relative to past work, it is important to emphasize that this paper covers a large period—over 10 years— relative to only a couple in past work (e.g., Christensen 1993). So, though mobile games may “disrupt” console games in the future, the period this paper covers is long enough and the market large enough, for console publishers to focus on this as a distinct market.

115 Appendix D: Euclidean Distance While the creation of a distance measure relies on a number of mathematical and statistical principles, it also relies heavily on institutional knowledge and the researcher’s judgment. While this led me to the use of Gower distance, in this Appendix I look to explain the intuition behind this choice further. Though there are many distance measures one could use as a comparison (see Cha 2007), it is helpful to provide a simulated example of why Euclidean distance comes into difﬁculty given its prevalence. To recall, the Euclidean distance (Deuc) between two games, i and j, with an υ number of features, k, each with weight ωk is measured as:

s υ euc 2 Di j = ∑ ωk(ik − jk) k=1

The key challenge when faced with a mixed data set is relating categorical variables to continuous variables. How should one think about the difference between a game that is $10.00 and one that is free, compared to the difference between an action game and a puzzle game? The most common way to address differences in units when using Euclidean distance is to standardize the data to have a mean of zero and unit standard deviation. While this makes intuitive sense for continuous variables, scaling categorical variables in this manner results in rare categories having exceptionally high values. For instance, in a group of 100 people, 49 with blonde hair, 49 with brown hair, and 2 with red hair, those with red hair will be of a signiﬁcantly higher “distance” from those with brown hair than those with blonde hair will be, as Table D1 shows. In the context of mobile games, the rarity of certain genres and sub-genres in this data set would result in a similar problem, with those in rare genres being extraordinarily different, more so than their game play would justify.

Table D1: Euclidean Distance and Categorical Variables Using Stylized Data

Hair Color Blonde Brown Red Initial Values Mean 0.49 0.49 0.02 Standard Deviation 0.5 0.5 0.014 Standardized Values Max 1.015 1.015 6.965 Min -0.975 -0.975 -0.142 Difference 1.99 1.99 7.107 Distances from: Blonde 0 2.815 7.381 Brown 2.815 0 7.381 Red 7.381 7.381 0

Note: The table above is illustrative, simulating data on 100 observations. In this example, 49 have brown hair, 49 have blond hair, and 2 have red hair.

116 An alternative approach would be to leave the binary variables at either zero or one, and to scale the continuous variables by bounding them between zero and one. While this resolves the problem of rare categories, it requires signiﬁcant transformation of the data in a non-traditional way. Nonetheless, using this approach, I reproduce the results using the same weights as described in Section 2.5.2. Figure D1 presents the distance densities and relationship between distance and revenue for mobile and console publishers. Overall, though the distance scale is different, the results remain very similar to those in Figure 2.4 with the mean of console publishers being on average much greater than for mobile publishers (p = 0.002). And, the opposing relationships between distance and revenue for the publishers remains—and, in fact, grows—in Figure D1b.

Figure D1: Deviation from the Dominant Design Using Eucliean Distance

(a) Distance Density Distributions (b) Distance and Revenue

Mobile Publishers Console Publishers ● Mobile Publishers ● Console Publishers 5 25

● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● 4 ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●●● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●●●●● ● ● ●● ●● ●● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ●●● ● ●●● ● 3 ● ● ● ● ● ●● ● ● ●● ● ●● ● ● 15 ● ● ● ● ● ● ● ● ● ● ● ●●●● ●● ●● ● ●● ● ●●●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ●●● ●● ● ● ● ●● ● ● ● ●● ● ●● ●● ● ● ●● ● ●● ● ● ● ● ● ● ●● ●●● ● ● ●●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●●●● ●● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ●● ●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●●● ● ● ● ● ●●●●● ●● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●●●● ● ●● ● ●● ●● ● ●●● ●● ●●● ●●● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● 2 ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● 10 ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● Density ● ● ● ● ● ●●● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● Log(Revenue) ●● ● ● ● ● ● 1 ●● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 0 ● ● ● ●● ● ● ● ●●● ● ●● ● ● ● ●●●●● ●● ●●● ●● ● ● ● ●● ● ● ● ● 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.3 5.4 5.5 5.6 5.7 5.8 5.9 Log(Distance) Log(Distance)

Note: Figure D1a shows the density distributions for games made by console publishers, in orange, and mobile publishers, in gray. Log(Distance) utilizes the Euclidean distance measure discussed above. The dotted orange line represents the mean for console publishers, while the dotted gray line represents the mean for mobile publishers. In Figure D1b, the logged revenue of each game is plotted on the vertical axis, while the logged Euclidean distance from the top ten mobile games by mobile publishers is plotted on horizontal axis. Games made by mobile publishers are represented by gray dots while games made by console publishers are represented by orange dots. The orange line represents the linear (OLS) fit for the console publisher games, and the gray line represents the linear (OLS) fit for mobile publishers’ games. The light gray area around each line represents the 95% confidence interval. In both plots, the ten games used to as the standard for the subsequent distance calculation have been removed from the analysis..

Repeating the regression analysis using Euclidean distance tells a similar story to Figure D1, above. As Table D2 shows, the negative relationship between distance and revenue remains large and statistically signiﬁcant at conventional levels, in Column 2. Similarly, the two types of publishers are not statistically distinguishable in terms of the relationship between logged downloads, as Column 4 reveals. Finally, the relationship between revenue per downloads is also large and statistically signiﬁcant in Column 6. Overall, these results are substantively the same, and, if anything, larger than the main results prevented above.

117 Table D2: Linear Regression: Euclidean Distance and Revenue

Log(Revenue) Log(Downloads) Log(Revenue per Download) (1) (2) (3) (4) (5) (6) Log(Distance) -5.444∗ -2.109 -6.543∗ -2.314 0.599 0.414 (2.760) (2.971) (3.591) (2.207) (0.549) (0.668) Console Publisher -79.173∗∗ -17.767 -19.126∗∗∗ (27.658) (24.187) (4.318) Console Publisher x Log(Distance) 13.982∗∗ 11.651∗ 3.144 0.347 3.407∗∗∗ 3.333∗∗ (4.941) (5.495) (4.408) (3.088) (0.763) (1.069) Observations 924 922 924 922 924 922 Nb. of Publishers 30 28 30 28 30 28 Year Fixed Effects Y Y Y Y Y Y Publisher Fixed Effects N Y N Y N Y Platform Fixed Effects Y Y Y Y Y Y R-Squared 0.475 0.548 0.538 0.645 0.174 0.265 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Note: The Table above presents the results from a linear (OLS) specification with standard errors (in parentheses) clustered in two dimensions: Publisher and Year. In the first two columns, the dependent variable is logged revenue, while in columns three to four the dependent variable is logged downloads. Finally, in columns five and six, the dependent variable is logged revenue per download. Distance is measured as Euclidean distance from the top 10 mobile games by mobile publishers. In addition to the fixed effects noted in the table, each model includes an indicator variable equal to one if the game data on size and price was recorded from App Annie, and equal to zero if it was compiled from another source. Note that Console Publisher drops out of the specification once publisher fixed effects are included. The 10 games used to as the standard for the subsequent distance calculation are removed from the analysis.

Given these results, why still use the Gower distance? While one can bend the data to make Euclidean distance work, the ﬁnal result is not straight-forward, nor is it consistent with the approach more generally of the data science and machine learning communities. Ultimately, I felt that Gower distance, by explicitly scaling the data and accounting for the difference in the data types, more clearly informs the reader how differences in the types of data are being modeled, leading to a more transparent approach.

118 Appendix E: Comparing Different Distance Weights In examining which variables to include and how to weight them, a researcher must take into account the curse of dimensionality, which implies that having few variables will results in too much similarity, while too many will result in too little. As discussed in Section 2.4, the data set in this paper is comprised of 7 relevant variables to measure distance: price, size (in mb), dominant color hue, perspective, genre, sub-genre, and IP. Note, that while the first 6 are mutually exclusive, IP consists of nine categories which are not mutually exclusive (e.g., a game can use both movie IP and be a console franchise). One option would be to create multiple different IP variables and weight them individually. However, this significantly increases the number of variables, thereby creating even greater distance between games. Instead, I divide the weight of IP evenly between each of the measures, so that while each individual IP category is given less weight, the total weight given to IP is comparable to the other categories. Though of course an infinite number of weights is possible, I choose to present three weighting schemes in addition to the one used in the main text to illustrate different perspectives of the distance measure, as outline in Table E1. The first column, “Main,” displays the weights (in percentages) of the results in the main text. In columns 2 and 3, I create weights to only use categorical and continuous variables, respectively. Finally, the fourth column is “Restricted” in that it only selects variables that are fundamental to game play. While the “Main” results can be found in the main text above, the results for the other three are presented below. For the sake of brevity, the figure and table notes have been removed from the below. They would be, however, the same as presented in the main text. In examining the results of the three other schemes below, two key points emerge. First, the overall relationship remains substantively the same. In all three schemes, console publishers produce games which are on average more different from the dominant design than mobile publishers (this difference remains significant at the p = 0.001 level for all three measures). And, they earn more revenue, and in particular, more revenue per download, on more distant games. Second, as one would expect, removing variables results in less distance, and therefore noisier results. While the final column showing revenue per download remains statistically significant in each table, the relationship between revenue and distance become noisier in each of the three, and remains significant at conventional levels only in the restricted specification.

Table E1: Comparison of Different Distance Weights

Weighting Scheme Variable Main Continuous Categorical Restricted Price 14.29% 33.33% 0.00% 20.00% Size (in MB) 14.29% 33.33% 0.00% 0.00% Dominant Color 14.29% 33.33% 0.00% 20.00% Perspective 14.29% 0.00% 25.00% 20.00% Genre 14.29% 0.00% 25.00% 20.00% Sub-Genre 14.29% 0.00% 25.00% 20.00% IP 14.29% 0.00% 25.00% 0.00%

Note: The Table above presents the different weighting schemed used in this paper. “Main” is the weighting scheme used in the main results, while the main results for the other weighting schemes are used below.

119 Continuous Variables Only

Figure E1: Deviation from the Dominant Design - Continuous

(a) Distance Density Distributions (b) Distance and Revenue

Mobile Publishers Console Publishers ● Mobile Publishers ● Console Publishers 1.25 25

● ● ● ● ● ● ●● ● ● ● 1.00 ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ●● ● ● ● ● 0.75 ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ●● 15 ●● ● ●●● ● ● ● ● ● ● ● ●● ●●● ● ● ●●● ● ●● ● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ●●● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ●● ● ● ●●● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ● ● ●●●● ● ● ● ● ●●● ● ● ●● ●●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● 0.50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10 ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ●● ● Density ●● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●●● ● ● ●● ● ●● ● ● ● ● ● ●● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● Log(Revenue) ● ● ● ● ● ● ● 0.25 ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.00 0 ●●● ●● ●●● ●●● ● ●● ● ● ● ● ●● ●● ●●●●●●●● ●●●● ●● ● ● ● ● 2.5 3.0 3.5 4.0 2.5 3.0 3.5 4.0 Log(Distance) Log(Distance)

Table E2: Linear Regression: Gower Distance and Revenue - Continuous

Log(Revenue) Log(Downloads) Log(Revenue per Download) (1) (2) (3) (4) (5) (6) Log(Distance) -0.374 0.198 -1.180 -0.599∗∗ 0.383∗∗∗ 0.382∗∗∗ (0.732) (0.437) (0.675) (0.237) (0.051) (0.106) Console Publisher -4.841 -1.077 -1.267∗∗∗ (2.753) (2.124) (0.147) Console Publisher x Log(Distance) 1.582 1.062 0.310 -0.085 0.489∗∗∗ 0.427∗∗∗ (0.965) (0.753) (0.822) (0.417) (0.026) (0.128) Observations 924 922 924 922 924 922 Nb. of Publishers 30 28 30 28 30 28 Year Fixed Effects Y Y Y Y Y Y Publisher Fixed Effects N Y N Y N Y Platform Fixed Effects Y Y Y Y Y Y R-Squared 0.464 0.542 0.533 0.647 0.231 0.306 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

120 Categorical Variables Only

Figure E2: Deviation from the Dominant Design Using Weights - Categorical

Mobile Publishers Console Publishers ● Mobile Publishers ● Console Publishers 25 3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 15 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Density ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Log(Revenue) ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3.25 3.50 3.75 4.00 3.25 3.50 3.75 4.00 Log(Distance) Log(Distance)

Table E3: Linear Regression: Gower Distance and Revenue - Categorical

Log(Revenue) Log(Downloads) Log(Revenue per Download) (1) (2) (3) (4) (5) (6) Log(Distance) -2.440 -0.676 -2.376 0.055 0.036 -0.194 (1.511) (1.457) (1.737) (1.033) (0.314) (0.297) Console Publisher -25.008∗ -11.482 -3.390 (11.282) (7.894) (2.716) Console Publisher x Log(Distance) 6.633∗ 5.318 3.023 0.981 0.956 1.194∗ (3.056) (3.157) (2.252) (1.709) (0.709) (0.601) Observations 924 922 924 922 924 922 Nb. of Publishers 30 28 30 28 30 28 Year Fixed Effects Y Y Y Y Y Y Publisher Fixed Effects N Y N Y N Y Platform Fixed Effects Y Y Y Y Y Y R-Squared 0.468 0.544 0.528 0.644 0.110 0.221 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

121 Restricted Variables

Figure E3: Deviation from the Dominant Design - Restricted

(e) Distance Density Distributions (f) Distance and Revenue

Mobile Publishers Console Publishers ● Mobile Publishers ● Console Publishers 3 25

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Table E4: Linear Regression: Gower Distance and Revenue - Restricted

Log(Revenue) Log(Downloads) Log(Revenue per Download) (1) (2) (3) (4) (5) (6) Log(Distance) -2.352 -1.159 -2.491 -0.775 0.273 0.214 (1.885) (2.040) (2.104) (1.332) (0.272) (0.356) Console Publisher -25.592∗∗ -4.847 -7.396∗∗∗ (10.830) (9.542) (1.550) Console Publisher x Log(Distance) 6.188∗∗ 4.999∗ 1.120 -0.108 1.851∗∗∗ 1.683∗∗∗ (2.696) (2.625) (2.469) (1.430) (0.382) (0.500) Observations 924 922 924 922 924 922 Nb. of Publishers 30 28 30 28 30 28 Year Fixed Effects Y Y Y Y Y Y Publisher Fixed Effects N Y N Y N Y Platform Fixed Effects Y Y Y Y Y Y R-Squared 0.466 0.542 0.528 0.644 0.163 0.255 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

122 Appendix F: Sensitivity Tests on Distributions This appendix examines the sensitivity of the distributions in regards to dominant design, as discussed in Section 2.5.2. In the main text, I use the top and bottom third of games, as measured by distance percentiles. Here, I report the results for the top 50%, top 25% and top 10%. For the sake of brevity, the ﬁgure notes are omitted. Figure F1 Shows the results for a median split - the top 50% of games and the bottom 50% of games by distance. In this graph, the difference in mean logged revenue for the most distant compared to the least-distant games for console publishers is relatively large (12.106 compared to 10.510, p = 0.001), while the difference for mobile for mobile publishers is negligible (11.816 and 11.334, p = 0.257). Thus, at the median split, console publishers do better, on average, for the more distant games than mobile publishers, but worse on average for the nearer games than mobile publishers. Mobile publishers, interesting, average roughly the same for both.

Figure F1: Distributions of Dominant Design and Revenue - Top and Bottom 50 pct.

Mobile Publishers Console Publishers 20

15 Near 10 5 0 20 15 Distant 10 Number of Games 5 0 $0 $5 $10 $15 $20 $0 $5 $10 $15 $20 Log(Revenue)

Figure F2 Shows the results for the top and bottom quartile of games, by distance. For console publishers, the result is very similar - the more dissimilar (distant) games average 12.322, while the more similar (near) games average 10.134 (p = 0.001). Mobile publishers exhibit a slightly larger difference here, though the difference remains quite noisy. Their games nearer the dominant design average 13.159, while their more distant games average 12.432 (p = 0.263).

123 Figure F2: Distributions of Dominant Design and Revenue - Top and Bottom 25 pct.

Mobile Publishers Console Publishers 20

15 Near 10 5 0 20 15 Distant 10 Number of Games 5 0 $0 $5 $10 $15 $20 $0 $5 $10 $15 $20 Log(Revenue)

Finally, in Figure F3, the results for the top and bottom 10% are shown. Here, the results become quite noisy for both. Console publishers continue to average more for their more distant games (12.673 compared to 11.748, p = 0.461), while mobile publishers’ results become indistinguishable (13.532 versus 13.250, p = 0.790), largely owing to their remarkably small number of observations at the more distant end.

Figure F3: Distributions of Dominant Design and Revenue - Top and Bottom 10 pct.

Mobile Publishers Console Publishers 20

15 Near 10 5 0 20 15 Distant 10 Number of Games 5 0 $0 $5 $10 $15 $20 $0 $5 $10 $15 $20 Log(Revenue)

Overall, the results are remarkably stable for console publishers. In each graph, the gap between the mean log revenue is much higher for “Distant” games as opposed to “Near” games for console publishers. In contrast, mobile publishers have a distinctly smaller gap across the board. That said, the sparsity of games in the top 10% of distance makes it difﬁcult to compare the means for mobile publishers, given the low number of observations.

124 Appendix G: Genre Though genre is relatively straight-forward to measure in the world of film, and, to some extent, novels, it is surprisingly difficult to measure in the world of video games (see Campbell-Kelly 2003; Herz 1997). Previous work in strategy and organizational theory has often used MobyGames’ genre measure (see, for example De Vaan et al. 2015; Mollick 2012); however, as noted in Section 2.4.1, the measure of genre provided by MobyGames (and, for that matter, ThinkGaming) was not internally consistent for mobile games. For example, different Angry Birds sequels were assigned to different genres though featuring virtually the same game play. This difficulty likely stems from the fact that MobyGames began by focusing on console and computer games and was therefore ill-equipped to categorize mobile games, which often feature very different gameplay. It is worth emphasizing, in light of the popularity of the MobyGames dataset in academic research, that I did not discover any genre problems for other forms of electronic gaming. But, given the importance of the genre measure to this study, I sought to improve this measure in two ways. First, I maintained the MobyGames categorization, but manually reviewed each game’s genre and compared it to descriptions of the game and screenshots to ensure that game was properly coded according to MobyGames’s classification scheme.23 Additionally, to account for the fact that the existing categories did not have a suitable category for many mobile games (e.g., Angry Birds), I included the “Idle” categorization scheme from IDTech as well. Second, I manually coded each game’s sub-genre based on the rubric provided by IDTech.24 I used IDTech’s rubric for two reasons: first, it provided significant granularity, allowing for a more precise measurement of games (e.g., listing games like Street Fighter and Mortal Combat as distinct types of fighting games). Second, it accounted for changes in video game technology overtime that affected different genres. Thus, it distinguishes between different types of adventure games based on the technology used in the gameplay, creating a helpful schemata as I attempted to categorize both very new games and ports from the 1980s or earlier. I then ensured that each sub-genre was linked to one and only one genre, creating a tree- like structure with 8 genres and 29 different sub-genres.25 This tree-structure was helpful, as it provided a middle-ground for games which were somewhat similar but not exactly the same (for example, “fighters” and “beat-em ups”). Table G1 provides a listing of each genre along with a description, while Table G2 provides a description of each sub-genre, along with the genre associated with each. That said, as these tables reveal, no categorization scheme is perfect. While the two-tiered genre measure greatly helps, there are still gaps. For instance, “sports-based fighting” games are much more similar to other types of combat games, which are coded as “action” games, than the above categorization scheme would suggest. While one could imagine modifying these categories further with these types of problems in mind, such an effort brings its own difficulties, not least of which is allowing the researcher too many degrees of freedom in cleaning the underlying data. For this reason, I determined to leave the IDTech scheme as-is, modifying the MobyGames categorizations slightly to allow for mobile games, and then use alternative measures, discussed in Appendix I, to measure the distance between games further.

23See https://www.mobygames.com/glossary/genres 24See https://www.idtech.com/blog/different-types-of-video-game-genres 25Note that ID tech lists 49 genres. However, this is for all types of electronic gaming, 20 of which I could ﬁnd no instance of in my sample (e.g., “Roguelikes”). Similarly, though Moby Games lists 11 genres, I remove games that fall into “DLC/Add On”, ”Special Edition”, and ”Compilation” as these are not new games but re-releases of old games.

125 Table G1: Genres Used in Sample

Genre Genre Description Action games are those in which the main mechanics re- Action volve around one or more of the following: accuracy, movement, quick decisions, reflexes, and timing. Adventure games emphasize experiencing a story Adventure through dialogue and puzzle solving. Gameplay mechanics emphasize decision over action. Puzzle games focus purely on solving puzzles usually Puzzle without much narrative. Role-Playing games (RPGs) belong to a wide and var- ied game genre that focuses on character development. RPG Additional aspects that are often found in RPGs include: amassing wealth, narrative, tactical combat. Simulation games can be one of many different types of simulations. What all simulations have in common is that Simulation they are more realistically modeled to real life situations and/or variables than most games. Sports games are games in which players control either Sports players or managers of a real or fictional sports. Strategy/Tactics games revolve around strategic and/or Strategy tactical usage of resources often in combat or managerial scenarios. Idle games are simplified games that involve minimal Idle player involvement, such as clicking on an icon over and over.

Note: The table above presents the genres used the sample. The descriptions for the ﬁrst 7 are re-printed from https://www.mobygames.com/glossary/genres, while the deﬁnition of Idle is re-printed from https://www.idtech.com/blog/different-types-of-video-game-genres. All descriptions have been lightly edited for clarity.

126 Table G2: Sub-Genres Used in Sample

Genre Sub-Genre Sub-Genre Description Beat-em up games, or brawlers, also focus on combat, but instead of facing a single opponent, Beat-em-Up players face wave after wave of enemies. Fighting games like Mortal Kombat and Street Fighter II focus the action on combat, and in Fighter Action most cases, hand-to-hand combat. Platformer games get their name from the fact that the game’s character interacts with platforms Platformer (usually running, jumping, or falling) throughout the gameplay. Rhythm games like Dance Dance Revolution and Guitar Hero are music-based games that Rhythm challenge players to keep in step with the rhythm of a song or soundtrack in the game by pressing a corresponding button on the controller at a precise time to accumulate points. Shooters let players use weapons to engage in the action, with the goal usually being to take out Shooter enemies or opposing players. Stealth games stress cunning and precision to resolve game challenges, and while other action Stealth or combat may help players accomplish the goal, stealth games usually encourage players to engage in the action covertly. Graphic adventure games allow the user select objects and solve puzzles to progress the game Graphic play. Adventure Extremely popular in Japan, most visual novels require players to build up character traits or Novel statistics to advance the gameplay. Real-time 3D games are similar to graphic adventure games, but offer larger worlds and the Real-Time-3D opportunity to interact with a three-dimensional world. Board Game Electronic versions of traditional games, such as chess, checkers, and backgammon. Casual Casual games exhibit basic game mechanics and are perfect for short, casual sessions. Idle Idle games are simplified games that involve minimal player involvement, such as clicking on Idle an icon over and over. Massive Multiplayer Onlline (MMO) also include a variety of game modes, where players can MMO cooperate or compete against one another. Logic A logic game requires players to solve a logic puzzle or navigate a challenge like a maze. Puzzle Trivia game players must answer a question before a timer runs out (or before another player Triva answers) to score points. Action role-playing games take game elements of both action games and action-adventure games. A defining characteristic of action RPGs is that the combat takes place in real-time Action RPG and depends on a player’s speed and accuracy to best foes, versus depending on high character Role-Playing-(RPG) attributes like charisma and dexterity. Massive Multiplayer Onlline Role Playing Games (MMORPG) involve hundreds of players MMORPG actively interacting with each other in the same world, and typically, all players share the same or a similar objective. Tactical role-playing games play more like traditional board games, wherein the turn-based Tactial RPG game action plays out over an isometric grid. Simulations may allow players to manipulate a character’s genetics or their ecosystem. Even Life the character’s reaction to a certain situation may be under the player’s control. Simulation SimCity is the most popular construction and management simulation of all time. The game Management simulates the building and management of a city, including street planning, zoning, and taxing city residents. Vehicle simulations aim to recreate the experience of flying an airplane, driving a race car, and Vehicle in some cases, driving a tractor on a farm. Fictional sport or competitive games fall into this category. eSport games like Overwatch and Competitive Team Fortress have also been assigned to this subgenre. Sports Racing In these games, players race against another opponent or the clock. Rooted firmly in the fighting game and sports genre, in these games the fighting is more realistic Sports-Based Fighting and can feature real-world fighters. Team Sports One of the earliest types of video games genres, team sports games simulate playing a sport. Multiplayer Online Battle Arena (MOBA) combines action games, role-playing games, and MOBA real-time strategy games. Real-time strategy games require the player to collect and maintain resources, like bases, while Strategy/Tactics Real-Time-Strategy advancing and developing both resources and combat units. Tower Defense In tower defense games, players must fend off computer-controlled enemies to win. Based on and mostly using realistic military tactics, turn-based tactics games pit combat forces Turn-Based Tactics against each other in volley-like gameplay.

Note: The table above presents the sub-genres used the sample and their corresponding genres. The deﬁnitions are re-printed from https://www.idtech.com/blog/different-types-of-video-game-genres and have been lightly edited for clarity.

127 Appendix H: Sample Selection As noted in Section 2.4, identifying a suitable sample of firms is a challenging, non-trivial task that requires significant judgment from the researcher. In this appendix, I seek to provide further insight on the process I followed and the sample that resulted. In selecting console publishers, I sought two criterion. First, given the super-star nature of the market, I looked for leading console publishers so as to avoid including extremely small firms, as this might limit the generalizability of the study. This led me to seek out the top publishers during the run-up to mobile games from 2005-2008.26 To measure “top,” I used data from VGChartz’s Yearly Global Index tables, summed sales by firm over the entire period, and selected the top 25 firms for further investigation.27 Second, I sought to ensure that the companies were “at-risk” of making a mobile game. Thus, from the above sample of 15, I purged 10 firms that went bankrupt between 2005 and 2010 on the basis that they were too moribund to be seen as a viable incumbent. While one could imagine including them in the sample, doing so would make the interpretation of the results more challenging. While most of the bankruptcies were relatively straightforward, it is worth highlighting that this decision also meant the removal of Atari, though games under the Atari brand continued to be produced over the period. Atari was nonetheless removed as it was bought out, liquidated, and in-turn sold-off multiple times between 2005 and 2018, making it difficult to point to a consistent “firm” under that name during the period. A further challenge is accounting for different brands owned by the same firm. While one could imagine folding the brands together, doing so would be likely to contaminate the results as different labels have distinct work forces, cultures, and capabilities. Alternatively, one could imagine controlling for differences using fixed-effects for owners. While this would be ideal, and this data was collected, this is challenging given the power limitations of a sample where n = 15. Consequently, I determined it best to examine each publisher, and link publishers which were shared a brand and workforce and treat the others as separate. Thus, where two publishers were operated jointly (e.g., Ubisoft’s various publishing arms), I merged the publishers together, while in other instances (e.g., Namco and Bandai), I treated them separately. Figure H1 shows the distribution of console sales for the resulting sample. Note that the smallest firm, Codemasters, is included in this sample, but has only 4.7% of the sales leading firm, Activision. Going beyond Codemasters then would make it very difficult to compare incumbents given the size differences. Additionally, removing the smaller firms would reduce the power of the study, therefore making it more difficult to draw conclusions.

26While one could imagine using a longer or shorter window, the relative stability of the gaming industry meant that the choice of window length had little to no effect on the sample, as I discovered by comparing 1, 2, 3, and 5 year windows, though these results are not presented here. 27See https://www.vgchartz.com/yearly/2018/Global/.

128 Figure H1: Top 25 Console Game Publishers

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Cumm. Console Sales (MM Games) Console Game Sales Rank

Note: The Figure above shows the cumulative sales of the 15 console publishers included in the sample between 2005 and 2008. The units for this graph are in the number of games sold, not their dollar value.

With this sample in hand, I then sought to create a sample of leading mobile firms. Similar to console publishers, I looked to identify the top firms in terms of cumulative revenue over the period. Ideally, I would have the annual sales statistics for mobile games, just as VGChartz provides for console games. This data, however, is not available for any gaming source I could identify (ThinkGaming, AppAnnie, or VGChartz). Instead, ThinkGaming provided daily revenue charts of top games for each day between 2013 and 2013, so I selected the end of each calendar quarter during that period, summed the totals by firm, and selected the top 15 mobile publishers. Unfortunately, no source provided clear sales statistics for earlier in the period. So, to identify these firms, I used the firm rankings from Pocket Gamer, a trade magazine, and compared the top 15 publishers from this sample to those suggested by the ThinkGaming approach.28 Thankfully, the top mobile publishers were also relatively stable, and therefore, the sample of firms suggested by Pocket Gamer mirrored those by sales. While in theory there is no reason to make the number of mobile publishers match the number of console publishers, in practice, 15 mobile publishers seemed a sensible cut-off, both for the sake of symmetry and to avoid using too small of mobile firms. This resulted in a sample of 30 publishers comprised of leading incumbents “at risk” of making a mobile gaming, and comparison group of 15 leading mobile publishers. The 30 firms, along with their cumulative mobile revenue are featured below, in Table H1. 28See https://www.pocketgamer.biz/list/17671/the-top-50-iphone-developers-of-2009/

129 Table H1: Publishers in Sample

Console Publishers Mobile Publishers Publisher Cumulative Mobile Revenue Publisher Cumulative Mobile Revenue Electronic Arts $2,262.29 King $12,409.65 Konami $1,426.30 Supercell $11,498.76 Square Enix $1,308.74 Machine Zone $5,814.09 Bandai $1,162.05 Niantic $4,172.70 Rockstar Games $219.36 Playtika $3,257.01 Ubisoft $213.62 Zynga $1,555.22 2k $139.33 Big Fish Games $1,523.72 505 Games $94.84 Kabam $1,229.04 Bethesda Softworks $94.29 Playrix Entertainment $1,169.92 Sega $89.79 Doubledown Interactive $868.80 Disney $86.08 Glu Mobile $799.79 Activision $42.48 Gameloft $764.92 Capcom $19.89 Jam City $718.98 Codemasters $7.41 Rovio $675.15 Namco $2.41 Miniclip $658.02

Note: The table above presents the publishers that comprise the sample and there cumulative revenue from 2013-2018 from mobile games in millions of USD.

130 Appendix I: Distance Variables As noted in Appendix G, genre provides a helpful measure of differences in games but it cannot fully account for all the relevant differences. In this appendix, I look to explain the remaining variables used in the distance measure which were not discussed fully in Section 2.4: hue, perspective, and intellectual property (IP).

Hue: In examining the different games, it became evident that the color palettes chosen by console publishers often differed from those of mobile publishers. In particular, console games often seemed darker and less-cartoonish. To quantitatively measure this, I created an algorithm to download a screenshot for each game, using the Google query ”mobile game screenshot + ‘gametitle’.” Then, I used a k-nearest-neighbor (knn) machine learning algorithm to identify the largest color hue for each image.29 This resulted in a continuous measure of color that could be used in the distance calculations above.

Perspective: Following the same logic, I sought to compare the different game perspectives. Perspectives represent the view the player has of the game. To get this measure for each game, I followed MobyGames’s rubric and manually reviewed screenshots of each game and coded each game accordingly. Note, however, that two MobyGames perspectives, audio and text-based, were not found in the sample. TableI 1 provides a description of each perspective contained in the sample.

Table I1: Game Perspectives

Perspective Description The player sees the game world as if through their own 1st-Person eyes. The protagonist is controlled using a third-person view, 3rd-Person but one that does not ﬁt the other options or is open to interpretation. These games make use of a camera that ﬂoats behind a Behind View character. An elevated view of an object from above. It is any angle Birds-Eye View above a behind view and below a top-down view. These games use a traditional “platforming” viewpoint Side View where the action is seen from the side. These games use a top-down view, also called an over- Top-Down head view or a helicopter view.

Note: The table above presents the perspectives used in the sample. Descriptions are re-printed from https://www.mobygames.com/glossary/genres and have been lightly edited for clarity.

29Hue was selected as the measure of color, as it allows for a continuous measure of color better than other measures of color (e.g., RGB).

131 Intellectual Property: Finally, I sought to create a measure of games’ IP. As I am not aware of a germane, previously established categorization scheme, I developed my own. To this end, I created nine different measures of IP, outlined in Table I2. Note that not all nine are mutually exclusive: they fall into three categories and are mutually exclusive only within each category. Also, it is important to emphasize that the ﬁnal measure, non-video game IP, contains signiﬁcant heterogeneity, ranging from sports licenses to movie characters. While it is tempting to partition this further, it remains such a small category—less than 3% of the sample—that I have opted to keep it as a single measure. To code these measures, I manually searched through the MobyGames database to identify if the title had been released before, or if it was built on previous worlds or characters. In rare cases were this was inconclusive I then resorted to Google searches to ensure accuracy.

Table I2: IP Descriptions

IP Measure Description Release Information This game title was created for a console or PC platform Port at least two years priors to its release on mobile platforms. This game title was released for both mobile and console Simultaneous Release platforms at its launch. Stand-Alone This game title was only released as a mobile title.

Franchise The game’s characters and/or worlds come originally Console Franchise from a console game. The game’s characters and/or worlds come originally Mobile Franchise from a previous mobile game. The game’s characters and worlds are completely origi- No Franchise nal. The game’s characters and/or worlds come originally PC Franchise from a PC game.

Outside IP The game’s characters and/or worlds are not original but Non-Video Game IP do not come from a previous game (e.g, movies, sports, or board games).

Note: The table above presents the different measures of IP used in the sample. All deﬁnitions are original to this study and manually coded by the author.

132 Appendix J: Post-2012 Results The fact that mobile game sales data does not appear to have been systematically before 2013 raises concerns that including earlier games may affect the results. To be clear, the data does contain sales data for games produced earlier than 2013, but, only for sales starting in 2013, leaving up to a 5-year gap (when notably the game’s earning power is at its peak). To ensure that this gap does not affect the results, the below presents the results using data from only the years 2013-2018. The table and ﬁgure notes are omitted for the sake of space but would be identical to those in the main text.

Figure J1: Deviation from the Dominant Design - Post-2012

(a) Distance Density Distributions (b) Distance and Revenue

Mobile Publishers Console Publishers ● Mobile Publishers ● Console Publishers 25

● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● 15 ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●●● ● ● ● ● ●● ● ● ● ● ●●● ●●● ●● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ●●● ● ● ● ● 10 ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● Density ● ● ● ● ● ● ● ● 1 ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●● ●● ●

Log(Revenue) ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● 0 0 ● ●● ● ● ● ● ●● ● ● ●●●●●● ● ● ● ● ● ● ● ● ● 3.3 3.6 3.9 3.3 3.6 3.9 Distance) Log(Distance)

Table J1: Linear Regression: Gower Distance and Revenue - Post-2012

Log(Revenue) Log(Downloads) Log(Revenue per Download) (1) (2) (3) (4) (5) (6) Log(Distance) -2.165 -0.713 -2.294 -0.174 0.034 -0.044 (1.211) (1.933) (1.386) (1.053) (0.359) (0.322) Console Publisher -29.827∗∗ -8.240 -7.516∗∗∗ (9.036) (6.063) (1.412) Console Publisher x Log(Distance) 8.013∗∗ 7.273∗ 2.109 0.941 2.128∗∗∗ 2.056∗∗ (2.484) (3.155) (1.796) (1.655) (0.404) (0.538) Observations 618 617 618 617 618 617 Nb. of Publishers 29 28 29 28 29 28 Year Fixed Effects Y Y Y Y Y Y Publisher Fixed Effects N Y N Y N Y Platform Fixed Effects Y Y Y Y Y Y R-Squared 0.479 0.550 0.511 0.626 0.151 0.273 Standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Overall, the results remain stable, and in fact, grow larger, alleviating concerns that the main results were driven by heavily selected data from the pre-2013 period. While it would be interesting to compare games over time more thoroughly to examine the period of “ﬁrmament,” the data do not allow for such a comparison. Nonetheless, these results do not suggest a reason for concern over the lack of initial sales data.

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