Capital Structure and Merger Waves in the Telecommunications Sector

Sumit K. Majumdar University of Texas at Dallas

majumdar@utdallas.edu

Rabih Moussawi Villanova University

Rabih.Moussawi@Villanova.edu

Ulku Yaylacicegi University of North Carolina at Wilmington

yaylacicegiu@uncw.edu

April 7, 2016

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Capital Structure and Merger Waves in the Telecommunications Sector

Abstract

This historical analysis evaluates the impact of corporate debt in influencing mergers of local exchange companies in the United States telecommunications industry between 1988 and 2001. Firms’ financial structures significantly affect behavior and performance; yet no evidence has shown how firms’ financial structures influence merger activities. The impact of corporate debt levels on the various mergers that took place in the merger wave in the sector is significantly negative for the first set of mergers carried out and significantly negative but with smaller impact for the second mergers. The results support the idea that firms with high debt levels can be monitored carefully, precluding engagement in potentially-risky mergers in a merger wave so as to not engender negative financial outcomes. Key words: corporate debt; industry consolidation; leverage; mergers; merger waves; local exchange carriers; retrospective historical evaluation; United States telecommunications industry JEL Codes: G34; L96

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1. Introduction 1.1 Introduction: In policy analysis, why firms have behaved strategically in particular ways

has emerged as a concern. Based on recent research on capital structure (Majumdar, 2016), we retrospectively evaluate the relationship between firms’ leverage and mergers of local exchange firms in the United States telecommunications industry. Past mergers that have been approved are historical natural experiments, so that pre- and post-merger assessments can be carried out, permitting an evaluation of strategy and competition policy decisions. If, after evaluation of an allowed merger, subsequent outcomes, measured via analysis of firms’ behavior, are below expectations, then results indicate that competition agencies may not have got economics correct (Carlton, 2009).

The use of contemporary historical panel data, from 1988 to 2001, of almost all the local

exchange carriers in the United States, containing pre and post-merger data for firms, for two different periods, enables tracking of dynamic merger history for each carrier (Majumdar, et al 2010; 2012), given the presence of sector merger waves. Table 1, extracted from Majumdar, et al (2012), lists ownership status and merger activity of local exchange telecommunications companies during that period, and which are used in our study.

Two issues can be investigated by retrospective historical assessments. First, whether a

specific merger decision was right or wrong can be assessed based on individual case studies. Second, the evaluation of a stream of mergers, over time, permits an understanding of the outcomes of merger policy. The second type of analysis, based on the empirical evaluation of historical time-series data, and assessment of a set of historical cases, permits the taking into account of detailed firm-specific, industry-specific and institution-specific factors that will have been in currency at various points in time when the merger decisions were made.1

By engaging in detailed retrospective merger analysis, it is possible to highlight under what

conditions merger outcomes are positive or negative. If these historical conditions can be established, then conclusions can be generalized to develop heuristics to provide insights to policy agencies to evaluate a merger based on expected outcomes, not just in terms of its price impact but also its impact on many other important economic parameters.

1.2 General: The link between the types of financing and firm strategic behavior is crucial,2

and the relationship between a firm’s capital structure and strategy is a key issue,3 with finance and

1 Retrospective historical assessments of consummated mergers have been reviewed by

Hunter, et al (2008), Kwoka (2013) and Pautler (2003). These historical assessments generate performance metrics useful knowledge for policy development (Kovacik, 2006). Retrospective merger appraisals permit the accumulation of knowledge about various behavioral, structural and competitive conditions in different industries.

2 To take one example, corporate finance and governance issues matter in influencing innovativeness and how financial concerns impact on firms’ technology performance is important (Bencivenga and Smith, 1991; Cantor, 1990; Hall, 2002; Hao and Jaffe, 1993; King and Levine, 1993; Lang, et al 1996; Majumdar, 2011, 2016; Mayer, 1990; O’Brien, 2003).

3 See Brown, et al (2009), Campello (2003), Kochhar and Hitt (1996) and Kovenock and

Phillips (1997).

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strategic behavior questions being closely interrelated.4 Debt is an important type of funds source (Corbett and Jenkinson, 1997), and capital structure is measured as the ratio of debt to total capital (Zingales, 1998).5 The findings in the leverage and strategic behavior literature indicate that increased debt held by firms softens aggressive strategic behavior.6 Financial structure matters strategically (Myers, 1977).7 Because of agency, signaling and information asymmetry issues,8 there is considerable heterogeneity between firms in financing decisions.9 The issues of whether to have debt, how much of it, and what happens as a result of borrowing, are important.10

Simultaneously, a large literature also discusses why firms merge, and how these mergers are

financed.11 Some reasons posited for mergers are to gain efficiencies (Stillman, 1983; Farrell and Shapiro, 1990; Trautwein, 1990), obtain reputation advantages (Dranove and Shanley, 1995), reduce risk (Amihud and Lev, 1981), diversify (Levy and Sarnat, 1970), enjoy spillovers (Tremblay and Tremblay, 1988), reconfigure resources (Steiner, 1975), and redeploy capabilities (Capron and Mitchell, 1998). Negative motives, such as hubris (Roll, 1986), also drive mergers. Mergers determine the extent of competition in a market, and create substantial variations in industry structure (Steiner, 1975) via the consolidations of firms. The historical evidence shows that concentration-enhancing structural variations permit firms to gain market power (Stigler, 1964; Scherer, 1988), to gain social power (Pfeffer and Salancik, 1978), to permit a quiet life (Hicks, 1935), to grow the firm (Mueller, 1969), and to permit rent-extraction (Marris, 1964).

4 See Balakrishnan and Fox (1993), Barton and Gordon (1987), Bettis (1983), Bromiley

(1990) and Oviatt (1988).

5 The defining feature of two components in a firm’s capital structure, debt and equity, are the differential rights of providers (La Porta, et al 1998), and relative levels debt can impact firms’ behavior.

6 Titman (1984) evaluates liquidation decisions; Lambrecht (2001) evaluates entry and exit

decisions; Phillips (1995) evaluates output and pricing decisions; Chevalier (1995), Kovenock and Phillips (1997) and Khanna and Tice (2000) evaluate strategic responses of firms, while Zingales (1998), and Campello (2003) highlight the links between capital structure, firm performance and survival; David, et al (2008) evaluate the impact of debt on long-term oriented R&D activities; Anderson and Makhija (1999) evaluate the relationship between debt and corporate growth opportunities in Japan, and Chirinko and Elston (2006) carry out similar analysis for Germany.

7 While an early piece (Modigliani and Miller, 1958) had showed that debt to equity ratios did not matter, a literature has emerged that shows these postulates to be irrelevant (Parsons and Titman, 2008).

8 See Jensen and Meckling (1976), Ross (1977) and Myers and Majluf (1984). 9 See Houston and James (1996), Krishnaswami, et al (1999), Cantillo and Wright (2000) and

Denis and Mihov (2003).

10 See Bettis (1983), Bromiley (1990), Grinblatt and Titman (2001), Grundy (1996) and Kochhar and Hitt (1996).

11 See Bessler, et al (2011) for a survey of the literature dealing with the financing of mergers

and acquisitions.

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There is a knowledge gap. Little evidence exists on how the leverage of firms influences

mergers. One speculation is that because of a limited liability effect, merging firms with high leverage are assumed to compete aggressively (Socorro, 2007). There is also a theoretical exploration of the relationship between leverage and merger timing (Morellec and Zhdanov, 2008), but the evidence as to specifically how leverage affects firms’ merger decisions is non-existent. Not only do mergers transactions by value account for a large proportion of the global gross domestic product, given that in the United States between 15,000 and 20,000 mergers occurred in the 2000s, but the role of debt in influencing economic activities such as mergers has historically been profound.12

The study empirically evaluates the relationship between firms’ leverage and mergers. There

is no clear-cut theory of the impact of leverage on mergers. Thus, hypotheses are conditional on the institutional arrangements to be observed. The historical mergers assessed are those occurring, in a wave, among the population of local exchange firms in the United States telecommunications industry in the last two decades. The sector provides a natural experiment setting for retrospectively evaluating, based on merger wave data, how leverage had impacted firms’ merger propensities. In infrastructure-based sectors, changes in firms’ strategic behavior have raised concerns that high debt levels could be leading to firms’ financial distress (Bortolotti, et al 2001). 2. Theory, Institutional Details and Hypothesis

2.1 Theory of Debt and Mergers: There are no clear propositions on the impact of

leverage on mergers. A strand of the literature, on leverage and aggressiveness in strategic behavior, is based on the long purse argument (Telser, 1966), suggesting that having ready access to capital allows a firm to sustain losses until it succeeds in eliminating competition. A firm with low leverage can raise more debt. This will enable it to engage in aggressive behavior leading to success. A firm with higher leverage can be vulnerable to competitors’ aggressive behavior. The presence of high debt levels in firms provides opportunities for rivals to weaken them financially by aggressive strategies. Highly-leveraged firms cannot respond similarly, since financial outcomes can be negative. Hence, mergers can be risky actions.

Firms with high debt levels are monitored. They may require periodic re-financing, and

negative financial parameters can lead to re-financing denial. Hence, firms with high leverage will be passive so as not to engender negative financial outcomes (Poitevin, 1989), and high leverage levels is associated with a less aggressive strategic stance (Riordan, 1985; Showalter, 1995). Concomitantly, firms with lower leverage can signal the potential to compete aggressively because of their ability to later tap into additional resources, and be less prone to creditors’ pressures (Poitevin, 1989).

Converse arguments are proposed. Lower leverage can be associated with less aggressive

behavior (Brander and Lewis, 1986). Managers in firms with higher leverage will promote aggressive strategies, because equity holders with limited liability will lose to the extent of their capital. They

12 The financial sectors of countries are based on debt or equity markets (Zysman, 1983). In

early industrialization, investment financing had come from retained earnings (Hall, 1994) or equity markets (Lazonick and O’Sullivan, 1997). With economic growth, debt financing became important, as in the United States and Western Europe. In Japan (Fruin, 1992) and South Korea (Amsden, 1989), banks have been important, and the growth of bank financing is a model suggested for other nations (Aoki and Patrick, 1994).

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can afford to sustain uncertain outcomes from risky strategies. Mergers can be risky strategies, in spite of the benefits espoused, given the evidence that mergers do not work (Andrade, et al 2001; Datta, et al 1992).

Managers will be aware of merger vicissitudes. Thus, firms may engage in risky mergers,

while espousing positive efficiency-enhancing, resource-pooling and technology-spreading gains, anticipating low probability of outcomes. In such cases, higher leverage permits firms to engage in a risky strategy knowing that likely negative outcomes will be borne to a limited extent by firms’ owners as negative financial consequences are passed on to debt holders.

2.2 Debt and Mergers Waves: Industry shocks, such as changes in technology or in the

regulatory environment, can lead to sequential mergers (Gort, 1969; Faulli-Oller, 2000; Jovanovic and Rousseau, 2002), leading to merger waves. Since then studies have documented the existence of merger waves (Andrade, et al 2001), finding these occur when over-valuations have occurred (Rhodes-Kropf and Viswanathan, 2004) or when several industry shocks are clustered (Harford, 2005). Given rapid industry changes, sequential mergers can rapidly increase market power and permit firms to obtain performance-enhancing economies of scale (Stillman, 1983; Farrell and Shapiro, 1990). Nevertheless, the participation of firms in merger waves compounds the positive and negative expectations that providers of debt associate with mergers.

2.3 Institutional Change and Mergers: Because firms’ decisions are driven by context

changes (Aldrich and Ruef, 2006; Nelson and Winter, 1982), environmental factors play a key role in influencing firms (Dill, 1958; Duncan, 1972). As environments become dynamic, firms seek advantage by continuous responses to changes (D’Aveni, 1994; Eisenhardt and Martin, 2000; Teece and Pisano, 1994), including enhancing resources for competitive strength (Kumar, 2006). Firms engage in scanning a variety of environments (Nelson and Winter, 1982), in assessing legal and institutional conditions (North, 1991), and in sensing opportunity-seizing situations (Teece, 2007).

Mergers can be motivated by changes occurring in fast-transforming environments. Triggers

for resource re-configuration are contingent on environment changes (Ambrosini, et al 2009). Thus, engaging in mergers is a capability enhancement that firms undertake (Teece, et al 1997), as mergers lead to reconfiguration of resources (Capron and Mitchell, 1998). Mergers allow firms to restructure business activities (Steiner, 1975). As part of a process in which firms with excess resources buy less well-endowed firms, mergers permit usage of combined resources to improve merged-firm capabilities (Pleatsikas and Teece, 2001). Reconfigurations economies include those from rationalization, stemming from disposal of duplicate assets, and restructuring arising from the recombination of capabilities (Brodley, 1987). These activities release resources for other activities.

2.4 Historical Details of Telecommunications Merger Waves: The detailed historical

flow of merger events, and the merger wave in the sector, is described next, and summarized in table 1. In 1984, with the break-up of the old AT&T, the sector consisted of 7 RHCs, which between them owned 22 stand-alone BOCs. There were 5 other groupings: Central Telephone, Continental Telephone, GTE, Southern New England Telephone and United Telephone, and two large independent companies: Cincinnati Bell and Rochester Telephone. These groupings owned several ILECs. See Appendix 1 for full institutional details.

A first set of mergers occurred between the 1984 divestiture and the introduction of

legislation in 1996. We have classified these as pre-1996 mergers. In the early 1990s, several RHCs amalgamated separate stand-alone ILECs. In 1991, the operations of Mountain States Telephone

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and Telegraph Company, Northwestern Bell Telephone Company and Pacific Northwest Bell Telephone Company were combined to form US West Communications. In 1992, the amalgamation of South Central Bell Telephone Company and Southern Bell Telephone and Telegraph Company operations, as Bell South, took place.

Simultaneously, several non-RHC groupings, with less-than adequate resources, or that were

territorially disparate, were acquired by other groupings. Thus, Continental merged its companies, such as Contel of California, Contel of New York, Contel of Vriginia and Contel of Texas with GTE. These events occurred in 1990. Thereafter, the operating companies belonging to United were acquired by Sprint in 1991. Thereafter, the operating companies of Central Telephone Company were acquired by Sprint in 1992.

A second wave of mergers occurred after the 1996 legislation. These extremely-large mergers

were significant attention-receiving events in the industry (Hazlett, 2000). We have classified these as post-1996 mergers. The key mergers, by deal size, were the merger of SBC and Pacific Telesis, in April 1996, between two RHCs; the merger of Bell Atlantic and NYNEX, in April 1996, between two RHCs; the merger of WorldCom and MCI, in October 1997, between two IXCs; the merger of AT&T and TCI, in May 1998, between an IXC and a cable operator; and the merger of SBC and Ameritech, in June 1998, between two RHCs.

The 1996 legislation, having opened the local exchange market to entry by CLECs,

motivated performance- and growth-enhancing mergers among the unattached RHCs. For example, Pacific Telesis, under financial strain in California, was merged with Southwestern Bell Corporation (SBC). To expand it national scope of operations, SBC acquired Southern New England Telephone (SNET) in 1998. In addition, inter-modal competitive threats from the cable sector emerged. AT&T, the long-distance company, purchased the cable companies, TCI, Media One and Lenfest by 1999. It also purchased the downtown Boston assets of Cable Vision, all for over $100 billion. The AT&T cable business could provide major competition to the local exchange carriers.

Several RHCs acquired other groupings. Ameritech was acquired by SBC, to obtain financial

scale for a major presence in all United States local markets. In 2000, GTE was acquired by Bell Atlantic. Bell Atlantic had also earlier acquired NYNEX, an RHC in its own right. The conglomerate Bell Atlantic re-named itself Verizon. The motive was to bring together complementary assets and strengths, create scale and scope economies, permit innovations and accelerate delivery of advanced services. Additionally, a motive was to tackle competition from cable giants such as AT&T.

Several ILECs went through two mergers during the wave. The Continental ILECs went

through two mergers. First, they were acquired by GTE. Then, GTE became part of Bell Atlantic. Similarly, the GTE and NYNEX local exchange companies went through two mergers. The first was when they were acquired by Bell Atlantic. The second was when Bell Atlantic acquired Puerto Rico Telephone Company and then re-consolidated and re-named the whole group, consisting of erstwhile Bell Atlantic, Contel, GTE, NYNEX and Puerto Rico Telephone Company, as Verizon. Pacific Telesis was first absorbed into SBC. Then, Ameritech was absorbed into SBC and the entire SBC structure recast.

2.5 Hypothesis: Given theory, historical and institutional conditions, we articulate our

expectations. The institutional environments have been different before and after 1996 in the telecommunications industry. We study the links between leverage and mergers. Before 1996, the local exchange sector consisted of regulated monopolies, where each firm had a pre-demarcated

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market with no competition possible due to institutional entry barriers. We have noted that the impact of the 1996 legislation has been vital in inducing entry by IXCs, CLECs and CSOs in incumbents’ markets. The pre- and post-legislation historical environments have each been profoundly different, affecting firms’ strategies.

Institutional entry barriers have been eliminated after 1996. Entry in firms’ territories has

been extensive, and considerable uncertainty introduced in incumbent firms’ markets. In such circumstances, after due scanning and searching, firms might engage in mergers to enhance resources for gaining additional competitive strength and to seize new opportunities. Yet, in spite of capability-augmenting possibilities, mergers become an inherently risky strategy because of the de novo introduction of competition in a historically-regulated environment. Nevertheless, firms may engage in risky mergers to ward off later competitiveness problems or enjoy what they believe are numerous new market opportunities.

Though positive efficiency-enhancing, resource-pooling and technology-spreading gains are

theoretically feasible, firms may anticipate a lower probability of these outcomes in a competitive and uncertain environment, relative to a placid and regulated milieu experienced earlier. In such cases, in anticipations of negative outcomes, higher leverage can constrain firms from engaging in a risky strategy.

A greater likelihood of negative financial outcomes can limit funds available for meeting

interest costs and capital repayments. Thus, the value of debt-holders’ security can be compromised. A flow of funds stoppage possibility, in an infrastructure sector, has many implications. Given debt is an important source of funds, if risky merger strategies create negative financial outcomes, with interest payments delayed and debts rolled over, because repayments are infeasible, these contingencies can preclude lenders from further lending.

The management of merged firms often turns out to be more difficult than anticipated,

leading to customer desertion and revenue decline. As firms merge, there is customer attrition, as old customers may no longer like receiving service from the new entity. In addition, the streamlining of administrative processes is expected, as mergers carry a message that things will change, but such administrative changes are rare. There are possibilities of mixed messages coming from two or more sales teams and coordination issues multiply. Unforeseen complexities arise in the post-merger integration of several firms, taking management attention away from business operational, leading to sales declines.

Other possibilities for negative fiscal consequences in merged firms can be due to

differences in culture. While such differences create opportunities for learning, beneficial effects occur if the differences are not great. If inter-firm differences are large, a substantial amount of management time goes in reconciling differences rather than in growing the businesses. These phenomena are compounded in case of sequential mergers that occur when firms participate in a merger wave. We posit that: we will observe a negative relationship between leverage extent and mergers, and the hypothesis evaluated is that of a negative relationship between the extent of debt and the occurrence of telecommunications sector mergers. 3. Analysis

3.1 Data: We analyze the impact of debt on mergers using a panel data set. Data are

available for each firm for the full period during which it was under prior and new ownership and

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have been used several times now for extensive sector analyses (Majumdar, et al 2010; 2012). A balanced panel from the Statistics of Communications Common Carriers (SCCC) are used for the period 1988 to 2001. Data are compiled for the principal local operating companies. These companies account for ninety nine percent of the telephone lines in the United States.

3.2 Merger Variables: A merger dummy variable denotes the occurrence of an event,

following which outcomes are evaluated. These merger events correlate with a list of mergers maintained at www.cybertelecom.org. The design of dummy variables to control for merger impact is based on prior research (Gugler and Yurtoglu, 2004; Majumdar, et al 2010; 2012).

The way the firms keep financial records, based on accounting and regulatory requirements,

each firm retains its accounting identity set up decades ago. Even if a firm has been taken over, by merger, this accounting identity remains and the data reported for the period 1988 to 2001 are based on these accounts-reporting identities. For every firm after merger, its behavior relative to itself in the past, when it had not merged, or relative to others in the same period which have been not taken over, or taken over, can be evaluated (Majumdar, et al 2010; 2012).

Table 2 variables used in our study and their definitions. The data property permits a pre-

and post-merger evaluation of outcomes for the same set of firms over a long time period, a type of analysis in demand (Carlton, 2009). We evaluate mergers to have occurred during the telecommunications merger wave, for which we have stated the hypothesis. A merger dummy variable for two merger and acquisition events denotes an event occurrence. The impact of leverage on two merger events, denoted as First Merger and Second Merger, which many companies have undergone, are evaluated. Table 3 provides descriptive statistics of variables used in the analysis. 4. Estimation

4.1 Treatment Effects Modeling: A merger strategy decision is an endogenous process,

but the taking-on of debt is also an endogenous strategic choice firms typically make. A firm’s debt level is not exogenously given (Parsons and Titman, 2008). The literature (McKay and Phillips, 2005; Myers, 1977; Titman and Wessels, 1988) has extensively dealt with the endogeneity of debt in firms, and why firms borrow. The analysis of historical micro-econometric causality helps ascertain factors influencing higher debt in firms, given that relative leverage can influence mergers.

In the historical micro-econometric causality analysis of debt and mergers, the debt

endogeneity concern (Parsons and Titman, 2008) is tackled using the treatment effects approach, in which a dummy explanatory variable denotes the existence, or otherwise, of the endogenous phenomenon the impact of which is evaluated on an outcome variable (Abbring and Heckman, 2008; Angrist and Krueger, 2001; Dehejia and Wahba, 1999; Heckman, 2005; Heckman and Vytlacil, 2005).

Firms that, as a matter of choice, have greater than average borrowing, measured as above

median borrowings subject themselves to a treatment. The treatment occurrence is given by the transition from below-median to above-median borrowing. Its outcome is evaluated in terms of resulting merger activity. A transition from below-median to above-median borrowing will have been a treatment experienced by firms. Such a transition involves changes in behavior influencing merger activity.

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The choice of whether to engage in a treatment is endogenous involving a selection bias, since not all firms will have engaged in such a transition but just a selected set of firms. In this analysis, the treatment effects approach (Heckman, 1976; 1979) is applied in assessing how choices with respect to greater leverage influences firms’ subsequent mergers, and a treatment effect is the average causal effect of a variable on an outcome, such as mergers.

A treatment effects model considers the median borrowing variable as a covariate

influencing mergers, after median borrowing has been modeled as a dummy endogenous variable influenced by other exogenous variables. Selection bias arises because treated firms differ from non-treated firms for reasons other than the treatment status, per-se. The process of incurring above-median borrowings is conditioned by several factors, as self-selection into treatment is at play when financial programs like raising debt, are decided on by firms.

Treatment effect models (Heckman, et al 1998; Hirano, et al 2003; Rubin, 1974) permit

natural experiments to be assessed (Angrist, 1998), where a response function, identifying strategic behavior, in this case of firms merging, embodies the effect of interest after the onset of an internal decision or institutional policy (White, 2011). Natural experiments are identifiable discrete shifts in within-firm or outside-firm environments such that there is a significant change in behavior (Angrist and Krueger, 2001). The treatment effects model is described in Cameron and Trivedi (2005), Guo and Fraser (2010) and Wooldridge (2010).

4.2 Variables in the Outcome Equation: We use the First Merger and Second Merger

variables as separate dependent variables in the primary outcome equations. In a treatment effects model, the treatment variable is a 1, 0 variable. We create the primary explanatory variable in the primary outcome equations as a measure of each local exchange company’s leverage (Leverage), which is coded as a binary indicator that takes the value of 1 which denotes if a firm has above median debt, relative to other firms, and it is coded as 0 denoting if a firm has below median debt, relative to other firms, in that time period.

Other control variables in the outcome equations are as follows: mergers are driven by

performance enhancement motives, and contemporaneous and performance indicators are used as merger-influencing variables; contemporaneous (Efficiency) and lagged (Efficiencyt-1) ratios of total operating expenses to total plant in service measures efficiency, and contemporaneous (Financial Performance) as well as lagged (Financial Performancet-1) ratios of total operating revenues to total assets measure financial performance (Cornett and Tehranian, 1992).

Since mergers are capability-acquisition activities, the relative technology deployment levels

among the firms will affect mergers. The quantum of fiber cabling is used as an indicator of technological capabilities, since fiber is the key broadband resource in a communications environment; thus, contemporaneous (Broadband) and lagged (Broadbandt-1) ratios of fiber optic cabling to total lines are used as explanatory variables.

Environmental factors likely to affect mergers are business lines and urban population ratios

(Sharkey, 2002). The business lines construct (Business) is the ratio of business lines to total access lines for each company. The urban population ratio (Urban) is the weighted average ratio of urban population to total population in each firm’s territory. This ratio is weighted by the fraction of lines each firm has operating rights to in a specific territory. The use of market share constructs proxies for market presence, though in regulated industries a high market share does not necessarily imply

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monopoly power (Spulber, 2002). The market share variable (Market Share) is constructed by taking the ratio of a firm’s lines in its operating territory relative to the total lines in the territory.

To control for industry-related factors, we use an: intensity of competition (Competition)

variable. The competition variable is the number of possible competitors given a license to operate in the various states. This variable represents the intensity of market competition in each territory. The competition data are collected from the FCC Competition in Telecommunications Industry reports. For each incumbent local exchange carrier, the competition variable is computed as the sum of the number of competitive local exchange carriers operating in the incumbent’s territory.

4.3 Literature on Covariates Determining Leverage: This analysis is based on Majumdar

(2016). The literatures cited earlier deal with the capital structure debate. The extent of firms’ leverage indicates expectations about earnings capacities and the abilities to repay debts. Yet, on what factors actually drive leverage levels, there is no universal set of covariates determining leverage (Myers, 2003). Factors relevant in explaining leverage variations are contingent on time and place specificities (Simerly and Li, 2000), industry factors (Vincente-Lorente, 2001), and firm-specific attributes (Kayhan and Titman, 2007; Kochhar, 1996; Morellec, 2004).

The relevant historical surveys (Frank and Goyal, 2009; Harris and Raviv, 1991; Rajan and

Zingales, 1995) highlight the key covariates influencing leverage. Leverage increases with fixed assets, non-debt tax shields, growth opportunities, and firm size; it decreases with volatility, spending on intangible assets and superior economic performance (Harris and Raviv, 1991). Others researchers (Rajan and Zingales, 1995) highlight firm size, profitability, possession of tangible assets and firm growth opportunities as variables significantly impacting leverage.

Frank and Goyal (2009) find six factors accounting for more than twenty seven percent of

leverage variations, with other factors contributing two percent. Firms in industries where the median firm has high leverage have high leverage; firms with greater tangible assets have higher leverage; firms with higher profits have lower leverage; larger firms have higher leverage; and firms with higher market-to-book ratios have lower leverage. While many of these measures are not applicable to our study since many of these telecommunications companies were not publicly traded at the time, we attempt to include as many of these measures as our data sample permits.

4.3.1 Variables Used in the Selection Equation: Given the literature, the following are

included as explanatory variables in the selection equation for Leverage: contemporaneous as well as lagged sales growth (Growth and Growtht-1), firm size (Size) measured as the log of total assets, financial performance (Performance) which is an asset utilization ratio, an important driver of profitability, measured as the ratio of operating revenues to total tangible assets, and the ratio of total long term assets to total assets (Assets).

4.4 Institutional Factors and Leverage: Institutional contingencies are relevant for

regulated industries, where output prices are regulated. Regulated entities, such as electric utilities, financial services and telecommunications firms, in the United States have high leverage (Barclay, et al 2003; Besley and Bolten, 1990; Bowen, et al 1982; Bradley, et al 1984; Hagerman and Ratchford, 1978; Taggart, 1981). Firms subject to rate of return regulation choose high leverage because interest costs are included in the rate base for calculating allowable returns (Dasgupta and Nanda, 1993; Rao and Moyer, 1994; Meyer, 1976; Sherman, 1977; Spiegel, 1996; Spiegel and Spulber, 1994; Taggart, 1985).

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High debt levels are attributed to the business environment regulators create, because regulators are not going to let firms die (Taggart, 1985). Regulators have incentives to set high regulated prices to lower the probability of regulated firms becoming financially distressed, allowing interest costs to pass-through to customers and hence permitting higher leverage to occur (Spiegel and Spulber, 1994). Price regulation in output markets provides regulated firms with incentives to use higher debt to finance operations, and the introduction of innovative regulations lowers leverage (Klein, et al 2002; Ovtchinnikov, 2010).

Sector regulatory changes have been important. The occurrence of regulatory changes is

modeled in assessing the extent of leverage. A shift in risk, from a firm’s customers to its shareholders, can follow from changes in pricing regulations, from rate of return to incentive regulation schemes such as a price cap regulation (Laffont and Tirole, 1993; Lewellen and Mauer, 1993). These institutional changes alter the nature of agency costs regulators face, and trigger changes in firms’ incentives for reducing costs (Sappington, 2002), as a result of which firms may reduce debt levels to reduce interest burdens. Hence, a shift away from a rate of return regime, to alternative schemes, can significantly influence debt levels in firms.

4.4.1 Regulation Variables in the Selection Equation: Based on the discussion,

explanatory variables reflecting the nature of price regulation faced by firms are included in the selection equation. There have been two polar types of regulation over prices in the telecommunications sector of the United States; the rate of return and price cap regulation schemes (Sappington, 2002). The diffusion of price caps scheme has not been instantaneous. Introduced in 1990, by 2001 over 40 states had implemented price cap regulations. The decline in rate of return regulation has been steady. In 1985 there were 50 states with rate of return schemes. By 2001, that number was six. Nevertheless, there was not a straight transition from rate of return to price cap regulation. There were other schemes, falling under the rubric incentive regulation, also implemented (Sappington, 2002).

Data on telecommunications sector regulatory changes have been used in many other works

surveyed in Sappington (2002). We construct variables for five different types of regulatory schemes: Rate of Return Schemes, Other Incentive Schemes, Earnings Sharing Schemes, Hybrid Price Caps, and Pure Price Caps. The base case regulatory scheme, left out in estimation and used for comparison purposes, is the Rate of Return Schemes variable.

4.4.2 Other Institutional Variables in the Selection Equation: Institutional factors

influence firm financing (La Porta, et al 1998; Rajan and Zingales, 1995). Thus, covariates are included in the selection equation to account for sector-specific institutional contingencies. In the sector, the Telecommunications Act of 1996 has required firms meet certain requirements, under Section 271 of the act, before they enter long distance markets (Economides, 1999). Based on Section 271 approvals data (Brown and Zimmerman, 2004), a dummy variable (Section 271) is added for the observations obtaining such approvals.

A competition intensity variable is the ratio of competitors in a firm’s territory relative to the

average number of industry competitors (Competitive Intensity), based on data for competitive entrants given a license to operate in the territories of the firms. A related factor is inter-modal competition (Loomis and Swann, 2005). A dummy variable (AT&T Cable), coded as 1 for the years of AT&T ownership of cable assets, and 0 otherwise, is included to account for inter-modal competition.

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4.5 Financial Market Considerations and Impact on Leverage: The structure of interest rates predicts real economic activity (Fama, 1986). The issue is, does the level of interest rates influence borrowings by firms, and do such borrowings influence capital investments? Since Friedman and Schwartz (1963) proposed the importance of monetary policies in influencing real sector activities, short-term interest rates have been used to influence the cost of capital and spending on fixed investment (Bernanke and Gertler, 1995). Monetary policy changes lead to balance sheet restructuring, including of leverage (Adrian and Shin, 2010; Kashyap, et al 1993). Financial policies propagate shocks. These constraints powerfully affect firms’ leverage decisions (Gomes, 2001; Korajczyk and Levy, 2003).13

4.5.1 Financial Variables in the Selection Equation: In assessing macro-economic

factors impacting firm level financial decisions, financial market controls are required. Two variables used are the interest rate on 30-year long term U. S. Treasury bonds (Interest Rate) and a variable measuring period-to-period changes in interest rates (Interest Rate Change).

According to table 3, a typical firm in our sample appears to rely significantly on leverage, at

about 50 percent of assets, but the cash flows from operations seem to provide cushion for such relatively heavily-levered operations. However, when looking at table 4, which provides the Pearson Correlations matrix, we find that highly levered companies are likely to be associated with poorer performance perhaps due to intense competition in the company’s environment. Leverage is additionally found to be positively correlated with increased competition, competitive intensity, and earnings sharing schemes. 5. Discussion

5.1 Results: The results are in tables 5 and 6, and relate to the treatment effects specification

of the leverage and merger relationships. There are two columns of results for the selection and outcome equations in each table. In table 5 and model (1), the First Merger variable is introduced on its own, with no controls added. In model (2), along with the First Merger variable the control variables are added. In table 6 and model (3), the Second Merger variable is introduced with no controls. In model (4), along with the Second Merger variable control variables are added. In table 5

13 The evidence on policy-induced constraints is, however, mixed. Some literature (Romer

and Romer, 1989; Bernanke and Blinder, 1992) has established monetary policy actions to be followed by real output movements lasting two or more years; and, a higher real funds rate has been associated with lower growth in future real output, with the negative correlation interpreted as high interest rates implying low investment opportunities (Estrella and Hardouvelis, 1991). Additional analysis finds insignificant effects (Laurent, 1988), or mixed evidence that interest rate differentials impact real economic activity (Harvey, 1988). Bernanke and Gertler (1995), summing up surveys by Chirinko (1993) and Cummins, et al (1994), remark that the quantitative identification of interest rate and financial effects on spending remains difficult, and suggest that non-financial factors may be of consequence in influencing spending patterns. Related analysis shows a modest or negligible proportion of the real output variance in the United States in the last five decades is attributable to monetary policy shifts (Leeper, et al 1996). The effects of monetary policy and interest rate changes could have limited consequences where capital expenditures are predetermined, where habits persist in such expenditure patterns, and sticky prices for capital assets influence the dynamics of technology deployment behavior. In such circumstances, interest rate levels may not affect leverage, since capital expenditures are planned in advance and subject to a number of important non-monetary influences (Woodford, 2003).

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models (1) and (2), the debt variable, Leverage, is negative and significant (p < 0.01). In table 6 models (3) and (4), too, the Leverage variable is negative and significant (p < 0.01). The results support the hypothesis we have advanced.

Because the dependent variables, First Merger and Second Merger, are 1, 0 dummy variables,

as is the Leverage variable, the impact of Leverage on mergers can be interpreted as the probability of merger disapproval, in the case of a negative coefficient, and merger support in case of a positive coefficient. Taking the results of models (1) and (2), with respect to the First Merger variable, the presence of above median debt in firms, as measured by the Leverage variable, leads to a 79 percent lower likelihood of a first merger occurrence, and, based on the results of models (3) and (4), with respect to the Second Merger variable a 10 percent lower likelihood of a second merger occurrence. The results stay consistent across the specifications. The telecommunications sector historical evidence shows that debt negatively affects the merger predilections of the firms studied.

5.2 Interpretation of the Evidence: There are two opposite predictions as to the impact of

high firm leverage on mergers. The results are interpreted in the light of the specific historical and institutional features of the sector assessed. Given the postulates of the long purse argument (Telser, 1966), requiring the access to capital so as to allow firm to bear negative outcomes until competitive success ensued, firms with low leverage might raise more debt to enable them to behave aggressively. Firms with higher leverage might be vulnerable to competitors’ aggression. High debt levels could engender weakening by rivals, and firms could be unresponsive. Thus, mergers could be risky. Such firms would be monitored, requiring re-financing, and firms with high leverage would be strategically passive so as to avoid negative fiscal outcomes. Thus, high leverage levels would be associated with less aggressive approaches by firms, and a lower merger propensity.

The historical first mergers by firms in the sector were carried out to consolidate operations

and pool resources. These early events were driven by performance-enhancing considerations, and many smaller firms such as Continental Telephone, Central Telephone and United Telephone consolidated their operations at that time. In the late 1980s and early 1990s, however, mergers were carried out to gain scope economies, such that the combined telecommunications entity could tackle larger players with deeper pockets. Hence, a-priori weak telecommunications firms were merging to form stronger telecommunications firms, and in such circumstances the presence of higher-than-average debt would further enhance the riskiness of such firms. Thus, we have observed that for first mergers the presence of high leverage led to a greater propensity, as revealed by the size of the Leverage coefficient, to not engage in mergers, as compared to the impact of high leverage on second mergers which led to a lowering of the intensity of the negative propensity to merge.

Most of the second mergers, in the telecommunications merger wave, were carried out to

exploit opportunities after the passage of the market-opening legislation in the sector. The first notable telecommunications merger of this genre was between Pacific Telesis and Southwestern Bell. Many of latter-period telecommunications mergers were undertaken by bigger firms that eventually consolidated to became Verizon and AT&T (the former SBC), to gain size and market presence. The latter-period mergers involved numerous companies which had to progress through more than one merger event. Several such sequential telecommunications mergers were undertaken to gain enhanced competitive leverage in evolving markets which would become contestable, given new entrants’ presence (Ferguson, 2004).

Such scaling-up of telecommunications firms would reduce the risk associated with higher

leverage; nevertheless, risk would still exist as the highly-competitive environment would also

15

become highly uncertain. Removal of restrictions keeping other types of firms from entering local exchange markets would bring in IXCs, CAPs, CLECs and CSOs to offer services to incumbents’ customers. As noted, there was extensive entry of new telecommunications firms (Loomis and Swann, 2005; Woroch, 2002). While size could mitigate the environmental uncertainties and risks, these might have remained large enough to ensure that the relationship between higher leverage and merger propensity would remain negative.

5.3 An Alternative Explanation: We advance an alternative explanation.14 Debt can also be

classified to be relational and transactional (Berger and Udell, 1995; Boot and Marinč, 2008), a classification scheme similar to insider and outsider debt (Carey, et al 1998; Rajan, 1992). Bank and financial institution loans are relational debts. Bonds and securities are transactional debts (Bhattacharya, et al 2004). Transaction-oriented lending focuses on one transaction, relating to a specific security issue. Transaction lending is an arms-length process and does not involve an information-rich and interaction-intensive relationship (Boot and Thakor, 2000). A firm can have numerous creditors among its transactional lenders. Because of the free-rider syndrome, such lenders can hardly engender collective action against firms’ managers to constrain risky strategies, such as engaging in mergers, and such creditors can also face subsequent hold-up problems at the hands of borrowers.

Conversely, relational debt-holder, such as bank lenders, can significantly influence firms’

strategies. Firms with a single lender or no public bonds outstanding can face pressures to engage in growth-enhancing opportunities (Houston and James, 1996), as well as constraints hindering them from taking decisions with potentially risky outcomes. This is the phenomenon of borrower holdup by monopolistic bank creditors (Sharpe, 1990), as bank lenders are deemed to be insiders (Ivashina, et al 2009).

Relational lenders monitor borrowers to obtain information needed for interventions, and

lending involves receipt of proprietary information (Allen, 1990; Diamond, 1984) to be used in multiple interactions (Greenbaum and Thakor, 1995). Where firms propose to engage in possibly-risky strategies, such as large mergers, relationship-based lenders can exert pressures to constrain such behavior, if they are not in long-term economic interest.

In old-established industries facing comprehensive institutional oversight, such as

telecommunications (Woroch, 2002), lenders and firms have engaged in business and interacted for decades (Majumdar, 2016). There will have been repeated interactions between borrowers and lenders over a long period. Hence, if firms were to take decisions involving large sums, say on mergers, and outcomes were uncertain, such lenders would constrain firms’ activities in order to preserve long-term financial performance outcomes as well as the security of the sums involved. In such a contingency, the negative relationship observed between high leverage and mergers would be engendered. 6. Conclusion

Our analysis is the first, that we are aware of, to evaluate the historical relationship between

the extent of firm leverage and merger propensity, an issue of considerable consequence given the importance of high levels of corporate debt in the economy as well as the general global importance of mergers. The extent of firm leverage can significantly influence merger decisions, either in

14 See Majumdar (2011) from which this discussion is abridged.

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retarding or promoting them. There are complexities involved in empirical analyses of debt and mergers because of the endogeneity of strategic financial decisions by firms. The need to account for debt as an endogenous variable is important, in empirical analyses, and the extent of debt as a strategic choice has to be appropriately modeled.

The literatures have dealt with the extent of debt as a major strategic decision to be modeled,

using a range of covariates to explain the debt-choice decision. We appropriately model the endogenous debt-choice issue, and enrich the numerous policy literatures and the corporate finance area, by demonstrating the disentangling of complexities inherent in simultaneously evaluating corporate financing and strategic decisions. To permit the evaluation, we use the treatment effects modeling approach. This enables the selection effects at play in strategic choice decisions, such as the taking on of corporate debt, to be accounted for econometrically. The technique enables the analysis of causality implicit in simultaneous strategic leverage and merger decisions. The use of the treatment effects modeling approach can enrich and advance the detailed historical analysis of firms’ strategic behavior in competition policy analysis and economic policy analysis.

Mergers are important global phenomena with substantial impacts on business activities,

competition policy, firm strategy and economic performance. How mergers are influenced is an important topic, with important policy consequences. Yet, there is no historical evidence of how firms’ financial structures influence them to undertake mergers. This article is sited at the confluence of the key literatures on business history, economic history, competition policy, corporate finance and corporate strategy.

The study has evaluated the extent of leverage of firms in influencing mergers in an industry

witnessing numerous large and sequential mergers. The historical analysis has considered the impact of corporate debt in influencing the numerous mergers of the local exchange companies between 1988 and 2001, these events being the most significant mergers of their era. The analysis shows that the impact of relatively higher corporate debt levels on merger events in the sector has been negative. The results are consistent with the idea of monitoring of firms with high debt levels and such firms will be strategically passive, by not engaging in potentially-risky mergers, so as not to engender potentially-negative financial performance outcomes.

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Appendix 1: Institutional Details The institutional background, while extremely well documented, is important for

understanding the relationship between leverage and mergers in the sector. Sector-related work had shown the Bell companies to be the dominant United States telephone operator by 1885 (Woroch, 2002). The Bell system controlled patents giving it market power. By 1893 and 1894, the Bell patents expired; a surge of entrepreneurs entered the industry; 87 telephone companies were established in 1894 alone. By 1902, over 4,000 independent telephone companies were competitors to Bell. Over half the Bell companies faced competition from an independent operator. Competition for franchises and customers stimulated infrastructure expansion.

By 1907, the independent competing telephone companies had made inroads into the Bell

system, controlling over 50 percent of telephone lines in the United States. In 1907, Theodore Vail became president of the Bell system and campaigned to acquire independent companies. By 1912, the telephone lines controlled by the independents declined to 45 percent. By 1921, this had declined to 36 percent. In 1921, the Congress passed the Willis-Graham Act permitting the Bell system to continue acquiring independent firms and link networks. By 1934, almost all telephone lines in the United States were interconnected to the Bell system, and the independent companies controlled just 21 percent of telephone lines.

The Communications Act of 1934 formalized the Bell monopoly. The Bell monopoly of

regulated telephone companies was not dismantled for fifty years, till 1984. After a Modification of Final Judgment (MFJ) order in 1982, and pursuant to a consent decree, in 1984 the Bell system divested its local telephone companies, called the Bell Operating Companies (BOCs), grouped into seven Regional Holding Companies (RHCs). In 1984, 22 BOCs were in existence, and 161 local access and transport areas (LATAs) were created. The BOCs were permitted to carry calls originating and terminating in one LATA.

Twelve years after the 1984 divestiture, the Telecommunications Act of 1996 (Public Law

104-104, 110 Stat. 56, codified at 47 U.S.C. 151, et seq.) recast underlying industry structure to introduce competition and remove monopoly power from the Bell companies (Cave, et al 2002). This structure-changing legislation was intended to bring competition into a hitherto-monopolized sector. The passage of the legislation would have fundamental impact in changing industry structural conditions (Lehman and Weisman, 2000).

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The implementation of the legislation eliminated the MFJ’s line-of-business restrictions keeping Regional Holding Companies (RHCs) from entering in-region inter-LATA markets, or into telephone equipment manufacturing. Restrictions keeping other types of carriers from entering local exchange markets were eliminated. Restrictions preventing the RHCs and other local exchange companies (LECs) from providing local telephone service outside their franchised territories were eliminated. Long distance interexchange carriers (IXCs), such as AT&T, competitive access providers (CAPs), such as new competitive local exchange carriers (CLECs) and cable system operators (CSOs) were allowed to offer local, intra-LATA, and inter-LATA telephone service to residential and business customers.

Competitive local exchange carriers (CLECs) were defined as incumbents' rivals, or firms

entering local phone markets after divestiture. The Telecommunications Act of 1996 and the FCC Report and Order of 1996 led firms to enter the local telecommunications market in three ways. First, CLECs could purchase a local service at wholesale rates and re-sell it to the end users. Second, they could lease various unbundled elements of an incumbent’s network through co-location. Third, they could set up networks as facilities-based competitors. The emergence of new firms was extensive (Loomis and Swann, 2005; Woroch, 2002).

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Table 1: Status of Firms and Merger Activity in the Local Exchange Sector of the US Telecommunications Industry before the Passage of the Telecommunications Act of 1996

This table describes the corpus of M&A activity for the population of the local exchange telecommunications companies including change in ownership, merger or acquisitions before the

passage of the Telecommunications Act 1996

Company Names Status 1988 to 1995

Illinois Bell Telephone Company* An original Ameritech company Indiana Bell Telephone Company Inc.* An original Ameritech company Michigan Bell Telephone Company* An original Ameritech company The Ohio Bell Telephone Company* An original Ameritech company Wisconsin Bell Inc. * An original Ameritech company

Chesapeake & Potomac Telephone Company* An original Bell Atlantic Company which became Verizon in 2000

Chesapeake & Potomac Telephone Company of Maryland*

An original Bell Atlantic Company which became Verizon in 2000

Chesapeake & Potomac Telephone Company of Virginia*

An original Bell Atlantic Company which became Verizon in 2000

Chesapeake & Potomac Telephone Company of West Virginia*

An original Bell Atlantic Company which became Verizon in 2000

The Diamond State Telephone Company* An original Bell Atlantic Company which became Verizon in 2000

The Bell Telephone Company of Pennsylvania* An original Bell Atlantic Company which became Verizon in 2000

South Central Bell Telephone Company* Stayed independent with operations amalgamated as Bell South in 1992

Southern Bell Telephone & Telegraph Company* Stayed independent with operations amalgamated as Bell South in 1992

Bell South* Stayed independent with operations amalgamated as Bell South in 1992

New Jersey Bell Telephone Company* An original Bell Atlantic Company which became Verizon in 2000

New England Telephone & Telegraph Company* An original NYNEX Company

New York Telephone* An original NYNEX Company Nevada Bell* An original Pacific Telesis Company Pacific Bell* An original Pacific Telesis Company Southwestern Bell Telephone Company* Stayed independent

The Mountain States Telephone and Telegraph Company*

Stayed independent till 1990 and operations amalgamated as US West Communications since 1991

Northwestern Bell Telephone Company* Stayed independent till 1990 and operations amalgamated as US West Communications since 1991

Pacific Northwest Bell Telephone Company* Stayed independent till 1990 and operations amalgamated as US West Communications since 1991

U S West Communications, Inc. * Combined operations of Mountain States Telephone and Telegraph Company, Northwestern Bell Telephone Company and Pacific Northwest

33

Bell Telephone Company from 1991 Cincinnati Bell Telephone Company* Stayed independent The Southern New England Telephone Company* Stayed independent Central Telephone Company of Virginia* Became a part of Sprint in 1992 Contel of New York* Became a part of GTE in 1990 Citizens Telecommunications Company Of New York Inc.

Stayed independent

Contel of Texas* Became a part of GTE in 1990 Contel of Virginia* Became a part of GTE in 1990 Contel of California Inc. * Became a part of GTE in 1990 GTE California, Inc. An original GTE Company GTE Florida, Inc. * An original GTE Company GTE Hawaiian Telephone Company, Inc. * An original GTE Company Contel of Missouri Inc. * Became a part of GTE in 1990 GTE Midwest Inc. An original GTE Company GTE North, Inc. * An original GTE Company GTE Northwest, Inc. * An original GTE Company GTE South, Inc. An original GTE Company GTE Southwest, Inc. An original GTE Company Lincoln Telephone & Telegraph Company* Stayed independent Puerto Rico Telephone Company* An original GTE Company Rochester Telephone Corporation* Stayed independent Carolina Telephone & Telegraph Company* Became a part of Sprint in 1991 United Inter-Mountain Telephone Company* Became a part of Sprint in 1991 Central Telephone Company of Florida* Became a part of Sprint in 1992 United Telephone Company of Florida* Became a part of Sprint in 1991 United Telephone Company of Indiana* Became a part of Sprint in 1991 United Telephone Company of Missouri* Became a part of Sprint in 1991 United Telephone Company of Ohio* Became a part of Sprint in 1991 United Telephone Company of Pennsylvania* Became a part of Sprint in 1991

Source of table: Reproduced and redeveloped from Majumdar, et al (2014a). * Company details used in the analysis. Some companies’ data aggregated and then ratios calculated for the years operations were amalgamated.

34

Table 2: Status of Firms and Merger Activity in the Local Exchange Sector of the US Telecommunications Industry after the Passage of the Telecommunications Act of 1996

This table describes the corpus of M&A activity for the population of the local exchange telecommunications companies including change in ownership, merger or acquisitions after the

passage of the Telecommunications Act 1996

Company Names Status 1996 to 2001

Illinois Bell Telephone Company* Became a part of SBC in 1999 Indiana Bell Telephone Company Inc.* Became a part of SBC in 1999 Michigan Bell Telephone Company* Became a part of SBC in 1999 The Ohio Bell Telephone Company* Became a part of SBC in 1999 Wisconsin Bell Inc. * Became a part of SBC in 1999

Chesapeake & Potomac Telephone Company* An original Bell Atlantic Company which became Verizon in 2000

Chesapeake & Potomac Telephone Company of Maryland*

An original Bell Atlantic Company which became Verizon in 2000

Chesapeake & Potomac Telephone Company of Virginia*

An original Bell Atlantic Company which became Verizon in 2000

Chesapeake & Potomac Telephone Company of West Virginia*

An original Bell Atlantic Company which became Verizon in 2000

The Diamond State Telephone Company* An original Bell Atlantic Company which became Verizon in 2000

The Bell Telephone Company of Pennsylvania* An original Bell Atlantic Company which became Verizon in 2000

South Central Bell Telephone Company* Stayed independent with operations amalgamated as Bell South in 1992

Southern Bell Telephone & Telegraph Company* Stayed independent with operations amalgamated as Bell South in 1992

Bell South* Stayed independent with operations amalgamated as Bell South in 1992

New Jersey Bell Telephone Company* An original Bell Atlantic Company which became Verizon in 2000

New England Telephone & Telegraph Company* An original NYNEX Company which became a Bell Atlantic Company in 1997 which became Verizon in 2000

New York Telephone* An original NYNEX Company which became a Bell Atlantic Company in 1997 which became Verizon in 2000

Nevada Bell* Became a part of SBC in 1997 Pacific Bell* Became a part of SBC in 1997 Southwestern Bell Telephone Company* Stayed independent The Mountain States Telephone and Telegraph Company*

Became a part of Qwest in 2000

Northwestern Bell Telephone Company* Became a part of Qwest in 2000 Pacific Northwest Bell Telephone Company* Became a part of Qwest in 2000 U S West Communications, Inc. * Became a part of Qwest in 2000 Cincinnati Bell Telephone Company* Stayed independent The Southern New England Telephone Company* Became a part of SBC in 1998 Central Telephone Company of Virginia* Stayed as a part of Sprint

35

Contel of New York* Stayed as part of GTE which then became part of Verizon in 2000

Citizens Telecommunications Company Of New York Inc.

Stayed independent

Contel of Texas* Stayed as part of GTE which then became part of Verizon in 2000

Contel of Virginia* Stayed as part of GTE which then became part of Verizon in 2000

Contel of California Inc. * Stayed as part of GTE which then became part of Verizon in 2000

GTE California, Inc. Became a part of Verizon with GTE takeover in 2000

GTE Florida, Inc. * Became a part of Verizon with GTE takeover in 2000

GTE Hawaiian Telephone Company, Inc. * Became a part of Verizon with GTE takeover in 2000

Contel of Missouri Inc. * Stayed as part of GTE which then became part of Verizon in 2000

GTE Midwest Inc. Became a part of Verizon with GTE takeover in 2000

GTE North, Inc. * Became a part of Verizon with GTE takeover in 2000

GTE Northwest, Inc. * Became a part of Verizon with GTE takeover in 2000

GTE South, Inc. Became a part of Verizon with GTE takeover in 2000

GTE Southwest, Inc. Became a part of Verizon with GTE takeover in 2000

Lincoln Telephone & Telegraph Company* Stayed independent

Puerto Rico Telephone Company* Became a part of Verizon with GTE takeover in 2000

Rochester Telephone Corporation* Became a part of Global Crossing in 1999 and Citizens Communications in 2001

Carolina Telephone & Telegraph Company* Stayed as part of Sprint United Inter-Mountain Telephone Company* Stayed as part of Sprint Central Telephone Company of Florida* Stayed as part of Sprint United Telephone Company of Florida* Stayed as part of Sprint United Telephone Company of Indiana* Stayed as part of Sprint United Telephone Company of Missouri* Stayed as part of Sprint United Telephone Company of Ohio* Stayed as part of Sprint United Telephone Company of Pennsylvania* Stayed as part of Sprint

Source of table: Reproduced and redeveloped from Majumdar, et al (2014a). * Company details used in the analysis. Some companies’ data aggregated and then ratios calculated for the years operations were amalgamated.

36

Table 3: List of Variables

Variable Description

Leverage Ratio of total long term debt to total assets Efficiency Ratio of digital lines to analog lines Financial Performance Ratio of total operating revenues to total assets Broadband Ratio of Broadband line to total lines Business Ratio of firm’s business lines relative to total lines

Urban Weighted average ratio of urban population to total population

Market Share Ratio of firm’s lines in states of operations relative to total lines in those states

Competition Number of competitors given a license to operate in the various states.

Growth Growth in Sales Size Log of total assets Performance Ratio of operating revenues to total tangible assets Assets Ratio of total long term assets to total assets Other Incentive Scheme Other incentive regulation Earnings Sharing Scheme Earnings share regulation Hybrid Price Caps Scheme Hybrid price caps regulation Pure Price Caps Scheme Pure price caps regulation

Section 271

Dummy if Section 271 is applicable; related to the procedures established in the communications act by which a Bell operating company may seek to provide services originating in one of its in-region States

Competitive Intensity Ratio of competitors in a firm’s territory relative to the average number of industry competitors

AT&T Cable Dummy variable coded as 1 for the years of AT&T ownership of cable assets, and 0 otherwise

Interest Rate Interest rate on 30-year long term U. S. Treasury bonds

Table 4: Descriptive Statistics

Variable Observations Mean Std. Dev. Min Max

First Merger Dummy 574 0.402 0.491 0 1

Second Merger Dummy 574 0.009 0.093 0 1

Leverage 574 0.514 0.500 0 1

Efficiency 562 0.073 0.014 0.025 0.134

Financial Performance 562 0.518 0.088 0.164 0.855

Broadband 573 0.053 0.039 0.004 0.226

Business 573 0.255 0.069 0.090 0.633

Urban 574 0.785 0.111 0.461 1

Market Share 574 0.490 0.390 0 1.682

Competition 574 8.589 14.612 0 108

Growth 521 0.033 0.104 -0.503 1.872

Size 562 13.903 1.291 11.443 16.708

Performance 562 0.357 0.036 0.266 0.488

Assets 562 0.822 0.076 0.384 0.992

Other Incentive Schemes 574 0.258 0.386 0 1

Earnings Sharing Schemes 574 0.128 0.293 0 1

Hybrid Price Caps 574 0.043 0.188 0 1

Pure Price Caps 574 0.389 0.466 0 1

Section 271 574 0.012 0.110 0 1

Competitive Intensity 574 1.000 1.254 0 5.604

AT&T Cable 574 0.214 0.411 0 1

Interest Rate 574 6.814 1.159 5.060 9.010

Interest Rate Change 533 -2.711 14.686 -23.00 25.92

Table 5: Correlation Table

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23)

First Merger Dummy (1) 1

Second Merger Dummy (2) 0.10* 1

Leverage (3) 0.07 -0.00 1

Efficiency (4) -0.05 -0.06 -0.00 1

Financial Performance (5) 0.23*** -0.04 -0.17*** 0.28*** 1

Broadband (6) 0.17*** 0.01 0.07 0.20*** -0.03 1

Business (7) -0.33*** -0.05 -0.07 -0.03 -0.04 -0.53*** 1

Urban (8) -0.17*** 0.03 0.00 -0.11* -0.27*** -0.52*** 0.49*** 1

Market Share (9) -0.24*** 0.05 -0.10* -0.17*** 0.02 -0.64*** 0.48*** 0.26*** 1

Competition (10) 0.24*** -0.01 0.17*** -0.35*** -0.00 -0.08 -0.11* -0.01 -0.06 1

Growth (11) 0.03 -0.15*** -0.03 0.11* 0.04 0.20*** -0.06 -0.07 -0.07 -0.02 1

Size (12) -0.04 -0.04 0.05 -0.32*** -0.08 -0.50*** 0.18*** 0.24*** 0.54*** 0.35*** -0.05 1

Performance (13) -0.19*** -0.00 -0.15*** 0.62*** 0.41*** -0.03 0.18*** -0.09* -0.04 -0.31*** 0.08 -0.20*** 1

Assets (14) -0.04 -0.19*** -0.05 0.07 0.16*** 0.17*** -0.04 -0.18*** 0.04 -0.17*** 0.09* -0.06 -0.03 1

Other Incentive Schemes (15) -0.15*** -0.06 -0.01 0.13** -0.11* 0.28*** -0.15*** -0.25*** -0.11** -0.20*** 0.01 -0.13** 0.20*** 0.24*** 1

Earnings Sharing Schemes (16) -0.06 -0.04 0.09* -0.01 -0.14** -0.08 0.20*** 0.15*** 0.08 -0.06 0.04 0.18*** 0.07 -0.04 -0.15*** 1

Hybrid Price Caps (17) -0.06 -0.02 -0.01 -0.13** -0.20*** -0.12** 0.10* 0.23*** -0.02 -0.05 -0.04 0.16*** -0.06 -0.10* -0.11* -0.08 1

Pure Price Caps (18) 0.31*** 0.02 -0.07 -0.18*** 0.45*** -0.10* -0.05 -0.14** -0.01 0.33*** -0.00 -0.01 -0.12** -0.10* -0.47*** -0.36*** -0.20*** 1

Section 271 (19) 0.10* -0.01 0.02 -0.03 0.01 -0.09* -0.03 0.03 0.10* 0.17*** -0.04 0.17*** -0.14** -0.01 -0.07 -0.05 -0.03 0.14*** 1

Competitive Intensity (20) -0.02 -0.05 0.16*** -0.19*** -0.32*** 0.03 -0.07 0.09* -0.16*** 0.56*** -0.01 0.29*** -0.14** -0.23*** 0.01 0.11* 0.18*** -0.06 0.06 1

AT&T Cable (21) 0.46*** 0.17*** -0.03 -0.32*** 0.22*** -0.20*** -0.13** 0.03 0.09* 0.36*** -0.10* 0.11* -0.32*** -0.17*** -0.29*** -0.14** -0.12** 0.47*** 0.22*** 0.00 1

Interest Rate (22) -0.39*** -0.10* 0.02 0.23*** -0.29*** 0.20*** -0.00 -0.02 -0.09 -0.44*** -0.02 -0.11* 0.25*** 0.24*** 0.45*** 0.15*** 0.09* -0.65*** -0.12** -0.00 -0.44*** 1

Interest Rate Change (23) 0.02 -0.02 0.00 -0.00 0.02 -0.01 -0.02 -0.00 0.01 -0.01 -0.05 0.01 -0.03 -0.03 0.01 0.01 -0.02 0.01 0.02 0.00 0.25*** 0.37*** 1

* p<0.05, ** p<0.01, *** p<0.001

39

Table 6: Treatments Effects Estimation Results for the First Merger Outcome Variable

Outcome Variable: First Merger

Model (A) Model (B)

Constant 0.826*** (0.052)

2.556 (4.095)

Leverage -0.796*** (0.087)

-0.691*** (0.082)

Efficiency 1.317

(2.886)

Efficiencyt-1 -2.114 (2.921)

Financial Performance -0.810* (0.553)

Financial Performancet-1 1.786*** (0.570)

Broadband 0.959

(2.074)

Broadbandt-1 0.318

(2.011)

Business -1.646*** (0.358)

Urban 0.327* (0.220)

Market Share -0.180** (0.069)

Competition 0.006***

(0.001)

Wald χ2 82.41 224.14

Selection Equations for Leverage Treatment Variable

Constant -1.056 (0.888)

0.444 (1.014)

Growth -0.519 (0.436)

-0.507 (0.683)

Growtht-1 0.004

(0.026) 0.0003 (0.028)

Performance -2.046* (1.278)

-3.608** (1.536)

Assets -0.793* (0.546)

-0.675 (0.611)

Other Incentive Schemes -0.148 (0.148)

-0.087 (0.159)

Earnings Sharing Schemes -0.041 (0.164)

-0.104 (0.178)

Hybrid Price Caps -0.57*** (0.190)

-0.731*** (0.212)

Pure Price Caps -0.065 0.027

40

(0.143) (0.154)

Section 271 -0.115 (0.297)

-0.057 (0.342)

Competitive Intensity 0.060* (0.035)

0.173*** (0.041)

AT&T Cable -0.065***

(0.134) -0.619*** (0.131)

Interest Rate 0.196*** (0.070)

0.176** (0.076)

Interest Rate Change -0000 (0.003)

0.00 (0.003)

Atanh ρ 1.399 1.304

Log σ -0.417 -0.561

Ρ 0.885 0.862

Σ 0.658 0.571

Λ 0.582 0.492

LR Test χ2 59.81*** 36.23***

N 509 509 *** p < 0.01, ** p < 0.05, * p < 0.10; standard errors in parentheses.

41

Table 7: Treatments Effects Estimation Results for the Second Merger Outcome Variable

Outcome Variable: Second Merger

Model (C) Model (D)

Constant 0.056*** (0.007)

0.069 (0.057)

Leverage -0.095***

(0.012) -0.103***

(0.012)

Efficiency -1.495** (0.567)

Efficiencyt-1 1.268**

(0.595)

Financial Performance -0.035

(0.116)

Financial Performancet-1 -0.082

(0.121)

Broadband 0.906**

(0.421)

Broadbandt-1 1.283***

(0.404)

Business -0.094

(0.072)

Urban 0.065*

(0.044)

Market Share 0.032**

(0.013)

Competition 0.000

(0.000)

Wald χ2 58.67 100.16

Selection Equations for Leverage Treatment Variable

Constant 1.169

(1.113) 1.975* (1.122)

Growth 0.794** (0.413)

0.915** (0.416)

Growtht-1 0.015

(0.033) 0.014

(0.032)

Performance -4.923*** (1.502)

-7.242*** (1.594)

Assets 0.669

(0.699) 0.656

(0.690)

Other Incentive Schemes -0.107 (0.176)

-0.092 (0.178)

Earnings Sharing Schemes 0.149

(0.201) 0.111

(0.201)

Hybrid Price Caps -0.308 (0.271)

-0.350 (0.271)

Pure Price Caps -0.143 -0.188

42

(0.179) (0.181)

Section 271 0.227

(0.416) 0.221

(0.410)

Competitive Intensity 0.102** (0.043)

0.121 (0.044)

AT&T Cable -0.409** (0.157)

-0.412** (0.157)

Interest Rate -0.032 (0.084)

-0.018 (0.085)

Interest Rate Change 0.005

(0.004) 0.004

(0.004)

Atanh ρ 0.767 0.817

Log σ -2.299 -2.309

Ρ 0.645 0.674

Σ 0.100 0.099

Λ 0.065 0.067

LR Test χ2 18.50*** 24.81***

N 509 509 *** p < 0.01, ** p < 0.05, * p < 0.10; standard errors in parentheses.