HANDBOOK OF TELECOMMUNICATIONS ECONOMICS VOLUME 1
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HANDBOOK OF TELECOMMUNICATIONS ECONOMICS VOLUME 1 STRUCTURE, REGULATION AND COMPETITION Edited by
MARTIN E. CAVE University of Warwick SUMIT K. MAJUMDAR Imperial College of Science, Technology and Medicine University of London and
INGO VOGELSANG Boston University
. . . . . . . . .
Elsevier The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands First edition 2002 Reprinted 2005, 2006 Copyright © 2002 Elsevier Ltd. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (⫹44) (0) 1865 843830; fax (⫹44) (0) 1865 853333; email:
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Ch. 1: Structure, Regulation and Competition
3
1. Introduction The last two decades have seen exceptionally fast rates of change in every aspect of the telecommunications industry. These include technology changes – especially the effects of digitalization; introduction of new products including internetbased services, and convergence associated with the coming together of the broadcasting, information technology and telecommunications industries. Simultaneously, market structure has changed through the replacement of the former monopolistic, vertically integrated telephone companies by a variety of competing firms. These developments have been accompanied by major legislative and regulatory developments, including the passage in the United States of the 1996 Telecommunications Act and the introduction of a large number of new laws and regulations in Europe and elsewhere. The same changes have seen a massive expansion of independent regulatory agencies. Their major role has been to determine the terms of competitive interactions between the existing incumbent operators and new entrants. This volume provides an economic review of and commentary on these changes in the landscape of what is arguably a very major industry. It attempts to provide a comprehensive account of the major applications of economic analysis to our field of interest, which is essentially two-way communications between parties that are not in physical contact with each other by means of fixed line telephony, mobile telephony, satellite telephony, cable TV telephony and Internet telephony. To a significant extent, voice communication is the process of interest. We propose in a second volume to focus our attention on the economic analysis of data communications, including in particular the development and use of the Internet, and on a number of other strategic, institutional and organizational issues of the incredibly fast-moving contemporary telecommunications landscape. The three principal sections of this volume contain fourteen chapters written by experts in the relevant areas. Each chapter is intended to be self-contained, and can be read independently. Together, however, the chapters provide a survey of the bulk of recent work in the economics of the telecommunications sector. The reader pursuing a particular subject, for example the application of engineering cost models, or the relation between wholesale and retail price regulation, is likely to want to consult several of them. Authorship of the chapters by different individuals carries the advantage that readers can gain access to their special expertise on the subject in question. It also exposes differences among authors that may exist on key current policy questions. For example, some authors, who have greater confidence in the potentially benevolent effects of regulation than others, would like to see the process of deregulation taking place more quickly. This diversity of opinion is an advantage, because it reflects genuine disagreement among economists and policy makers on matters which are still of major topical concern. Kuhn (1962) has described the early stages of the evolution of a field as
4
M. Cave, S. Majumdar and I. Vogelsang
consisting of phenomena reporting in the absence of a unifying theoretical framework. Thus, a plurality of perspectives may be observed being brought to bear on issues. Even in the presence of theories that may be seemingly logical, the empirical phenomena noted might be at odds with the assumptions that underlie the framework. The existence of multiple views enables us, as editors, in this introductory chapter, to highlight some of these differences without necessarily attempting to reconcile them. It also enables us to articulate the key conceptual issues that we think ought to form the core of the discipline of telecommunications economics that is evolving. Although we have sought to be comprehensive, particularly in explaining concepts that underlie policy making and in reviewing the literature, the single most important bias of the current volume is its focus on highly developed countries, the U.S. in particular. The main reasons are that the most important work has been on American telecommunications problems and that most of our authors are U.S. based. Critical policy experiments were carried out in the U.S., and the extant literature covers U.S. experience in the main. Nevertheless, general consequences from U.S. policies are lessons drawn by the authors and in this introduction. Two features of the U.S. telecommunications sector are of particular importance – the ownership structure and the degree of vertical integration. Most countries in the world have a dominant telecommunications carrier that is or was state owned. In contrast, the U.S. Bell system was always in private hands1. In spite of this difference, the resulting policy issues and outcomes are strikingly similar across countries. The U.S. telecommunication sector has been less vertically integrated than elsewhere, since AT&T was split up in 1984. This vertical separation has provided an interesting policy experiment and has justified a separate chapter on long-distance competition. Our second bias is the omission of macro-economic consequences of telecommunications. The design of regulatory institutions was planned as a chapter but could not be completed on time. However, this area is partially covered in several chapters, notably in the chapters dealing with the historical overview, in interconnection practice, mobile telephony and local network competition. We also take it up in this introduction. The contents of the Handbook are set out in three main sections. The first section deals with the economic characteristics of the sector. These characteristics define the structure of the industry, and we discuss how different aspects of structure can interact to influence industry development. The next section deals with regulation and the third section deals with competition. In theory, the section on competition should precede that on regulation. In practice, regulatory issues 1
This particular volume of the Handbook does not cover the topic of privatization. Nevertheless, given the salience of the topic, some comments are in order and these comments are included at various points in this introductory chapter.
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data followed by dead time with no transmission. Davies recognised that the existing circuit switching structure did not handle that approach efficiently. He suggested a structure for data communication with many messages using a shared high capacity channel. Davies designated the units of a message as ‘packets’ using an analogy with small packages. His group built the first packet switching network on a small scale at the National Physical Laboratory. A third conceptual justification was developed by the U.S. Department of Defense Advanced Research Projects Agency (ARPA) which was concerned with allowing greater sharing of computer resources among various ARPA sites. ARPA proposed a plan to connect remote computers in 1967. After refinement from the efforts from Davies and Baran, along with Wesley Clark and numerous others, ARPA developed the first cross-country packet switching network. The first node of the ARPANET was installed in 1969 at UCLA, by a team led by Leonard Kleinrock and including graduate students Vint Cerf, Steve Crocker and John Postell, who played major roles in the development of computer networking. The network had four nodes by the end of 1969. Graduate students from the first sites began communication with each other on developing ways to make the network useful. They developed many of the early protocols that were used in the later Internet. The approach utilised in the ARPANET was a departure from traditional communication or computer practices in which one large company sponsored a particular protocol or language. Instead, the graduate students formed a loosely organised network working group which communicated regularly with each other and distributed proposals known as ‘‘Request for Comments.’’ The proposals eventually became standard parts of the Internet if they achieved widespread acceptance, but there was no formal procedure to adopt standards. The ARPANET was a single centrally managed network. Each ARPANET site required a particular minicomputer running particular programs, and that computer provided the interface between the network and the host computers on that site. However, other packet switched networks were developed using different standards. During 1973 Vint Cerf and Bob Kahn (then at ARPA) developed a protocol for exchanging packets among networks operating on different standards. After further refinement, their paper was developed into the transmission control protocol (TCP, later refined into the TCP/IP protocol) that would allow data packets to be encapsulated in standard ‘envelopes’ that could be passed across networks. The TCP protocol was crucial for the later development of the Internet. It provided a standard way of carrying dissimilar packets. The TCP protocol also borrowed an idea from the French Cyclades network in which error correction was entirely the responsibility of the host computers. The network was assumed to be unreliable, and hosts were responsible for sending packets again if there was no acknowledgement of correct transmission. In contrast the ARPANET considered the network responsible for reliability, and built extensive acknowledgement and error correcting mechanisms into the message processors,
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S.J. Liebowitz and S.E. Margolis
effects as independent variables. Gandal uses Lotus file compatibility and the ability to link to external databases to measure the strength of network effects. Brynjolfsson and Kemerer use Lotus menu structure and installed base as a measure of network effects. In general, these variables do not measure network effects distinctly. The value attributed to Lotus file compatibility will be the result of two influences. The first is that consumers who are upgrading from older products will benefit from the ability of the new product to read their old files. This is not a network effect. The second influence is the network effect of compatibility with others. Similarly, the ability to link to external databases is a useful function, but it is not a network effect since it does not depend on the number of other 1-2-3 users. The Lotus menu structure variable measures the importance of remaining compatible with one’s old learned habits, but this too is not a network effect4. The installed base variable suffers from a similar problem, since if there were no new buyers (but only upgrade buyers) it might merely pick up the value of being compatible with old files. These variables might have measured the effects that they were intended to measure if the sample of users were limited to first time buyers, but they were not. The empirical importance of virtual network effects in any specific market, therefore, is still an open question. The empirical importance of network effects in literal networks may seem so obvious that empirical testing may have seemed redundant. Nevertheless, there have been several recent attempts to measure or test for network effects in the case of the facsimile market and automated teller machines. Saloner and Shepard (1995) attempt to measure the relative importance of network effects and scale economies in the adoption of ATMs (automated teller machines). The idea is that consumers receive additional value from having access to a large network of ATMs rather than a small network, certainly a reasonable assumption. Their time period was the decade of the 1970s, however, a period prior to interconnected ATM networks of multiple banks that are common now. They find that the probability of a bank installing at least one ATM is positively related to the number of branches, evidence that they believe confirms the importance of network effects. Although their results are expected and entirely plausible, their analysis can not be considered to have confirmed the hypothesis due to some very basic problems with their estimation. For one thing, random adoption of ATMs by bank branches would also create a positive measured relationship, although Saloner and Shepard test for this randomness effect and find its impact limited. Second, Saloner and Shepard find that the relationship does not hold for the specific states, within the United States, that do not restrict the number of branches that banks 4
Except for the unlikely case where it is important to consumers that other random individuals be able to borrow their computers and easily use their spreadsheet program.
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Ch. 4: Customer Demand Analysis
121
The index is intended as a measure of a household’s community of interest, the interpretation being that the higher is the index, the more concentrated is the household’s community of interest. The concentration index is included in the model both directly and interacted with price. The final variables included in the model are dummy variables for AT&T, MCI, and Sprint referring to the household’s choice of primary (i.e., 1-plus) interexchange carrier and a dummy variable indicating whether or not the household subscribes to an optional calling plan30. Since not all households make toll calls, the analysis proceeds in two stages. In the first stage, a discrete-choice probit model is estimated that explains the probability that a household has a non-zero amount of toll calling. A Mills ratio is calculated from this probit equation and its inverse is then used as an independent variable in a second-stage model that explains the demand for toll calling for those households with non-zero calling activity31. The base sample underlying the analysis consisted of 6,461 households, and was the sample used in estimating the first-stage probit models from which Mills ratios were calculated. The secondstage models (i.e., the toll-usage models) were then estimated from sub-samples of households whose total month’s toll activity was between 4 and 500 minutes and whose average toll cost per minute was less than 35 cents32. Sample sizes range from about 2,450 households for the interLATA/intrastate category to about 4,600 for interLATA/interstate and for interLATA/interstate combine. The results yielded an estimated price elasticity for intraLATA toll of about 0.44 and an elasticity for interstate toll of about 0.50. On the whole, different categories of toll calling are found to be substitutes, although the effects are indicated to be quite small. Call concentration is found to have a strong inverse effect on the size of the price elasticity, with low concentration being associated with a large (in absolute value) elasticity and vice versa. With regard to inter-exchange carrier effects, AT&T customers, on the average, have lower toll activity than nonAT&T customers, but for those households who subscribe to an optional calling plan, AT&T customers have higher calling volume than non-AT&T customers. 30
Toll minutes and price are household-specific as obtained from the household’s telephone bill. Income and the socio-demographic variables are measured as means for the ZIPþ4 postal areas in which a household resides. 31 The Mills ratio (or more precisely, the inverse of the Mills ratio) adjusts for the mean of the error term in the second-stage model not necessarily being equal to zero as a consequence of the second-stage sample including only those households with non-zero toll-calling activity. For a discussion of how the Mills ratio arises and how it is calculated, see Greene (1997). 32 The subsamples were truncated at the upper end of toll usage in order to eliminate toll activity that was almost certainly primarily business motivated, and at the lower end in order to eliminate households whose toll calling was unusually infrequent or was low during the month of the sample because of travel or unavoidable absence from home. Also, some households who subscribe to an optional calling plan had (for whatever reason) little or no toll activity during the month of the sample. Because of the monthly subscription fee, the average prices per minute will be unduly high in these cases, and their presence would clearly bias the estimation. Restricting the sub-samples to households having a calculated average price per minute of less than 35 cents eliminates the distortion that these artificially inflated average prices per minute would cause.
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W.W. Sharkey Table 1 Digital transmission systems
Digital service level
Voice grade equivalents
Line rate10
DS-0
1
DS-1
24
1.544 mbps
64 kbps
DS-3
672
45 mbps
carrier rates which currently support transmission levels from OC-3 and higher rates11. Looking forward, optical fiber technologies have been deployed having a carrying capacity of more than a terrabit per second (11012 bits per second)12. These systems use wave division multiplexing to combine multiple colors of light with each color carrying OC-48 or higher on one fiber pair. Switching: The purpose of switching is to interconnect transmission paths, which makes it possible and economically feasible for point to point communications to be made throughout the network without the need to establish a direct transmission link between every pair of points. The first switch consisted of a manual switchboard in which a human operator made connections based on verbal instructions from calling parties. Various automatic switching machines were introduced during the early 1900s including step-by-step, panel and crossbar designs (Bell Laboratories, 1980, pages 236 to 45). These innovations introduced common control features to switching machines in which switching functions were disaggregated and placed under a controlling device that interpreted the caller’s dialing code. This design allowed for a more efficient utilization of switching capacity, since the individual functions could be directed to new tasks before a previous call setup was completed13. More recently, control features have been implemented in software programs, resulting in cost savings associated with adding new customers or new features to the switch. Stored program control switches were first based on analog ‘spacedivision’ methods, in which all internal connections were accomplished through direct metallic contacts or semiconductor cross-point devices. Typical telephone 10
DS-0 at 64 kbps results a sampling rate of twice the highest frequency sampled. This results in a rate of 2 4000, and with 8 bits per sample this equals 64,000 kbps. 11 In the SONET standard, bandwidths begin where previous digital transmission technologies reach their upper limits. An OC-1 transmission level corresponds to a DS-3 level of approximately 51 Mbps. Outside of North America, a closely related system known as the synchronous digital hierarchy (SDH) is used. 12 Wildeman and Flood (2000, p. 11). Current SONET transmission rates extend to OC-192 and higher levels. 13 The migration of switching technologies toward greater utilization of common control features has also shifted the cost structure of switching by increasing the initial fixed cost of the switch and reducing the usage sensitive variable cost.
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218
W.W. Sharkey
Fig. 7. Interoffice SONET rings in Portugal.53
translog cost specification popularized by Christensen, Cummings and Schoech (1983)54. The translog functional form approximates a wide variety of cost functions and possesses some degree of flexibility which makes it one of the most favored specifications by economists. One must recognize, however, that historically lack of satisfactory data rather than good techniques has been the problem in the vast majority of empirical studies of the production process. The estimation of an econometric model using the translog or any other functional form requires a sufficiently large number of observations to make the estimation of all parameters practical. These data requirements can be met either through a time series on one firm, a cross-section of many firms, or a panel data set. However, all of these data types are subject to serious limitations from the perspective of policy analysis. A time series of one firm requires retrospective data. Typically, the interval available for firms in the U.S. is at best quarterly. Thus, variation in the data comes about from examining relatively long series, which will include periods from different technological eras corresponding to data on production processes with different technological characteristics.
53
The ring paths illustrated in Figure 7 are for illustrative purposes only and do not represent actual outputs of the HCPM interoffice model. 54 See the chapter by Fuss and Waverman in this Handbook for a review of this literature.
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Ch. 7: Price Regulation
243
some customers that it could serve at lower cost than its competitors if it had the pricing flexibility to do so. For all these reasons, RORR has been declining in popularity in recent years. As the discussion in Section 2 reveals, price cap regulation (PCR) has replaced RORR in many jurisdictions. (Recall Table 3.) In theory at least, PCR can overcome all of these problems with RORR. By divorcing allowed revenues from realised costs, PCR provides expanded incentives for innovation and cost reduction. When the firm is permitted to retain as profit all of the reductions in costs it achieves, the firm has strong incentives to reduce its operating costs. Incentives for cost-reducing innovation are also enhanced under PCR to the extent that lower prices are induced under PCR than under RORR. The lower prices lead to higher production levels, so a given reduction in marginal cost provides a larger reduction in total cost, and thus a larger increase in profit (Cabral and Riordan, 1989; Clemenz, 1991). PCR can also reduce technological distortions and the costs of regulation. When it does not link authorised earnings to capital or any other particular input, PCR can avoid over-capitalisation and related input distortions in production. And if reviews of price cap plans are scheduled infrequently, the costs of regulation can be reduced under PCR. Costs are reduced further when the regulated firm is authorised to change prices within well-specified bounds. By delegating pricing authority in this manner, regulators can avoid many costly and contentious hearings to analyse proposed rate changes. PCR also shifts risk from consumers to the regulated firm. When it agrees to a price schedule that does not vary with realised costs, the firm bears all the risk – both favourable and unfavourable – associated with cost variation. When it severs the link between authorised revenues and realised costs, PCR also eliminates incentives for shifting accounting costs between regulated and unregulated activities, undertaking inappropriate levels of diversification and innovation, and adopting inefficient technologies (Braeutigam and Panzar, 1989). These undesirable incentives disappear when the firm earns an extra dollar of profit for every dollar of cost reduction it achieves, regardless of whether the reduction is realised in common costs or the costs of providing particular regulated or unregulated services. And when PCR affords the regulated firm significant freedom to vary individual service prices quickly and unilaterally, an incumbent producer will be better able to prevent less efficient competitors from serving customers29. 4.2. Incentive regulation in practice Although PCR and RORR can provide very different incentives in principle, it is not clear that they do in practice (Barnich, 1992; Waterson, 1992). The potential distinctions between PCR and RORR become blurred as they are implemented in 29
For additional comparisons of incentive regulation and RORR, see, for example, Acton and Vogelsang (1989), Braeutigam and Panzar (1993), and Liston (1993).
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282
D.E.M. Sappington
Majumdar (1997) examines the impact of various incentive regulation plans on a measure of technical efficiency rather than total factor productivity growth. Employing an approach developed by Banker et al. (1984), Majumdar constructs a measure of the relative efficiency of 45 local exchange companies in the United States between 1988 and 199389. The efficiency measure relates to the companies’ relative performance in transforming inputs (switches, lines, and employees) into outputs (local, intraLATA toll, and interLATA toll calls), and requires no explicit assumptions about the firms’ production technologies. Majumdar finds that: (1) price cap regulation is associated with significant improvement in efficiency after a lag of two years; (2) the positive impact of price cap regulation is diminished when earnings sharing provisions are added to price cap regulation; and (3) earnings sharing regulation alone is associated with a long-term reduction in measured efficiency. As Majumdar (1997) points out, it is important to determine whether his findings persist in studies that employ a longer time series and more recent data. Alternative measures of efficiency should also be explored. In addition, the endogeneity of the selected regulatory regime should be accounted for explicitly. Better controls for the competitive pressures the firms face and relevant differences in their operating conditions (e.g., customer population density) would also be useful. 7.5. Earnings There is some evidence that the earnings of the regulated firm increase when it operates under incentive regulation, particularly price cap regulation. The Federal Communications Commission (1992) reports that AT&T’s average annual rate of return was 13.2 percent under the first thirty months of price cap regulation, whereas its prescribed rate of return was 12.0 percent just prior to the imposition of price cap regulation in July 1 1989. Tardiff and Taylor (1993) find no significant relationship between earnings and incentive regulation, broadly defined, between 1984 and 1990. Armstrong et al. (1994, pp. 204–205) report that British Telecom’s rate of return on capital increased from less than 17 percent in 1984 (when price cap regulation was instituted) to more than 21 percent in 1991 and 1992. Of course, this increase reflects a variety of effects, including the change from public to private ownership of BT. Ai and Sappington (1998) report that RBOC earnings were higher (by approximately 16 percent) under price cap regulation than under rate of return regulation between 1991 and 1996. However, they do not find evidence of higher earnings under other forms of incentive regulation. Of course, studies of the effects 89
Majumdar (1995), Jha and Majumdar (1999), Fraquelli and Vannoni (2000), and Resende (2000) employ related methodologies and find that competition and/or incentive regulation have motivated suppliers of telecommunications services to increase their operating efficiencies in recent years.
Ch. 7: Price Regulation
283
of regulatory policy on earnings are difficult to conduct because firms often have significant discretion in allocating revenues and costs across time periods. 7.6 Service quality Incentive regulation can promise the regulated firm substantial financial reward for realised cost reductions. Consequently, the possibility arises that a firm that operates under incentive regulation may be tempted to reduce service quality unduly in order to reduce operating costs90. The evidence on this issue is mixed. AT&T experienced some large-scale service outages while operating under price cap regulation in 1990 and 1991. However, after careful study of the circumstances surrounding the outages, the U.S. Federal Communications Commission (1992) reported no evidence of a link between price cap regulation and AT&T’s service quality. Tardiff and Taylor (1993) also find no evidence that incentive regulation promotes reduced service quality in the U.S. telecommunications industry. Armstrong et al. (1994) report widespread dissatisfaction with the service quality provided by British Telecom (BT) during the early years of price cap regulation. An audit by the regulatory body Oftel revealed that although BT’s service quality was not as poor as it was generally perceived to be, it could have been substantially higher than it was on many dimensions. BT was required to publish certain service quality performance statistics twice each year, and was also subjected to financial penalties for poor service quality. Increased competition was also encouraged, and appeared to have had its intended effect. To illustrate, 17 percent of BT’s pay telephones were out of order on average in 1986. The corresponding statistic in 1987 was 23 percent. In 1987, BT’s primary competitor, Mercury, was authorised to provide pay telephone service. By 1989, less than 5 percent of BT’s pay telephones were out of order (Rovizzi and Thompson, 1992). Ai and Sappington (1998) find that during the mid 1990’s, the service quality provided by the RBOCs in the United States was lower on some dimensions under incentive regulation than under rate of return regulation. Most notably, the RBOCs remedied reported service problems more slowly under price cap regulation, earnings sharing regulation, and rate case moratoria than they did under rate of return regulation. Residential customers also reported more service problems (per access line) under earnings sharing regulation than under rate of return regulation. But Ai and Sappington (1998) also report higher service quality on some dimensions under incentive regulation. For example, residential and business customers both registered fewer complaints with their public utility commission under price cap regulation, earnings sharing regulation, and rate case moratoria than under rate of return regulation. Residential and business customers also received more timely installation of new telephone service under earnings sharing 90
Norsworthy and Tsai (1999) stress the need to account for realised service quality when designing reward structures for regulated firms.
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They then attempt to proxy ct with two separate variables, an index of access prices and a simple time trend. The latter variable is intended to measure ‘‘ . . . other components of cost that trend smoothly over time . . . 70’’ No justification is provided for the implicit assumptions that: (1) the time trend variable is highly correlated with non-access cost changes; and (2) that variable is not correlated with other, excluded variables. Also, perplexingly, these authors indicate that ‘‘et is the price elasticity of demand for a good at time t calculated using Ward’s results’’71. This use of a prior econometric estimate of the price elasticity of demand for AT&T’s service as a right-hand variable in Equation (14) begs the question of how this prior elasticity estimate can vary over the authors’ sample period. In addition, this use of an econometric estimate as an explanatory variable introduces an errors-in-variables problem that is likely to bias the resulting parameter estimates. Fitting this revised version of Equation (14), Taylor and Zona obtain an estimate of y equal to 2.55, which is significant at the 0.01 level72. From this estimate, the authors conclude that: ‘‘ . . . our results reject the hypothesis that AT&T acts as a competitive firm in the interstate longdistance market. Thus, if its prices are not constrained by regulation or the threat of antitrust intervention, then AT&T would wield substantial market power because of coordinated pricing behavior among the IXCs73.
This conclusion, then, tends to support MacAvoy’s (1995) contention, discussed earlier, that the major long-distance carriers – AT&T, MCI, and Sprint – have successfully coordinated their pricing behavior in order to raise and maintain market price above marginal cost. That is, both of these papers argue that tacit collusion is present in this industry. That conclusion, however, is inconsistent with much of the prior literature in this area74. In order to investigate this issue further, we have developed and 69
The plus sign is apparently a typo, as a minus obviously should precede the last term. Taylor and Zona (1997, p. 243). 71 Taylor and Zona (1997, p. 243) indicate that the source for these elasticity estimates is the study by Michael Ward reported in Reply Comments of the Staff of the Bureau of Economics of the Federal Trade Commission, In the Matter of Revisions to Price Cap Rules for AT&T, CC Docket No. 93-197, October 25, 1993. 72 The data used for this estimation are Laspeyres indices for output and access prices constructed by the authors. These indices are calculated quarterly from 1984:3 through 1994:4. 73 Taylor and Zona (1997, p. 244). 74 See our earlier discussion of this issue in Section 2, above. In addition, the results reported in Section 3 regarding the impact on price of reduced regulation also tend to reject the tacit collusion hypothesis. 70
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$)$, *.$ !$$$ " .-$& $ $ - *-'$ - !$ $ " )$*-% $$3 .-' "-* * $ G 1*. !.+-% -& "*/-$ G - $& $ *.$ )$-%% * " - !-0-'$ " /.%$-%$ -& $*-% $)$3 * -!!$-, ./$)$, *.-* *.$ -)' * *$'-*' !*$- * $*/0 * * -& $? !$* - "-* ' * '$-*3 * *.$$ * -* *.-* - !$-%* - !-* %- *-'$ " *.$ !!%+ .- - -0$ !#*-1%$ $*+3 * ""$ - & $(!%--* " /.+ %-% *$%$!.$ -$ "*$ *5 $ . -*)*$ - &$ /$ -*$-$ -& 1%%' -& %%$*3 .$$ -% - !*-* *-*$' $- /.+ )$*-%%+ *$'-*$& $)$ !)&$ .-)$ 1$$ &)$*' !*$- !$-* - !$** -*$-%$ &/*$-3 &)$*$& $*$ %$*, / $*, -%+ 1$- $ * *$ -/ * " *$$* 1 +' $? !$* " - !-+ /*. /. *.$+ !$*$& $)$3 !*-* !!$*+ " *$%$ -* *, $!$-%%+ "-* * $ )$*$*, * 0$3F $ !%$*$&, . $? !$* -& "-%*$ .-)$ %**%$ $ )-% $ $ *.$ *.- " /.-* *.$+ -$ *$&$&3 1$ -1%$ 1 $& 1$$-*. *.$ *$$* ! %%$& *. '. &$' & & * %*$-%%+ -& $-%%+ 03 0 )$*$* .- !*-* *-*$' !%-* " %-% $*/0 5 !$** &$!$&' * 4$ -& /. -0$ *.$ )$*$*3 .$ 1$* )$* 0 "-%*$, *.$+ .-)$ $*)$ * * !$ &/ * -)&-1%$ *3 $.%'$ *.-* & * )%)$ .$-)+ 0 * - 1$ . $ $!)$ * $*-, .-'' &*3 *-$, /$%$ *$.%'+ *$& * 1$ %$ 0 *.- /$%$ *$.%'+:3 *.$ $(*$* *.-* *-**$ - 1$ )$& *.$ +*$ - 1$ $*#**$& " -*.$ /$%$ *-&-&, /$%$ *$.%'+ .$-!$ * $3 *-*, -1%$ !-$ .-)$ %$-$& *.-* - $*/0 !*$& " $5/-+ &$%)$+ " %*5.-$% )&$ $*$*-$* * $-%+ $#' $& * &$%)$ */5/-+ )$ -& &-*- $)$3 001 +' ' $.%'+ " %-% *$%$ -*, %0$ -+ -* & *+, !)' -!&%+ -& !$&*-1%+3 .-* $*-, ./$)$, *.-* * -$ "-%%' -& -!-1%*$ -$ $(!-&'3 &)-$ $%$* .-)$ %/$$& /*.' * - !$ -& !$"-$ " $$*-% &'*-% '-% !$' -& -!!%-* !$# !$ .! *$-&%+ !)$3 .$ $%$*%$ -. " !)$$* !*-% *- .- $-$& *.$ *. '.! * " - #1$ /.%$ %/$' *.$ * " - "-* $ -& *-%%-* " #1$3 *.$ !$"-$5-&D *$& * " /*.' -& *- "-%% *'$*.$, ./ *.$ *-&$5"" & $& -1)$ /%% 1-%-$ * $- * 1$ $$3 :
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$ !*-* " ! !$, ./$)$, -$ *.$ !-* *.-* .-'$ -%$ $$ .-)$ %-% !$**3 '.%+, *$.%'-% .-'$ .- $& $& )$-%% * " !)&' %-% $)$, 1 * /*. - $-$ #($& * $%-*)$ * )--1%$ *3 1-%-$, *.$ $""$* !$** -1' 3 $*-* *.-* -&!* $/ *$.%'+, *.$$1+ "-%*-*' $*+ - -.$)$ %/$ *A 1 * *.$ $%-*)$%+ .'.$ #($& * -$ , $& ' *.$ 1$ " )-1%$ #3 !%-*$ -**$, $/ *$.%'$ "*$ $ %* !)$& $.-$& $)$ * %-*' &$-& -& %0$%+ !!*' $ #3 '*-% /*.' -& *- .-)$ !" &%+ $& $& * -& !)$& $)$ " %-% -* $*/03 '*-%-* !!* -&)-$& '-%%' $*/0 -& /*.' "$-* $ *.-* $(!-& *.$ $)$ ""$$& * *$3 '*-%-* -% $%$)-*$& *.$ %$ " "*/-$ *.$ %-% $*/03 *+!-% &'*-% /*. 1+ !'- .-)' $ *.- - %% %$ " &$ $-1%' - $(!-&' --+ " -!-1%*$3 !$ -%'*. !-0 $-' - * " "-* - !*-% #1$ $3'3, &$$ /-)$ &) %*!%$(' - -& .-$% $3'3, 8 /$%$ *-&-&, '$-*%+ $%-(' -!-*+ %*3 !-$& * $-%+ *$, *.$ %-% $*/0 * - !$-$*, *-* !%-*" " $)$ &$%)$+3 -% -$ - $#' $ /*.' -& '-%%' "*5 /-$ * %- . $/ -%%' "$-* $ -*!-*$& /.$ *.$ /*. /- #* *-%%$&3 *.$ -$ " 1 $ $)$, *. *% -+ 1$ !%-$& *.$ .-& " *.$ $3 -$ - -% * 1%$ .* -& -*- *.$ $*/0 $%$*-%%+ " - $*-%, $'-% !$-* $*$, $& ' *.$ $$-+ * 0 %%3F - $ %*, $%-1%*+ -& $ *+ " /$%$ -& /$%$ $*/0 .-)$ '$-*%+ !)$&:63 .$ !-* " )-* $*/0 -.*$* $ %-% !$** $ &"# %* * !$&*3 $(-!%$ / %& 1$ *.$ &$!%+$* " #1$ ' 1- -$- -*.$ *.- *.$ *-&*-% *- $*/0F /*. %! $--*' " $*-% "#$ /*.$3 $*-%+ *. $/ -.*$* $ /- - '& #* " F *-*$'+ * $*$ 1+ '-*.$' .'. )% $ *-"# - &$$%+ !! %-*$& 1 $ -$-3 .$$ $*/0 $$& /*.' G $%-*)$%+ $$*%+ *-%%' -$5'-&$ /*.$ G *-0' -&)-*-'$ " *.$ %/ * -& .'. -!-*+ " #1$3
$ $$*%+ -$ .-)$ 1$$ )' */-& *$$* *% -.*$* $ !-**$$& "" *.$ /-+ *.$ *$$* #' $&3 .$$- *.$ *-&*-% $*-%$& $*/0 *$%%'$$ /*.' -& '-%%', *.$ *$$* !%-$ *$%%'$$ -* *.$ $&'$ " *.$ $*/03 .$ *$$* -& * 1$ -&$ ! " & 1 !!$ -& -* *$-%, !-$& * *.$ /.$$ *.$ *$-% -$ & 1 -& *$%%'$$ $&$ *.$ $*/03 -0$*5/*.$& $*/0 $ " -%*$-*$ *' !$)-)$ 1 * /*. &""$$* !%-* " /*.' -& *-3 *.-* -$ "-* % &' )$ -& *.$ *-"# -$ !-0$&F -* *.$ $, -& $-. !-0$* :6
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&$%F * '$$-*$ * $*-*$ " 1 &%$& $*/0 $%$$* /.., * , / %& 1$ $& * $* !$ " *.$$ $)$:3 $* &$-& -& * --%+$ "-%% "- .* " "' *.$ * '$* ? $* &' %-% $*/0 !$**3 * " $$*+, $$* &$%%' " *.$$ !.$$- .- .-& * $%+ &-*- &-/ " 1$* !%* -& *-*$5/$& $*$!$ /.$ -*$ -& $)$ ""$' /$$ .'.%+ *%%$&3 *.$ $/ $- " %-% !$**, /.-* $$&$& -$ $*-*$ " *.$ * " $*-* !!%+ - /$%% - *.$ " - 1$* /. "-$ 5 !$**3 . -+ )%)$ - '$$#$%& * * " - $*/0, * -+ $%+ 1 &%$& $*/0 $)$ * )-+' &$'$$3 .$ %-**$ $!$$* -%*'$*.$ $/ /.%$-%$ $)$ *.-* * 1$ -&&$& * *.$ 1$*F !& * %$3 -&&*, *.$ $*-* /%% %0$%+ ""$ - &""$$* --+ " $)$ *.- *.$ 1$*3 * *.$ $-%*+, ./$)$ *.-*, - " +$*, !$** .- 1$$ * %*$& -& * 1$" * !)&$ - -&$? -*$ &-*-$* * *$* *.$$ 0& " !!*3
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= $-& 6, 9, :56, 6956<, 8, =, 86, 8;, 8=58>, ;, ;=5;:, 9;, 9=59>, <, <:, =<5==, >:, :9566, 685666, 668566=, 66:, 665686, 68856;, 69<, 69>, 6<=, 6><, 6>>, 6:6, 6:856:;, 6:>, 8, :566, 6<, 8, ;<, 9659, 9;5<, =5=;, >:, 858;, 8<586, 865868, 869586>, 858, 8;589, 8=588=, 8;, 8;, 8;;58;<, 8;>, 8965899, 89=589>, 8<8, 8<9, 8<=58=, 8=;58=:, 8>6, 8:=58:>, ;8, ;6, ;6;, ;=5;:, ;865;8<, ;8:, ;;65;;, ;;95;;<, ;96, ;985;9;, ;9=, ;<8, ;=:, ;:5;:, ;:<, ;::, 9:596, 96;, 9, 9;, 985986, 98;598<, 9;;59;>, 995996, 999599<, 99>599:, 9<6, 9=;, 9==, 9>59><, <9, <=, <6=5<6:, <6, <8, <<5<>, <8, <8, <8>, <;65<;, <;95<;<, <9=, <<5<<6, <<85<<:, <=8, <=95<=<, <>6, <>9, <>>, <:85<:;, <:<, =5=<, =:5=6, =6;5=6< 70- &$-& 9>8
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% $**. 9=59=6 "($& /$%$ 6:=56:>, <96, <9<, <<6, <==5<=:, <>;, <>:, <::, = /$%$ -!!%-* !*% 9<< /$%$ %-% !$** <;6, <:: /$%$ %-% %! 8=, = %& <, ;, ;:=, 9859;, 9=, 96<, 9;, 9= %& -&$ '--* ;6; -& -$ !' =, 88=, 8>, =6 -& 1 &%' >, 8>, 8>: 1- -'$$$* $%$ -* <>> %& &$ $1 <= -&*0 $' %-* 9, 8858;, 9, :8