Executive Migration and Interorganizational Competition 1

Jesper B. Sørensen University of Chicago

June 1998

Forthcoming, Social Science Research

Approximate Word Count: 9,000

1 This is a revised version of papers presented at the 1995 Annual Meetings of the Social Science History Association, the Social Organization of Competition Workshop at the University of Chicago Graduate School of Business, and the 1996 Annual Meetings of the American Sociological Association. Funds from the Graduate School of Business at the University of Chicago supported the writing of this paper. This work has benefitted from comments by William Barnett, James Baron, Diane Burton, David Grusky, Heather Haveman, David Kang, Rakesh Khurana and Joel Podolny. Discussions with Ron Burt, Toby Stuart and Olav Sorenson led to substantial improvements. I am especially grateful for advice from Michael Hannan and Patricia Mei Yin Chang. Please direct correspondence to Jesper B. Sørensen, Graduate School of Business, University of Chicago, 1101 East 58th St., Chicago, IL 60637 or [email protected]. Abstract

This paper examines how the patterns of managerial careers affect change processes among formal organizations. By conceptualizing interorganizational mobility as a form of interorganizational relation, I operationalize the idea that the career choices made by individuals result in structures that constrain organizations. An analysis of the television industry shows that stations perform worse in the market for viewers when they recruit managers from the same sources as their competitors, net of controls for the station’s overall recruitment activity. Theoretically, the results suggest that managerial mobility is a mechanism by which firms move through the competitive landscape, both intentionally and unintentionally, and that careers therefore are a source of evolutionary change in organizational populations. The strategic implication is that firms must attend not only to their positioning in product markets, but also to their positioning in factor markets, particularly the market for managerial talent. In recent years, the long-standing interest of organizational scholars in the consequences of executive succession has been complemented by a greater interest in how patterns of managerial mobility drive processes of renewal and change in formal organizations (Fligstein

1990; Boeker 1997; Gelatkanycz and Hambrick 1997; Rao and Drazin 1998). This research typically proceeds from the assumption that organizational behavior is partly determined by the background characteristics of managers, and by the social influences to which they are subject

(Cyert and March 1963; Hambrick and Mason 1984). Recruitment patterns are therefore important determinants of firm behavior because they transform an organization’s managerial capabilities. They do so in two ways: 1) by altering the mix of managerial outlooks on the top management team; and 2) by reconfiguring the management teams pattern of external ties, and thus its exposure to outside influences.

Hitherto, theoretical and empirical research on the consequences of executive migration have employed a focal organization perspective. In other words, studies have focused on how a manager’s insights or understandings change decision making processes and outcomes within the firm. The large literature on CEO succession has typically focused on the dynamics of change within firms (e.g., Grusky 1963; Carroll 1984; Haveman 1993), as do studies of executive turnover more generally (e.g., Virany, Tushman and Romanelli 1992). In this vein, Boeker

(1997) explores how the movement of top managers across firms influences decisions to enter new product markets in the semiconductor industry. Geletkanycz and Hambrick (1997) investigate how the top management team’s pattern of external ties (including those formed through mobility) influences the firm’s strategic positioning. Rao and Drazin (1998) examine how the mobility of mutual fund managers affects the introduction of new mutual funds by investment companies.

While they demonstrate that executive migration impacts firm behavior, the atomistic approach of these studies seems limited. The migration of executives between firms generates interorganizational networks (Baty, Evan and Rothermel 1971). Managerial mobility therefore embeds organizations in networks of opportunity and constraint. Network theories of organizations suggest that a firm’s performance and behavior depend on its location in such networks relative to other firms (e.g., Burt 1992). Therefore, in order to fully understand the consequences of executive migration, one must also consider its effects the distribution of firms in such interorganizational networks.

In this paper, I explore the implications of adopting an ecological approach to understanding the consequences of managerial mobility for organizational outcomes. If it is true that firm behavior is influenced by the career experiences of its managers, it stands to reason that if firms draw on managers with similar experiences , they will be more likely to engage in similar types of behavior. Patterns of recruitment and mobility embed firms in the larger structure of the market for managerial talent and hence determine the extent of overlap in managerial capabilities.

From an ecological perspective, this suggests that patterns of career mobility shape competition between firms (Hannan and Freeman 1989). I argue that for firms facing a common environment, the extent to which they draw on similar pools of labor determines the degree to which they compete for other resources.

The remainder of the paper is organized as follows. The next section develops theory and hypotheses concerning the relationship between patterns of executive migration and interfirm

Careers and Competition 2 competition. The next two sections describe the setting for the study – the commercial television broadcasting industry – and the data and methods used. I then describe the results, followed by a discussion of their implications.

Executive Migration and Interorganizational Competition

Baty, Evan and Rothermel (1971) were among the first to suggest that personnel flows should be viewed as interorganizational relations. They suggested that as managers move between organizations, they bring with them the knowledge and information acquired with their previous employer. For this reason, organizations are constrained by the labor markets they operate in:

When new members are recruited, particularly in technical or professional occupations, new bodies of knowledge and skills are imported which are often the sources of innovative ideas in organizations. ... In this respect, the goals of an organization are constrained by the labor market; that is, the kinds of people that it can recruit. (Baty, Evan and Rothermel 1971: 430-431) Despite this lead, and a similar idea advanced by Pfeffer and Leblebici (1973), little sustained attention has been paid to the importance of personnel flows in shaping organizational outcomes.

While the topic receives scattered theoretical mention (e.g., Granovetter 1995 [1974]: 108;

Hannan and Freeman 1989: 55; Galaskiewicz 1985: 287; DiMaggio 1991: 91), it is only recently that researchers have turned to an empirical examination of the issue of how flows of managerial labor affect organizational behavior (Geletkanycz and Hambrick 1997; Boeker 1997; Rao and

Drazin 1998).

Studies of the effects of executive migration typically start from the premise that various aspects of firm behavior, including strategic decision making, innovation and firm performance are

Careers and Competition 3 affected by the characteristics of top management team members. The suspicion that managerial characteristics should influence managerial decision making flows directly from standard theories of bounded rationality and behavioral theories of the firm (March and Simon 1958; Cyert and

March 1963). Bounded rationality arguments suggest that the interpretive abilities of an organization’s top managers are typically overwhelmed by a large volume of complex, ambiguous stimuli from both inside and outside the organization. In arriving at decisions, managers must therefore distill and interpret this information in order to arrive at an understanding of the organization’s situation. Hambrick and Mason (1984; also see Finkelstein and Hambrick 1996) suggest that this occurs through a three-stage filtering processes, with an executive’s background characteristics and patterns of social ties affecting each stage. First, managers limit their field of vision by only attending to information gleaned from certain sources while ignoring other stimuli.

A manager’s pattern of social contacts play an important role in shaping the types of information to which he or she is exposed (Geletkanycz and Hambrick 1997). Second, a manager’s background characteristics (including career experiences) can influence the salience attached to different types of information, such that some types of information are priveleged while others are ignored. For example, managers may place disproportionate emphasis on information related to their functional background (Dearborn and Simon 1958). Finally, managerial characteristics may affect the interpretations that managers impose on different stimuli (Finkelstein and Hambrick

1996). For example, Fligstein argues that “having spent careers analyzing business problems in a certain way, managers come to view all problems through a certain lens” (Fligstein 1990: 357).

This suggests that organizational behavior will be affected by the experiences and social ties of top management teams. Turnover and recruitment are crucial processes that drive change

Careers and Competition 4 in managerial characteristics, both by changing the set of experiences and perspectives that the firm draws upon, and by reconfiguring the organization’s exposure to external influences. I consider the ecological implications of each of these in turn.

First, turnover in the top management team leads to change in an organization’s stock of managerial experiences and background knowledge. We can therefore expect the direction of organizational change to be affected by the types of managers recruited. Boeker (1997) found that recruiting an executive from a firm with a different product portfolio increases the probability that the recruiting firm will enter one of the market segments occupied by the executive’s former employer. This suggests that executive migration is an important vehicle for the diffusion of managerial capabilities. Moreover, an ecological perspective suggests that the overall pattern of movement between firms will have consequences for the distribution of managerial capabilities across firms, and ultimately for the structure of competition. Overlap in managerial capabilities will increase to the extent that firms recruit from the same sources.

Turnover and recruitment also lead to a reconfiguration of a top management teams pattern of ties to its external environment. New employees bring with them social ties to external sources of information and insight (Baty, Evan and Rothermel 1971; Geletkanycz and Hambrick

1997). Such ties facilitate the transfer of ideas and information between organizations by making otherwise remote events more proximate and concrete to organizational decision makers

(Haunschild 1993). Moreover, interpersonal ties are avenues for social influence processes as they become the locus of pressures for conformity to social norms (DiMaggio and Powell 1983).

Interorganizational networks therefore play a crucial role in the diffusion of organizational practices. For example, numerous studies have suggested that director interlocks between firms

Careers and Competition 5 are important conduits for the diffusion of corporate strategies and practices (Haunschild

1993,1994; Davis 1991; Palmer, Jennings and Zhou 1993; Galaskiewicz and Wasserman 1991).

Geletkanycz and Hambrick (1997) stress the importance of the extent to which external ties are to organizations in the same competitive context (e.g., industry or market) as opposed to organizations in different settings. They argue that top management teams with ties to actors outside the focal firm’s industry will be exposed to a greater diversity of information and influences and therefore be more likely to deviate from industry norms. Note, however, that from an ecological perspective, the value of information gathered through external ties is contingent on its uniqueness, and therefore on the structure of the broader network of inter-firm mobility (Burt

1992; Hannan and Freeman 1989). To the extent that a firm’s competitors have external ties giving them access to the same information, the focal firm gains no competitive advantage. In fact, the firm may find itself at a competitive disadvantage if all of its external ties are duplicated by its competitors. Firms that find themselves recruiting from the same sources as their competitors should be subject to greater competitive pressure.

In summary, a consideration of the broader pattern of executive migration between firms suggests that differences between firms in managerial capabilities will depend in part of their respective locations in this network. Firms occupying similar positions in the managerial mobility network will possess overlapping managerial capabilities. One implication of such an overlap in capabilities is increased competition for resources. Overlap in managerial capabilities may increase competitive pressures in two ways. First, firms that depend on similar managerial capabilities may pursue similar strategies. Geletkanycz and Hambrick (1997) show that firms

Careers and Competition 6 which draw heavily on intraindustry experiences are more likely to conform to prevailing strategic practices in the industry.

Even among firms pursuing similar strategies, however, there may be competitive advantages to relying on different managerial capabilities. The experiences of managers may also affect the ways in which strategies are executed (Gunz and Jalland 1996). If firms relying on similar managerial capabilities are more likely to implement their strategies in a similar fashion, they will compete more intensely for resources. For example, two television stations may both pursue the same strategy of showing local news at 11, but they may execute these strategies quite differently depending on the experiences and capabilities of their management team. To the extent that the two stations adopt approaches to news broadcasts that draw on the same environmental resources, they will compete more intensely. Thus both stations may decide to place a heavy emphasis on coverage of local crime. If so, they will compete for access to at least two types of resources – criminal events and viewers who prefer this type of news. A station emphasizing local politics and education would by contrast experience relatively less competition for resources.

Thus overlap in managerial capabilities may increase competitive pressures even in setting where strategic positions are highly institutionalized.

The simplest way in which firms come to occupy similar positions in the labor market is when one firm recruits directly from its competitors. By the arguments outlined above, recruitment from a competing station should lead to lower growth in viewership:

Hypothesis 1: The growth rate of television stations will be a negative function of the degree to which they recruit personnel from their competitors. The first hypothesis focuses on the simple case of direct exchanges of personnel between competitors. Indirect ties between organizations play no role in this formulation, thereby

Careers and Competition 7 obscuring the consequences of the structure of the network as a whole. Such indirect ties can assume varying degrees of complexity. The simplest case is where two (or more) stations recruit personnel from the same third station. These stations occupy more crowded positions in the labor market network and are therefore likely to see their performance suffer:

Hypothesis 2: The growth rate of television stations will be a negative function of the degree to which they recruit managers directly from the same stations as their competitors. This formulation is relatively narrow, in that organizations are considered equivalent (in the same niche) only if they recruit from the exact same third parties. Two stations will, in this formulation, not occupy the same position as long as they recruit from different sources.

However, the different third parties themselves may occupy similar positions in the social structure, for example by recruiting from each other or from the same station. A measure based on direct ties alone, such as the one for Hypothesis 2, may therefore underestimate the extent to which stations occupy similar positions in the labor market. For the third hypothesis, I therefore consider two stations to be tied to the same third station if the third station is reachable by either party in two steps or less:

Hypothesis 3: The growth rate of television stations will be a negative function of the degree to which they are tied, through one- and two-step paths, to the same stations to which their competitors are tied through one- or two-step paths. In modeling competition in local television broadcasting, it is important to attend to potential differences between generalist and specialist firms. In general, the competitors of a television station are easily identified as the other stations in a market. However, the presence of the major broadcast networks (ABC, CBS, NBC) effectively creates two distinct strategic groups within most markets: network affiliates and independent stations. As a consequence of the network programming they carry, network affiliates pursue generalist strategies in trying to appeal

Careers and Competition 8 to broad spectrum of the television audience. Independent stations, by contrast, typically pursue specialist strategies by targeting their programming at particular audience segments, for example by broadcasting local sports teams. Network affiliates are more constrained in their programming choices, but their generalist strategies also provide them with more slack to cope with fluctuations in audience tastes (Hannan and Freeman 1989). The success of independent stations, on the other hand, is more closely tied to their choice and implementation of programming strategies. Overlap in managerial capabilities should therefore more detrimental to specialist firms than to generalist firms, because the success of a specialist strategy is contingent on its not overlapping with other competitors; specialists thrive by exploiting niches that others are uninterested in. Generalists, on the other hand, succeed not by choosing market segments so as to avoid competitors, but by having superior capabilities.

These differences suggest that the implications of firm recruitment behavior may differ for network affiliates and independent stations. Specifically, specialist firms (i.e., independent stations) should — because they have less slack with which to cope with strategic missteps or overlap with other stations — be more susceptible to niche overlap in the labor market than generalist firms.

Hypothesis 4: The negative effects of direct recruitment and niche overlap for firm performance will be stronger for independent stations than for non-network affiliated stations. This hypothesis can be tested by estimating separate niche overlap effects for network affiliated stations and independent stations.

The Setting: Commercial Television Stations

Careers and Competition 9 The empirical analyses that follow investigate how the growth rates of local commercial television stations depends on their relative location in a network of relations defined by the interfirm mobility of station executives. I analyze data on executive mobility and organizational performance among commercial television stations in the United States. The world of commercial television stations is an ideal setting for this study for a number of reasons. First, there is substantial turnover among television station employees. In major markets, annual turnover among supervisors and managers (the groups studied here) exceeds 15%. For station department heads, the annual turnover rate approaches 20% Some studies estimate that one fifth to one third of station employees change jobs in a given year (see summary in Berg 1991). The main advantage of such high turnover for the present study is that the interfirm mobility matrices will be less sparse.

Second, the television industry is characterized by a high frequency of strategic change and a tight linkage between strategic changes and readily available performance indicators.

Programming decisions are the most important strategic decisions made by television stations

(Eastman, Head and Klein 1989). As most television viewers can confirm, broadcasters constantly adjust their programming content in the search for improved ratings. Programming decisions are vital to a station's success. The revenue of television stations depends largely on the size of the audience it reaches (Fisher, McGowan and Evans 1980). Local television stations generate revenue by selling broadcasting time to three basic groups: broadcast networks (who in turn sell time to national advertisers), local advertisers and national advertisers. The prices that advertisers will pay depends on the size of the audience, as well as the demographic characteristics of the audience that is reached by different programs. Since programmers cannot

Careers and Competition 10 change the amount of time they broadcast beyond an upper limit (i.e., they cannot indefinitely expand production in any conventional sense), revenues depend on advertiser demand. This demand in turn depends on the success of programming in reaching desired audiences.

The high degree of uncertainty involved in the programming process makes strategic change (and performance) highly susceptible to social influence processes (Bielby and Bielby

1994). Television stations are distinctive in this respect, since their product (programming) is amenable to rapid changes that do not require any large-scale retooling of the organization's core technology. While some syndicated programs require substantial capital investments, stations are relatively flexible with respect to rearranging their programming schedule (Eastman, Head and

Klein 1989). A given series can be tried out in a number of different time slots. By contrast, in other industries, such as the semiconductor industry, new product designs or production processes require massive investments in new production facilities and human capital. The ease and low cost associated with programming changes helps make such change more frequent. The extremely close attention paid to small fluctuations in television ratings also increases the frequency of strategic change, since industry actors evaluate programs over a very short time horizon.

Data and Methods

To determine the effects of overlap on performance and competition, I estimate models of growth in a station’s net weekly circulation, defined as the total number of homes that tuned in to a station at least once during a given week. The circulation measures used in this study are measures of the scale of operations, similar to measures of the assets of financial institutions or

Careers and Competition 11 the telephone company subscribers. In fact, a station's audience is its greatest asset. We can express the growth of organizations as a function of an organization's size and a number of covariates characterizing organizational and environmental characteristics:

S i,t%1 "&1 $xit%,i,t%1 ' Sit e (1) Sit where S is a time-varying measure of organizational size, " is an adjustment parameter that indicates how growth rates depend on organizational size, and $ is a vector of parameters characterizing the effects of organizational and environmental covariates.

The Sample

I analyze a complete record of commercial television stations in 15 broadcast markets from 1961 to 1988. Data from years prior to 1961 were also collected and used in the construction of the interfirm mobility matrices. The data on station characteristics were collected from yearly volumes of the Television Factbook (subsequently entitled the Television and Cable

Factbook). The Factbook is an industry publication used as a reference work by industry members and television advertisers. For each station in existence, the Factbook includes information on ownership, technical facilities, network affiliations and executive personnel.

Starting in 1961, the Factbook includes circulation information for each station, indicating the number of households reached by each station according to surveys by the market research firm

Arbitron. (The Factbook was not published in 1962, 1975 and 1983.) Some of the information in the yearbooks is culled by the publishers from the broadcast licenses on file with the FCC. Other

Careers and Competition 12 information, such as circulation figures, are supplied by market research firms. The stations themselves provide the remainder of the information, including the personnel listings.

Data were collected on the characteristics of all commercial television stations in 15 broadcast markets. These markets are listed in Table 1, along with descriptive information on market sizes. Entering data on a yearly basis from all of the over 200 markets in the United States would have been prohibitively time-consuming and expensive. By 1988, there were over 1,000 television stations on the air. In selecting which markets to include, I chose to concentrate on the

15 largest markets (in terms of the number of television households) in the United States in 1988.

These markets reached approximately 40% of all viewers in the United States at that time.

I focused on the 15 largest markets, as opposed to a random sample of markets, for a number of reasons. First, by concentrating on the largest markets, I increased the average number of stations as well as the proportion of independent (not network affiliated) stations. Second, by including the markets that are currently the largest markets, this sample allows me to include markets that have grown rapidly since the 1960s (such as Miami), as well as the markets that have declined in relative size over time (such as Cleveland).

Nonetheless, the nature of the sample of markets raises questions of sample selection bias.

The selection of markets adjacent to each other in the status hierarchy of markets is however preferable to a random sample of markets. Interorganizational mobility within the industry to a large extent takes place between markets (Sørensen 1996). An important means of upward mobility in the industry is to join a station in a larger market. By choosing markets near each other in the status hierarchy, I minimized the number of interorganizational moves that would be missed because a higher status market was not included.

Careers and Competition 13 Only data on local, commercial broadcast stations were collected. One could argue that several other types of organizations should have been included in the population. Public and educational television stations were excluded. Since the Factbook is oriented to advertisers, it generally does not include information on the station personnel or on the circulation levels of public television stations. Such stations might arguably have been included for two reasons.

First, public television stations compete with commercial stations in the market for viewers. Their performance therefore affects the growth in circulation of commercial stations. Second, managerial mobility between non-commercial and commercial stations undoubtedly occurs, although it is probably less frequent than mobility between commercial stations.

Similar concerns arise with respect to other types of organizations, most notably cable television franchises and radio stations. Both cable operators and radio stations compete with local television stations for the attention of viewers (listeners) and for advertising revenues. In the early history of the television industry, radio and television competed intensely for advertising revenues, although television quickly gained the upper hand. In the 1980s, cable television and local television stations competed intensely for advertising (Dimmick, Patterson and Albarran

1992). Thus a given television station competes not only with other stations in its market, but also with cable franchises and (to a lesser extent) radio stations. The models presented here therefore do no account for the full range of competitive influences that television stations are subject to. Nonetheless, the main competitors faced by a commercial station are the other commercial stations in its market (Eastman, Head and Klein 1989).

Careers and Competition 14 Executive Migration

For each television station, the yearly Factbooks include a listing of top managerial personnel. The Factbook asks each station to provide a list of the management personnel. The self-reports of station personnel are a potential source of bias, since the stations themselves determine how many, if any, station employees to list, and since there are no standard criteria for assigning job titles. However, inspection of the job titles suggests that they are quite standardized across organizations. A total of 6,321 unique names were collected in the fifteen markets studied.

The mean number of employees declined steadily over time. In 1961, the mean number of team members listed was 9.2. The average team size declined gradually, eventually reaching a mean of

7.4 in 1987. The decline in team sizes is largely attributable to the growth of independent stations, which for a variety of reasons have smaller staffs on average. For example, independent stations often do not have news departments.

These listings were compared in subsequent years to detect turnover and interorganizational mobility. Potential interorganizational moves were flagged if the names matched exactly, and if less than two years elapsed between a person’s departure from one station to his reappearance at another. Each potential move was then inspected individually to determine whether an interorganizational move could reasonably be said to have taken place. For example, if a name seemed particularly common (e.g., “John Smith”) and the job titles implied quite different managerial skills (e.g., “News Director” vs. “Sales Manager”) or otherwise implausible transitions, an interorganizational move was not coded. In general, my strategy was to be conservative and err on the side of not coding interorganizational moves if there was any reasonable doubt.

Careers and Competition 15 In addition to inter-industry moves, there are two types of interorganizational moves within the population that are not observed in this data set. First, a person may move between a station in one of the markets studied and a station in one of the excluded markets. Only approximately 15% of the stations in the nation are included in this study. Second, people may move between stations in the sample but not have their interorganizational mobility identified: they may join the sample from the lower ranks of another station in the sample, or they may leave the sample and join the lower, unlisted ranks of another sampled station. The sample design therefore only provides a partial picture of the patterns of interorganizational mobility in the population. The number of moves between stations in the sample will in general be underestimated, while the number of moves out of the sample will be overestimated. However, the observed network patterns can be considered a lower bound estimate of the interconnection of firms through the labor market; the study design is therefore conservative.

Altogether, a total of 377 interorganizational ties through managerial mobility were coded.

Moves between stations that are members of the same ownership group are excluded from this count and from all analyses, since these are intracorporate moves. Two-thirds (254) of these moves are between stations in different broadcast markets, while the remaining third are between stations that are direct competitors. The yearly number of mobility events approximately doubles from the early 1960s to the late 1980s, from less than ten moves per year to approximately twenty moves per year.

A natural concern that arises when attributing causal power to patterns of executive migration is that these patterns may simply reflect the pre-existing structure of competition between stations. If this were the case, any observed effects of overlap could not be attributed to

Careers and Competition 16 the consequences of executive migration. However, inspection of the patterns of mobility suggests that this is not the case. For example, while moves between independent and network affiliated stations are less frequent than moves within either group, 30% of the mobility events in this sample are of this type. Similarly, 65% of mobility events in the sample are between stations in different markets, that is, between stations that do not compete with each other. Furthermore, more detailed analyses (not shown here) suggest that the moves are essentially evenly distributed between downward, upward and lateral changes in relative station size. In general, movement occurs both up and down the status hierarchy. This increases our confidence that the movement of executives between stations does not simply reflect the pre-existing structure of the industry.

A skeptic might argue that any observed negative relationship between crowding around a firm’s position in the labor market and performance is due to increased competition for personnel and not to strategic overlap in the product market. At a given level of resources, the labor market facing organizations in more crowded areas of the labor market will be tighter, which may have adverse effects on organizational performance. Tight labor markets have two implications for firm outcomes. First, it is more difficult for the firm to acquire the best quality talent of a given type. As the number of organizations competing for the services of a set of individuals with particular attributes and skills increases, firms will be forced to recruit from lower in the quality distribution. This will either lead to the concentration of the most talented managers in a small number of firms, or in the general dilution of the quality of management teams. In either case, management teams will on average be less competent; firms will perform more poorly as managers fail to operate with maximal effectiveness. Second, in order to ensure that they have the best talent, organizations in densely populated regions of the labor market will have to devote

Careers and Competition 17 greater attention to the recruitment and retention of managerial personnel. To the extent that they fail to retain personnel, high levels of managerial turnover can lead to more organizational disruptions. The tight labor market can in this way lead to higher labor costs as well as the diversion of organizational attention from operational and strategic issues to personnel issues.

With less resources to devote to product market competition, by this reasoning, organizations in crowded regions of the labor market should perform worse than firms in less crowded regions.

The data do not allow for a direct adjudication of these competing explanations.

However, the issue can be approached indirectly by including controls for the frequency of recruitment as measures of the extent to which a station is forced to devote resources and attention to labor market issues. If labor market overlap has a negative effect net of such controls, the strategic isomorphism argument can be accepted with greater confidence.

Variables

Niche Overlap

I have argued that competition between television stations increases to the extent that they occupy overlapping niches in the labor market, where an organization’s labor market niche is defined by its pattern of relations in the labor market with other organizations. The hypotheses developed above each require different measures of the overlap caused by executive migration patterns.

The geographical dispersion of television stations is a fundamental structural feature of the industry that must be considered when developing these measures. The Federal Communications

Commission defines local broadcast markets and assigns broadcast licenses by market. These

Careers and Competition 18 broadcast markets segregate competition for viewers; a station in New York does not compete for viewers with a station in Chicago. While the rapid spread of cable television in the 1980s, and the attendant emergence of “super-stations”, has weakened the boundaries between markets somewhat, this occurred at the tail end of the sample analyzed in this paper. As a consequence, I define overlap, for each hypothesis, as the extent to which a station recruits from the same sources as other stations in its market.

The simplest case is associated with Hypotheses 1. To test the claim the growth rates decline to the extent that stations recruit personnel from their competitors, I simply compute a count of the number of direct competitors from which a station has recruited personnel. Let X be

a sociomatrix of station-to-station ties defined by interfirm mobility in a given year, with xij = 1 if a

manager has moved from station i to station j and xij = 0 otherwise. (The matrix X is therefore a directional, or asymmetric matrix.) The number of ties to competitors through recruitment is thus given by

r ' x m j j ij ij (2) i

where mij is a dummy variable equal to 1 when station i and j are in the same market. This measure will depend on two factors: the number of people a station is recruiting in a given year, and the number of stations in the market. I therefore divide by the volume of direct ties, and by the number of other stations in the market.

j xijmij i Rj' (3) (j xij)(Nm&1) i

Careers and Competition 19 where Nm is the number of stations in market m. This measure ranges from zero, indicating that a station did not recruit from its competitors, to one, indicating that a station recruited from each of its competitors and did not recruit from any other sources (i.e., stations in other markets). The volume of ties is a measure of the size of a station’s labor market niche, meaning that this measure expresses overlap relative to the size of a station’s niche. Thus for a station with a large labor market niche, one overlapping tie will have less effect than for a sation with a small volume of ties.

For the remaining hypotheses, the issue is whether two stations, i and j, overlap in their recruitment patterns. As a simple measure of niche overlap is the number of ties that are identical between two actors -- i.e., the number of common ties to third parties (Podolny, Stuart and

Hannan 1996). I compute this measure for stations in the same market:

t ' q(k) m j j ij ij (4) i

where the q(k)ij are dichotomous outcomes generated by comparing the elements of columns i and j of the matrix X:

1 if xki ' xkj ' 1 q(k)ij' (5) 0 if xki … xkj

As with the previous measure, we normalize tj by the volume of ties and the number of stations in station i’s market:

Careers and Competition 20 j q(k)ijmij i Tj' (6) (j xij)(Nm&1) i

For Hypothesis 2, we compute this measure on the matrix of direct station-to-station ties.

Tj will then increase to the extent that a station recruits from the same other stations as its competitors.

Hypothesis 3 considers the effects of indirect ties as well. For this hypothesis, xij = 1 if station j can be reached from station i in two steps or less. In other words, if station j sends a

manager to station k, which in turns sends a manager to station i, then xij = 1. Thus the sociomatrix for Hypothesis 3 is a two-step reachability matrix, created by summing the matrices of one-step and two-step ties (i.e., X+ X2) (Wasserman and Faust 1994: 160). This sociomatrix is

then used to compute the measure of similarity in recruitment patterns (Tj) in equation 8.

All niche overlap measures are computed for each year of available data. Moves between stations are recorded in the year in which they were completed; in other words, in the year when the manager arrived at the recruiting station. I do not aggregate moves over time: only moves completed in a given year are included in the calculation of the niche overlap measures. Separate analyses suggest that the results are robust to different aggregations over time.

Careers and Competition 21 Control Variables

Previous research suggests a number of control variables that should be included in the models of organizational growth. Controlling for factors that are known to influence growth rates will strengthen our conclusions about the effects of niche overlap.

Structural differences between television stations contribute to differences in growth rates.

I consider a number of different organizational characteristics. Previous research has suggested that growth rates decline with size (" < 1) . Barnett (1994) finds that the rate of increase in the number of telephone subscribers declined with the size of telephone companies in early 20th- century Pennsylvania. Barron, West and Hannan (1994) found that the growth rates of credit unions in New York State declined with credit union size. Baum and Mezias (1993) also found negative size dependence in the growth rates of day care centers in Toronto. The negative effect of organizational size on growth rates suggests that larger organizations exploit opportunities for growth less efficiently than smaller stations.

I also control for a station’s audience size relative to other stations in a market with a measure of the station’s log circulation to the maximum log circulation in the market in a given year. This is intended as a partial means of capturing the generalist/specialist distinction within the context of a fixed-effects model, the idea being that stations that are relatively small compared to the largest station may be exploiting a specialist niche.

The demographic characteristics of an organization's top management team can also affect organizational growth rates (Pfeffer 1983; Hambrick and Mason 1984). Pfeffer (1983: 348) argued that “demography is an important, causal variable that affects a number of intervening variables and processes and, through them, a number of organizational outcomes.” The most

Careers and Competition 22 important general effect of demography is on the social integration of groups. If the social

integration in top management teams is low, organizational performance is expected to suffer. I

include several demographic aspects of management teams that have been seen as important

predictors of organizational performance: team size (measured as the number of employers listed in the Factbook), team tenure (measured as the mean across members of their tenure on the team).1 Social integration arguments suggest that integration is more difficult in large groups, and that growth rates should therefore decline with team size (Smith et al., 1994). In turbulent environments, such as that characterizing the television industry, higher levels of mean team tenure can lead to lower rates of organizational growth, as the perspectives formed when executives joined a station become increasingly out of touch with the state of the environment

(Virany, Tushman and Romanelli 1992).

I include a dummy variable indicating whether a firm has recruited in the past year, as well as a measure of managerial turnover (measured as the mean number of new team members over the preceding four years). To the extent that these measures capture the diversion of resources from “production” to recruitment activity, they provide a test of the argument that niche crowding in the labor market lowers growth rates by deflecting attention and resources from the product market.

Organizational ecology theory suggests a number of environmental and population-level factors that may influence organizational growth. Given the local nature of competition among television stations for viewers, it is best to specify many of these factors at the level of broadcast

1 In models not shown here, a measure of team heterogeneity (measured as the coefficient of determination of team tenure) was also included. This variable had very weak and non- significant effects and was therefore excluded.

Careers and Competition 23 markets. A central tenet of ecological theory is that growth is constrained by the carrying

capacity of organizational environments (Hannan and Freeman 1977). As the size of a population

of organizations approaches the carrying capacity, expansion becomes more difficult and growth

rates decline. Similarly, if the abundance of resources increases, the prospects for organizational

growth improve. The size of a station’s audience should therefore increase with increases in the

size of the market. A natural measure of the carrying capacity in television broadcast markets is

the number of households with a television. Yearly estimates of the number of television

households are taken from the “spot television market” volumes of the Standard Rate and Data

Service.

Density dependence theory (Hannan and Carroll 1992) suggests that the number of organizations in a population affects the growth rates of individual organizations. When the time series for these analyses begins (1961), 87% of American households had a television, suggesting that television was securely taken for granted and that increases in density would have few legitimating effects. We can therefore expect that the density of stations in a market will have a

monotonic, negative effect on growth rates.

I also include a number of control variables peculiar to the television industry. I control

for each station’s visual broadcast power. The increased resources that derive from membership

in ownership groups should also promote circulation growth. Finally, the performance of

network affiliates can in part be attributed to the performance of the network they are affiliated

with; affiliates suffer if a network’s prime time schedule is unpopular, and reap the benefits of

successful network programming strategies. To control for variations in the performance of the

major networks, I include a separate dummy variable for each of the major networks (ABC, CBS,

Careers and Competition 24 and NBC). The models include two additional control variables. Data gap indexes years that follow a missing year of data due to the unavailability of the Factboook. The Same Network dummy is coded one in those rare instances where an affiliate is exposed to competition from another station affiliated with the same network.

Estimation

We wish to estimate parameters from the model given in Equation 1. If we take the log of that equation and rearrange terms, we have the following log-linear model:

ln(Si,t%1) ' "ln(Sit)%$xit%,i,t%1 (7)

The data is arranged in the form of a pooled cross-section/time series data set, with each television station contributing a time series of observations of differing lengths. The length of each station's time series may differ because of missing data, or because a station is founded or fails during the observation period. Pooling data in this way leads to gains in efficiency and allows us to correct for the bias arising from the inclusion of a lagged dependent variable (Hannan and Young 1977). To correct for autocorrelation, I use a fixed effects or least squares with constants estimator (Balestra and Nerlove 1966; Tuma and Hannan 1984), which includes a dummy variable for each station in the sample.

The interpretation of the parameters from a fixed effects model requires special discussion.

The inclusion of the organization- specific terms removes all between-organization variance from the model, which means that the estimates of " and $ are estimates of within-organization effects.

Careers and Competition 25 A negative coefficient estimate for a variable in this model would indicate that growth rates are lower during times when the value of this variable exceeds its historical mean.

Results

Table 2 presents the descriptive statistics for the variables included in the growth models, computed across station-year spells. A graphical inspection of the univariate distributions led me to transform certain variables to correct for skewness. Specifically, team size and mean team tenure were logged, while I took the square root of the mean number of new employees. There are a total of 2,474 station-year spells and 164 stations included in the sample. However, missing values reduce the effective sample size in the estimated models to 2,060 station-year spells. Data on a total of 131 stations are included in the estimation. The proportional overlap measures (the last two lines of Table 2) are generally very low, to a large extent because most firms do not recruit from another station in the sample in a given year.

Table 3 reports the bivariate correlations of the variables included in the models; these correlations are based on the within-station variation, as is the case in the fixed-effects model.

These correlations are generally low and should cause little concern with respect to the estimation of the multivariate models. The exceptions are the correlations between log station age and the

(log) number of television households in a market (a measure of carrying capacity) and the number of stations in the market (density). These high correlations reflect the fact that for each individual station, the age variable is simply a time trend; this trend correlates highly with secular increases in the size of the television market and in the number of television stations, both of

Careers and Competition 26 which show basically linear growth patterns. Models run without station age resulted in nearly identical results.

Turning to the fixed-effects models, the first column in Table 4 presents estimates of a baseline model of growth in net circulation among television stations. The effects of the control variables are generally in line with expectations. The estimate of the effect of lagged net circulation (" in Equation 1) is significantly less than unity (0.760), which indicates that the rate of growth in television circulation decreases as the level of circulation increases. This is consistent with a number of other studies that have shown that growth rates decline with organizational size

(Barnett 1994; Barron, West and Hannan 1994; Baum and Mezias 1993). Growth rates depend strongly on a station’s size relative to the largest station in the market: as this ratio increases, growth rates decline significantly. Relatively small organizations may experience rapid growth as they take advantage of previously unexploited resources. As their audience size approaches that of their largest competitors, growth rates decline. Contrary to previous studies (Barron, West and Hannan 1994) station age does not have a significant negative effect on growth rates.

Separate analyses found a significant negative age effect in models that excluded the circulation ratio measure, suggesting that the age effect was picking up the size of a station relative to its largest competitor.

Growth rates do not depend on the size of the management team. However, growth rates do decline as the mean tenure of the management team increases, as organizational demographers would expect (e.g., Pfeffer 1983; Smith et al. 1994). Turnover, however, has no significant effect on growth rates in this sample. Neither the mean number of new employees hired in the past four years, nor the dummy variable indicating recruitment in the past year, have a significant effect on

Careers and Competition 27 growth rates. This suggests that increases in recruitment activity do not negatively impact firm performance.

The positive effect of the number of television households in a market suggests that growth rates depend positively on changes in the carrying capacity of the organization’s environment, as ecological theory would predict. As a market increases in size, the growth rates of local television stations increase. Contrary to expectations, however, changes in the density of stations in a market do not have a significant, negative effect on growth rates. This may be a consequence of the regulatory strength of the FCC, or of collinearity with the number of television households.

Hypothesis 1 claims that the growth rates of television stations would be a negative function of the extent to which a station recruits directly from its competitors. The estimates in the second column of Table 4 presents a test of this hypothesis by including a measure of the

proportion of recruitment ties that are to other stations in the market (Rj). The coefficient estimate for this variable is negative and significant. Firms, in other words, pay a penalty (in the form of lower growth rates) when they recruit from their competitors. Note that this effect is net of the station’s recent history of turnover (as measured by the mean number of new employees) and its recruitment behavior in general (as measured by the recruitment dummy variable).

Recruiting executives from one’s competitors does not appear to have coordination benefits; rather, these results suggest that an unintended consequence of such recruitment is lower growth rates. This suggest that “cherry-picking” managers from the ranks of your competitors is a poor idea. These data, of course, give little indication as to whether the recruiting firm in fact set out to lure managerial talent from its competitors. Separate analyses suggest that within-market

Careers and Competition 28 moves are slightly more likely to flow from a more successful station to a less successful station than vice-versa, but the difference is not great. Whatever the reason for mobility between competitors, such movement seems to put the recruiting firm at a disadvantage. This makes intuitive sense: if recruitment pushes the recruiting firm’s strategy in the direction of the new employee’s former employer, then the recruiting station is by necessity a follower and not a leader in a strategic sense.

Hypothesis 2 claims that growth rates will be lower to the extent that a firm recruits from the same stations as its competitors. The third column in Table 4 presents a test of Hypothesis 2

by including the proportional overlap measure Tj computed on a matrix of direct ties between stations. The coefficient estimate for this variable is less than half its standard error, so

Hypothesis 2 is not supported.

The measure based on direct ties, however, may underestimate the extent to which stations occupy the same position in the labor market. The fourth column of Table 4 includes a measure of niche overlap density based on a two-step reachability matrix, thereby taking into account indirect ties as well. The effect of this variable is negative and highly significant, lending strong support to Hypothesis 3. Thus firms that occupy positions in the labor market similar to those of their competitors experience greater competition and therefore have lower growth rates.

The negative effect of overlap in direct and indirect ties may simply be picking up the negative effect of recruiting from one’s competitors, such that overlap has no effect net of direct recruitment. The results in the fifth column of Table 4, where both variables are included in the model, suggest that this is not the case. In fact, the overlap variable dominates while the direct recruitment variable is reduced to insignificance.

Careers and Competition 29 On balance, the results in Table 4 support the argument that organizations that find themselves in more crowded regions of the managerial labor market perform worse than firms in less densely populated regions. Proximity to other competitors does not appear to facilitate tacit collusion or mutual adjustment processes. As argued earlier, niche overlap in the labor market harms firms, either by forcing them to devote more resources and attention to recruiting managers, or by inducing firms in similar locations to adopt similar product market positions.

While the models here do not adjudicate between these alternative interpretations directly, the fact that neither a firm’s recent turnover history nor a firm’s recruitment behavior in a given year have an impact on growth rates suggests that the effort directed at recruiting personnel does not impact overall growth rates in the product market. This lends credence to the claim that occupancy of equivalent positions in the market for managerial talent causes firms to compete more intensely in their product markets.

Hypothesis 4 suggests that the effects of a firm’s location in the labor market differ for generalists and specialists; specifically, we expect the negative consequences of direct recruitment to be stronger for independent stations than for network affiliates. This hypothesis is tested in

Table 5 through the use of interaction effects with network affiliation status. (For ease of presentation, the estimates for the control variables included in the models in Table 4 are not presented in Table 5. However, the estimates for the interaction effects are from models that include the full set of control variables.) Model 1 in Table 5 includes a main effect of recruiting from competitors, and an interaction effect between this variable and a dummy variable coded 1 if a station is affiliated with one of the three major networks (ABC, CBS, NBC). Models 2 and 3 present corresponding interaction effects for the two overlap measures. In Model 1, the inclusion

Careers and Competition 30 of the interaction effect reduces the effect of direct recruitment from competitors to insignificance; the effect for major network affiliates is insignificant as well. The estimates for the effects of overlap through direct ties (Model 2 in Table 5) are insignificant as well, as they were in

Table 4. However, when overlap is measured using direct and indirect ties, the effects of overlap do differ significantly for major network affiliates and independent stations. For independent stations, overlap with other stations in the labor market has a significant negative effect on growth rates. The positive interaction effect for network affiliates, however, effectively offsets this negative effect making the net effect of overlap zero for network affiliates. (A Wald test of the null hypothesis that the coefficients for the main effect and the interaction effect sum to zero cannot be rejected at the p < 0.05 level.)

The final model in Table 5 explores the effects of recruiting from competitors in more detail. For this model, the other stations in a market are differentiated according to whether they are of the same network affiliation status as the focal station (i.e., network affiliate or independent). I then differentiate according to whether a firm recruits from a competitor of the same affiliation status or of a different affiliation status, and explore how the effects of these two counts differ for network affiliates and independents. The results suggest that, for both network affiliates and independents, recruitment from stations of a different affiliation status (e.g., from generalist to specialist) has no significant effect on growth rates. The main effect of recruiting from stations of the same network affiliation status is however negative and significant, suggesting that independent stations pay a penalty when they recruit from other independent stations.

Moreover, we again find a positive interaction effect for network affiliates which largely cancels

Careers and Competition 31 the negative main effect, suggesting that network affiliates do not pay a penalty for recruiting from other network affiliates.

Overall, the results in Table 5 suggest that specialist organizations (i.e., independent stations) are more sensitive to processes of niche overlap than are generalist organizations. For specialists organizations operating with less slack, strategic missteps or strategic overlap with competitors are more consequential for the firm’s viability than they are for generalist organizations.

Further analyses of the differences between specialists and generalists (not shown here) cast further light on the question of whether the negative effects of labor market overlap are due to the ill effects of tight labor markets or to strategic isomorphism. Generalist organizations, with more slack, should be better able to afford any diversion of resources necessitated by a tight labor market. This suggests that the two measures of recruitment activity should have a stronger negative effect for specialists than generalists. However, interactions with network affiliation status suggest this is not the case; the effects of recruitment behavior are not significant for either type of organization. On balance, these results suggest that the consequence of niche overlap in the labor market is to push organizations toward similar strategic positions in the product market.

Discussion and Conclusion

In this paper, I have addressed a neglected topic: how the patterns of individual careers shape change processes among formal organizations. Prior research has adopted a focal organization perspective which neglects the implications of executive migration for the structure of relationships between firms. By conceptualizing interorganizational mobility as a form of

Careers and Competition 32 interorganizational relation, I have operationalized the notion that the choices made by individuals result in structures that constrain organizations. Managerial mobility is a mechanism that drives the dynamics of competition between firms.

The findings demonstrate that the networks formed through executive migration have the expected consequences for competitive dynamics: when television stations find themselves in crowded regions of the market for managerial talent, their performance in the market for viewers suffers. Controls for a station’s frequency of recruitment suggest that this result is not simply due to the tight labor markets faced by organizations in crowded regions of the labor market. Rather, it is the pattern of recruitment that matters, as evidenced by the dominance of the overlap effects over the direct recruitment effects. The strategic implication of this result is that firms must attend not only to their positioning in product markets, but also to their positioning in factor markets, particularly the market for managerial talent.

The results furthermore suggest that career dynamics are a partially endogenous source of change in the competitive relationships between firms. Recruitment is a form of organizational interaction, one whose traces constrain the behavior of organizations and affect the structure and dynamics of interorganizational competition. It is, however, difficult for firms to behave strategically with respect to their position in the market for managerial talent, since career patterns result from the at least partially autonomous choices of individual managers. Firms are constrained in whom they can recruit by virtue of their position in the labor market and how attractive they are to potential employees. Moreover, employers cannot prevent employees from departing for new opportunities, so their exposure to the labor market is often beyond their

Careers and Competition 33 control. Labor market dynamics are therefore also an exogenous source of change in organizational populations.

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Careers and Competition 39 Table 1: Size Measures of Sampled Broadcast Markets, 1961-1988

Stations Television Households Market 1961 1988 1961 1988 New York 8 13 4,431,045 6,686,700 Los Angeles 7 15 2,214,861 4,609,200 Chicago 5 13 2,153,474 3,050,700 Philadelphia 4 9 1,772,729 2,564,700 San Francisco 5 14 1,034,656 2,133,000 Boston 5 12 1,231,557 1,980,500 Detroit 3 6 1,174,445 1,614,100 Dallas-Fort Worth 4 9 620,869 1,584,700 Washington D.C. 4 7 716,273 1,550,700 Houston 3 7 504,514 1,469,700 Cleveland 4 12 1,167,944 1,379,400 Atlanta 3 7 464,179 1,207,500 Minneapolis-St. Paul 5 8 640,771 1,186,900 Seattle-Tacoma 6 12 475,530 1,180,900 Miami-Ft. Lauderdale 4 9 381,074 1,156,000

Careers and Competition 40 Table 2: Descriptive Statistics

Variable Mean Overall F Between F Within F Spells Stations Net Circulation (log) 13.579 1.275 1.501 0.419 2,218 150 Circulation Ratio 0.946 0.084 0.110 0.024 2,148 145 Same Network Dummy 0.181 0.385 0.288 0.203 2,474 164 Station Age 18.101 11.033 9.924 6.810 2,474 164 Visual Power (000) 0.963 1.382 1.657 0.490 2,474 164 Ownership Group Member 0.711 0.453 0.399 0.291 2,474 164 Team Size 8.569 3.916 3.698 1.876 2,474 164 Mean Team Tenure 4.638 3.036 2.400 1.971 2,458 163 Mean New Employees 1.802 1.170 0.843 0.822 2,306 150 Market TV Households (log) 14.318 0.665 0.611 0.188 2,474 164 Market Density 7.962 2.949 2.641 1.685 2,474 164 Recruited in Past Year 0.101 0.301 0.164 0.284 2,474 164

Recruit from Competitors (Rj) 0.004 0.027 0.017 0.026 2,474 164

Proportional Overlap (Tj): Direct Ties 2.919E-04 0.006 0.013 0.005 2,474 164 Direct & Indirect Ties 0.002 0.018 0.014 0.018 2,474 164

Careers and Competition 41 Table 3: Bivariate Correlations Based on Within-Station Variation

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

1. Net Circuation (log) 1.000

2. Circulation Ratio 0.508 1.000

3. Same Network Dummy -0.141 0.000 1.000

4. Station Age 0.693 0.299 -0.121 1.000

5. Visual Power (000) 0.332 0.292 -0.033 0.323 1.000

6. Ownership Group Member 0.130 0.002 0.070 0.267 0.050 1.000

7. Team Size 0.182 0.031 0.004 0.206 0.067 0.017 1.000

8. Mean Team Tenure 0.189 0.188 -0.039 0.305 0.137 -0.009 0.061 1.000

9. Mean New Employees 0.016 -0.109 0.018 0.011 -0.045 0.068 0.349 -0.515 1.000

10. Market TV Households (log) 0.632 0.046 -0.104 0.798 0.135 0.289 0.221 0.127 0.158 1.000

11. Market Density 0.488 0.068 -0.111 0.717 0.155 0.204 0.185 0.124 0.085 0.679 1.000

12. Recruited in Past Year 0.041 -0.009 0.020 0.077 0.008 0.012 0.024 -0.125 0.183 0.082 0.082 1.000

13. Recruit from Competitors (Rj) -0.029 -0.019 0.072 -0.011 -0.025 0.004 -0.009 -0.093 0.085 0.005 -0.006 0.484 1.000

Proportional Overlap (Tj): 14. Direct Ties 0.035 0.011 0.000 0.031 0.020 0.015 0.022 -0.055 0.065 0.055 0.023 0.156 0.217 1.000

15. Direct & Indirect Ties -0.018 0.010 0.083 0.006 -0.003 -0.012 0.013 -0.125 0.087 0.018 0.023 0.365 0.402 0.351 Table 4: Fixed-effects Models of Growth in Net Circulation

Variable (1) (2) (3) (4) (5)

Lag Net Circulation (log) 0.760† 0.761† 0.760† 0.759† 0.760† (0.018) (0.018) (0.018) (0.018) (0.018) Circulation Ratio -0.617† -0.620† -0.616† -0.603† -0.606† (0.239) (0.239) (0.239) (0.238) (0.238) ABC -0.053† -0.052† -0.053† -0.052† -0.051† (0.022) (0.022) (0.022) (0.022) (0.022) CBS -0.100† -0.097† -0.100† -0.099† -0.097† (0.031) (0.031) (0.031) (0.031) (0.031) NBC -0.040 -0.038 -0.040 -0.039 -0.038 (0.025) (0.025) (0.025) (0.025) (0.025) Year 0.004† 0.004† 0.004† 0.004† 0.004† (0.002) (0.002) (0.002) (0.002) (0.002) Data Gap 0.019* 0.019* 0.019* 0.019* 0.019* (0.010) (0.010) (0.010) (0.010) (0.010) Same Network Dummy -0.007 -0.006 -0.007 -0.004 -0.004 (0.017) (0.017) (0.017) (0.017) (0.017) Station Age (log) -0.035 -0.035 -0.035 -0.033 -0.034 (0.022) (0.022) (0.022) (0.022) (0.022) Visual Power (000) 0.030† 0.030† 0.030† 0.030† 0.030† (0.009) (0.009) (0.009) (0.009) (0.009) Ownership Group Member -0.036† -0.036† -0.036† -0.037† -0.037† (0.013) (0.013) (0.013) (0.013) (0.013) Team Size (log) 0.010 0.010 0.010 0.011 0.011 (0.016) (0.016) (0.016) (0.016) (0.016) Mean Team Tenure (log) -0.033† -0.034† -0.033† -0.035† -0.035† (0.010) (0.010) (0.010) (0.010) (0.010) Mean New Employees (sqrt) -0.012 -0.013 -0.012 -0.013 -0.013 (0.014) (0.014) (0.014) (0.014) (0.014) Market TV Households (log) 0.177† 0.175† 0.177† 0.180† 0.178† (0.042) (0.042) (0.043) (0.042) (0.042) Market Density -0.005 -0.005 -0.005 -0.005 -0.005 (0.003) (0.003) (0.003) (0.003) (0.003) Recruited in Past Year -0.003 0.007 -0.003 0.006 0.012 (0.011) (0.013) (0.012) (0.012) (0.013)

Recruit from Competitors (Rj) -0.232* -0.155 (0.140) (0.145)

Proportional Overlap (Tj): Direct Ties -0.152 (0.592) Direct & Indirect Ties -0.439† -0.386* (0.184) (0.190)

R2 within 0.825 0.825 0.825 0.825 0.826

Note: † p < 0.01 (one-sided) * p < 0.05 (one-sided) Based on 2,060 spells and 131 stations; the average station is observed for 15.7 years. Table 5: Interaction Effects of Labor Market Overlap and Network Affiliation Status

Variable (1) (2) (3) (4)

Recruit from Competitors -0.250 (0.223) Recruit from Competitors * Network Affiliate 0.026 (0.260) Proportional Overlap (Direct Ties) -0.119 (0.778) Proportional Overlap (Direct Ties) * Network Affiliate -0.077 (1.187) Proportional Overlap (Indirect Ties) -0.975† (0.319) Proportional Overlap (Indirect Ties) * Network Affiliate 0.765* (0.372) Own Recruitment -0.552† (0.190) Other Recruitment 0.104 (0.126) Own Recruitment * Network Affiliate 0.484† (0.209) Other Recruitment * Network Affiliate -0.211 (0.171)

R2 within 0.825 0.825 0.826 0.826

Note: † p < 0.01 (one-sided) * p < 0.05 (one-sided) Based on 2,060 spells and 131 stations; the average station is observed for 15.7 years. Results are from full models including all of the control variables in Table 5. Only the effects of labor market overlap variables and interaction effects are shown as the effects of the control variables do not change substantially with the inclusion of interaction effects. “Own Recruitment” refers to when a network affiliate or independent station recruits from a station of the same network affiliation status as itself. “Other Receruitment” refers to when a network affiliate or independent station recruits from a station with a different network station from its own.