Managerial discretion and internal alignment ... - Wiley Online Library

Strategic Management Journal Strat. Mgmt. J., 28: 1089–1112 (2007) Published online 17 May 2007 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/smj.628 Received 22 July 2005; Final revision received 27 March 2007

MANAGERIAL DISCRETION AND INTERNAL ALIGNMENT UNDER REGULATORY CONSTRAINTS AND CHANGE MARGARET PETERAF1 * and RANDAL REED2 1 2

Tuck School of Business at Dartmouth, Hanover, New Hampshire, U.S.A. Department of Economics, Morgan State University, Baltimore, Maryland, U.S.A.

This paper investigates the effects of regulatory constraints and their relaxation on managerial discretion and internal fit in the context of the U.S. airline industry. Our results suggest that when managers’ discretion is limited in one realm of choice, they compensate by using their greater level of discretion in some other arena to achieve internal fit. We show that the pursuit of fit matters, in the sense of having measurable efficiency consequences, and that fit trumps ‘best practice,’ at least in this context. In this respect, our findings provide a validation of the contingency perspective on internal fit. The ability to achieve fit under changing conditions may express a dynamic managerial capability necessary for adaptive organizational change. Copyright  2007 John Wiley & Sons, Ltd.

INTRODUCTION Despite the extensive criticism of contingency theory (e.g., Miller, 1981; Schoonhoven, 1981; Van de Ven and Drazin, 1985; Venkatraman, 1989), its core concept of fit remains one of the most enduring in the management field (Zajac, Kraatz, and Bresser, 2000). The notion of fit suggests an alignment among things internal to a firm, such as strategy and structure (Chandler, 1962) or strategy and organizational activities (Porter, 1996). It also suggests external alignment, matching organizational structure with the contextual environment (e.g., Burns and Stalker, 1961; Lawrence and Lorsch, 1967; Pennings, 1992; Donaldson, 2001) or matching strategy with environmental needs (Hofer and Schendel, 1978; Hambrick, 1983a, 1983b). A key hypothesis is that both internal and external fit enhance firm performance. A stronger view is that Keywords: managerial discretion; internal fit; complementarities; adaptation; dynamic managerial capabilities; dynamic fit; contingency theory

∗ Correspondence to: Margaret Peteraf, Tuck School of Business at Dartmouth, 100 Tuck Hall, Hanover, NH 03755, U.S.A. E-mail: [email protected]

Copyright  2007 John Wiley & Sons, Ltd.

strategic fit may provide a basis for sustainable competitive advantage (Porter, 1996; Miller, 1996; Rivkin, 2000). This study, at its most fundamental level, is a validation of contingency theory.1 It follows a long tradition of research supporting a connection between internal alignment and superior organizational performance (e.g., Khandwalla, 1973; Rumelt, 1974; Gupta and Govindarajan, 1984; Govindarajan, 1988; Gomez-Mejia, 1992; Whittington et al., 1999). What distinguishes our work is that we answer the major objections to contingency theory with an innovative approach and a new methodology involving counterfactual analysis. We also add to the resurgence of work on fit and complementarities, providing econometric evidence of a link between firm performance and systemic internal alignment that supports the case study, simulation, and interaction approaches of prior studies (Sigglekow, 2001, 2002; Rivkin and Sigglekow, 2003; Carmeli and Tischler, 2004).

1 We thank an anonymous referee for encouraging us to bring out this point.

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To answer the charge of ‘determinism’ against contingency theory (Child, 1972, 1997; Astley and Van de Ven, 1983; Hrebiniak and Joyce, 1985; Miller, 1988), we draw on managerial discretion theory (Hambrick and Finkelstein, 1987) to enrich the contingency perspective. This allows us to portray managers as taking an active role in adapting to varying constraints and to consider the effect of those constraints on their actions. To address the problem of ‘reductionism’ (Schoonhoven, 1981; Miller, 1981; Van de Ven and Drazin, 1985; Meyer, Tsui, and Hinings, 1993), we employ a methodology that allows us to capture the effects of the complex interdependencies among managers’ strategic choice variables, without having to specify the exact nature of the linkages involved. This methodology is more flexible than the configurational or systems approach to fit (Drazin and Van de Ven, 1985; Miller, 1986) and more closely reflects an economic understanding of the underlying complementarities (Milgrom and Roberts, 1990, 1995). To answer the call for a consideration of dynamics (Venkatraman, 1989; Miles and Snow, 1994; Zajac et al., 2000), we utilize longitudinal data encompassing a period of significant environmental change to explore the question of how managers alter their choices as conditions change. Our study is of the U.S. airlines industry as it changed from a regulated industry to one that was largely deregulated. As is widely recognized, regulation significantly affects the degree of managerial discretion, or ‘latitude of action,’ constraining strategic choice (Hambrick and Finkelstein, 1987; Finkelstein and Hambrick, 1996). We investigate the effects of regulatory constraints and their relaxation on two broad types of managerial choices in this industry: those that were constrained directly by regulation, which we dub ‘operational variables,’ and those that were constrained only indirectly, which we call ‘administrative practices.’ By looking at fit with respect to these two types of strategic choices, we avoid the methodological problems associated with specifying strategic choice variables more discretely (Snow and Hambrick, 1980; Reger, Duhaime, and Stimpert, 1992). We consider the relationship of these choices to organizational outcomes in terms of measurable efficiency effects, using data on airline costs. We employ a methodology that is distinctive in several respects. We begin conventionally, by estimating airline cost functions for each of the two eras—the regulated era and the post-deregulation Copyright  2007 John Wiley & Sons, Ltd.

era—checking to be sure that our cost estimates are consistent with prior economic studies of this sort. To consider the effects of the different discretionary environments on the two types of strategic choices, we decompose an airline cost function into its component parts: the X vector and the estimated betas. The X vector reflects the choices of airline managers with respect to what we call the ‘operational variables’: variables that were constrained directly by regulation during the regulated era. We suggest that the betas of the cost function also reflect managerial choices—choices which regulation did not constrain directly, but which were likely affected indirectly. We refer to these choices as ‘administrative practices,’ since they encompass the practices and processes that affect the way in which operational inputs are transformed into service outputs in the airline industry. In short, we view the cost function as a socially constructed transformation function. While the estimated betas cannot provide information about the choices made by the managers of any specific firm, they do provide information about choices in the industry on average. Our results, then, shed light on the choices of the average airline management team and corresponding changes in administrative practice at the level of an ‘industry recipe’ (Spender, 1989). To examine the consequences of these choices, we employ a counterfactual analytical technique related to the Blinder–Oaxaca decomposition technique from labor economics (Blinder, 1973; Oaxaca, 1973). This technique permits us to provide answers to ‘what if’ questions about alternative strategic choices. Finding that the unconstrained choices of managers in the deregulated era resulted in large overall efficiency gains, we asked the following question: What if managers had employed the new types of administrative practices that emerged after deregulation during the regulatory era? That is, suppose they had been able to apply the new ‘best’ practices to the old, constrained set of operational variables. Would this change alone have improved their efficiency? Or does the fit between the administrative practices and the operational choice set matter (Porter, 1996; Siggelkow, 2001, 2002)? This question is reminiscent of that which contingency theory was originally developed to address: Are there any universally beneficial practices or are there only contextually appropriate practices? Despite the challenge from contingency Strat. Mgmt. J., 28: 1089–1112 (2007) DOI: 10.1002/smj

Managerial Discretion and Internal Alignment theory, the universalist perspective remains viable (Delery and Doty, 1996; Pfeffer, 1997). Modern examples include the TQM movement (Sitkin, Sutcliffe, and Schroeder, 1994), high-performance work practices (e.g., Pfeffer, 1994), and the general notion of ‘best practice.’ The results of this study suggest that, at least in this context, fit trumps ‘best practice.’ Somewhat surprisingly, there would have been no efficiency gains from applying the new administrative practices to the old set of operating variables. Rather, we find that pairing the new set of practices with the old operational variables would have increased costs significantly. The more general result is as follows: Given the operational variable set from either era (the regulated or the deregulated), costs are lowest when the administrative practices from the same era are matched to it.

These results indicate that the internal alignment between the overall set of administrative practices and the activities implied by the operational choice set does indeed matter (Milgrom and Roberts, 1995; Porter, 1996; Rivkin and Sigglekow, 2003). Internal alignment matters in the sense that it has measurable efficiency consequences for the firm. It also suggests an important role for top management, even in constrained discretionary environments (Finkelstein and Hambrick, 1996). Normatively, the implication for practitioners is the following: ‘When managers are sharply constrained on some fronts, they are well served by adopting administrative practices that accommodate those constraints—even if those practices are not the most advanced, enlightened, or sophisticated practices available.’2

Our findings, while only suggestive, indicate that airline managers, on average, were surprisingly adaptive, even under regulation (Haveman, Russo, and Meyer, 2001). The regulatory constraints on managerial choices regarding operational variables, such as entry, exit, pricing, and route network, were rather restrictive. In contrast, the regulatory constraints on the intangible factors that constitute administrative practices were only indirect. Managers seem to have taken advantage of this 2

With thanks to an anonymous referee.


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differential in the regulatory constraints on their choice sets. On average, they chose a set of practices and processes that fit the constrained choice variables and allowed them to operate relatively efficiently. This suggests an adaptive capacity and degree of organizational resiliency during the regulatory era that may be insufficiently appreciated (Meyer, Brooks, and Goes, 1990). ‘Constraint,’ as Hambrick and Finkelstein (1987: 374) remind us, ‘is the obverse of discretion.’ They suggest that under long-term environmental constraints the decision-making capacity of organizations may atrophy. Under such conditions, performance is determined primarily by other factors, suggesting that management matters relatively little. The results of our study suggest otherwise. We find that even under regulatory constraints strategic choice plays an important role in the efficient management of organizations (Birnbaum, 1984; Hrebiniak and Joyce, 1985). What is more interesting is that our results shed light on how managers might mitigate the constraints on choice. When their discretion is limited within one realm of choice, they compensate by using the greater level of discretion afforded in another area. Hambrick and Finkelstein (1987) suggested this as a possibility in their portrayal of managers under different degrees of discretion. We provide some empirical corroboration of this. We also suggest a more specific mechanism underlying a dynamic managerial capability (Adner and Helfat, 2003) for achieving dynamic fit, facilitating organizational adaptation under changing conditions. In the next section, we trace the history of the contingency perspective on fit, focusing on the critical concerns that arose in the 1980s and on new developments that have facilitated an effective response to those concerns. Following that, we link managerial discretion theory to the contingency perspective on internal fit, in light of our airline industry context, before describing our empirical approach and results.

THE RISE AND FALL AND RISE AGAIN OF CONTINGENCY THEORY Contingency theory, despite its influence within organizational theory as well as the strategy field, is more properly characterized as a perspective than a well-specified theory per se (Schoonhoven, Strat. Mgmt. J., 28: 1089–1112 (2007) DOI: 10.1002/smj

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1981). Its subject matter varies widely (Van de Ven and Drazin, 1985). What unites the contingency perspective is its core construct of fit, implying co-alignment or consistency among a set of factors (Drazin and Van de Ven, 1985; Venkatraman and Prescott, 1990). Contingency perspectives include structural contingency theory (Burns and Stalker, 1961; Lawrence and Lorsch, 1967; Thompson, 1967), with its emphasis on external fit, as well as work concerned with internal fit, such as studies relating strategy and structure (Chandler, 1962). Configurational approaches (Miles and Snow, 1978; Miller and Friesen, 1977; Miller, 1986) fall into this latter category, although some authors have taken pains to distance this work from the contingency theory label.3 In the 1960s and 1970s, contingency theory was a dominant paradigm within organizational studies (Pfeffer, 1997). In the 1980s, it lost currency because of its inability to address theoretical and methodological problems that had surfaced (Van de Ven and Drazin, 1985). Devastating critiques by Schoonhoven (1981), Miller (1981), Mohr (1982), and others brought these flaws to light. While these critiques were aimed largely at structural contingency theory, they applied in some measure to other forms of contingency theory as well.4 Confusion over the core concept of fit, as well as dissatisfaction with mixed empirical results, stemmed the flow of contingency studies, the work of respected scholars such as Hambrick (1983a, 1983b), Gupta and Govindarajan (1984), and Miller (1986) notwithstanding. Pennings (1992: 268) observed that structural contingency theory ‘lost some of its stature and popularity during the eighties.’ Miller (1996: 506) laments that ‘for all its promise, the literature on configuration remains undeveloped.’ Zajac et al. (2000: 429) note that perceived problems ‘hindered the theoretical development and empirical testing of the concept of strategic fit.’ 3 While Miller (1981) referred to configurational work as the ‘new contingency approach’ and Van de Ven and Drazin (1985) include such work as one of three approaches to fit within structural contingency theory, Delery and Doty (1996) as well as Meyer et al. (1993) argue that the contingency label should not apply. 4 The problem of mixed empirical results, for example, applies as much to the configuration studies as it does to structural contingency theory (Miller, 1996). This is particularly evident once one understands configurations studies to include studies of strategic groups (Ketchen et al., 1997).


The criticisms of contingency theory are largely of five different types, the last two of which are closely related. The first, leveled most strongly at the early contingency theorists, was that the theory was overly deterministic (Miller, 1981). This term referred to the notion that within structural contingency theory the environmental context was viewed as determining the structure appropriate to it. More generally, it was a complaint regarding an assumption of unidirectional causation (Meyer et al., 1993). The complaint was not unique to structural contingency theory. Miller (1981) pointed to Chandler’s (1962) well-known dictum that ‘structure follows strategy’ as an example of determinism within contingency perspectives on internal fit. The selection approach to fit described by Van de Ven and Drazin (1985) and associated with population ecology (Hannan and Freeman, 1977; Aldrich, 1979) is essentially deterministic (Pennings, 1992). The second major criticism was that contingency research was reductionist (Van de Ven and Drazin, 1985; Meyer et al., 1993). The empirical models employed were seen as underrepresenting the complexity of the interactions among organizational elements and ignoring nonlinearities (Schoonhoven, 1981; Miller, 1981; Meyer et al., 1993). In essence, they treated organizations as if they were decomposable into independent elements, whose effects could be examined independently and then aggregated to understand the entirety (Van de Ven and Drazin, 1985). If the sum of the individual components does not represent the whole, then attempts to study the system piecemeal are misguided at best (Venkatraman and Prescott, 1990). A ‘systems approach to fit’ within structural contingency theory (Drazin and Van de Ven, 1985) that relied on the identification of internally consistent gestalts, or ideal types, emerged as a reaction against reductionism. Within the field of strategy, such approaches are called configurational (Miller, 1986, 1996; Meyer et al., 1993). They include studies involving typologies, such as Miles and Snow’s (1978), as well those employing taxonomies, such as Miller and Friesen’s (1977).5 A third complaint is the static orientation of contingency theory (Miller, 1981; Venkatraman, 1989; Zajac et al., 2000). Within structural contingency 5 The distinction between typologies and taxonomies is that the latter are empirically derived configurations.

Strat. Mgmt. J., 28: 1089–1112 (2007) DOI: 10.1002/smj

Managerial Discretion and Internal Alignment theory, in particular, cross-sectional models dominate. This is curious, given that the achievement of fit implies the need for adaptation, itself a dynamic concept. Both Pennings (1992) and Venkatraman (1989) called for research to shift toward more longitudinal studies. Miles and Snow (1994) draw attention to the importance of dynamic fit. Zajac et al.’s (2000) study is one of a few concerned with understanding fit dynamically. The fourth criticism concerns empirical difficulties, including equivocal evidence regarding the performance effects associated with fit (Pfeffer, 1997; Schoonhoven, 1981; Drazin and Van de Ven, 1985). The mixed results are as characteristic of configuration studies as they are of structural contingency theory. Ketchen et al. (1997) report only partial support in their meta-analysis of organizational configurations and performance. The mixed results regarding the performance effects of strategic groups, which are most often defined as configurations, provide a case in point (Thomas and Venkatraman, 1988). As Drazin and Van de Ven (1985) suggest, these mixed results may be due to methodological problems, which are a concern even apart from the results. A number of these problems focus on the analysis of fit as a set of hypothesized interactions. Drazin and Van de Ven (1985) identify several methodological problems facing survey researchers taking this approach. Schoonhoven (1981) points to more general methodological problems, including a lack of theoretical guidance regarding the form of the interactions. She notes that the analytical models employed often impose restrictive assumptions that may not be warranted. Doty, Glick, and Huber (1993) report that similar empirical difficulties are common in tests of configuration theory. Simple empirical approaches are often used, but are inadequate due to the inherent complexity of configurations. Meyer et al. (1993) discusses the particular problems of empirical work with typologies. The fifth major criticism is closely related to the charge (above) of methodological problems and inconclusive empirical results. It is that the definition of the core concept of fit lacks precision (Galbraith and Nathanson, 1979; Venkatraman and Camillus, 1984; Van de Ven and Drazin, 1985). This, of course, leads directly to problems in trying to operationalize the concept, including how to measure fit and test for its existence as well as its effects (Venkatraman, 1989; Drazin and Van de Copyright  2007 John Wiley & Sons, Ltd.

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Ven, 1985). Schoonhoven (1981: 351) decried the lack of specificity as to the nature of fit and noted that ‘the mathematical function of the implied interaction . . . is seldom made explicit.’ Although a number of authors (e.g., Van de Ven, 1979; Venkatraman and Camillus, 1984; Drazin and Van de Ven, 1985; Venkatraman, 1989) have attempted to articulate more carefully the alternative views of fit, the concept remained problematic. After years of waning interest, academic interest in fit and the contingency perspective appears to be on the upswing once again. This is particularly evident in the field of strategic management. Sigglekow (2001: 838) notes ‘a remarkable upsurge of interest in the concepts of interaction and fit.’ There are a number of possible reasons for this renewed attention to the topic of strategic fit. Certainly, a major driver is the increasing attention commanded by economists’ work on complementarities among elements of a firm’s strategy (Milgrom and Roberts, 1990, 1995). By rigorously modeling mutually reinforcing interactions within a mathematical framework, this work has helped to legitimate the concept of fit, particularly internal fit. It has also resolved a number of prior definitional problems that plagued research on fit, providing guidance as to how to test for and measure fit. As Whittington et al. (1999: 584) assert, the work on complementarities ‘extends’ the contingency and configuration approaches, ‘both by making stronger claims for the value of coherence and by suggesting an alternative approach to performance testing.’ The more recent work on organizational adaptation along rugged fitness landscapes (Levinthal, 1997) has had a similar legitimating effect. This work employs sophisticated NK modeling (Kauffman, 1993) to study interaction effects among firm attributes and the implications for organizational performance and change. It has contributed not only new insights regarding interaction effects, but has introduced simulation techniques to the contingency perspective toolbox as well (e.g., Levinthal, 1997; Rivkin and Sigglekow, 2003). In addition, the resurgence of interest in fit may stem from the centrality of the concept to other increasingly influential theoretical perspectives. Porter’s widely known Harvard Business Review article, ‘What is Strategy?’ (Porter, 1996), featured the fit concept prominently, tying it explicitly to sustainable competitive advantage. His notion of activity systems is based on the idea Strat. Mgmt. J., 28: 1089–1112 (2007) DOI: 10.1002/smj

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of complementarities among interrelated organizational elements. Rivkin’s (2000) work on the imitability of complex strategies builds on this theme. Similar ideas are found within the resourcebased view (Wernerfelt, 1984; Barney, 1991; Peteraf, 1993a). Penrose (1959), Rumelt (1984) and others have emphasized the importance of bundles of resources and capabilities. Like activity systems, their complexity and ambiguity have implications for both organizational heterogeneity as well as sustainable competitive advantage (Lippman and Rumelt, 1982; Hoopes, Madsen, and Walker, 2003). Within the dynamic capabilities literature, the concepts of co-specialized assets (Teece, Pisano, and Schuen, 1997) and complementary assets (Dierickx and Cool, 1989) also reflect the theme of contingent fit. Recent empirical work has begun to connect the resource-based view to the concepts of strategic fit and complementarities even more explicitly (e.g., Zajac et al., 2000; Carmeli and Tishler, 2004). The topic of contingent fit continues to be one that resonates as having relevance for both management scholarship and practice. It is a theme that applies to a wide array of topics including agency theory, transaction cost economics, and organizational ecology (Nickerson and Zenger, 2002). While the contingency label is not yet free of its association with past criticisms, new work is rapidly overcoming the hurdles that impeded progress in the recent past. In this paper, we add to this body of new work using an approach that is consistent with an economic understanding of complementarities and that addresses remaining criticisms of the contingency perspective. In the next section we discuss managerial discretion in the airlines industry as it relates to our period of study. We follow this with an explanation of our methodological approach.

MANAGERIAL DISCRETION IN THE AIRLINE INDUSTRY In the contingency literature, the complaint that the theory was overly deterministic emerged early. Indeed, Child (1972, 1997) developed his model of strategic choice in direct response to this concern. Strategic choice drew attention to the active Copyright  2007 John Wiley & Sons, Ltd.

role that managers play in influencing the organization’s situation, including its strategy, structure, processes, and competitive context. While Hrebiniak and Joyce (1985) explored the middle ground between choice and determinism, Hambrick and Finkelstein (1987) developed more fully the concept of managerial discretion in terms of managers’ ‘latitude of action,’ which varies predictably with environmental, organizational, and managerial characteristics. This study lies at the intersection of contingency theory and managerial discretion theory. It accommodates variation in the degree of managerial discretion from two different sources. The first is the nature of the task environment (Hambrick and Finkelstein, 1987). The second has to do with the characteristics of the organizational choice sets. An important aspect of the task environment affecting managerial discretion is the regulatory environment (Hambrick and Abrahamson, 1995; Finkelstein and Hambrick, 1996). Regulated industries are characterized as low-discretion environments, in relation to their deregulated or unregulated counterparts (Rajagopalan and Finkelstein, 1992; Magnan and St-Onge, 1997; Finkelstein and Boyd, 1998). We examine the effect of differences in the discretionary environment on managerial choice and organizational performance within the U.S. airline industry, over a time frame that includes both a regulated and a deregulated period. During the regulated era, managerial discretion was circumscribed by regulatory controls that centered largely on market entry and exit, as well as on pricing (Keeler, 1981; Bailey, Graham, and Kaplan, 1985). Entry and exit regulation gave regulators effective control over an airline’s route structure and network configuration, as well as cities served.6 Moreover, it essentially constrained choice regarding fleet composition, since a carrier’s fleet needed to be matched to the structural and demand characteristics of its routes. Following the Airline Deregulation Act of 1978, entry, exit, and pricing regulations were phased out over a 4year period. By the end of 1981, the regulatory

6 Route structure refers to the structural characteristics of a citypair route, such as the number of stops, the number of plane changes, and the stage length of each leg of the route. An airline’s network refers to the overall configuration of the route system.


Managerial Discretion and Internal Alignment task environment was no longer a consequential factor limiting managerial discretion.7 Variation in managerial discretion may also derive from the characteristics of the organizational activity sets being managed.8 Some kinds of activities lend themselves more easily to oversight by other constituencies, whether by regulators or rival inside forces, such that a manager’s latitude of action is reduced. For example, tasks whose performance is readily monitored and whose outcomes are easily discernible are those that may be amenable to external control. Activity sets that are more complex, harder to specify, tacit in nature, and whose outcomes are causally ambiguous (Lippman and Rumelt, 1982) are less susceptible to external interference. Constraints on such activities come indirectly and are less binding as a consequence. Even with indirect constraints, managers will have greater discretion in designing and performing these kinds of tasks. Within the airline industry, activities that are relatively discrete, well understood, and observable, with parameters that can be clearly specified, are susceptible to direct constraints by regulators seeking to limit managerial discretion. Examples of such activities include cities served, routes, number of stops and connections, number of departures, route structure, and network configuration. In contrast, complex activity systems involving strategy, intangibles, and many tacit components are hard for external parties to control for the same reasons that they are hard to imitate (Porter, 1996; Rivkin, 2000). It is spheres of activities such as these over which managers are likely to have greater latitude of action, even if constrained indirectly. As an example of such a sphere, consider Southwest’s widely admired system of activities for minimizing the turnaround time required to get its planes back 7 Although the regulatory authority did not cease operations until 1985, its controls over routes and fares were effectively gone by the end of 1981 (Kaplan, 1986). 8 While the impact of the general characteristics of internal organization on discretion has been acknowledged (Hambrick and Finkelstein, 1987), there has not yet been any explicit discussion of how discretion varies with the characteristics of the organizational activities or choice sets. Our view is that this is an opportunity for further theoretical development within managerial discretion theory. For example, contracts impose legal constraints limiting managerial discretion (Hambrick and Abrahamson, 1995). Just as monitoring problems inhibit the use of contracts in transaction cost theory (Williamson, 1975), such problems can limit attempts to rein in managerial discretion as well. Some activities sets, with certain kinds of characteristics, are more easily monitored than others.


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into the air after landing. In this study, we refer to organizational choices associated with limited managerial discretion as operational choices; we refer to those choices conferring greater managerial discretion as administrative practices. Below, we present a model of managerial choice that centers on a set of hypothetical cases regarding the ways in which the constrained task environment could have affected managerial choice with respect to the two types of choice domains. By conducting a set of empirical tests, we can discriminate among these cases to see which are supported by our data. Doing so reveals information regarding the nature of the regulatory constraints on managerial choice. In addition, it reveals information about how managers respond under conditions of constrained choice.

A MODEL OF MANAGERIAL DISCRETION Modeling the elements of managerial choice Under the two regulatory eras, we consider two broad types of managerial choices: choice regarding operational elements, such as routes and departures, and choice regarding administrative practices. Conceptually, one can conceive of the administrative practices as the way in which the operational elements are put together to form a coherent system. If the function of a firm, in simplest terms, is to transform various inputs into more highly valued products or services, then the set of administrative practices shapes this transformation process. It describes the overall way in which the firm is managed on a day-to-day basis. Administrative practices vary from firm to firm. But on average they conform to what is known as an ‘industry recipe’ (Spender, 1989). This recipe reflects the mean choices of individual firms and may change over time. Although such choices, by their intangible and complex nature, are difficult to measure directly, there is a natural way to capture them indirectly. A firm’s production function describes the transformation process alluded to above. It is, in essence, a mathematical representation of the recipe by which a firm organizes and configures its production process. Since the cost function is the dual of the production function, the administrative practices of the average firm can be captured equally well by estimating a cost function (Varian, 1978; Wernerfelt, 1984). Strat. Mgmt. J., 28: 1089–1112 (2007) DOI: 10.1002/smj

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If the cost function is represented by the equation C = β(X), then β describes the transformation process and thus the choice of administrative practices, on average. This conforms to the way that an economist would think about the cost function. The X vector in this equation describes the set of operational choice variables. As an empirical matter, production choices that can be easily represented by measurable variables have the potential for external controls. In this case, those variables include output mix, factor prices, and variables describing service attributes, such as points served and network structure. Service attributes such as frequency of departure represent quality of service in the airline industry (Douglas and Miller, 1974). C denotes the average unit cost of producing the firm’s output, at given factor prices, with a given level of quality. Below, we describe how we use this cost function model to explore the effects of regulatory constraints and their relaxation on managerial choice. A model of hypothetical choice under regulation and reform In this section, we model the average airline firm’s reaction to regulatory reform as a set of hypothetical choices. Our concern is with the choices the average airline management team makes regarding X and β under alternative regulatory regimes and the extent to which regulation constrains their choice set. For ease of interpretation, we refer to the period with regulatory restrictions as pre and the unrestricted period as post in our notation below. During the regulated regime, the firm’s choice set is constrained, although only the choice of X is constrained directly. The choice of β, while affording greater managerial discretion, may be constrained indirectly. We represent this restricted choice set by the terms βpre and Xpre . Once the regulatory constraints are lifted, following regulatory reform, firms have several possible choices. In broad terms, they can choose to change the variables described by the X vector to Xpost , or they can choose to remain at Xpre . They can change X in such a way as to either increase or decrease both output and quality. In addition, they can change their sets of administrative practices and processes. That is to say, they could change from βpre to βpost , or they could keep the old administrative practices described by Copyright  2007 John Wiley & Sons, Ltd.

βpre. Summing up, the four choice possibilities are described by the following combinations: {(βpre , Xpre ), (βpost , Xpre ), (βpre , Xpost ), (βpost , Xpost )} It is immediately clear that if the average firm’s choice after deregulation remains at (βpre , Xpre ), then either regulation imposed no restrictions on the original choice set, or managers (on average) chose not to change their way of doing business after deregulation. The latter scenario is possible if the costs to switching the routines they established during the regulatory period are high and the gains from switching are small. We denote this hypothetical case as the No Regulatory Distortion Hypothetical (H1) to indicate the limited nature of any regulatory constraints on managerial behavior that this case describes. This scenario contradicts most observers’ beliefs about the airline industry’s reaction to deregulation, but it serves as a useful baseline case. To distinguish among the cases that follow, it is important to look more deeply at the effect of these choices on costs. If the average firm chooses (βpost , Xpre ) under deregulation, and C(βpost , Xpre ) < C(βpre , Xpre ), then regulation led managers to use an inefficient set of administrative practices. This is evident, since there is a cost savings from deregulation due to the change in β alone. Administrative practices were not constrained directly by regulation. Nevertheless, this result is possible due to indirect regulatory constraints from, say, distorted incentive mechanisms that alter the normal profit-maximizing concerns of management. We denote this hypothetical case as the Indirect Constraints Only Hypothetical (H2) since it describes an indirect regulatory constraint on the choice of β, but no constraints on the choice of X. X could appear to be unconstrained under regulation if the constraints imposed were not binding. The next two cases describe regulatory restrictions that affect only the firm’s choice of the X vector. The first assumes that regulation primarily restricted the firm’s output, while the second assumes that regulation distorted the firm’s choices regarding the quality of output. They are distinguished by the effect of regulatory reform on the firm’s costs. We denote the first hypothetical choice as the Output Restriction Hypothetical (H3). In this Strat. Mgmt. J., 28: 1089–1112 (2007) DOI: 10.1002/smj

Managerial Discretion and Internal Alignment case, the average firm chooses (βpre , Xpost ) in response to regulatory reform and C(βpre , Xpost ) < (βpre , Xpre ). This suggests that regulation restricted managerial choice over the components of the X vector in a fashion that increased costs. There are several explanations. Regulation may have restricted firm output in the presence of economies of scale. Similarly, it may have restricted the firm’s network structure in the presence of economies of network density. Alternatively, the quality of output may have been ‘too high’ under the regulatory regime, raising the firm’s costs. All of these possibilities are consistent with the literature describing the effects of regulation on the airline industry. See, for example, Bailey et al. (1985) and Meyer and Oster (1981). The hypothetical choice in the second of these two cases is the same, but the effect on costs differs. Here, the average firm chooses (βpre , Xpost ) after deregulation even though C(βpre , Xpost ) > C(βpre , Xpre ). This would be a rational choice if quality was too low under regulation (or output was too high with diseconomies of scale). We denote this as the Quality Restriction Hypothetical (H4). This possibility does not conform to the view of most observers of airline firm behavior in the wake of deregulation. The more general view is that regulation constrained output and promoted non-price competition, driving quality to higher than optimal levels (Bailey et al., 1985). The next set of hypotheticals describes cases in which the firm’s choices of both β and X were restricted during the regulatory period (X directly and β indirectly). Each of these cases assumes that the average firm chooses (βpost , Xpost ) in response to regulatory reform, presumably since costs decline such that C(βpost , Xpost ) < C(βpre , Xpre ). The underlying effects, however, are significantly more complex. To distinguish carefully among the types of regulatory distortions and the managerial response to deregulation, we consider three possible cases. In the first case, the following four conditions hold:

The first two inequalities focus on the effects of the change in β after regulatory reform, holding the X vector constant. Together, they state that regardless of whether the X vector is held constant at its pre or post deregulation values, the change to the new β results in lower costs. Similarly, the last two inequalities state that the change to the new X vector reduces costs, regardless of whether the β is held constant at the new or old values. Altogether, what these four inequalities indicate is that the new X vector and the new administrative practices are both unambiguously more efficient than those employed under regulation. This suggests that regulation distorted managerial choices regarding both β and X. We denote this as the Restriction on Output and Administrative Practice Hypothetical (H5). Most of the literature on the airline industry seems to point toward this case as being the most likely (Bailey et al., 1985). In the second case the following four conditions hold: C(βpost , Xpre ) < C(βpre , Xpre ) C(βpost , Xpost ) < C(βpre , Xpost ) C(βpre , Xpost ) > C(βpre , Xpre ) C(βpost , Xpost ) > C(βpost , Xpre ) As in the prior case, the first two inequalities indicate that the new β is unambiguously more efficient. The second two, however, tell a different story. They state that if you change the X vector while holding β constant at either its pre or post position, average costs will increase. This suggests that the new X reflects a choice to increase service quality—an expensive option. Since such a result would indicate that regulation constrained both quality levels in X and the set of administrative practices (β), we designate this as the Restriction on Quality and Administrative Practice Hypothetical (H6). In the final case the following four conditions hold:

C(βpost , Xpre ) < C(βpre , Xpre )

C(βpost , Xpre ) > C(βpre , Xpre )

C(βpost , Xpost ) < C(βpre , Xpost )

C(βpost , Xpost ) < C(βpre , Xpost )

C(βpre , Xpost ) < C(βpre , Xpre )

C(βpre , Xpost ) < C(βpre , Xpre )

C(βpost , Xpost ) < C(βpost , Xpre )

C(βpost , Xpost ) < C(βpost , Xpre )


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In this case the new X vector is unambiguously cheaper to produce but the new administrative practices are not unambiguously more efficient. Note the sign change in the first inequality. It says that a change in β alone, without a change X, will not lower average costs. Indeed, the choice of (βpre , Xpre ) leads to a more efficient outcome. Taken together, the first two inequalities suggest that average costs are lower when administrative practices (β) are matched to their X counterpart than they are when only the new β is adopted. The regulatory output is more efficiently produced with the administrative practices of that same era, while the post-reform output is more efficiently produced with the updated set of practices. Summing up, these hypothetical conditions suggest the following: regulation constrained the choice of both the X vector variables as well as the administrative practices (β). Regardless of these constraints, however, managers, on average, chose practices that best suited the requirements for producing X most efficiently. In essence, this scenario describes firms attending to optimal internal alignment or fit (Porter, 1996) under constrained regulatory conditions. Thus we characterize this case as the ‘Internal Alignment’ Matters Hypothetical (H7). Testing to distinguish among the hypothetical choices In this section we lay the groundwork for empirically investigating the effect of U.S. airline regulation on managerial choice and the average response of firms to regulatory reform. Table 1 displays a set of testable conditions in relation to the hypothetical cases outlined above. Across the top of the table, H1 through H7 represent the seven hypothetical cases under investigation. They are: Table 1.

The No Regulatory Distortion Hypothetical The Indirect Constraints Only Hypothetical The Output Restriction Hypothetical The Quality Restriction Hypothetical The Restriction on Output and Administrative Practice Hypothetical H6: The Restriction on Quality and Administrative Practice Hypothetical H7: The ‘Internal Alignment’ Matters Hypothetical Along the left side of the table are six testable conditions, indicated by T1 through T6. These will be used to distinguish among the hypotheticals and determine which are supported empirically. The body of the table shows which of the testable conditions on the left are consistent with the hypotheticals along the top. The symbol Y indicates that the condition is consistent with the hypothetical case, whereas the symbol N indicates that it is inconsistent. The symbol (−) denotes that the test is not applicable. As an example of how this table works, consider Hypothetical 2 (H2). This is the case in which indirect regulatory constraints affect the choice of administrative practices (β), while the regulatory constraints on the X vector are non-binding. Thus, T2 is inconsistent with this hypothetical, while T1 is consistent. Recall, in addition, that the effect on costs of this scenario was described by T3. Thus T3 is consistent with H2 as well. Hypothetical 6 (H6) provides a more complex illustration. This is the case in which firms, on average, choose (βpost , Xpost ) after deregulation, in response to regulation that directly constrained service quality (reflected in X) and indirectly constrained administrative practices (reflected by β). The average firm’s choice to change both β and X suggests that neither T1 nor T2 is consistent with

Tests to distinguish among the hypothetical cases H1–H7

Testable conditions T1 T2 T3 T4 T5 T6

H1: H2: H3: H4: H5:

Xpost = Xpre βpost = βpre C(βpost , Xpre ) < C(βpre , Xpre ) C(βpost , Xpost ) < C(βpre , Xpost ) C(βpre , Xpost ) < C(βpre , Xpre ) C(βpost , Xpost ) < C(βpost , Xpre )

H1

H2

H3

H4

H5

H6

H7

Y Y — — — —

Y N Y — — —

N Y — — Y —

N Y — — N —

N N Y Y Y Y

N N Y Y N N

N N N Y Y Y

H1: No Regulatory Distortion; H2: Indirect Constraints Only; H3: Output Restriction; H4: Quality Restriction; H5: Restriction on Output and Administrative Practice; H6: Restriction on Quality and Administrative Practice; H7: ‘Internal Alignment’ Matters. Copyright  2007 John Wiley & Sons, Ltd.


Managerial Discretion and Internal Alignment this scenario. T3 through T6 lay out the distinguishing conditions that were described when this hypothetical case was introduced above. Of these, both T5 and T6 are inconsistent with H6, since the higher quality levels in Xpost raise costs relative to those associated with Xpre . This is the case regardless of whether β is held constant at its pre or post values. Conducting the six tests requires a series of steps in the empirical analysis. First, we must estimate the airline cost function and examine how it changed from the regulatory period to the period in which the industry was deregulated. We explain the empirical cost estimation model in the next section. In subsequent sections, we describe the data and then present the results of the cost estimation. With the results from this cost estimation in hand, we can finally conduct the tests of the hypotheticals that we have just described. We provide the results of these tests, prior to a more general discussion and some concluding remarks.

COST FUNCTION ESTIMATION The airline industry produces service output of a heterogeneous nature, including not only different classes of passenger service, but freight service as well. To accommodate this variation in output composition, we employ a cost function that includes an indexed measure of the various types of physical output, as well as a vector of input prices and operating characteristics, following Friedlaender and Spady (1981). The use of an output index is standard procedure for studies of transportation industries, where there are complementarities among the various service or output types.9 The functional form The basic form of the cost function is C = f (y, w, t) where y is an indexed measure of output, w is a vector of factor prices and t is a vector 9 The use of an index permits this study to be compared to prior cost studies, which employed indices. Moreover, it facilitates interpretation of results.


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of operating and dummy variables. The dummy variables capture firm and time-based fixed effects, as described below. Details regarding the output index are provided in the data section below and in Table 7 in the Appendix. The operating variables include average stage length, average load factor, the number of points served, the number of departures and the number of routes served. This is similar to the wellknown model of Caves, Christensen, and Tretheway (1984), which employs the first three measures. Average load factor is the percentage of available seats that are filled by paying passengers, weighted by miles.10 The number of points served measures the number of nodes in an airline’s route network, while stage length measures the length of the links connecting the nodes, weighted by departures. We add to the model of Caves et al. (1984) the number of departures, to capture a large part of the operating costs of airlines and to control for plane size. In addition, we incorporate the number of routes served, in order to measure the number of connections among the network points. This enables us to capture the effects of changes in an airline’s network structure, which is particularly important as the airlines move toward a hub and spoke type of system. We specify intercept shifts for each firm, as well as for each season (quarter) and year. The inclusion of time-based fixed effects prevents biases in the estimated coefficients due to the omission of variables that are year or season specific, but affect all firms in the same way. Technological improvements in airplanes are an example of a time-based fixed effect. The effect of winter weather on flight patterns and costs is an example of a seasonal fixed effect. The cost function with a regime switch The distinguishing feature of our model is that it allows for changes in the parameters of the cost function as the industry was deregulated. Free from regulatory restrictions on entry and exit, carriers reorganized their route structures into hub and 10 Average load factors are influenced by frequency of departures and both are standard indicators of service quality in the airline industry. Greater frequency provides greater convenience for travelers, in terms of their time preferences. Lower load factors allow travelers greater comfort and a higher probability of successfully booking flights at the last minute (Douglas and Miller, 1974).


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spoke systems, and implemented new ways to manage their expanding systems (Levine, 1987). To capture this transformation of the cost function, we allow the basic cost model to vary between two eras—one comprising the regulatory era and a second for the later years, when the regulatory agency no longer had any authority over routes, and carriers had full latitude of action. We model this structural change in the cost function by estimating separate cost functions in each of the two eras, with the break point occurring between 1981 and 1982. This is the most natural break point for such a study, since 1981 was the last year in which the Civil Aeronautics Board (CAB) exercised control over route structures. To confirm the reasonableness of this choice, we test the stability of the cost function over this period and find evidence of a structural break, as expected.11 We estimate the cost function using a Cobb– Douglas model (Greene, 1997). This functional form has the advantage over more flexible forms, such as the Translog, in that it conserves degrees of freedom. More importantly, it is the only functional form for cost functions that has a structural interpretation (Butler and Monk, 1985). Two estimations are carried out (one for each subsample), in which the coefficients and errors from the two samples are treated independently. The cost functions for the estimations are given by

where C is total cost; the subscripts i and t represent the firm and the quarterly time period, respectively; y is the indexed output measure; p is the number of points served; l is the average load factor; s is the average stage length; d is departures; r is the number of routes served; w, f, k, m are the prices of labor, fuel, capital, and materials respectively; δa are firm fixed effects; δt are yearly fixed effects; δ q are seasonal dummies; and β, λ, and γ are the parameters to be estimated. The last line of the cost equation contains cross terms capturing the interaction between factor prices and the firm-specific dummies. This permits carrier specific heterogeneity to enter the cost share equations given below. This equation is estimated separately for each subsample (and for the full sample for tests of stability). Following Christensen and Green (1976), we jointly estimate the cost equation with the factor cost share equations, given below. This procedure results in more efficient parameter estimates, effectively providing many additional degrees of freedom without adding any unrestricted regression coefficients. The factor cost share equations are given by

ln Cit = β0 + βy ln yit + βp ln pit + βl ln lit

a

+ βs ln sit + βd ln dit + βr ln r + βw ln wit + βf ln fit + βk ln kit + βm ln mit + γa1 δa + γt1 δt +

q

+

a

γq1 δq +

t

λaw δa ln wit

a

λaf δa ln fit +

a

+

λaw δa + εwit

a

shfit = βf +

λaf δa + εfit

a

shkit = βk +

λak δa + εkit

where shw represents the share of costs attributable to labor, shf is the share of costs due to fuel, shk is the cost share of capital, i indexes the firm, and t indexes the time period. The material cost share equation is dropped in order to avoid singularity in the system of equations. The estimation methodology is not sensitive to which share equation is omitted.

λak δa ln kit

a

λam δa ln mit + εit

a

11 A Chow test resulted in a chi-squared statistic that is significant at the 0.1 percent level. An examination of our connectivity data revealed that connectivity changed markedly over the two time periods, indicative of a significant change in network characteristics.


shwit = βw +

Estimation methodology The estimation methodology is a three-stage leastsquares estimation of the parameters, with output as an endogenous variable.12 The regression treats 12 A 3SLS regression is necessitated by the additional factor share equations, which improve the estimation efficiency. The instruments employed are GNP (gross national product), CPI


Managerial Discretion and Internal Alignment the four equations (the cost equation and the three factor share equations) as a system represented by Y = Xβ + ψ where 

tc   sh  Y = w shf shk



xtc  x  X= w xf xk



ε  ε  ψ = w εf εk

and tc is log of total cost and sh stands for factor cost share as above. The regressors (with the exception of the dummy variables) are all normalized by removing their sample means. This procedure is applied to each subsample to allow for a structural break in the cost function. To avoid singularity in the firm, quarter, and year dummy variables, each set omits one dummy. American Airlines is the omitted carrier, the first quarter (winter) is the omitted season, and the middle years from each period (1979 for pre and 1984 for post) are the omitted years. This implies that the three intercepts reflect the total cost of American Airlines in the first quarter of 1979 and 1984, respectively. In addition, the factor share coefficients represent the shares for American Airlines at the mean of the data. Data The cost function is estimated with quarterly panel data for 16 firms, spanning a 10-year period, from 1977 to 1986. These firms comprise the entire set of formerly regulated and certificated carriers, with two notable exceptions. Pan American is excluded from the sample because most of its routes were international rather than domestic. Northwest is excluded because of missing factor price data and nonsystematic reporting of other data (Sickles, Good, and Johnson, 1986). The firms are listed in Table 8 in the Appendix, along with the time period for which we have data for each firm. There are 300 observations for the regulatory period (pre) and 235 observations for the deregulated period (post). (consumer price index), population, and their cross terms. While it may be desirable to view load factor as endogenous as well, no good instruments are available for this variable. Treating load factor as endogenous using the instruments listed above does not qualitatively change any of the results. Copyright  2007 John Wiley & Sons, Ltd.

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The data employed in the study are taken from the Department of Transportation records. The primary source of data was Form 41. Data on the number of routes come from Service Segment Data. Gross national product (GNP) and consumer price index (CPI) are obtained from Federal Reserve Board publications. The data on factor prices are identical to that of Sickles et al. (1986), who employ indices of the component prices. Output is also represented as an index, since airline output is multi-product and includes several service categories. Output includes first-class passenger-miles, coach passenger-miles, first-class charter passenger-miles, coach charter passengermiles, and freight ton-miles. We summarize this via an index number that represents a weighted combination of these output types. The weights for each category are the percentage revenues obtained from each category of output. Further details regarding the construction of these and other variables are provided in Table 7 of the Appendix. Means of the data for each of the two sample periods are given in Table 9 in the Appendix.

COST FUNCTION ESTIMATES The results of the cost function estimations in abbreviated form are given in Table 2, with the full set of results reported in Table 10 in the Appendix. Since these results are not the main focus of our interest, but instead provide the input for the empirical tests we conduct below, we discuss them here only briefly. Table 2. First-order coefficients of cost functions 1977–81 Intercept Output Points served Load factor Stage length Departures Routes Labor price Fuel price Capital price Materials price

19.424∗∗ 0.210∗∗ 0.032∗∗ −0.072 0.448∗∗ −0.001 0.063∗∗ 0.416∗∗ 0.234∗∗ 0.145∗∗ 0.205∗∗

(0.017) (0.039) (0.011) (0.048) (0.049) (0.027) (0.014) (0.007) (0.009) (0.003) (0.003)

1982–86 19.354∗∗ 0.415∗∗ 0.075∗∗ −0.201∗∗ 0.180∗ 0.082 0.062∗∗ 0.383∗∗ 0.214∗∗ 0.144∗∗ 0.259∗∗

(0.034) (0.057) (0.023) (0.067) (0.090) (0.053) (0.018) (0.005) (0.008) (0.004) (0.006)

Coefficients are significant at the †10% level, at the ∗ 5% level and at the ∗∗ 1% level. N = 300 for 1977–81; N = 235 for 1982–86 Strat. Mgmt. J., 28: 1089–1112 (2007) DOI: 10.1002/smj

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What is notable is simply that these results are similar to those obtained in other airline cost studies, suggesting that our methods are trustworthy. As in prior studies, there is evidence of strong economies of traffic density (Caves et al., 1984; Brueckner and Spiller, 1994; Reed, 1999) as well as economies of network size (Caves et al., 1984; Sickles et al., 1986; Kumbhakar, 1990; Reed, 1999).

without the factor price variables. The factor price variables can be excluded, since these variables are not part of the managerial choice set, although they clearly affect costs. Under the null hypothesis of no change, the test statistic is distributed chi-squared. As the results plainly show, the tests strongly reject the null hypothesis of equal means across the two time periods, regardless of whether the price variables are included. That is to say: Xpre = Xpost

EMPIRICAL TESTS OF THE HYPOTHETICAL CASES With the results of the cost estimation for the two sample periods in hand, we are now able to conduct the discriminatory tests of the hypothetical cases described earlier. Recall that there are six such tests, summarized in the left-hand side column of Table 1. The aim now is to determine which of these tests are supported, using the results of our cost estimations. By observing the pattern of support and rejection for the tests, we can then see which of the hypothetical cases provides a plausible scenario, regarding how regulatory constraints and their removal affected managerial choice, on average.

It is well documented that the airline industry underwent much change over the period examined, so these results are not a surprise. Regulation did indeed constrain managerial choice of the elements of the X vector. The results of this test have further use, however, in differentiating among the seven hypothetical cases. The evidence from these results supports Hypotheticals H3, H4, H5, H6 and H7, while ruling out H1 and H2. In sum, the results enable us to eliminate the No Regulatory Distortion Hypothetical (H1) and the Indirect Constraints Only Hypothetical (H2). Test for bpost = bpre (T2 )

Test for Xpost = Xpre (T1 ) We begin our series of tests by determining whether the means of the set of X variables are the same across the two time periods, in order to ascertain whether the choice of X was constrained by regulation. Table 9 in the Appendix provides the mean value of each variable for the two subsamples in non-log form. The joint test for the difference in means is a likelihood ratio test, the results of which are given in Table 3.13 We perform this test on the means of the logged variables from the estimation, with and Table 3. Joint tests for difference between means (T1)

All variables Non-price variables

1977–81 vs. 1982–86

Critical value at 1% level

807.16 69.50

21.7 15.1

13 Tests of the difference in the means of the individual variables indicate that nearly all of these changed significantly as well.


To test for the stability of the coefficients over the two subsamples and determine whether regulation indirectly constrained managers’ average choices regarding the administrative practices, we conduct a Chow test. Because of the multi-equation nature of our framework, the Chow test uses a test statistic that is distributed chi-squared under the null, rather than the F -statistic more commonly employed with a single equation framework. The results of this test are provided in Table 4. Clearly, the null hypothesis of no change in administrative practices is rejected, since the chisquared statistic far exceeds the critical value, even at the 0.1 percent level. This suggests that the coefficients differ significantly between the two Table 4.

Test for stability of coefficients over time (T2)

1977–86 vs. subsamples

Chi-squared statistic

Critical values at 1% and 0.1% levels

1946.37

99.1, 111.1


Managerial Discretion and Internal Alignment periods of time such that βpre = βpost The results of this test lend support to our rejection of Hypothetical H1. Moreover, they allow us to reject, in addition, hypotheticals H3 and H4, the Output and Quality Restriction hypotheticals. This leaves us with only the hypothetical cases that are compatible with a change in both the operational choice vector and administrative practices, which are the last three hypotheticals in Table 1. Tests for relative efficiency (T3 , T4 , T5 , T6 ) The remaining tests involve counterfactual analysis in the spirit of the Blinder–Oaxaca technique (Blinder, 1973; Oaxaca, 1973). That is, they require a comparison of the estimated average costs, from either the regulatory period (pre) or the deregulated period (post), with a set of counterfactual estimates. T3, for example, is the test of whether C(βpost , Xpre ) < C(βpre , Xpre ), where C represents average cost. It requires us to compare a counterfactual estimate of average costs, in which the old X vector from the regulated period is paired with the new administrative practices from the deregulated period, with an actual estimate of average costs during the regulatory period, based on the practices and X vector from that same time period. T6 , in contrast, compares the same counterfactual average cost estimate with the actual estimated average costs for the post deregulation period. To make the cost comparisons required by these four tests, we need to make two sets of calculations. First, we calculate predicted total costs, on the basis of our regression results, for each of four hypothetical choice possibilities discussed earlier. Recall that these choices, which include two counterfactual possibilities, are {(βpre , Xpre ), (βpost , Xpre ), (βpre , Xpost ), (βpost , Xpost )} Table 5 reports the predicted total costs for each of these cases. Each of the cells in this table is constructed as follows. The mean value of X from either the pre or post period, as indicated by the column headings, is multiplied by the estimated coefficients from the pre or post period, as indicated by the row headings. Thus the upper right Copyright  2007 John Wiley & Sons, Ltd.

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cell is a counterfactual average cost. It is what total costs would be if managers, on average, chose to produce the post reform X vector with the regulatory era administrative practices indicated by βpre . That is to say, this cell holds the value for TC(βpre , Xpost ). The second set of calculations involves converting the total costs, given in Table 5, to average costs, in order to control for the differing output levels across the two time periods. For a meaningful comparison, we also need to control for input prices, by establishing a base time period from which to draw the factor prices. This is because input prices are not a choice variable for managers; they changed exogenously over the two periods. We chose the deregulatory prices for the base in these tests.14 Finally, in order to make the cost comparisons among the four choice possibilities more transparent, we normalized the average costs, such that the average costs for the choice represented by (βpost , Xpost ) is equal to 1. With this as a base, one can quickly compare the costs of each of the other choices with this base case, in percentage terms. The results of our average cost calculations are presented in Table 6. The numbers in parentheses are the standard errors of the forecasts after normalizing. With this table, we can determine whether the data supports any of the four remaining tests. For T3, we need to examine the cells in the first column to see if C(βpost , Xpre ) < Table 5. Predicted total costs Admin practices/ operational variables βpre βpost

Xpre

Xpost

274,843,205.72 315,191,146.70

424,027,322.60 381,394,667.23

Table 6. Predicted (normalized) average costs Admin practices/ operational variables βpre βpost

Xpre

Xpost

1.2868 (0.0027) 1.1118 (0.0071) 1.47572 (0.0017) 1 (0.0028)

14 The alternative choice of the regulatory period’s price vector results in only a trivial quantitative difference and no qualitative difference in the analysis. It is the qualitative results that are of interest in this study.


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C(βpre , Xpre ). Since C(βpost , Xpre ) is not less than C(βpre , Xpre ), and instead is nearly 19 percent greater (1.476–1.287 = 0.189), this test fails. By checking Table 1, we can see that the only hypothetical case that is consistent with this result is H7. This is the case that we called the ‘Internal Alignment’ Matters Hypothetical. To confirm that Hypothetical H7 is indeed the only one fully supported, we can run through the remaining cost comparisons. T4 is the test of whether C(βpost , Xpost ) < C(βpre , Xpost ). This average cost comparison can be made by examining the second column of Table 6. This indicates that C(βpost , Xpost ) is indeed less than C(βpre , Xpost ), since the average costs of (βpre , Xpost ) are than more than 11 percent higher. Clearly, this test is passed as well, lending additional support to Hypothetical H7. For T5 , we need to determine whether C(βpre , Xpost ) < C(βpre , Xpre ) by comparing the cells of the first row of Table 6. This is indeed the case, further supporting H7. (Observe that 1.287 exceeds 1.112 by 17.5 percentage points.) T6 is the test of whether C(βpost , Xpost ) < C(βpost , Xpre ). By examining the bottom row of Table 6, we see that this test is also passed, in final support of Hypothetical H7. (Note that the average costs of (βpost , Xpre ) are more than 47 percent higher than those associated with (βpost , Xpost )). This clinches the case, suggesting that the managerial response to regulation and deregulation in the U.S. airline industry is consistent, on average, with Hypothetical H7, which we termed ‘Internal Alignment’ Matters. The response is inconsistent with each of the other hypothetical cases.

DISCUSSION What do these results suggest? How are we to interpret them? First let us consider what has been ruled out. The fact that Hypotheticals H1, H3, and H4 were rejected suggests that airline regulation constrained not only the choice of routes, departures, and other operational variables represented by the X vector; it constrained, indirectly, the choice of administrative practices (represented by the betas) as well. That is to say, regulation constrained not only the tangible operational choice variables, but also the intangible choice variables, Copyright  2007 John Wiley & Sons, Ltd.

such as organizational processes. It indirectly constrained the way that airline managers, on average, chose to manage their firms. This in itself is interesting for managerial discretion theory. It suggests that even when activities have characteristics that are likely to frustrate attempts at direct control by external parties, they may still be subject to constraints imposed indirectly. That said, it is still likely that greater managerial discretion is afforded for such activities in comparison to other types, even when there are indirect constraints. In rejecting Hypothetical H5, we learned that managers did not adopt the new X and the new β in response to the lifting of regulatory restrictions because both were unambiguously more efficient. Nor is it the case that they switched to a new X and β because both βpost and Xpost were unambiguously more welfare enhancing (with a higher cost, but higher quality Xpost ). The rejection of H6 rules out this interpretation. Quality was not constrained under regulation. Instead we find support for a different type of scenario, in H7. Recall first that Hypothetical H7 is the one in which the following four conditions hold: C(βpost , Xpre ) > C(βpre , Xpre ) C(βpost , Xpost ) < C(βpre , Xpost ) C(βpre , Xpost ) < C(βpre , Xpre ) C(βpost , Xpost ) < C(βpost , Xpre ). Support for H7, then, suggests the following: from the bottom two inequalities, we see that managers, on average, chose the new X vector because it was unambiguously more efficient. It leads to lower costs than the old X vector under either set of administrative practices. This suggests that regulation restricted output in the presence of economies of scale and scope. It suggests also that regulation restricted the managerial choice of network structure in the presence of economies of network density. It may also suggest that regulatory distortions resulted in wasteful levels of quality in the form of costly meals and other amenities. Each of these interpretations is consistent with economic studies of how regulation and deregulation affected the U.S. airline industry. (See, for example, Bailey et al., 1985; Moore, 1986; Brueckner and Spiller, 1994, and Baltagi, Griffin, and Rich, 1995). At the same time, the top two inequalities tell a rather different, more managerial story. They Strat. Mgmt. J., 28: 1089–1112 (2007) DOI: 10.1002/smj

Managerial Discretion and Internal Alignment suggest that ‘internal alignment’ matters, in the following sense: for any given X vector, costs will be lowest if it is managed with the average set of administrative practices (β) from the same regulatory era. This can be seen most clearly by rewriting the topmost inequality so that the set of inequalities appears thus: C(βpre , Xpre ) < C(βpost , Xpre ) C(βpost , Xpost ) < C(βpre , Xpost ) This suggests that even though airline managers, on average, made adjustments in their administrative practices once they were freed from regulatory restraints, it was not simply because they found a superior way to operate—one that they would have chosen if the choice had been available to them earlier. While a new ‘industry recipe’ for these practices (Spender, 1989) clearly emerged in the new era, managers did not choose this recipe because it represented a new, universally superior, set of practices that stemmed from the ‘march of progress.’ Rather, they were adopting a new recipe (the new average administrative practices), to fit the new conditions of the deregulated environment and the altered X vector. Given the vast changes that were made to the X vector variables, in terms of operations, route structures, etc., there was a need for a new way of managing. Managers, on average, altered their administrative practices to meet this need. So, for example, the move to hub network structures, with many flights from feeder routes arriving at approximately the same time, called for new organizational processes designed to manage connecting flights and the quick turnaround of airlines. The results also suggest that, given the choice, managers, on average, would not have preferred to employ the new, more ‘modern’ administrative practices implied by βpost during the regulatory era. From an efficiency perspective, managing the pre-reform X vector variables with the pre-reform administrative practices is a superior choice. Managing Xpre with the updated set of organizational practices and processes described by βpost , ironically, would have raised costs by nearly 19 percent. In some respects, this is actually a rather startling result. It flies in the face of the usual expectations about the effects of regulation on managerial behavior. Regulation, for example, is known to increase bureaucratic costs (Russo, 1992), distort Copyright  2007 John Wiley & Sons, Ltd.

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input mix choices (Averch and Johnson, 1962), and blunt the stimulus to innovate (Capron, 1971). It is believed to limit the incentives of firms to operate efficiently (Peteraf, 1993b; Kole and Lehn, 1997). Our results suggest something quite different. We find that even though their choice of administrative practices was constrained in the regulatory era, managers developed an ‘industry recipe’ that was relatively efficient, given the constraints on their choices regarding route structure, etc. In other respects, our results are less surprising. Indeed, they accord with the long-standing contingency view that there is no one best way to manage. They provide evidence that fit can trump ‘best practice,’ even under presumably blunted incentives and constrained choice conditions. They show not only that fit matters, but how it matters in terms of measurable efficiency effects. These results add to the body of accumulated work on the importance of complementarities and internal fit (e.g., Whittington et al., 1999; Sigglekow, 2001, 2002). But more significantly, they support a resurgence of scholarly interest in fit, with an approach that addresses earlier criticisms that had tarnished the legitimacy of the contingency and configurational perspectives. In addition, they have implications regarding prospects for achieving dynamic fit (Miles and Snow, 1994; Zajac et al., 2000), given that our study spans a period encompassing significant environmental change. While our findings are not conclusive, they suggest that managers, as a group, are remarkably adaptive, even under the constrained conditions of governmental regulation. The evidence is consistent with managers exhibiting an adaptive capacity and a degree of resiliency, under long periods of constrained managerial choice, which has not been sufficiently recognized (Meyer et al., 1990). Our efficiency results are also consistent with the view that regulation may spur managers to improve the way that they operate (Porter and van der Linde, 1995; King and Lenox, 2002). Our focus, however, is not on efficiencies that arise due to regulatory constraints. Rather, it is on efficiencies that are attributable to the discretionary choices of managers operating within a partially constrained choice environment. Given that the direct constraints of regulation were on the operational choice variables, and not on the choice of administrative practices, perhaps this makes sense. Managerial choice with regard Strat. Mgmt. J., 28: 1089–1112 (2007) DOI: 10.1002/smj

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to the operational variables was severely circumscribed by the environment under regulation. Managerial choice with respect to administrative practices was only constrained indirectly, due to the need for the administrative practices to fit the directly constrained X vector variables. Subject to these limits, managers were free to choose a set of administrative practices that would permit them to operate as efficiently as they could. Our results suggest that they did so. Theoretically, this is an interesting result regarding managerial discretion in relation to mechanisms for adaptation. It suggests that when managers find that their discretion is limited in one arena, they adapt by exercising their discretion in a less constrained arena. In this case, since their control over operational variables was directly constrained under regulation, they achieved internal fit by making adjustments in their administrative practices, where they had greater discretion. Hambrick and Finkelstein (1987) suggested something similar with their portrayal of how managers with limited environmental discretion, but greater organizational discretion, might find ways to break out of the environmental limitations. This study provides empirical evidence that supports this conjecture and provides a specific new mechanism. In addition, it blunts the charge that contingency theory is overly deterministic, by suggesting that managers can find ways to exercise strategic choice in achieving fit, even under the most restrictive of environmental conditions.

LIMITATIONS AND FUTURE RESEARCH DIRECTIONS Limitations Like all empirical studies, this one has its limitations. Intriguingly, the two main limitations of this study actually strengthen its results and suggest opportunities for further study. The first is that this study of managerial choice is conducted at the industry level, by examining average costs over the two periods. At first blush, one might wonder how industry-level results can tell us anything about managerial choices at the firm level. Clearly, much more could be learned by taking a firm-level approach to our questions. A more thoughtful consideration, however, reveals the following. First, our estimations are based on firm-level data. Second, we make no Copyright  2007 John Wiley & Sons, Ltd.

claims regarding the behavior of any specific individual firms or managers. Rather, our results shed light on the choices of the average management team and resulting changes in administrative practice at the level of an ‘industry recipe’ (Spender, 1989). Since variation within averages tends to mask differences, a finding of significant differences in the average across periods is actually a very strong result. Moreover, this result is consistent with new results regarding the behavior of firms in industries with significant complementarities, such as the airline industry. According to Lenox, Rockart, and Lewin (2005), firms are relatively more homogeneous in industries where fit is important. This suggests that the average may well represent typical managerial behavior in the airline industry, even allowing for minor variations in choice. This may be particularly apt, since our study excludes new entrants and former intrastate carriers, such as Southwest, who are more likely to deviate from norms established under regulation. The second such limitation stems from the fact that the airline industry was deregulated in a gradual fashion (Bailey et al., 1985). As a result, our two regulatory regime periods are not pure cases of a regulated and a deregulated environment. There was not a clean break point at the end of 1981. As a consequence, managers may not have had time to fully adjust the fit between their administrative practices and the operational choice variables during either period that we examined. If this is so, then our cost estimates may be higher than the costs would have been if all adjustments in alignment had been made. The fact that it is hard to adjust fleet composition quickly strengthens this point.15 Recall, however, that our results indicated that internal alignment led to lower costs, regardless of which period we considered. Given that further alignment would likely had led to even lower costs, our results might have been even stronger had our time periods of study allowed for more compete alignment in each era. This limitation, then, rather than weakening our results, actually increases their strength. Similarly, a misspecification of the break point would have further diluted our results, by mixing a regulated period with 15 From another standpoint, their investments in sunk equipment gave carriers an incentive to adapt more quickly (Walker, Madsen, and Carini, 2002).


Managerial Discretion and Internal Alignment a deregulated period. Thus a better specification would only increase the impact of our results. Lastly, other changes in the environment not fully captured by our model, such as changes in demand or technology, would tend to mask the results. The mere fact that we have results under these conditions is a testament to their strength. Future directions for research As an industry-level study, ours is a first-cut attempt to address the questions driving this research. This suggests a natural follow-on. The rich data available for the airline industry may make it possible to conduct a study similar to our own, but with more of a firm-level focus. Future research could explore the question of whether organizational responses to institutional change, in terms of how individual firms manage their internal alignment, have differential efficiency consequences (Walker, Madsen, and Carini, 2002). Doing so would address the question of whether the management of internal alignment can lead to competitive advantage. Moreover, it would enable one to quantify the amount of the value advantage, in terms of cost savings (Peteraf and Barney, 2003). Our findings suggest several other fruitful avenues for future research. One possibility is to conduct a similar study to see if the differential responses to regulation and change vary by strategic type, such as Miles and Snow’s (1978) categories. Another avenue stems from our results regarding the role of administrative practices in attaining internal alignment. As complex, intangible phenomena, such practices are difficult to study empirically. Our innovative method of capturing average practices (or ‘industry recipes’) could spur further research on different types of administrative practices and ‘industry recipes’ in other industries and settings. Finally, this study suggests some opportunities for new research on the topic of managerial discretion (Hambrick and Finkelstein, 1987). Our portrayal of managers as employing the differential in degrees of managerial discretion with respect to different realms of choice to achieve their objectives suggests a phenomenon worthy of investigation at a more micro level. This type of behavior might also be characterized in terms of how managers develop and exercise a dynamic capability for maintaining fit over changing conditions Copyright  2007 John Wiley & Sons, Ltd.

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(Helfat et al., 2007). Lastly, there is an opportunity to develop more fully the theory regarding the relationship between activity or task characteristics and managerial discretion.

ACKNOWLEDGEMENTS We owe a debt of gratitude to the following people for their comments, help, and sage advice: Mary Benner, David Besanko, Theresa Cho, VG Govindarajan, Syd Finkelstein, Don Hambrick, Connie Helfat, Andy King, Michael Lubatkin, Tammy Madsen, Joe Mahoney, Cathy Maritan, Alan Meyer, Will Mitchell, Ken Smith, Jim Walsh, and Paul Wolfson. Thanks also to Robin Sickles, who generously provided us with his factor price data.

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APPENDIX Table 7.

Variable construction

Variable name

Definition

Factor prices: (W) wages (F) fuel (K) capital (M) materials

The factor prices are Tornqvist indices of the component prices. For example, the price of labor is an index of the six component categories (pilots and copilots, flight attendants, mechanics, passenger handling, cargo handling, and other (primarily management)). These prices are normalized to 1 in the third quarter of 1970. These data are identical to those of Sickles et al. (1986), who provided the data and describe more fully the construction of each index in their paper Output is again an index. The index is constructed using ton-miles of five categories (first class, coach, first class charter, coach charter, and cargo). The weights are obtained from the percentage of revenues from each category The number of airports served by a carrier during the quarter The percentage of available seat-miles that are sold by the carrier during the quarter The average distance flown per departure The number of revenue departures for the carrier from all airports during the quarter The number of routes served by the carrier during the quarter The population variable (employed in the first stage) is the average population at the destinations served by the carrier during the quarter Standard figures obtained from the Federal Reserve Board Publications

Output (Y) Points Served (P) Load Factor (L) Stage Length (S) Departures (D) Routes Served (R) Population (Pop) GNP and CPI

Table 8.

Airline carriers in the sample

Carriers in the sample

AA—American AL—Allegheny (US Air) BN—Braniff CO—Continental DL—Delta EA—Eastern FL—Frontier NC—North Central OZ—Ozark PI—Piedmont RC—Republic SO—Southern TI—Texas International TW—TWA UA—United WA—Western


Table 9. Means of variables by subsample

Time period of data availability (years and quarters) 1977I–1986IV 1977I–1986IV 1977I–1982I 1977I–1986IV 1977I–1986IV 1977I–1986I 1977I–1986III 1977I–1979II 1977I–1986II 1977I–1986IV 1979III–1986II 1977I–1979II 1977I–1982II 1977I–1986IV 1977I–1986IV 1977I–1986IV

Output (Y) Points served (P) Load factor (L) Stage length (S) Departures (D) Routes (R) lY(log Y) lP lL lS lD lR lW lF lK lM

1977–1981

1982–1986

223,324,130 99.92 0.5066 499.97 69,363.88 297.41 18.6146 4.5480 −0.6850 6.0746 10.9951 5.5721 0.9193 1.6418 0.9803 0.6563

317,157,590 101.66 0.5197 600.67 78,989.88 333.32 19.1944 4.5849 −0.6578 6.3400 11.1306 5.6923 1.2560 1.9636 1.3756 1.0671


1112


Table 10. Cost function estimates 1977–81 Intercept lY lP lL lS lD lR lW lF lK lM AL BN CO DL EA FL NC OZ PI RC SO TI TW UA WA y77 y78 y80 y81 y82 y83 y85 y86 q2 q3 q4 lWAL lWBN lWCO lWDL lWEA lWFL lWNC lWOZ lWPI lWRC lWSO lWTI lWTW lWUA lWWA lFAL lFBN lFCO

19.424∗∗ 0.210∗∗ 0.032∗∗ −0.072 0.448∗∗ −0.001 0.063∗∗ 0.416∗∗ 0.234∗∗ 0.145∗∗ 0.205∗∗ −0.392∗∗ −0.333∗∗ −0.326∗∗ −0.083∗∗ −0.030† −0.699∗∗ −0.636∗∗ −0.669∗∗ −0.661∗∗ −0.447∗∗ −0.795∗∗ −0.858∗∗ 0.020∗ 0.029∗∗ −0.397∗∗ −0.012∗ −0.011∗ 0.029∗∗ 0.041∗∗ 0 0 0 0 0.005 −0.001 0.012∗∗ 0.001 −0.100∗∗ −0.048∗∗ 0.007 −0.004 0.022∗ 0.049∗∗ 0.003 −0.018† −0.059∗∗ 0.030∗ 0.004 0.001 0.031∗∗ −0.017† 0.005 0.062∗∗ 0.030∗

(0.017) (0.039) (0.011) (0.048) (0.049) (0.027) (0.014) (0.007) (0.009) (0.003) (0.003) (0.029) (0.013) (0.016) (0.016) (0.015) (0.030) (0.036) (0.032) (0.033) (0.035) (0.034) (0.030) (0.011) (0.010) (0.016) (0.006) (0.005) (0.005) (0.007)

(0.005) (0.005) (0.004) (0.010) (0.010) (0.009) (0.010) (0.010) (0.010) (0.013) (0.010) (0.010) (0.012) (0.013) (0.010) (0.010) (0.009) (0.010) (0.012) (0.012) (0.018)


Table 10. 1982–86 19.354∗∗ 0.414∗∗ 0.075∗∗ −0.201∗∗ 0.180∗ 0.082 0.062∗∗ 0.383∗∗ 0.214∗∗ 0.144∗∗ 0.259∗∗ −0.259∗∗ 0 −0.337∗∗ −0.010 0.006 −0.596∗∗ 0 −0.615∗∗ −0.342∗∗ −0.336∗∗ 0 0 −0.034 −0.036 −0.377∗∗ 0 0 0 0 0.032∗∗ 0.012∗ 0.023∗∗ 0.063∗∗ −0.005 −0.007 0.010† 0.034∗∗ 0 −0.103∗∗ 0.043∗∗ 0.001 −0.01763∗ 0 0.002 −0.023∗∗ −0.031∗∗ 0 0 0.006 0.014† −0.021∗∗ 0.001 0 0.054∗∗

(0.034) (0.057) (0.023) (0.067) (0.090) (0.053) (0.018) (0.005) (0.008) (0.004) (0.006) (0.040) (0.019) (0.023) (0.016) (0.044) (0.046) (0.044) (0.034) (0.016) (0.010) (0.027)

(0.007) (0.006) (0.006) (0.007) (0.006) (0.007) (0.005) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008)

(Continued ) 1977–81

lFDL lFEA lFFL lFNC lFOZ lFPI lFRC lFSO lFTI lFTW lFUA lFWA lKAL lKBN lKCO lKDL lKEA lKFL lKNC lKOZ lKPI lKRC lKSO lKTI lKTW lKUA lKWA lMAL lMBN lMCO lMDL lMEA lMFL lMNC lMOZ lMPI lMRC lMSO lMTI lMTW lMUA lMWA

0.020 −0.0002 −0.016 −0.057∗∗ −0.028∗ 0.015 0.081∗∗ −0.016 0.005 0.008 −0.022† 0.005 −0.010∗ 0.009† 0.006 0.004 0.014∗∗ −0.021∗∗ 0.025∗∗ 0.005 −0.014∗∗ 0.003 −0.003 −0.019∗∗ −0.008† 0.011∗ 0.004 0.004 0.030∗∗ 0.012∗∗ −0.031∗∗ −0.010∗ 0.015∗∗ −0.017∗∗ 0.020∗∗ 0.017∗∗ −0.025∗∗ −0.011∗ 0.009∗ −0.001 −0.021∗∗ 0.008∗

(0.012) (0.012) (0.012) (0.017) (0.012) (0.013) (0.016) (0.017) (0.012) (0.012) (0.012) (0.012) (0.005) (0.005) (0.005) (0.005) (0.005) (0.005) (0.006) (0.005) (0.005) (0.006) (0.006) (0.005) (0.005) (0.005) (0.005) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.005) (0.004) (0.004) (0.005) (0.005) (0.004) (0.004) (0.004) (0.004)

1982–86 0.008 0.0001 0.012 0 0.004 −0.02241† 0.016 0 0 0.023 −0.001 0.034∗∗ −0.009† 0 0.025∗∗ 0.003 0.028∗∗ 0.018∗∗ 0 0.000004 0.031∗∗ 0.028∗∗ 0 0 −0.002 0.013∗ −0.003 −0.026∗∗ 0 0.024∗∗ −0.053∗∗ −0.029∗∗ −0.012 0 −0.007 0.014 −0.021∗ 0 0 −0.026∗∗ −0.027∗∗ −0.009

(0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.011) (0.012) (0.005) (0.005) (0.005) (0.005) (0.006) (0.006) (0.005) (0.006) (0.005) (0.005) (0.005) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009)

Coefficients are significant at the † 10% level, at the ∗ 5% level and at the ∗∗ 1% level. N = 300 for 1977–81; N = 235 for 1982–86.

(0.008) (0.008) (0.008) (0.012) (0.012)


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