Reaching for the stars: The importance of reputational rank in creative career development

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Reaching for the stars: The importance of reputational rank in creative career development Michael Jensena, Heeyon Kimb a b

University of Michigan, Stephen M. Ross School of Business, Ann Arbor, MI, 48109, United States Cornell University, SC Johnson College of Business, Ithaca, NY, 14853, United States

ARTICLE INFO

ABSTRACT

Keywords: Creative careers Reputation Status Reference groups Directors Film industry

This study draws on vacancy competition and the role-theoretic perspective on reputation to develop and test a sociological theory of how reputation formation and creative career development coevolve in project-based labor markets in the cultural industries. We use vacancy competition to emphasize the importance of identifying the correct reference groups and the roletheoretic perspective on reputation to emphasize the importance of reputational rank within the correct reference group. Specifically, we distinguish between local reputational rank among the peers at the same career stage, entry reputational rank among the peers from the same entry cohort, and global reputational rank among all the peers in an industry. However, unlike prior sociological research on creative careers and reputation, we argue that local reputational rank, not entry cohort or global reputational rank, predicts career development. We find support for our arguments using a comprehensive sample of complete career trajectories of successful and unsuccessful aspirant film directors that graduated from The National Film School of Denmark between 1968 and 2013.

1. Introduction Sociologists have long argued that professional reputation formation and creative career development are intertwined in the cultural industries, such as the film industry, in which temporary single-project organizations coordinate and control short-term projects. Moving from project to project to develop a career depends on the formation of a reputation for quality, and the formation of a reputation for quality depends, importantly, on the continued participation in different projects over time (Bielby & Bielby, 1999; Faulkner, 1983; Lutter, 2015). Despite the recognition that reputation formation and career development coevolve in the cultural industries, the actual processes through which they coevolve have not been theorized and remain poorly understood (O’Mahony & Bechky, 2006). By theorizing how reputation and creative careers coevolve, we provide new answers to core sociological questions about how career mobility occurs and why over-supply of labor exists in the cultural industries despite strong and easy-to-observe cumulative advantages (Caves, 2000; DiPrete & Eirich, 2006; Faulkner, 1983; Menger, 2006). We provide a foundation for answering these questions by developing a new sociological theory of how professional reputation and creative careers coevolve in project-based labor markets that combines insights about localized competition from vacancy competition within organizations (Sørensen & Sharkey, 2014; Sørensen, 1979) and the role-theoretic perspective on reputation (Jensen, Kim, & Kim, 2012). Vacancy competition implies that competition for jobs in organizations is most intense among people at the same hierarchical level, and that the individuals with most human capital tend to win (Sørensen, 1979). We argue similarly that competition for projects in project-based labor markets is most intense among people at the same career stage and that those with higher reputational E-mail addresses: [email protected] (M. Jensen), [email protected] (H. Kim). https://doi.org/10.1016/j.poetic.2019.101396 Received 6 April 2019; Received in revised form 6 September 2019; Accepted 10 September 2019 0304-422X/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Michael Jensen and Heeyon Kim, Poetics, https://doi.org/10.1016/j.poetic.2019.101396

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rank at each career stage tend to prevail. Specifically, we define reputation in terms of deviations from performance expectations at a particular career stage (Jensen et al., 2012) and reputational rank as the implicit or explicit ranking of people by such reputation at that career stage. By theorizing careers in project-based labor markets as a series of career-stage-dependent competitions, we make the relationship between career development and reputation formation explicit. Although cumulative experience and performance affect reputation (Cattani, Ferriani, & Allison, 2014; Rossman, Esparza, & Bonacich, 2010), they do not capture what is uniquely sociological about reputation, thus emphasizing the need to rethink reputation in project-based labor markets. Rather than cumulative experience or performance itself, we argue that meeting the performance expectations to each career stage is the inflection point from which positive and negative reputations form: Exceeding expectations results in a positive reputation and more career opportunities, whereas failing to meet expectations results in a negative reputation and fewer career opportunities (Jensen et al., 2012). Our study contributes to research on reputation and creative careers. First, by distinguishing between career stage and reputational rank in theorizing how reputation and creative careers coevolve, we address a core puzzle in career research: What explains the co-existence of cumulative advantage, which helps entrench individuals in their status positions, and career mobility, which captures that movement nevertheless happens in the status hierarchy? Cumulative advantage implies that individuals of “considerable repute” get more recognition for similar work, and more opportunities to work, than those “who have not yet made their mark” (DiPrete & Eirich, 2006; Merton, 1968b: 58). We argue, however, that defining reputational rank by career stage mitigates cumulative advantage because it limits competition to a smaller set of similar individuals, the reference group.1 We show accordingly that reputational rank defined by career stage is more important for creative career development than reputational rank defined by the entry cohort, which is the most common approach in research on cumulative advantage in careers (e.g., Allison, Scott Long, & Krauze, 1982; Allison & Stewart, 1974) and the entire industry, which is the most common approach in reputation research (e.g., Cattani et al., 2014; Lutter, 2015). Even if most advantage cumulates at the top of the hierarchy, reputational rank within each career stage provides a different mechanism for career mobility: Having the best reputation in the labor market is not necessary, it is sufficient to have a better reputation than those at the same career stage with whom competition is most direct. Second, by emphasizing localized competition for career opportunities within each career stage, we address the over-supply puzzle in research on creative careers in the cultural industries: Knowing that developing a professionally and economically sustainable career is extremely difficult, why are the cultural industries nevertheless characterized by a permanent over-supply of labor (Caves, 2000; Menger, 1999)? Different compelling reasons exist for the over-supply of creative labor including the non-pecuniary benefits such as self-actualization that unpaid involvement in the cultural industries provides (see Menger, 2006). We add that our focus on career-stage-dependent reputational rank implies that most aspiring artists are not faced directly with the less likely prospect of developing a successful career, not to mention the extremely unlikely prospect of making it to the top of the hierarchy, but are instead faced with the more manageable prospect of simply making it to the next career stage. Although making it to the next career stage is not easy, particularly early in the career, it is still a less daunting task to be ranked relatively high compared to peers at a similar career stage than to be ranked relatively high compared to everybody in the labor market. Localizing competition through reputational rank contributes, in other words, to an over-supply of artistic labor by systematically underestimating the likelihood of successfully transitioning to the next career stage to develop a sustainable career. It simply appears easier to reach the stars than to actually reach them. We develop our perspective on reputation formation and career development in short-term project-based labor markets in the context of the Danish feature film industry. Different specialized individuals, such as producers, directors, screenwriters, cinematographers, editors, sound designers, and actors, contribute to film production. We focus on the reputation and careers of directors. While producers are responsible for all the economic and creative aspects of film projects, directors are in charge of the creative aspects and they are, together with the lead actors, the most visible and important contributors to a film project. In theorizing career development, we distinguish between aspirant directors seeking to enter their directing career by directing their first film (career entry) and novice directors seeking to continue their director career by directing more films over time (career continuation). We focus on career continuation (moving from project to project) rather than career advancement (moving from less prestigious to more prestigious projects) because “[i]n a project-based labor market such as the film business, the most basic measure for career advancement is survival” (Lutter, 2015: 334). We test our arguments using unique data on the complete career histories of all the 137 aspirant directors that graduated from The National Film School of Denmark from the inaugural class of 1968 to the class of 2013 regardless of their success or failure in commencing and continuing their director careers, thus avoiding a common source of sample selection bias in career research. 2. Careers and reputation in project-based labor markets Most sociological research on careers focuses on careers in organization-based labor markets, that is, careers that develop within and across permanent organizations (Bidwell & Mollick, 2015; Sørensen & Sharkey, 2014). A career is defined accordingly as “a succession of related jobs, arranged in a hierarchy of prestige, through which persons move in an ordered (more-or-less predictable) sequence” (Wilensky, 1961: 523). The process through which people move from job to job within organizations is commonly 1 Merton (1968a) defined reference group as the group to which individuals compare themselves but to which they do not necessarily belong. We agree with (Merton, 1968a: 285) that ‘group’ refers to individuals “of the same status or in the same social category”, but use reference group in the broader sense for the group to which other individuals compare the focal individual.

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theorized as “vacancy competition” (Sørensen, 1977, 1979). Vacancy competition implies that people only move between positions in the job structure when the destination position is vacant and that access to the vacant position depends on the human and social capital, including ability, education, and experience, of the people in the positions below the vacant position (Sørensen, 1979). Vacancy competition in organization-based labor markets represents, in other words, a hierarchical chain of localized competitions for career advancement in which performance expectations and reference groups change at each hierarchical level. Despite the differences between organization-based and project-based labor markets discussed below, vacancy competition is a useful starting point for theorizing career development and reputation formation in project-based labor markets because of its emphasis on localized competition and changing reference groups. Careers in project-based labor markets are different from careers in organization-based labor markets (Borkenhagen & Martin, 2018; Leung & Koppman, 2018). Career development in organization-based labor markets implies moving vertically to successively higher-ranked jobs with more formal authority, whereas career development in project-based labor markets implies moving horizontally from project to project to increase professional experience but not necessarily formal authority (Barley, 1989; Faulkner, 1983: 241). Moreover, competition in project-based labor markets stems not from a mismatch between organizational job structures with relatively few vacancies compared to potential candidates but from the relatively scarce demand for new projects (i.e., vacancies) compared to the supply of potential projects and participants. Finally, the sequence of projects a person moves through in project-based labor markets does not necessarily represent a prestige hierarchy in which later projects are higher prestige than earlier projects (Giuffre, 1999; Leung, 2014). The sequence of projects represents an experience hierarchy, however, because each project increases the experience of the participants, which implies that the number of completed projects can be used to localize competition and define reference groups. Indeed, Faulkner (1983: 12) uses “productivity” or the number of completed films to index career stage in Hollywood rather than “associated events” such as “Academy recognition, film rentals, and network location.” Despite its importance for creative careers, sociologists have not theorized reputation in project-based labor markets beyond noting that “capabilities and reputations cumulate across contracting and recontracting events” (Faulkner & Anderson, 1987: 881). Most sociologists draw instead on reputation theory based in economics (Jensen et al., 2012). Reputation forms accordingly through a sequential Bayesian updating process with audiences updating their probability estimates that social actors will behave in a particular manner as they acquire new information (Noe, 2012). Bayesian approaches to reputation explain that reputations based on past behaviors should be updated after current behaviors have been evaluated but they do not theorize how to evaluate current behaviors or, once evaluated, how to use reputation to compare social actors in competitive contexts. As a remedy, Stiglitz (2000) notes that it is necessary to integrate economics, psychology, and sociology to understand how individuals form expectations and how signaling conventions are created. We argue that expectations about future behaviors are shaped by both past behaviors and the social systems, including reference groups, in which past and future behaviors are embedded (Merton, 1968a). Specifically, by emphasizing localized competition and changing reference groups at each status or career stage, the role-theoretic perspective on reputation extends vacancy competition to project-based labor markets to help theorize how reputational rank facilitates career development. Building on traditional sociological definitions of status as a position in a social system and roles as the sets of behavioral expectations including the beliefs, norms, and rules associated with that status (Linton, 1936; Merton, 1968a), the role-theoretic perspective on reputation defines reputation as “a prediction of future behaviors that is based on an assessment of how past behaviors meet the role expectations that follow occupying a particular social status” (Jensen et al., 2012: 148). The general implications for reputation formation and upward status mobility of the role-theoretic perspective on reputation are straightforward. When individuals earn positive reputations by exceeding the role expectations to their current status, they rank higher within their reference group, which makes them more likely to move to a higher status with different role expectations and a reference group. The specific implications for career development are also straightforward. The role-theoretic perspective on reputation extends vacancy competition to project-based labor markets by positing that career development is the outcome of a reputational rank competition within each career stage and reference group that focuses on who exceeded the performance expectations the most. In our context, we use career stages to designate status positions as career stages represent the extent to which participants have accumulated enough credits to gain a “sufficient foothold to maintain a sustainable career” (Zuckerman, Kim, Ukanwa, & Von Rittmann, 2003: 1031). In other words, moving from project to project, people gain experience, build capabilities, and their reputations are updated and, implicitly or explicitly, ranked relative to other people at the same career stage based on prior project performances. The updated reputation affects, in turn, access to the next project and, therefore, the new expectations and reference group used to evaluate subsequent project performance. Moreover, although reputation formation begins with the completion of the first project, newcomers are met with expectations prior to their first project. Even aspirants attempting to enter a labor market are met with minimum performance expectations, as Baker and Faulkner (1991: 285) colorfully reported: “This little dickhead came to me and said ‘I’m a screenwriter’ but all the little shit had was a three-page treatment.” Having yet to build a reputation, minimum role expectations function as entry barriers. 3. Film directors, reputation, and career development A director may acquire a professional reputation for quality in different aspects of film production (Bechky, 2006; Faulkner & Anderson, 1987; Jones, 1996). Directors acquire technical skills and project management capabilities such as managing different types of actors, overseeing technical crews, and staying within time schedules and financial budgets as they gain experience. Technical skills and project management capabilities are more like taken for granted aspects of experience-based human capital,

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which makes it less valuable for directors to develop positive reputations for these aspects of film production (although negative reputations can be disastrous). Having a positive reputation for commercial success or artistic acclaim is more likely to be rewarded (Baumann, 2001; Delmestri, Montanari, & Usai, 2005; Ebbers & Wijnberg, 2012). Commercial success implies that a film is popular with general audiences and therefore in high demand in movie theaters (and other platforms), whereas artistic acclaim implies recognition for artistic merit by expert audiences such as film critics and industry peers (Cattani et al., 2014; Kim & Jensen, 2014). Commercial success makes it easier to finance future film projects, and artistic acclaim tends to increase commercial success and advance the careers of the cast and crew (Holbrook & Addis, 2008; Jensen & Kim, 2015). We distinguish accordingly between commercial and artistic reputational rank because they are the key performance dimensions in the film industry and many directors perform differently commercially and artistically. The commercial and artistic expectations are typically lower for inexperienced directors with few film credits than for experienced directors with many credits (cf. Lawrence, 2006 on the positive association between career stage and performance expectations). Mike Nichols, the Oscar winning director of The Graduate (1967), highlighted the increasing role expectations: “[when] somebody talented starts… expectations get higher and higher as they go along. And, finally, it gets hard because you don’t get credit for still doing it” (Weinraub, 1993). Reputation is not simply cumulative experience or performance, however, because expectations become more demanding, linearly or non-linearly, as directors gain experience and reference groups change. Reputation is therefore not the same as the number of directed films (Rossman et al., 2010), cumulative performance (Bielby & Bielby, 1999; Cattani et al., 2014), or average performance (Delmestri et al., 2005; Ebbers & Wijnberg, 2012). Moreover, while industry insiders and external experts broadly know what to expect from directors at each career stage, it is typically impossible to codify the exact expectations to a career stage. Rather than trying to codify tacit expectations, we define reputational rank in the film industry as the cumulative performance relative to directors at the same career stage to endogenize expectations within a career stage. Like vacancy competition, reputational rank implies that directors are ranked within a particular reference group and that the highest ranked directors are the most likely to direct another film. Compared to the reference groups in vacancy competition in organizations-based labor markets, i.e., the employees with similar job titles, the reference groups used to define reputational rank in project-based labor markets are typically more ambiguous. The lack of explicit career paths to define common career trajectories makes it harder to determine who competes with whom and, therefore, how to define their relevant reference group. Two different approaches dominate prior research. Research on cumulative advantage in the sciences explicitly defines the reference group narrowly as the entry cohort (e.g., Allison et al., 1982; Allison & Stewart, 1974), whereas research on reputation in the cultural industries implicitly defines the reference group broadly as everybody within an industry (e.g., Cattani et al., 2014; Lutter, 2015). Both approaches assume that the reference group does not change as people move from career stage to career stage. By localizing competition at each career stage, we define the reference group more broadly than the entry cohort but more narrowly than all directors within the film industry and we allow the reference group to chance throughout a career. Specifically, building on Faulkner (1983) and Zuckerman et al. (2003), we use experience or the number of films directed to indicate career stage. Experience is also a commonly used indicator within the film industry to distinguish between directors and to calibrate performance expectations.2 The career-stage approach has two advantages. First, it allows the reference group to vary over time, which is important in project-based labor markets because failure to get new projects typically means exit from the profession, thus making the entry cohort an increasingly irrelevant reference group for the surviving cohort members. Second, it ensures that local reputational rank is orthogonal to career stage, thus allowing the reputational ranks of directors at different career stages to be compared, even if competition is most intense within career stage. To compare the reputational ranks of directors at different career stages seems unfair to early stage directors. For example, exceeding early role expectations, Alexander Payne, a later nominee for three Best Director Oscars and a Best Screenplay winner, commented that “[it] just seems hyperbolic” that critics compared him to “satiric greats like Preston Sturges or Billy Wilder” after having directed two films only (Carson, 2000). From a theoretical perspective, however, defining reputational rank by career stage makes it possible to distinguish between reputation and experience, thus suggesting that even though Payne, Sturges, and Wilder were at different career stages, their equally high reputational ranks within their respective career stages made them comparable. In other words, because they are at different career stages and therefore have different reference groups, the local reputational rank of an emerging inexperienced director may be higher than the local reputational rank of a declining experienced director – a “rising star” versus “dead wood” (Borkenhagen & Martin, 2018: 2). In sum, because local reputational rank based on within-career-stage reference groups avoids confounding reputation and experience, we argue it is a more precise (less noisy) predictor of career continuation than reputational rank based on reference groups encompassing either the entry cohort only (entry reputational rank) or the entire industry (global reputational rank). Combining localized competition from vacancy competition and reputational rank from role-theoretic reputation, we hypothesize that local commercial and artistic reputational rank, but not entry or global commercial and artistic reputational ranks, increase the likelihood that directors continue their careers by directing more films.

2 For example, the expectations to experienced Stanley Kubrick and Eyes Wide Shut, his fourteenth film, were high as it was to “the most speculated-about film of the nineties” (Lacey, 1999), whereas the expectations to inexperienced Damien Chazelle and La La Land, which he pitched unsuccessfully for six years when “he was just 25 years old and coming off his directorial debut” (Buchanan, 2017), were low.

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4. Methods 4.1. The Danish film industry The Danish film industry is relatively small with an annual production of 20–25 feature films. Similar to Hollywood, film production is organized around project-based labor markets and production companies that compete for audiences in an open domestic market (no protective quotas for Danish films). An important difference between film production in Denmark and Hollywood is the reliance on public financial support from the Danish Film Institute (DFI) despite the smaller average budgets for Danish films (typically less than $5 million).3 DFI is a national agency for film and cinema culture that operates under the Ministry of Culture to support the development, production, and distribution of Danish films. A few films have been produced without financial support from DFI since the late 1970s but the vast majority of film projects (more than 95%) depends on DFI support. DFI support accounts typically for 30–60% of the production budget and is mostly a prerequisite for production companies and external investors to agree to finance the balance. DFI provides financial support to film projects based on an overall assessment of the artistic and commercial potential of the film itself and the experience and talent of the applicant (mostly the director or producer) behind the project (Wiedermann, 2009). The number of applications for DFI support greatly exceeds the number that can be supported. The CEO of DFI, Henrik Bo Nielsen, noted that for every applicant granted DFI support, ten are denied (Vuorela, 2013). DFI support for Film School graduates is mostly granted through the commissioner scheme (roughly 80% of films) and the market scheme (roughly 10% of films).4 By focusing on both artistic and commercial reputational rank, we capture potential differences in role expectations among art-focused film commissioners and market-focused committee members. Although DFI emphasizes director experience in prioritizing film projects and granting support, experience is not the only important factor. Indeed, the CEO and the VP of Film Support at DFI, Henrik Bo Nielsen and Claus Ladegaard, thought that experience is not valued enough, which has resulted in too many aspirant directors being supported (Hjortshøj & Vuorela, 2013). In contrast, sixty percent of directors and scriptwriters responded in a survey about DFI film support that DFI focuses too much on experienced directors (Thorsen, 2015). The disagreement about the importance of experience does not mean that experience is not important but more likely that experience is neither a necessary nor a sufficient condition for obtaining DFI support. The reputational rank approach implies that DFI and other key decision makers implicitly or explicitly compare directors at the same career stage and then tend to favor the highest ranked directors at each career stage. 4.2. Film school director careers The Danish film industry is an appropriate empirical setting to test our hypotheses because data is available on the complete career histories of all the 137 aspirant feature film directors that have graduated from The National Film School of Denmark from the inaugural 1968 class to the 2013 class, including the graduates that failed to become directors. Operating under the Ministry of Culture, the Film School graduates on average six producers, directors, cinematographers, sound designers, editors, and screenwriters every other year after a four-year educational program. The Film School is the oldest (founded in 1966), most influential, and most prestigious film school in Denmark, with Film School alums directing almost 40% of Danish films in 2013.5 Directors from the Film School have won Oscars for Best Foreign Picture at the Academy Awards and Palme d’Ors at the Festival de Cannes including Lars von Trier for Dancer in the Dark (2000) and Susanne Bier for In a Better World (2010). After removing a few directors from our statistical analyses due to data limitations, we use the remaining 104 feature film directors that graduated after 1974 in the career development analyses (data on ticket sales was not collected systematically earlier) and after 1968 in the career entry analyses (ticket sales not needed). We used different databases to collect data about the films and crews of feature films produced in Denmark between 1963 and 2013 including Nationalfilmografien (DFI), Lumiere (European Audiovisual Observatory), and IMDB (imdb.com). We collected data on the full film career histories of all the Film School directors (and directors not educated at the Film School) and demographic information such as birth year and birth country using the above mentioned databases plus archival data from the Film School (several directors kindly provided missing data on their graduation films). We include all the directors in the Danish film industry 3 Another difference is that film production tends to be director centric in Denmark, whereas it tends to be producer centric in Hollywood (like most of Europe, auteur theory (Sarris, 1962) is important in Denmark). Film School directors are rarely ‘hired’ by producers but originate most film projects themselves and work together with a producer to secure funding and production. Reputation is therefore not a simple proxy for the price a producer would pay to hire a director even though it likely would be highly correlated with price in a producer-centric market. 4 In the commissioner scheme, three film commissioners, each appointed for a three-year period, make their own decisions (subject to approval by the DFI management and board) to support film projects based primarily on artistic merit. In the market scheme, a committee comprised of three external members from the film industry, such as individuals appointed by the Association of Film Distributors, the Association of Film Producers, and the Association of Film Directors, and two internal members representing DFI makes collective decisions to support film projects based on cinematic quality and popular appeal. 5 By focusing on directors from the National Film School, we have selected an elite group of aspirant directors that may be more likely to develop a successful career than aspirants who did not attend the National Film School. Unfortunately, it is impossible to identify everybody who would like to become a director but failed because it is impossible to define and observe the relevant risk set. We still use all the directors active in the Danish film industry to calculate our reputational rank measures, as discussed below, to ensure that they reflect the rank of our focal directors among all directors in Denmark.

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Fig. 1. Career Trajectories of Film School Director Graduates 1968–2013. A. Number of Feature Films Directed until 2013. B. Years after Graduation to Feature Film Number.

including directors not educated at the Film School in the comparison sets to calculate reputational rank because they compete with Film School directors for the same set of resources. We focus on Film School directors in our statistical analyses because we have their full career trajectories regardless of their success or failure to enter the industry by directing their first film. Unfortunately, it is not possible to model career entry for the directors that did not graduate from the Film School because we only observe these directors after they have directed their first feature film. 4.3. Event history approach to director careers Following Zuckerman et al. (2003), we split careers into stages by the number of films directed. Defining stages by the number of films makes sense because traditional career stage indicators such as formal promotions and job title changes are not directly applicable to the film industry, there are “no clearly marked line of career progression” (Faulkner, 1983: 48). Moreover, because careers in the film industry do not advance “through a stable hierarchy of [formal] positions but from one discrete event [film] to another” (Faulkner, 1983: 238), the number of films directed is a meaningful indicator of career stage that is often used by film critics to calibrate their reviews, particularly early in a director career (measured in either the natural log of number of films directed or binary variables capped at eight or more films for career stage). Fig. 1 summarizes the careers of the 137 directors that graduated from the Film School after having completed their graduation films (more about graduation films below). Fig. 1A shows that entering the film industry by directing the first film is difficult: fewer 6

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than 50% of the 137 aspirant directors became novice directors (although some directors from the last few cohorts may still make the transition). Fig. 1B depicts the average number of years after graduation it takes to make a certain number of films. The vertical lines that intersect the graph show that there is considerable variance in the number of years it takes to make the same number of films (the ‘fastest’ director took fifteen years for seven films, for example, whereas the ‘slowest’ director took forty). To capture the variety in career trajectories, we use a repeated event history approach in which we model the ‘risk’ of directing a feature film and use the count of years between films as the clock (Box-Steffensmeier & Jones, 2004). A director enters the risk set upon graduation (film 0) and continues in the risk set until death, career change, or career failure. We define career failure as the lack of any credited activity (not limited to directing feature films) in the film industry for five years (longer dry spells are rare) after the last credited activity. As robustness checks, we also used 14 years, the longest time observed between credited feature films, and not considering career failure at all by including all the years without any credited activities. The results did not change substantively. The repeated event history approach could raise concerns about unobserved heterogeneity because some directors directed more than one film (event) and the variables may not capture all the reasons some directors direct more films than other directors (films are correlated within directors). We follow Cleves, Gould, and Marchenko (2016) and report robust standard errors using the HuberWhite sandwich estimator of variance because observations are not independent within director. In addition, we re-estimated all the models using a shared frailty specification with a gamma and an inverse-Gaussian distribution for unobserved director characteristics or “frailties” (Cleves et al., 2016: 342–343). The frailty effects were not significant and the results were not different from the reported results using robust standard errors. We use parametric estimation because we expect the baseline hazard to increase with time after each film (non-parametric Cox proportional hazard models are reported in our robustness checks). Following Cleves et al. (2016: 284), we use the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) to determine the best fitting parametric model. The Weibull model is unanimously the best fitting model for the full career development models (lowest AIC and BIC). 4.4. Reputational rank To measure reputational rank, it is necessary to first identify the reference group of directors among whom the focal director is ranked artistically and commercially (see Grote & Hall, 2013, on reference groups in career research). We distinguish theoretically between three different reference groups: Entry cohort (entry reputational rank), career stage (local reputational rank), and entire industry (global reputational rank). For entry reputational rank, we define the reference group as the aspirant directors graduating from the Film School the same year (as a robustness check, we also defined entry cohort by the Film School and non-Film School directors who had their directorial debut in the same year as well as the same year plus/minus one year and found similar results). For local reputational rank, we define the reference group as the Film School and non-Film School directors who had completed the same number of films as the focal director in the last five years (directors with eight or more films are grouped together).6 For example, the reference group for Lars von Trier in 1996 when he had completed his fifth film, Breaking the Waves, is all the directors that completed their fifth film in the five years from 1992 to 1996. For global reputational rank, rather than limiting the reference group to directors at comparable career stages, we define the reference group more broadly as all the Film School and non-Film School directors active in the last five years regardless of career stage. To calculate commercial reputational rank, we used the cumulative number of tickets sold in Denmark including the tickets sold for the film defining the current career stage (for Lars von Trier in 1996, for example, the cumulative number of tickets includes the first five films).7 The cumulative number of tickets by the focal director is divided by the average cumulative number of tickets of the directors in the reference group (results are robust to using the highest grossing and most awarded films by the focal director and reference group). We calculate artistic reputational rank similarly by using the cumulative number of awards including: 1) Bodil and Robert wins for Best Picture, Best Director, and Best Actor/Actress in Leading and Supporting Roles; 2) Oscar wins and nominations for Best Foreign Film; and 3) Cannes, Berlin, and Venice Film Festival main award wins and nominations.8 A reputational rank above (below) one indicates that the focal director is ranked above (below) average. Entry, local, and global reputational rank are calculated the same way except that the reference group is the directors from the same Film School cohort, the directors with the same number of films in the last five years, and all the directors active in the last five years for entry, local, and global reputational rank respectively. Although we theorize rank as an ordinal number defining a position within a status group, we measure reputational rank continuously by how much an individual is above or below the average individual within the reference group. By using relative 6

We use five-year reference group windows for two reasons: First, by restricting the reference group to five years, we limit including directors who have effectively ended their careers and therefore are less relevant for the reference group. Second, by broadening the reference group beyond one year, we acknowledge that most film projects take multiple years and that comparisons of films and directors are relevant for several years but not necessarily forever. Our results are robust to using three- and seven-year windows. 7 We use Danish ticket sales only because reliable data on foreign ticket sales is not available before 1996. Not including foreign ticket sales underestimates the ticket sales for films targeting an international audience including many of Lars von Trier’s films and films produced outside Denmark by foreign Film School graduates. To adjust for the lack of data on foreign ticket sales, we include binary control variables for films in nonDanish language (films targeting an international audience are typically in English) and for non-Danish directors. 8 The Bodil Award, first given by the Danish National Association of Film Critics in 1948, and the Robert Award, first given by the Danish Film Academy in 1984, are the two most prestigious film awards in Denmark. Although we code more international awards than Danish awards, the Danish awards dominate the artistic reputational rank measure because Danish directors win foreign awards relatively rarely. 7

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reputation instead of ordinal rank, we acknowledge that key resource providers do not know the exact rank of all directors, but instead have a rough sense of how directors compare to each other. We assume only that resource providers are able to rank pairs of individual directors above or below each other (see Gigerenzer & Goldstein, 1996; Goldstein & Gigerenzer, 2002) even though they typically know what films each director has directed and the commercial and artistic performance of these films. By measuring reputational rank in terms of relative performance above or below the average within the reference group, we acknowledge also that the differences between ranks are not always the same (the difference between first and second might be much larger than the difference between sixth and seventh). Despite the advantages of our measure, we nevertheless created ordinal reputational rank measures and report the results as a robustness check in Table 3. 4.5. Control variables We control for characteristics related to the last film a director directed as they may be particularly important. First, we control for the number of Bodil wins that the actors in the last film have cumulated during their careers and the average prominence of its five most prominent actors defined by their eigenvector centrality in film credit networks (see Appendix A for details). Second, we control for the last film being a Dogme 95 film. Dogme 95 was a visible film movement started in 1995 by Film School directors Lars von Trier and Thomas Vinterberg requiring film production to abide by the chaste ‘Dogme 95 Manifesto’ including only filming on location, using handheld cameras, and not crediting directors (Stevenson, 2003).9 Third, some directors pursue most of their film careers outside of Denmark and others participate occasionally in foreign film productions. Not all the foreign films open in Danish cinemas and foreign films by Danish directors tend to perform worse than Danish films by the same directors in Denmark, thus suggesting that we control for the last film being a foreign film. Finally, we control for the size of the cast (number of actors) because a higher cast size indicates that a director is perceived to be capable of directing a directing a more complex film with a higher budget (Lutter, 2015; Rossman et al., 2010). We use the relative size of the cast (cast size divided by the average cast size of films in the same year) because the average cast size has increased over the years as more and more actors are credited. We control for different director and film industry characteristics. We use the prominence of the actors in the aspiring directors’ graduation film (see the Appendix A for description of graduation films and prominence measure) to control for the a priori expectation among industry insiders that an aspiring director would be able to successfully become a director (Bowness, 1989; Stuart, Hoang, & Hybels, 1999). We include binary indicators for being born outside Denmark and for being female because being foreign born and being a female could be disadvantage in a film industry dominated by Danish born males. The number of production companies is potentially important for career development because a higher number of production companies provides more directing opportunities. We also control for decade to account for unobserved temporal characteristics of the film industry such as broad social, legal, and economic factors that could affect the transition from aspirant director to novice director. Finally, we control for the number of years since graduation to be able to depreciate graduation film actor prominence by interacting years since graduation with actor prominence in the career development models. Table 1 presents summary statistics and bivariate correlations. 5. Results 5.1. Main results Table 2 presents the repeated event Weibull models of director career development. Model 1 contains control variables only. Model 2 adds career stage, defined by director experience or the number of directed films, the simplest indicator of reputation and human capital (Faulkner, 1983; Rossman et al., 2010), and it shows that director experience increases the likelihood of directing another movie significantly (2.36; p < 0.001). Model 3 adds cumulative awards and cumulative ticket sales, two commonly used indicators of director reputation (Ebbers & Wijnberg, 2012; Lutter, 2015). Cumulative awards is statistically significant (0.25; p < 0.05), whereas cumulative ticket sales is not significant. Model 4 adds entry artistic and commercial reputational rank, which uses the directors from the Film School cohort as the reference group. Entry commercial and artistic reputational rank are not statistically significant. Model 5 adds global artistic and commercial reputational rank, which uses all the directors having directed films in the last five years as reference group. Global artistic reputational rank is marginally significant (0.05; p < 0.10), whereas global commercial reputational rank is not statistically significant. Model 6 adds local artistic and commercial reputational rank, which uses the directors that have directed the same number of films in the last five years as reference group. Both commercial and artistic local reputational rank are statistically significant, thus providing initial support for the conclusion that local commercial (0.16; p < 0.01) and artistic (0.14; p < 0.01) reputational rank increase the likelihood of directing another film. This conclusion is further strengthened in Model 7, which includes all the reputation measures and it shows that local reputational rank continues to be a significant predictor of career development, whereas the alternative reputation measures are not significant (the marginally significant negative effect of entry cohort commercial rank is caused by collinearity with cumulative ticket sales, as shown in Model 10). Model 8 replaces the continuous director experience variables with a series of binary variables indicating the 9 Genre specialization is not common among Film School directors. The dominant genres for Film School directors are realistic drama and romantic comedy but movement between them is common. The relative emphasis on commercial or artistic success captured in our reputational rank measures is the dominant form of specialization for Film School directors. Film School directors did not direct sequels during our sample period.

8

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35.

Local Reputational Rank: Commercial (5y) Local Reputational Rank: Artistic (5y) Entry Reputational Rank: Commercial (5y) Entry Reputational Rank: Artistic (5y) Global Reputational Rank: Commercial (5y) Global Reputational Rank: Artistic (5y) Director Cumulative Ticket Sales (ln) Director Cumulative Awards (ln) Director Experience (ln) Actor Prominence (Graduation Film) Last Film: Number Actor Bodil Wins Last Film: Top 5 Actor Prominence Last Film: Dogme 95 (1,0) Last Film: Relative Cast Size Last Film: Foreign Film (1,0) Foreign Born Director (1,0) Female Director (1,0) Years Since Graduation Number of Production Companies 1980s (1,0) 1990s (1,0) 2000s/2010s (1,0) Completed Second Film (1,0) Completed Third Film (1,0) Completed Fourth Film (1,0) Completed Fifth Film (1,0) Completed Sixth Film (1,0) Completed Seventh Film (1,0) Completed Eight or More Films (1,0) Graduation Film Awards (ln) Number of Films Last Year by Own Cohort Number of Films Last 2 Years by Prior Cohort Directed TV Series (# Depreciated 5 years) Directed Short Features (# Depreciated 5 years) Director Assistant (# Depreciated 5 years)

Table 1 Summary Statistics and Bivariate Correlations.

0.35 0.64 1.27 1.06 0.33 0.60 5.07 0.49 0.59 0.32 0.66 0.64 0.01 0.97 0.13 0.27 0.31 8.86 10.99 0.28 0.37 0.21 0.11 0.08 0.05 0.03 0.02 0.01 0.02 0.78 0.52 1.22 0.25 0.21 0.09

Mean 0.85 1.49 1.95 2.04 0.81 1.87 5.83 0.94 0.69 0.23 1.34 0.90 0.12 0.45 0.34 0.44 0.46 7.58 3.66 0.45 0.48 0.41 0.32 0.27 0.23 0.17 0.13 0.11 0.13 1.10 0.70 1.25 0.62 0.45 0.42

S.D. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00 0.00 0.00 4.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Min. 12.69 11.82 9.00 9.00 8.90 21.92 15.09 5.02 2.64 1.00 10.00 4.40 1.00 2.91 1.00 1.00 1.00 37.00 19.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 4.69 3.00 5.00 5.40 3.20 6.00

Max. 0.34 0.41 0.25 0.56 0.22 0.55 0.31 0.38 0.09 0.27 0.48 0.12 0.12 0.06 −0.18 −0.03 0.27 0.13 −0.03 −0.01 −0.02 0.15 0.09 0.12 0.08 0.07 0.05 0.09 0.42 0.07 0.01 0.23 −0.09 0.09

1

0.48 0.57 0.34 0.48 0.51 0.72 0.40 0.07 0.32 0.38 0.20 0.05 0.14 −0.12 −0.02 0.27 0.14 0.03 0.07 −0.09 0.15 0.12 0.11 0.10 0.05 0.04 0.11 0.61 0.03 0.03 0.03 −0.08 −0.05

2

0.54 0.43 0.43 0.74 0.55 0.60 0.17 0.44 0.62 0.12 −0.06 0.09 −0.15 −0.04 0.38 0.11 0.07 0.01 −0.16 0.18 0.17 0.14 0.13 0.10 0.08 0.23 0.51 −0.02 0.03 0.07 −0.03 −0.03

3

0.33 0.50 0.55 0.66 0.54 0.13 0.35 0.41 0.14 0.00 0.23 −0.06 −0.01 0.44 0.09 0.12 −0.05 −0.18 0.14 0.17 0.13 0.08 0.09 0.18 0.26 0.49 −0.06 −0.05 0.03 −0.05 −0.04

4

0.63 0.58 0.66 0.63 0.08 0.42 0.50 0.16 0.10 0.20 −0.20 0.05 0.49 0.19 0.17 −0.04 −0.14 0.06 0.12 0.17 0.24 0.19 0.17 0.53 0.53 −0.01 −0.06 0.07 −0.13 −0.03

5

0.43 0.84 0.56 0.02 0.34 0.33 0.16 0.12 0.22 −0.16 −0.04 0.42 0.13 0.20 −0.06 −0.13 −0.01 0.06 0.12 0.16 0.14 0.14 0.73 0.53 −0.04 −0.12 −0.02 −0.06 −0.06

6

0.65 0.88 0.18 0.58 0.81 0.16 −0.06 0.22 −0.13 −0.09 0.69 0.22 0.09 −0.02 −0.23 0.36 0.32 0.25 0.23 0.18 0.15 0.21 0.65 0.01 −0.01 0.14 −0.15 −0.05

7

0.72 0.08 0.47 0.50 0.20 0.07 0.25 −0.17 −0.04 0.56 0.19 0.18 −0.02 −0.19 0.09 0.21 0.21 0.28 0.20 0.16 0.47 0.66 −0.03 −0.08 0.04 −0.11 −0.09

8

0.12 0.57 0.72 0.16 −0.10 0.38 −0.05 −0.03 0.83 0.27 0.18 −0.03 −0.27 0.26 0.34 0.35 0.30 0.26 0.24 0.35 0.62 0.01 −0.06 0.05 −0.13 −0.11

9

0.16 0.19 −0.04 0.11 −0.07 −0.13 −0.12 0.02 0.05 −0.13 −0.08 0.15 0.04 0.12 0.00 0.06 0.02 0.03 0.00 0.01 0.00 0.01 −0.04 0.07 0.12

10

0.69 0.03 0.02 0.07 −0.13 −0.01 0.44 0.07 0.09 −0.07 −0.16 0.13 0.21 0.23 0.20 0.09 0.12 0.19 0.41 −0.05 −0.10 −0.01 −0.10 −0.06

11

0.12 −0.06 0.07 −0.13 −0.11 0.52 0.17 0.05 −0.05 −0.13 0.29 0.24 0.17 0.24 0.13 0.14 0.17 0.51 0.00 −0.01 0.13 −0.10 0.00

12

9

0.03 −0.07 0.01 −0.09 −0.01 0.03 0.01 0.02 −0.03 −0.08 −0.09 −0.04 −0.05 −0.02 0.10 0.12 0.02 −0.01 0.01 −0.09 0.08

14

0.24 0.01 0.43 0.13 0.18 −0.06 −0.15 0.23 0.06 0.12 0.01 0.02 0.14 0.17 0.23 0.03 −0.06 −0.06 −0.09 −0.09

15

(continued on next page)

−0.01 −0.05 −0.02 0.00 0.09 0.04 0.06 0.03 −0.06 0.15 0.01 −0.03 0.12 −0.02 0.21 −0.02 0.26 0.05 −0.06 0.05 −0.04 −0.03

13

M. Jensen and H. Kim

Poetics xxx (xxxx) xxxx

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35.

Local Reputational Rank: Commercial (5y) Local Reputational Rank: Artistic (5y) Entry Reputational Rank: Commercial (5y) Entry Reputational Rank: Artistic (5y) Global Reputational Rank: Commercial (5y) Global Reputational Rank: Artistic (5y) Director Cumulative Ticket Sales (ln) Director Cumulative Awards (ln) Director Experience (ln) Actor Prominence (Graduation Film) Last Film: Number Actor Bodil Wins Last Film: Top 5 Actor Prominence Last Film: Dogme 95 (1,0) Last Film: Relative Cast Size Last Film: Foreign Film (1,0) Foreign Born Director (1,0) Female Director (1,0) Years Since Graduation Number of Production Companies 1980s (1,0) 1990s (1,0) 2000s/2010s (1,0) Completed Second Film (1,0) Completed Third Film (1,0) Completed Fourth Film (1,0) Completed Fifth Film (1,0) Completed Sixth Film (1,0) Completed Seventh Film (1,0) Completed Eight or More Films (1,0) Graduation Film Awards (ln) Number of Films Last Year by Own Cohort Number of Films Last 2 Years by Prior Cohort Directed TV Series (# Depreciated 5 years) Directed Short Features (# Depreciated 5 years) Director Assistant (# Depreciated 5 years)

Table 1 (continued)

−0.12 0.01 0.10 −0.10 0.17 −0.02 0.05 −0.06 −0.08 −0.07 −0.05 0.03 −0.08 −0.02 0.07 0.07 −0.11 0.14 −0.04

16

−0.02 −0.12 0.21 −0.05 −0.09 −0.09 −0.05 0.12 −0.05 −0.01 0.03 0.04 −0.07 −0.07 −0.01 −0.10 0.01 −0.06

17

0.29 0.19 −0.07 −0.33 0.22 0.27 0.27 0.28 0.26 0.23 0.27 0.43 −0.01 −0.12 0.07 −0.09 −0.12

18

−0.32 0.18 0.37 0.09 0.09 0.06 0.09 0.08 0.06 0.09 0.27 0.13 0.28 0.17 0.01 −0.05

19

−0.47 −0.32 −0.03 0.03 0.06 0.04 0.03 0.16 0.22 0.05 −0.13 −0.18 −0.07 −0.11 −0.04

20

−0.40 0.04 0.00 −0.01 −0.03 −0.01 −0.07 −0.10 0.11 0.25 0.28 0.05 0.17 −0.08

21

−0.09 −0.10 −0.10 −0.07 −0.07 −0.06 −0.07 −0.11 −0.02 0.14 0.12 −0.02 0.14

22

−0.10 −0.09 −0.06 −0.05 −0.04 −0.05 0.19 0.01 0.06 0.10 −0.10 −0.03

23

−0.07 −0.05 −0.04 −0.03 −0.04 0.17 0.04 0.01 0.05 −0.08 −0.06

24

−0.04 −0.03 −0.03 −0.03 0.24 −0.03 −0.07 0.02 −0.05 −0.05

25

−0.02 −0.02 −0.02 0.14 −0.01 −0.10 −0.02 −0.06 −0.04

26

−0.01 −0.02 0.08 −0.02 −0.07 −0.02 −0.05 −0.03

27

−0.01 0.10 −0.02 −0.06 −0.04 −0.04 −0.02

28

0.27 −0.08 −0.06 −0.06 −0.02 −0.03

29

0.05 0.05 0.11 −0.13 −0.10

30

0.16 0.10 0.13 0.08

31

0.19 0.07 0.03

32

−0.01 0.03

33

0.06

34

M. Jensen and H. Kim

Poetics xxx (xxxx) xxxx

10

1.06

(0.35)

(0.04)

(0.06)

11

0.88***

0.14

0.27

Last Film: Dogme 95 (1,0)

Last Film: Relative Cast Size

Last Film: Foreign Film (1,0)

(0.22)

(0.16)

(0.25)

(0.22)

0.01 (0.03)

0.03

1.08***

0.99**

Years Since Graduation

Number of Production Companies

1980s (1,0)

1990s (1,0)

(0.33)

(0.31)

(0.03)

0.05

−0.22

(0.26)

(0.37)

(0.29)

0.39

(0.25)

0.69**

(0.03)

0.04

(0.04)

−0.13***

0.02

−0.17

0.03

(0.13)

0.26*

(0.28)

0.45

(0.12)

0.19

(0.05)

0.08

(0.16)

(0.22)

(0.10)

Female Director (1,0)

Foreign Born Director (1,0)

0.56***

Last Film: Top 5 Actor Prominence

(0.05)

0.11*

−0.15*

(0.06)

−0.07

Last Film: Number Actor Bodil Wins

Actor Prominence * Years Since Graduation

1.43*

Actor Prominence (Graduation Film) (0.61)

1.70***

2.36***

Director Experience (ln)

(0.69)

0.25*

Director Cumulative Awards (ln)

(0.31)

0.31

(0.29)

0.65*

(0.03)

0.05†

(0.04)

−0.12***

(0.16)

0.14

(0.23)

0.01

(0.26)

0.03

(0.12)

0.17

(0.32)

0.25

(0.12)

0.13

(0.05)

0.07

(0.06)

−0.14*

(0.61)

1.36*

(0.40)

(0.12)

0.05

Director Cumulative Ticket Sales (ln)

Entry Reputational Rank: Artistic (5y)

Entry Reputational Rank: Commercial (5y)

0.05†

Global Reputational Rank: Artistic (5y)

(0.29)

0.42

(0.25)

0.68**

(0.03)

0.04

(0.04)

−0.13***

(0.16)

0.04

(0.22)

−0.16

(0.27)

0.02

(0.13)

0.24†

(0.32)

0.39

(0.12)

0.19

(0.05)

0.08

(0.06)

−0.15*

(0.62)

1.38*

(0.36)

2.35***

(0.30)

0.36

(0.27)

0.62*

(0.03)

0.05

(0.04)

−0.13***

(0.17)

0.05

(0.22)

−0.11

(0.27)

0.04

(0.14)

0.17

(0.28)

0.38

(0.12)

0.21†

(0.05)

0.08

(0.06)

−0.15*

(0.60)

1.52*

(0.37)

(0.34)

2.34***

(0.30)

0.23

(0.26)

0.69**

(0.03)

0.05†

(0.04)

−0.14***

(0.15)

0.06

(0.23)

0.02

(0.26)

−0.09

(0.13)

0.23†

(0.36)

0.13

(0.11)

0.10

(0.06)

0.07

(0.06)

−0.13*

(0.64)

1.22†

(0.35)

0.27

(0.32)

0.72*

(0.03)

0.05*

(0.04)

−0.16***

(0.19)

0.13

(0.23)

0.07

(0.28)

−0.03

(0.12)

0.16

(0.37)

0.08

(0.12)

0.13

(0.06)

0.07

(0.07)

−0.12†

(0.67)

1.26†

(0.43)

2.19***

(0.23)

−0.04

(0.05)

0.04

0.00

(0.05)

0.05

(0.04)

−0.11†

(0.05)

0.07

(0.07)

−0.01

(0.07)

0.14*

0.17**

(0.06)

Model 7

(0.06)

2.21***

(0.05)

(0.05)

Model 6

(0.05)

(0.02)

0.05

(0.08)

Model 5

Global Reputational Rank: Commercial (5y)

−0.03

Model 4

0.14**

Model 3

Local Reputational Rank: Artistic (5y)

Model 2

0.16**

Model 1

Local Reputational Rank: Commercial (5y)

time at risk = 1348

Coefficients: ln(hazard ratios) n = 104,

Table 2 Weibull Parameticl Event History Analysis: All Films - Career Continuation.

(0.38)

0.26

(0.33)

0.76*

(0.03)

0.06*

(0.04)

−0.16***

(0.19)

0.22

(0.23)

0.09

(0.34)

0.02

(0.16)

0.05

(0.42)

−0.02

(0.12)

0.13

(0.06)

0.09

(0.06)

−0.12*

1.43* (0.68)

(0.22)

−0.16

(0.08)

0.05

(0.05)

0.02

(0.07)

−0.12†

(0.06)

0.15*

(0.10)

−0.01

(0.07)

0.14*

0.18*

(0.08)

Model 8

(0.31)

0.19

(0.29)

0.66*

(0.03)

0.06*

(0.04)

(0.34)

0.18

(0.30)

0.70*

(0.03)

0.06*

(0.04)

−0.16***

(0.18)

0.22

(0.25)

0.07

(0.29)

−0.06

(0.17)

0.04

(0.44)

−0.03

(0.12)

0.14

(0.06)

0.09

(0.07)

−0.12†

1.39* (0.69)

(0.05)

0.00

(0.06)

−0.09

(0.06)

0.11†

(0.09)

−0.01

(0.05)

0.13*

0.22*** (0.07)

Model 10

(continued on next page)

−0.14***

(0.17)

0.12

(0.23)

0.08

(0.28)

−0.05

(0.16)

0.12

(0.42)

0.10

(0.13)

0.09

(0.06)

0.09

(0.06)

−0.14*

1.37* (0.66)

(0.05)

0.15**

0.18**

(0.06)

Model 9

M. Jensen and H. Kim

Poetics xxx (xxxx) xxxx

12

−269.91

(0.05)

0.55***

60.05***

−239.88

(0.07)

0.88***

(0.45)

−6.17***

8.27*

−235.75

(0.07)

0.89***

(0.51)

−6.22***

1.62

−239.07

(0.07)

0.89***

(0.45)

−6.18***

(0.07)

0.90***

(0.46)

−6.08***

2.37

−238.70

*** p<0.001, ** p<0.01, * p<0.05, † p<0.10; Robust standard errors in parentheses; Two-tailed tests. 1 Model 2 compared to Model 1; all others compared to Model 2.

Likelihood Ratio Testⁱ

Log Pseudolikelihood

ln(p)

(0.48)

16.52***

−231.63

(0.07)

0.91***

(0.47)

−6.28***

21.20***

−229.29

(0.07)

0.94***

(0.54)

−6.30***

4.73***

Completed Eight or More Films (1,0)

−5.50***

4.49**

Completed Seventh Film (1,0)

Constant

4.87***

27.69***

−226.04

(0.08)

0.97***

(0.56)

−6.45***

(1.24)

(1.43)

(1.04)

(1.08)

(0.85)

(0.91)

(0.93)

Completed Sixth Film (1,0)

(0.68)

1.52*

(0.45)

−0.07

Model 8

4.52***

(0.42)

−0.05

Model 7

Completed Fifth Film (1,0)

(0.37)

−0.02

Model 6

3.16***

0.14

(0.38)

Model 5

Completed Fourth Film (1,0)

0.24

(0.37)

Model 4

2.99**

0.17

(0.39)

Model 3

Completed Third Film (1,0)

0.19

(0.37)

Model 2

2.44**

0.67

(0.46)

Model 1

Completed Second Film (1,0)

Completed First Film (1,0)

2000s/2010s (1,0)

time at risk = 1348

Coefficients: ln(hazard ratios) n = 104,

Table 2 (continued)

21.28***

−229.25

(0.07)

0.94***

(0.49)

−6.30***

(0.72)

5.59***

(1.03)

4.82***

(0.59)

5.16***

(0.68)

4.59***

(0.52)

3.33***

(0.45)

3.13***

(0.40)

2.52***

(0.34)

1.47***

(0.39)

−0.08

Model 9

26.36***

−226.71

(0.08)

0.96***

(0.49)

−6.34***

(0.97)

5.10***

(1.19)

4.78***

(0.65)

5.18***

(0.76)

4.76***

(0.57)

3.45***

(0.48)

3.26***

(0.47)

2.78***

(0.42)

1.79***

(0.41)

−0.16

Model 10

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career stage (number of films directed). It confirms the earlier local reputational rank results and that the return to career stage increases at a decreasing rate from the first to the third film and then plateaus after the third film. To remove concerns about multicollinearity among the different reputation measures, Model 9 contains the local reputational rank measures only and it strengthens the support for our local reputational rank arguments. Finally, Model 10 confirms the conclusion that local reputational rank is a better predictor of film directing than entry and global reputational rank. The effects of local commercial and artistic reputational rank are also substantively significant. Based on Model 9, increasing local artistic reputational rank one standard deviation, for example, increases the hazard of directing a film by 24% ((exp (0.15) − 1) × 1.49), whereas increasing local commercial reputational rank one standard deviation increases the hazard by 17 percent ((exp(0.18) − 1) × 0.85). 5.2. Robustness checks Table 3 presents a series of robustness checks. An alternative to our reputational rank approach would be to interact director experience with cumulative ticket sales and cumulative awards (logged). Building on Model 3, Model 11 and Model 12 show that the interactions between director experience and cumulative ticket sales and between director experience and cumulative awards are not significant. Rather than focusing on reputational rank based on cumulative commercial and artistic performance, the performance of the last film could be all that matters. Specifically, in the film industry, “the reputational value of an employee’s work product atrophies rapidly over time” (Bielby & Bielby, 1999), which suggests that, in the extreme, “you’re only as good as your last job” (Blair, 2001). Building on Model 9, Model 13 shows that neither the ticket sales nor the awards of the last film are significant, whereas local reputational rank continues to be significant. In Model 14, we use ordinal local reputational rank measures instead of continuous local reputational rank measures used in our other analyses. The director scoring highest on the continuous reputational rank measure is given the ordinal rank one, the second highest two, and so on (capped at fifty for commercial rank). Because the number of ranked directors vary by career stage, we control for the number of directors among whom the focal director is ranked (it is better to ranked one of thirty than one of three). Our results are robust to using an ordinal rank measure (a negative coefficient or a lower ordinal rank means better reputation compared to peers). Building on Model 9, Model 15 uses a Cox proportional hazard specification rather than a Weibull specification, thus avoiding assumptions about the shape of the baseline hazard. Model 15 shows that the reputational rank results are robust to the Cox specification. Building on Model 9, Model 16 focuses on career continuation only by modeling the time to each of the films following the first feature film (from 1 to right censoring) and it confirms the earlier local reputational rank results. Finally, to ensure that reputational rank predicts career continuation at all career stages, we re-estimated Model 9 seven times, each time truncating the career with one film. Fig. 2 shows that both local reputational rank coefficients are statistically significant at each stage of the career (when directors have only directed one film, two films, and so on), thus supporting our early assertion that reputational rank through localized competition facilitates career mobility at all career stages, which may help explain the over-supply of labor by making success appear more manageable. Together, the main analyses and the robustness checks corroborate that local reputational rank, not entry or global reputational rank, is a statistically and substantively significant predictor of career continuation. The career-stage-based reference group in local reputational rank avoids confounding director experience and cumulative performance, thus providing a less noisy measure of reputational rank than global reputational rank. 6. Discussion This study combined insights from vacancy competition and the role-theoretic perspective on reputation to develop and test a sociological theory of how professional reputation formation and creative careers coevolve in project-based labor markets in the cultural industries. We used vacancy competition to emphasize localized competition and reference groups and the role-theoretic perspective on reputation to emphasize reputational rank within the correct reference group. Distinguishing between entry, local, and global reputational rank, we argued that local reputational rank among directors at the same career stage is a statistically and substantively significant predictor of director career development, whereas entry reputational rank among directors from the same Film School cohort, global reputational rank among all directors, and simple cumulative performance are not. We found support for our arguments in a sample of aspirant directors from The National Film School of Denmark. Controlling for career stage, higher local commercial and artistic reputational ranks increase the likelihood that directors can continue their directing careers by directing more films, whereas entry and global reputational rank and traditional reputation measures such as cumulative commercial and artistic performance typically did not predict career continuation. 6.1. Theoretical contributions In addition to the direct contributions to research on creative career and professional reputation discussed in the introduction, our study also contributes more broadly to sociological research on reputation and reference groups. By theorizing reputation as of deviations from career-stage-based performance expectations, our study develops a uniquely sociological perspective on reputation. Reputation has received little theoretical attention in sociology (Jensen et al., 2012). Most sociological research on reputation adopts the decontextualized definition of reputation from economics as a prediction of future behavior based on past behaviors (Bielby & Bielby, 1994; Kollock, 1994; Raub & Weesie, 1990). To deepen our understanding of 13

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Table 3 Robustness Checks: Weibull Event History Analysis (Models 21 and 22: Cox Proportional Hazard). Coefficients: ln(hazard ratios) n = 104, time at risk = 1348

Model 11

Model 12

Model 13

Ordinal Reputational Rank: Commercial Ordinal Reputational Rank: Artistic Number of Directors in Reference Group Last Film: Ticket Sales (ln) Last Film: Awards Local Reputational Rank: Commercial (5y) Local Reputational Rank: Artistic (5y) Experience * Cumulative Awards Experience * Cumulative Ticket Sales Director Cumulative Ticket Sales (ln) Director Cumulative Awards (ln) Director Experience (ln) Actor Prominence (Graduation Film) Actor Prominence * Years Since Graduation Last Film: Number Actor Bodil Wins Last Film: Top 5 Actor Prominence Last Film: Dogme 95 (1,0) Last Film: Relative Cast Size Last Film: Foreign Film (1,0) Foreign Born Director (1,0) Female Director (1,0) Years Since Graduation Number of Production Companies 1980s (1,0) 1990s (1,0) 2000s/2010s (1,0) Completed First Film (1,0)

−0.01 (0.05) 0.06 (0.04) 0.26† (0.15) 1.75** (0.57) 1.34* (0.63) −0.14* (0.06) 0.07 (0.05) 0.13 (0.12) 0.25 (0.33) 0.17 (0.12) 0.03 (0.28) 0.01 (0.23) 0.14 (0.17) −0.12*** (0.04) 0.05† (0.03) 0.64* (0.29) 0.30 (0.32) 0.17 (0.39)

Completed Second Film (1,0) Completed Third Film (1,0) Completed Fourth Film (1,0) Completed Fifth Film (1,0) Completed Sixth Film (1,0) Completed Seventh Film (1,0) Completed Eight or More Films (1,0)

14

−0.04 (0.14) 0.05 (0.05) 0.32 (0.28) 1.74*** (0.43) 1.32* (0.64) −0.13* (0.06) 0.07 (0.06) 0.14 (0.12) 0.23 (0.33) 0.18 (0.13) 0.02 (0.27) 0.01 (0.23) 0.14 (0.16) −0.12*** (0.04) 0.05† (0.03) 0.65* (0.29) 0.29 (0.32) 0.17 (0.39)

0.02 (0.06) 0.27 (0.20) 0.18* (0.08) 0.10† (0.06)

1.42* (0.67) −0.13* (0.06) 0.08 (0.06) 0.09 (0.12) −0.22 (0.49) 0.07 (0.18) 0.06 (0.32) 0.12 (0.23) 0.15 (0.17) −0.14*** (0.03) 0.06* (0.03) 0.66* (0.28) 0.19 (0.31) −0.04 (0.39) 1.23* (0.55) 2.28** (0.69) 2.80*** (0.68) 2.97*** (0.67) 4.37*** (0.90) 4.80*** (0.72) 4.46*** (1.22) 5.12*** (0.93)

Model 14

Model 15

Model 16

−0.04** (0.01) −0.05† (0.03) 0.06** (0.02)

1.48* (0.63) −0.16* (0.06) 0.12† (0.07) 0.06 (0.13) 0.31 (0.37) 0.03 (0.17) 0.10 (0.31) 0.16 (0.22) 0.02 (0.18) −0.15*** (0.04) 0.05† (0.03) 0.68* (0.30) 0.02 (0.35) −0.16 (0.42) 0.86† (0.51) 2.25** (0.70) 3.06*** (0.79) 3.47*** (0.90) 4.78*** (1.06) 5.52*** (0.98) 5.00*** (1.46) 5.86*** (1.12)

0.11** (0.04) 0.11** (0.04)

0.13* (0.06) 0.13** (0.05)

0.89 (0.55) −0.07 (0.05) 0.06 (0.05) 0.11 (0.10) 0.21 (0.26) 0.11 (0.11) −0.11 (0.25) 0.03 (0.18) 0.05 (0.14) −0.05† (0.03) 0.04 (0.03) 0.59* (0.23) 0.32 (0.27) 0.06 (0.37) 0.84** (0.28) 1.31*** (0.34) 1.62*** (0.41) 1.65*** (0.45) 2.31*** (0.51) 2.68*** (0.52) 2.10** (0.75) 2.85*** (0.53)

0.57 (0.98) −0.09 (0.07) 0.07 (0.06) 0.13 (0.12) 0.09 (0.42) 0.19 (0.15) −0.04 (0.27) −0.43 (0.39) 0.17 (0.19) −0.14*** (0.04) 0.06† (0.03) 0.92** (0.32) 0.33 (0.32) 0.14 (0.43) 0.84** (0.29) 1.40*** (0.32) 1.46*** (0.44) 2.63*** (0.47) 3.11*** (0.42) 2.70*** (0.78) 3.34*** (0.45)

(continued on next page)

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Table 3 (continued) Coefficients: ln(hazard ratios) n = 104, time at risk = 1348

Model 11

Model 12

Model 13

Model 14

Constant

−6.21*** (0.52) 0.89*** (0.08) −235.73 0.03

−6.22*** (0.51) 0.89*** (0.07) −235.70 0.11

−6.31*** (0.50) 0.94*** (0.07) −228.08 2.33

−5.93*** (0.85) 0.96*** (0.07) −228.11 83.59***

ln(p) Log Pseudolikelihood Likelihood Ratio Testⁱ

Model 15

Model 16

−906.18

−4.48*** (0.66) 0.89*** (0.08) −148.49

*** p<0.001, ** p<0.01, * p<0.05, † p<0.10; Robust standard errors in parentheses; Two-tailed tests. ⁱ Models 11 and 12 are compared to Model 3 (Table 2); Model 13 is compared to Model 9 (Table 2); Model 14 is compared to Model 1 (Table 2).

Fig. 2. Reputational Rank Regression Coefficients With 95% Confidence Intervals.

reputation, we draw instead on status and role theory (Linton, 1936; Merton, 1968a) to anchor reputation in status-based role expectations in general and career-stage-based performance expectations in particular. By emphasizing that deviations from expectations shape reputation formation, we stress that positive and negative reputations can be built on little cumulative experience if individual behaviors deviate sufficiently from expectations. Similarly, reputations can be created and destroyed quickly depending on individual behaviors, thus helping to account for the surprising speed at which reputations of individuals and organizations sometimes change. We document the value of our perspective on reputation by showing that anchoring reputational rank in career-stagebased performance expectations avoids confounding the effects of reputation and those of cumulative experience and performance, a common problem in research on reputation and creative careers. More generally, by comparing reputational rank using three different reference groups, we highlight the importance of reference groups in creative career and reputation research. Defining reputational rank within a career stage not only identifies whom within a reference group is most likely to move ahead, it also enables competition between individuals at different career stages by allowing trade-offs between experience and reputation. Should resources be used on an early-career-stage director with a high reputational rank (a rising star) or on a late-career-stage director with a low reputational rank (a fading veteran)? We use career stages to demarcate status positions in our context but status hierarchies can, in other contexts, be defined based on job titles such as assistant, associate, and full professors or external certifications such as winning major awards (Ertug & Castellucci, 2013; Jensen & Kim, 2015). Regardless of the empirical specification of status positions, our role-theoretic perspective on reputational rank helps explain differences in career mobility across different types of project-based labor markets: The higher (lower) the importance of reputational rank compared to status position or career stage, the more (less) career mobility within a project-based labor market. For example, because popular music requires less technical music skills than classical music, reputational rank likely matters more in popular music than in classical music, which suggests that there is more career mobility and less stable status hierarchies in popular music than in classical music. 6.2. Scope conditions and limitations The scope conditions to our theory are important. Our theory of career development and reputational rank is most applicable when reputation is valuable, i.e. when it is hard to specify and evaluate the quality of a product a priori. Reputation is, therefore, less important in project-based labor markets focused on modular tasks with well understood performance drivers, easily identifiable individual contributions, and strong external evaluation systems (Leung, 2014). Reputation is also less important in traditional internal labor markets (see Bidwell, Briscoe, Fernandez-Mateo, & Sterling, 2013) dominated by objective hiring and promotion criteria, such as seniority and tenure, external certifications and credentials documenting skills, or membership in specific trades or unions (Ferguson & Hasan, 2013). Nevertheless, we think that our role-theoretic perspective on reputational rank can be extended beyond project-based labor markets to most contexts in which competition is localized and reputation is important. When high-status 15

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actors fail to meet their relatively high role expectations, for example, they will be penalized to a degree over and beyond that of lower-status actors with the same absolute performance (Graffin, Bundy, Porac, Wade, & Quinn, 2013; Jensen, 2006; Rhee & Haunschild, 2006). In other words, although the operationalization of status and reputation vary by empirical context, our theoretical conceptualization of reputational rank within a status position as a mechanism for status mobility can be generalized and applied to different types of career development as well as organizational status mobility. Our study is not without limitations. First, our reputational rank theory allows for increases and decreases in status and reputation, but our empirical focus on director careers and equation of status position and career stage does not allow for decreases in status. We acknowledge that not allowing for decreases in status empirically is a limitation but view this limitation as a reasonable implication of focusing on career development and how aspirant directors develop their careers. It is difficult to imagine directors who have directed five films, for example, being treated as novice directors with one film only and therefore evaluated using role expectations for novice directors, although they may not be able to raise as much money for their projects as before, thus emphasizing the importance of controlling for cast size. It is also true that explicit decreases in status happens in many other contexts (Rider & Negro, 2015) including demotions in organizations with clearly defined job hierarchies, thus suggesting an important next step in developing our role-theoretic perspective on careers would be to focus on contexts with increases and decreases in status. Second, by focusing on Film School directors to ensure that we have complete career histories of successful and unsuccessful aspirants, our analyses and results are ultimately limited to this subgroup of aspiring directors. We focus on Film School directors and their complete career histories to avoid important sources of sample selection bias stemming from only observing directors with at least one film in the general population. As a result, our study does not address important questions related to potential differences in status and reputation between Film School directors and directors not attending the Film School including whether or not the Danish Film Institute favors projects by Film School directors. Because our main focus is on developing and testing the role-theoretic perspective on reputational rank and career development, we prioritize avoiding sample selection bias rather than broadening our sample to include directors with systematically incomplete career histories. Third, the Danish film industry is unique due to its relatively small scale and the importance of the Danish Film Institute in funding film projects. We are not surprised that local reputational rank is more important than global reputational rank in this context because the DFI deliberately balances exploiting known talent (established directors) and exploring new talent (aspirant and novice directors). We believe nevertheless that the portfolio approach to film projects used by the DFI is similar to the portfolio approach to film projects used by the major Hollywood studios in which they invest disproportionally in potential blockbusters using known talent but also invest in minor productions using new talent (Elberse, 2013). Generalizing our arguments from the Danish film industry to Hollywood may, however, require refining how reference groups are defined. For example, because genre specialization or film budget size is more important in Hollywood, reference groups may have to be defined by genre or cumulative budget size. Similarly, moving from the film industry to other empirical contexts would almost certainly require focusing on other reputation dimensions than commercial and artistic performance such as focusing on research and teaching in university settings. We think our theoretical framework is flexible enough, however, to accommodate these types of empirical adjustments, thus broadening the potential applicability of reputational rank and localized competition outside the Danish film industry. 7. Conclusion By combining insights from vacancy competition and the role-theoretic perspective on reputational rank to theorize and test how reputation and careers coevolve, our study sheds new light on career development in project-based labor markets. We focused on project-based labor markets in the film industry but our emphasis on local reputation rank applies to other labor markets including markets for corporate executives and directors, who tend to develop different reputations with different corporate stakeholders such as investors, customers, employees, and communities. An important next step in advancing our approach to career development and reputation formation would therefore be to examine how reputational rank affects career development in other labor market contexts in which reputation helps people develop their careers. Finally, although careers obviously are highly uncertain undertakings, an uplifting implication of our study is to always focus locally on the current project and competition rather than the structural mobility constraints: Reaching the stars incrementally is less daunting. Declaration of Competing Interest None. Appendix A To fully understand career development, it is necessary to also understand why some directors got to enter the directing career by directing their first film. We present, therefore, a set of supplemental career entry models using all Film School graduates to predict who gets to enter the profession and to develop a professional reputation by directing their first film. We use actor prominence in the graduation film to model career entry because prominent insiders, such as lead actors with extensive experience in working with directors, have the expertise to evaluate emerging talent and reputational incentives to avoid endorsing less promising aspirants (Bowness, 1989; Rider, 2012; Stuart et al., 1999). A graduation film is a short feature film (typical length about 20 min) completed during the last year at the Film School by a crew of Film School students. Graduation films are typically comprised of crew and cast assembled by the graduating director, with the crew including a graduating producer, 16

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Table A1 Parametic Event History Analysis: First Film - Career Entry

n = 129, time at risk = 834

Gamma

Weibull

Acclerated Failure Time

Acclerated Failure Time

Model 17

Model 18

Actor Prominence (Graduation Film) Graduation Film Awards (ln) Relative Cast Size Number of Films Last Year by Own Cohort Number of Films Last 2 Years by Prior Cohort Directed TV Series (# Depreciated 5 years) Directed Short Features (# Depreciated 5 years) Director Assistant (# Depreciated 5 years) Foreign Born Director (1,0) Female Director (1,0) Years Since Graduation Number of Production Companies 1980s (1,0) 1990s (1,0) 2000s/2010s (1,0) Constant ln(σ) κ ln(p) Log Pseudolikelihood Likelihood Ratio Test

−0.20* (0.09) −0.11* (0.05) 0.02 (0.05) −0.02 (0.02) 0.01 (0.01) −0.02 (0.02) 0.01 (0.03) −0.02† (0.01) −0.06 (0.04) −0.00 (0.04) 0.14*** (0.02) −0.00 (0.01) 0.03 (0.04) 0.01 (0.06) 0.04 (0.07) 1.34*** (0.16) −2.88*** (0.43) 2.18* (0.88)

−0.10* (0.05) −0.03 (0.05) −0.02 (0.02) 0.01 (0.01) −0.02 (0.02) 0.00 (0.03) −0.02† (0.01) −0.07* (0.04) 0.00 (0.03) 0.14*** (0.01) −0.00 (0.01) 0.01 (0.04) 0.01 (0.06) 0.02 (0.07) 1.32*** (0.16) −2.97*** (0.43) 2.36* (0.96) −66.24

−62.54 7.40*

Model 19

−0.11* (0.05) −0.03 (0.04) −0.02 (0.02) 0.01 (0.01) −0.01 (0.02) 0.02 (0.03) −0.02 (0.01) −0.09* (0.04) 0.01 (0.03) 0.15*** (0.01) −0.00 (0.01) 0.01 (0.04) 0.01 (0.06) 0.03 (0.07) 1.32*** (0.15)

2.19*** (0.23) −67.76

Model 20 −0.21* (0.09) −0.13** (0.05) 0.01 (0.05) −0.02 (0.02) 0.01 (0.01) −0.02 (0.02) 0.03 (0.03) −0.02 (0.02) −0.08† (0.04) 0.00 (0.04) 0.14*** (0.01) −0.00 (0.01) 0.02 (0.05) 0.02 (0.06) 0.06 (0.07) 1.35*** (0.15)

2.17*** (0.23) −63.98 7.55*

*** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.10; Robust standard errors in parentheses; Two-tailed tests.

scriptwriter (typically the director), cinematographer, editor, and sound designer and the cast including two to six unpaid volunteers ranging in experience from unknown amateurs to known professional actors. Following Rossman et al. (2010), we measure actor prominence by their normalized (the actor with the highest prominence gets the score of one) eigenvector centrality (Bonacich, 1987) in the film credit network. Specifically, we used all the Danish films produced between 1963 and 2013 to construct rolling five-year networks in which the cells indicate the proportion of films in which an actor is credited above other actors. We use the most prominent actor in each graduation film in our analyses because less prominent actors agreeing to participate may represent less of an endorsement of the aspirant director and more a desire to work with the more prominent actor. In addition to the control variables in the previous models, we control for differences in intra-cohort productivity by the number of films by the other directors from the focal director’s cohort in the last year and for the differences in inter-cohort productivity by the number of films in the last two years by the directors from the prior cohort. Differences in intra-cohort productivity could affect the other aspirant directors positively by increasing the visibility of the entire cohort or negatively by crowding out the less fortunate director aspirants. Differences in inter-cohort productivity could also affect aspirant directors if a very productive cohort crowds out the next cohort or a less productive cohort makes room for the next cohort. Moreover, as an aspirant director may take different intermediary steps, we control for the cumulative number of assistant director films, directed short feature films, and directed television series depreciated linearly over five years (the best model fit was achieved by depreciating over five years but alternative depreciation schemes including not depreciating provided similar prominent endorsement results). Finally, the ability of graduating directors to convince prominent actors to participate in their graduation films functions as an a priori indicator of the quality of the 17

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graduating directors, the graduation film itself may be considered particularly strong and win a graduation film award, thus suggesting that we control for graduation film awards in our entry analysis.10 Table A1 presents the parametric event history analysis of the likelihood of becoming a novice director by directing the first film. We report both the gamma and the Weibull models because the AIC and BIC model-specification tests are inconclusive. Model 17 and Model 18 use the gamma specification and show, in accelerated failure times (hazard ratios are not available in gamma models), that the more prominent the graduation film actors, the shorter the time to directing the first film (−0.20; p < 0.05). Model 19 and Model 20 use the Weibull specification and show, also in accelerated failure times, consistently with the gamma models, that the more prominent the graduation film actors, the shorter the time to directing the first film (−0.21; p < 0.05). The effect is substantively significant as well. Increasing actor prominence one standard deviation, for example, increases the hazard of directing the first film by 123% ((exp(1.85) − 1) × 0.23). Finally, regardless of the approach to controlling for director experience in Tables 2 and 3, all the models confirm that actor prominence is associated with higher likelihood of directing films, but also that the endorsement effect decreases over time, as shown in research on the decreasing effect of status following market entry (Jensen, 2003).

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10 Unlike awards for feature films, a number of different film organizations and festivals have given awards for graduation films over the years, none of which have given graduation film awards systematically throughout our time period, which makes graduation film awards a less useful indicator of director quality.

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W., Kim, T.-Y., Ukanwa, K., & Von Rittmann, J. (2003). Robust identities or nonentities? Typecasting in the feature-film labor market. The American Journal of Sociology, 108, 1018–1073. Michael Jensen is a professor of strategy at the Stephen M. Ross School of Business, University of Michigan, and an international research fellow at the Oxford University Centre for Corporate Reputation. He received his Ph.D. in management and organizations from Northwestern University. His research focuses on the role of social structures in markets, with a particular emphasis on status, reputation, and identity. Heeyon Kim is an assistant professor of strategy at the School of Hotel Administration and management and organization at the SC Johnson College of Business, Cornell University. She received her PhD in strategy from the University of Michigan. Her research explores how social evaluations of firms, such as social status, reputation and market identity, affect their behavior and performance. She focuses empirically on creative industries including feature films, fashion, and music as well as the international expansion of firms.

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