FTC Staff Reply Comment Before the Federal Communications Commission In the Matter of Revisions to Price Cap Rules for AT&T
us-ftc-advocacy-filings-9f9df38cdbc6ad3a209bec8503892303FTC Staff Reply Comment Before the Federal Communications Commission In the Matter of Revisions to Price Cap Rules for AT&TAdvocacy Filing
Matter Number
V930026
V930026 COMMISSION AUTHORIZED Before the FEDERAL COMMUNICATIONS COMMISSION Washington, D.C. 20554 In the matter of Revisions to Price Cap Rules for AT&T ) ) ) CC Docket No. 93-197 Reply Comment of the Staff of the Bureau of Economics of the Federal Trade Commiasion" October 25, 1993 "This comment represents the views of the staff of the Bureau of Economics of the Federal Trade Conunission. They are not necessarily the views of th9 Commission or any individual Commissioner. Inquires regarding this comment should be directed to Michael R. Ward (202-326-2096) of the FTC's Bureau of Economics.
i I Table of Contents I. Incroduction and Summary. . . . . . . . . . . . . .. 1 II. Expertise of the Staff of the Federal Trade Commission 3 III. Background. 4 IV. The Analysis of Market Power in Long Distance Services 7 V. Implications of the Analysis for the Streamlining of Optional Calling Plans and Commercial Services 10 VI. Conclusion . . . i 13
Before the
FEDERAL COMMUNICATIONS COMMISSION
Washington, D.C. 20554
In the matter of
Revisions to Price
Cap Rules for AT&T
)
)
)
CC Docket No. 93-197
Reply Comment of the Staff of
the Bureau of Economics
of the Federal Trade Commissionl
I. Introduction and Summary
The staff of the Bureau of Economics of the Federal Trade
Commission (FTC) appreciates this opportunity to submit this
comment in response to the Federal Communications Commission's
(FCC) Notice of Proposed Rulemaking 2 ("Notice" ) concerning
proposals to revise price cap rules for AT&T. 3 In the Notice, the
FCC requests the submission of data and comment regarding 1)
IThis comment represents the views of the staff of the Bureau
of Economics of the Federal Trade Commission. They are not
necessarily the views of the Commission or any individual
Commissioner. Inquires regarding this comment should be directed
to Michael R. Ward (202-326-2096) of the FTC's Bureau of Economics.
2Notice of Proposed Rulemaking In the Matter of Revisions to
Price Cap Rules for AT&T, CC Docket No. 93-197 Released July 23,
1993) .
3This cor,1ffient addresses issues re... ating to economic efficiency.
It does not take a position on other policy consider~tions that may
be of relevance to the FCC.
I , removing Optional Calling Plans (OCPs) from the price-cap regulatory framework to a more streamlined form of regulation 4; 2) removing commercial services from the price-cap regulatory framework to a more streamlined form of regulation 5; 3) revising AT&T's quality monitoring; and 4) revising the regulatory treatment of 800 Directory Assistance and analog private line service. This comment addresses only the first two proposed revisions. The analysis contained in this comment discusses the conditions under which consumers would benefit from adoption of proposals 1) and 2) , streamlining the regulatory procedures for optional calling plans and commercial long distance services. This comment summarizes and interprets an empirical analysis of market power in long distance communications that has been conducted by a sta:f member of the Bureau of Economics of the Federal Trade Commission. o The analysis estimates the drop in the quantity demanded that a price increase would induce for AT&T and the other long distance companies for Basket 1 services. 7 The 4"ReachOut America" is AT&T's most popular optional calling plan, but AT&T has also recently introduced "the i plan." "Friends and Family" and "the Most plan" are MCl's and Sprint's more popular optional calling plans. 5AT&T has proposed that long distance calls made by customers who pay business or commercial local telephone service rates would be classified as commercial services. 6A copy of this analysis is attached to this comment. 7Basket 1 services include traditional long distance, calling card and international calling and are primarily purchased by residential and small business customers. Baskets 2 and 3 comprise 800 service and private lines that are primarily purchased by large businesses. 2
I , results suggest that the market for Basket 1 long distance services, tuought to be less competitive than Baskets 2 and 3, is nonetheless quite competitive. Specifically, the potential welfare costS due to the exercise of market power by an unregulated AT&T is estimated to be between 0.25% and 1.26% of industry revenues (i.e., between $138 million and $696 million per year). Furthermore, to the extent that competition for optional calling plan customers and cormnercial service customers (who are among those included in Basket 1) is more vigorous than that measured in the analysis, any potential welfare costs due to supra-competitive AT&T prices for these services would be smaller. If the FCC finds that the benefits of streamlining outweigh the potential welfare costs from a possible increase in the opportunity for AT&T to exercise market power, then streamlining would enhance total economic efficiency. II. Expertise of the Staff of the Federal Trade Commission The FTC is an independent administrative agency charged with maintaining competition and safegu.arding the interests of consumers. 9 The staff of the FTC, upon request, often analyzes the competitive or economic efficiency implications of regulatory or legislative proposals. In the course of this work, as well as in antitrust and consumer protection research and litigation, the staff applies established principles and recent developments, both 8This is discussed in section IV. 9 1 5 U.S.C. §§ 41 et seq. The FTC Act declares unlawful unfair methods of competition and unfair or deceptive acts or practices. 3
empirical and theoretical, to competition and consumer protection issues. FUL example, the staff recently submitted a comment to the FCC on its proposals to modify the regulations concerning the local transport of interstate long distance traffic. 10 The staff of the Bureau of Economics of the FTC has studied various economic aspects of the telecommunications industry. These studies include: the effects of price and entry regulations on long distance telephone servicell , the cost attributes of local telephone companies l2 and issues relating to bypass of the local telephone network by long distance companies. 13 III. Background As we understand it, this proceeding is designed to tailor regulation of telecommunications under the Communications Act of 1934, as ammended,14 to an increasingly competitive long distance market. One premise of the 1984 court-ordered AT&T divestiture was IOComment of the Staff of the Bureau of Economics of the Federal Trade Commission regarding Expanded Interconnection with Local Telephone Company Facilities (CC Docket No. 91-141 Phase I and CC Docket No. 80-286) (March 5, 1993). llSee Alan D. Mathios and Robert P. Rogers, The Impact of State Price and Entry Regulation on Intra-State Long Distance Telephone Rates, FTC Bureau of Economics Staff Report (November 1988) . J2Shin, Richard and John T. Ying, "Unnatural Monopolies in Local Telephones," RAND Journal of Economics 23 (1992) 171-183 and Ying, John T. and Richard Shin, "Costly Gains to Breaking Up: LECs and the Baby Bells," Review of Economics and Statistics 32 (1993) 357-361. 13Parsons, Steven G. and Michael R. Ward, "Telecommunications Bypass and the 'Brandon Effect,'" FTC Working Paper 199, (1993). 14 47 U.S.C. §§ 151 et. seq. 4
that long distance telecommunications were potentially competitive. Effective competition was deemed to require equal access to the local telephone companies' end user connections. IS Equal access allowed AT&T's competitors to introduce services that were comparable to AT&T's. While AT&T's share of the revenues of the long distance market was 95% in 1982, by 1987 its market share had fallen to 80%, and it is currently about 60%.16 By 1991, MCI's and Sprint's revenue market shares had climbed to 17% and 10% respectively, and the two next largest firms, WilTel and Cable & Wireless, had market shares of somewhat less than 1%. Since the divestiture, industry output, measured by the number of long distance calling minutes, has nearly tripled. Even though AT&T's share has declined to 60%, its output has increased by two-thirds over 1984 levels. By the late 1980'S, the FCC decided that traditional rate-of- return regulation would hinder the transition from monopoly to competition because of the difficulty in setting a "fair" rate of return, the regulatory lag between the date the rate is filed and the date it becomes effective, the imposition of administration lSUnited States v. AT&T, 552 F. Supp. at 195-200 (D.D.C. 1982), aff'd sub nom. Maryland v. United States, 460 U.S. 1001 (1983). See also United States v. GTE Corp., 603 F. Supp. 730 (D.D.C. 1984) and MTS and WATS Market Structure Phase III, 100 F.C.C. 2d 860, 57 R.R.2d 1303 (1985) (FCC extended equal access obligations to non- Bell local telephone companies) . 16Kwoka, John E., liThe Effects of Divestiture, Privatization and Competition on Productivity in U.S. and U. K. Telecommunications, II Review of Industrial Organization (1993) 49- 61; and Statistics of Communications Common Carriers, Industry Analysis Division, FCC, 1992. 5
costs, and the perverse inefficiency incentives (i.e., the Averch- Johnson Effect) .17 To facilitate the transition toward full competition, the FCC adopted a price-cap regulatory framework in 1989. 18 The price-cap regulation adopted for AT&T divided services into three "baskets" depending on the level of competition in the market for each service. "Basket 1" included direct dialed and operator assisted basic measured toll services (MTS), international services and calling card services; "Basket 2" was limited to 800 number services; and "Basket 3" contained all remaining services, principally those offered to large businesses. Each basket had its own price-cap, and sometimes a floor, that increased with inflation and decreased with a productivity factor and "exogenous" changes in costs, mainly carrier access charges. In its Interexchange Proceeding l9 , the FCC concluded that certain long distance services were sufficiently competitive that an even simpler regulatory framework was warranted. Under "streamlined" regulation, AT&T is able to change prices more 17See Ghosh, Sutapa, "The Future of FCC Dominant Carrier Regulation: The Price Caps Scheme, 41 Fede~-al Communications Law Journal 401 (1989). 18policy and Rules Concerning Rates for Dominant Carriers (Price Caps), Final Rules, 54 Fed. Reg. 19,836 (1989). 19Competition in the Interstate Interexchange Marketplace, CC Docket No. 90-132, Report and Order, 6 FCC Rcd 5880 (1991), (Interexchange Order), recon., 6 FCC Rcd 7569 (1991), (Sua Sponte Reconsideration Order), further recon., 7 FCC Rcd 2677 (1992) (Further Reconsideration Order), pets. for recon. pending; see Competition in the Interstate Interexchange Marketplace, Memorandum Opinion and Order of Reconsideration, 8 FCC Rcd 2659 (1993). 6
quj.:kly, prices are presumptively competitive, ::>nd reporting requirements are relaxed. In October, 1991, streamlined regulation was adopted for all of Basket 3 except analog private line~, a relatively small service with a shrinking demand. Likewise, following deployment of 800 number portability technol ogy 20 in May 1993, streamlined regulation was adopted for all of Basket 2 except 800 Directory Assistance. The current Notice solicits comments on whether competition for two additional services, optional calling plans and commercial services, is sufficiently vigorous to warrant streamlined regulation for them as well. IV. The Analysis of Market Power in Long Distance Services Attached to this comment is a paper analyzing market power in long distance telecommunications that was prepared by a staff member of the Bureau of Economics of the FTC. The analysis estimates the potential welfare loss due to AT&T's exercise of market power by measuring the firm-specific own-price elasticity for AT&T and its chief competitors. 21 Economic theory maintains that the reciprocals of these elasticities, called "Lerner 20 800 number portability allows a customer to keep its 800 telephone number when it changes long distance companies. Since companies make investments specific to a telephone number (e.g., advertisements, printed material), switching costs arise if the number must be forfeited. See also Kaserman, David L. and John W. Mayo, "Competition for 800 Service," Telecommunications Policy (1991) 395-410. 21This elasticity represents the percentage change in the amount demanded of a firm's service that would occur if the firm increased its price by one percent and the prices of the other firms in the industry remained unchanged. 7
indicesll,equal the pric2 markups over marginal costs for profit maximiz:;'u'::f firms. 22 With supra-competitive pricing, potential customers that value the service above its marginal cost but below its price choose not to buy the service even though they and the producer could benefit from the provision of such service. The difference between these customers' valuations and the additional cost of serving them is a social welfare loss. Accordingly, we calculate the loss in economic welfare due to supra-competitive pricing, or the uead\tie:ight as the difference between the demand curve (measuring the customers' value) and the marginal cost for all customers not served who would be served if price equaled marginal cost. 23 The analysis focuses on residential and small busLless services, i.e., those belonging in price-cap Basket 1. The long distance prices used in the estimation procedures are the rates filed at the ~CC for basic interstate service. These prices do not include prices for WATS or 800 services, term or volume discounts, OCPs, or any other deviation from the basic rates. 24 The long 22Landes, William M. and Richard A. Posner, IIMarket Power in Antitrust Cases, II Harvard Law Review 94 (1981) 937-983. 23Some customers purchase the service despite its supra- competitive price. On economic efficiency grounds, the price-cost difference for these customers represents a transfer from consumers to producers and not a social welfare loss. 24Since price data were not available for AT&T's competitors other than Mcr and Sprint, demand elasticities may by biased toward zero, causing inflated price-cost margins. See the attached paper, page 25. 8
distance quantities are carrier common line .minutes. These quantlt.1.eS are primarily purchased at basic rates out can also include some 800 and optional calling plan purchases. They exclude large business services which use private lines or bypass facilities. The conclusions from the analysis about market power are as follows. The lower-bound estimate of AT&T's own-price elasticity is -2.9, implying an AT&T upper-bound Lerner index of 0.337. For reasons discussed in the paper 25 , the estimated elasticity is likely to be a short-run elasticity. Since long-run demand is more elastic, the estimated Lerner index overstates the long-run price- cost markup. When estimates of the Lerner index bias due to the use of short-run, rather than lony-run, demand elasticities are accounted for, AT&T's price markup over long-run marginal COStd is, at most, 18.6% of price. At this upper-bound value, the deadweight loss from supra-competitive pricing for the 1988 to 1991 time period was less than 1.26% of total industry revenues ($696 million per year). Similarly, a lower-bound deadweight loss estimate of 0.25% of total industry revenue ($138 million per year) was also calculated. The upper-bound estimate is likely to overstate the actual welfare loss. For example, accounting for AT&T's fall in market share since the 1988 to 1991 period from 67% to 60% decreases the upper-bound deadweight loss estimate to 0.71% of revenues. 26 ~See the attached paper, pages 20-21. ~See the attached paper, page ~3. 9
V. Impli'cations of the Analysis for the Streamlining of Optional Calling Plans and Commercial Services On economic efficiency grounds, the adoption of a more deregulatory framework is warranted if the social benefits of such a change outweigh its social costs. The costs of adopting streamlined regulation emanate from the increased opportunity for AT&T to set prices above marginal costs. It is possible that price-cap regulation is constraining AT&T's prices from the profit maximizing level more than streamlined regulation would constrain them. The difference between the deadweight losses under these two scenarios represents the economic efficiency loss due to the change in regulatory framework. 21 For example, if price-cap regulation perfectly constrains AT&T's prices to marginal cost and streamlined regulation has no constraining effect at all, then the entire potential deadweight loss discussed in the previous section represents the loss in economic efficiency. In practice, the difference between the deadweight losses under the two regulatory frameworks is likely to be less because streamlined regulation will likely retain some constraining influence and because price-caps likely do not perfectly constrain prices to marginal costs. The potential deadweight loss calculations in the attached analysis pertain to an aggregation of many different services, including optional calling plans and commercial services. To the extent that these specific services face more competition than the 21 AS explained in footnote 23, above, the economic efficiency loss does not include a potential transfer from producers to consumers. Such a transfer does not represent a loss to society as a whole. 10
average service, their price-cost margins will be smaller than the average price-cost margin. Accordingly, the potential welfare loss due to supra-competitive pricing will be proportionately lower for such services. Competition in optional calling plans and commercial services is likely to be more vigorous than for the average service included in the analysis. Optional calling plans tend to offer discounts to high volume callers. Since these customers tend to have larger monthly bills, they have more to gain from searching for lower prices. Thus, for optional calling plan customers, demand for long distance service is likely to be more elastic. For commercial service customers, AT&T reports a relatively high customer turnover rate of 13%.28 This is evidence that its customers can switch to lower priced (or higher quality) alternatives. More elastic demand for these services means that the potential deadweight loss as a percentage of the relevant revenues is smaller than the estimated 0.25% to 1.26%. The benefits of adopting a streamlined regulatory framework stem from the removal of regulatory costs imposed by price-cap regulation that are not incurred under streamlined regulation. Just as streamlining Baskets 2 and 3 eliminated specific sources of social costs, so could deregulation of AT&T's Basket 1 services. First, direct administration costs incurred by the FCC and AT&T would be saved, as well as the costs incurred by AT&T and its competitors in advocating changes in the rules. Not only does the 28N . 6 otlC~, para. . 11
FCC devote fewer resources to the regulation of Baskets 2 and 3, but AT&T and its competitors may also incur fewer costs due to the submission of requests, waivers, opposition comments, and studies. Second, with increased pricing flexibility, AT&T would be better able to respond to differences in cost conditions across customers. For example, term contracts for the private line services allow AT&T to reduce costs by making investments specific to a customer. Likewise, pricing flexibility allows 800 service prices to reflect marginal costs, which decrease with customers'. calling volume. Third, with streamlined regulation, AT&T's costs of introducing new services would fall and more product variety would likely ensue. As the pace of introduction of new electronic technologies increases, their adoption becomes increasingly tied to the telecommunications companies' ability to accommodate them. If these cost savings are greater than the welfare cost due to supra- competitive pricing, then streamlining would be warranted. The benefits from streamlining services for business customers that the FCC recognized iIi the Interexchange Order could also result from streamlining Basket 1. These included: the expedition of "new services and price reductions"; an increase in AT&T's ability "to react to market conditions and customer demands" with more flexible pricing; a decrease in "regulatory delay and uncertainty"; an increase in competitive pressure on AT&T's competitors; and an increase in "AT&T's incentives to initiate pro- consumer price and service changes. ,,29 ~Interexchange Order, para. 78-80. 12
VI. Conclusion This comment reviews an analysis conducted by a membe~ of the staff of the Federal Trade Commission that attempts to measure the level of competition in long distance markets. The results of this analysis provide estimates of the potential deadweight loss if AT&T were completely unconstrained. The social costs of adopting a streamlined regulatory framework are then shown to be the difference in the actual deadweight losses resulting from price- caps and streamlined regulation. If the social benefits from the elimination of price-cap regulation exceed these social costs, then adoption of a streamlined regulatory framework would enhance economic efficiency. 13
" Market Power in Long Distance Telecommunications' Michael R. Ward Economist. Federal Trade Commission September 16, 1993 ABSTRACT: AT&T's long distance prices are regulated in order to avoid supra- competitive pricing. Th is rationale presumes that AT&T possesses sufficient market power to raise prices substantially above marginal cost. This paper estimates firm specific demand elasticities for AT&T and the other large long distance companies using monthly data for 1988 through 1991 from five states. Lerner indices are calculated from these elasticities and interpreted as price markups over marginal costs. An upper bound for the price markup for AT&T is estimated to be 18.6% of price, which implies a potential deadweight loss of less than 1.26% of current long distance revenue. This is likely to overestimate the current deadweight loss because competition has subsequently increased. lhe views expressed here are the author's and do not necessarily reflect those of the Federal Trade Commission or any individual commissioner. I would like thank Tim Daniel, Jan Pappalardo and Steve Parsons for comments and Dolly Howarth for research assistance.
Table of Contents I. Introduction II. The Long Distance Telecommunications ~rket 3 A. Carrier Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 4 B. Regulation of Telecommunications . . . . . . . . . . . . . . . . . . . . . . . . . . . . " 6 III. Empirical Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 8 A. Two-level budgeting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 8 1. Upper Level Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. II 2. Lower Level Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 12 B. Econometric Issues 13 I. The Instrumental Variables Method . . . . . . . . . . . . . . . . . . . . . . .. 14 2. Reverse Regressions 16 C. The Lerner Index . . . . . . . . . . . . . . . . . . . . . . . . .. 16 IV. Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 21 A. Upper Level Estimation ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 21 B. Lower Level Estimation '" . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 22 I. Demand Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 22 2. Potential Measurement Errors if! the Long Distance Prices 24 3. Instrumental Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 25 4. Some Tests of the Instrumental Variables. . . . . . . . . . . . . . . . . . . .. 27 v. VI. VII. Demand Estimation Results . . . . . . . . Potential Deadweight Loss Calculations Conclusi", 29 31 35 References . . . . . 36
I. Introduction Price regulation is desirable only when its benefits outweigh its costs. These benefits include the increase in consumer welfare and economic efficiency from reducing supra-competitive prices, while the costs include direct administration costs as well as reductions in productivity arising from disincentives directly caused by the regulation. For the telecommunications industry, there is growing evidence that regulation has led to substantial productivity losses (Mathios and Rogers (1989,1990), Kaestner and Kahn (1990), Olley and Pakes (1992), Crandall (1991), Kwoka (1993), Ying and Shin (1993». While most experts agree that competition has increased in the long distance market, AT&T's prices are still regulated. The evidence on the extent of residual market power in the industry is still unclear. Some studies examining the structure (Egan and Waverman (1991» and pricing behavior (Levin (1991» of the long distance telecon:ununications industry conclude that competition already exists. Others interpret the structural evidence differently (Selwyn, Cornell, Taschdjian and Woodbury (1991», or conclude tilat full implementation of fiber optic technology will render the industry a natural oligopoly that will support supra-competitive prices (Huber, Kellogg, and Thorne (1993)). This paper attempts to measure the degree of market power in the small business and residential market for long distance telecommunications by estimating the degree of substitutability between AT&T's and its rivals' service. Before the 1984 divestiture, AT&T was thought to exert market power in two different ways. First, it was feared that AT&T-controlled monopolies in local telephone service would subsidize otherwise competitive long distance service, or that AT&T would discriminate against rival long distance carriers by providing inferior access to end users (Brennan (1987». Second, it was feared that AT&T would wield market power in the long distance market directly by charging supra-competitive prices. The first threat did not materialize, as evidence strongly suggested that the subsidy flowed from the more competitive long distance markets to the regulated monopoly local service markets (Temin and Peters
(1985a, 1985b), Kaserman, Mayo and Flynn (1990), Shin (1993)). Nevertheless, AT&T's divesture of its local service operations in 1984 was explicitly designed to remedy the subsidy and discrimination complaints (Brennan (1987)). As a result, it would seem that if the long distance industry could be shown to be sufficiently competitive, there would be no compelling economic arguments for continued price regulation of AT&T's long distance service. This paper measures the degree of competition in the long distance telecommunications market primarily through estimates of firm-specific demand elasticities. This demand elasticity indicates the extent of a firm's loss in quantity demanded due to unilaterally raising prices - that is, the extent of the firm's market power (Landes and Posner (1981)). The reciprocal of the own-price elasticity, the Lerner index, provides an estimate of the percentage price markup over marginal cost for an unconstrained, profit maximizing firm. The applicability of the Lerner index to AT&T is discussed below. Finally, estimates of this price-eost margin provide the basis for measuring the potential deadweight loss from supra-competitive pricing. The paper's general conclusions are as follows. The estimate of AT&T's short-run own-price elasticity is -2.9, implying a short-run AT&T Lerner index of 0.337. Adjusting this index with an estimate of the bias due to the use of a short-run, rather than long-run, elasticity estimate implies that AT&T's price markup over long-run marginal cost is less than 18.6% of price. At this upper-bound value, the potential deadweight loss from supra-eompetitive pricing was less than 1.26% oftotal revenues for the 1988 to 1991 time period. Because the market likely has become even more competitive since the period analyzed in this study, this estimate is likely to overstate the current loss. For example, adjusting for AT&T's subsequent fall in market share decreases the upper-bound potential deadweight loss estimate to 0.71 % of revenues. These estimates of deadweight loss are substantially less than estimates of the efficiency gains due to past deregulatory actions. 2
" II. The Long Distance Telecommunications Market The FCC's Specialized Common Carrier decision in 1971 opened up the long distance market to competition. AT&T's tough posture toward its new competitors led to many private antitrust lawsuits, and culminated in the Justice Department's massive antitrust suit. The 1982 settlement of this suit provided for the 1984 divestiture of AT&T's long distance operations from newly created regional local telephone companies. As part of the settlement, the divested local telephone companies were obligated to install switching equipment that allowed for "equal access" by any long distance company. This allowed AT&T's competitors to introduce services that were comparable to AT&T's. While AT&T's share of the long distance market's revenues in 1982 was 95%, by 1987 its market share had fallen to 80%, and it is currently about 60% (Kwoka (1993), Statistics of Communications Common Carriers (1992)). By 1991, MCl's and Sprint's revenue market shares had climbed to 17% and 10% respectively, and the two next largest firms, WilTel and Cable & Wireless, had market shares of somewhat less than 1% each. Since the divestiture, industry output, measured in the number of calling minutes, has nearly tripled. Even though AT&T's share has declined to 60%, its output has increased by two-thirds over 1984 levels. A long distance company operates a communications network that connects local telephone exchanges (hence, it is often called an Interexchange Carrier, or "IXC"). A long distance company's network terminates in different local telephone companies' jurisdictions. The local telephone companies, such as the regional Bell Operating Companies and GTE, transport telephone calls between the customers' premises and the long distance network. These services are called "carrier access," and they currently represent nearly 40% of all long distance costs. Because these carrier access costs play an important role in the empirical analysis carried out below, it is worthwhile to describe them in some detail. 3
A. Carrier Access Almost aU carrier access rates are regulated by the FCC or state regulatory commission~. For standard toll service, long distance companies purchase "switched access" from local telephone companies. Switched access prices are divided into three main components: carrier common line, local switching, and local transport. Carrier common line charges are levied to cover that portion of the local telephone distribution plant assigned to the long distance companies for capital recovery. Local switching charges are levied because a long distance telephone call must be switched through the local network, thus tying up switching capacity that has alternative uses. Local transport charges are levied as a rental of the line between the long distance network and the relevant local switch. All three of these charges are levied per minute of use at each end of the telephone call using switched access. The carrier common line charge is levied specifically to defray the costs of the local telephone distribution plant. and not the costs of completing long distance calls (hence, it is sometimes called the "non-traffic sensitive" charge), and has a long and tortured history. Half a century ago, the courts ruled that, because AT&T's long distance service used local exchange network loops, a portion of the cost of these local network loops should be recovered through long distance rates. The portion of the local loop assigned to long distance service steadily grew to 27% in 1982 with little relation to underlying economic costs.' The FCC's 1983 Access Charge Plan formalized this cost assignment as the carrier common line charge, which was to be levied as a per minute charge despite its fixed cost nature. At first, the non- AT&T long distance companies (collectively known as the Other Common Carriers or OCCs) had inferior connections to the local telephone companies (for example, customers were required to dial extra digits to reach the long distance company). This sort of access is called "non-premium access" and its associated carrier common Iine charge was set at 45 % of the premium charge paid by AT&T. The FCC IOriginally the proportion of the local network loop costs assigned to long distance operations was the fraction of calls going over long distances. In the early 1950s, this was less than 3%, by 1982 it was 8.3% and in 1991 It was 14.4%. 4
has since required the local telephone companies to install equipment to provide equal access to any long distance company asking for it. Equal access equipment is now in place for nearly all (91 %) long distance company customers, and non-premium service accounts for only 3 % of acc service. A subscriber common line charge of $3.50 per month for each residential line and $6.00 per month for each business line was phased in between 1985 and 1991; this new charge gradually replaced most of the carrier common line charge revenues. The reduction in the carrier common line charge, which has fallen to about one-sixth its 1984 level (total switched access prices fell about 60%), accounts for much of the price reductions in long distance service (faylor (1991». Local switching and transport services are together called "traffic sensitive services" because their costs depend on the volume of traffic. For both of these services, there are many different rate elements that make the price dependent on various switching services and the distance of the transport. In general, however, the rates for these services have dec! ined only slightly since divestiture. These rates are generally believed to be considerably above the services' marginal costs. Cost studies have put incremental costs (Mitchell (1990)) and long-run marginal costs (Shin (1993» of switched access at one- tenth to one-third of the switched access rate. Long distance companies also purchase a different form of carrier access, special access, from local telephone companies. Special access lines are not switched by the local telephone company and are leased by the month at rates corresponding to their capacity and distance. Actual usage is not metered. For a sufficient volume of traffic, special access can represent significant cost savings over switched access. Special access competes with third party, or facility bypass, access provision. Long distance companies use special access to connect directly to end users with high volumes of traffic or nonstandard technical requirements and to connect different long distance nodes within a metropolitan area as a substitute for the local transport portion of switched access. 5
Long distance company carrier access costs per minute differ due to differences in the mix of carrier access services that the companies purchase. First, different local telephone companies can have different prices, and the smaller, more rural local telephone companies tend to have higher prices. Long distance companies that carry a disproportionate share of the calls originating or terminating with these local telephone companies will tend to have higher carrier access costs. Second, the amount of local transport purchased for a typical call can differ across long distance companies. As a long distance company serves more urban customers, who tend to be closer to its local network connection, or adds more local network connections, its average local transport distance and corresponding charge per minute tend to fall. Third, switched access costs will depend on the amount of lower priced, non-premium access purchased by a long distance company; however, this cost difference is becoming negligible due to declines toth in non-premium usage and the carrier common line charge. B. Regulation of Telecommunications States regulate prices for intrastate services (local services, intrastate carrier access and intrastate long distance), and the FCC regulates interstate services (interstate carrier access and interstate long distance). While the FCC regulates AT&T prices directly, the accs and the third party-access providers are not regulated directly, although the accs file prices with the state PUCs and the FCC similar to tariff filings of regulated firms. Price-eaps are increasingly replacing· rate-of-return as the form of regulation in the telecommunications industry. Generally, price-eap regulation allows the regulated firm to charge any price below a regulated price-eap that is periodically adjusted to reflect changes in exogenous cost factors (Liston (1993». The FCC decided to move to price-cap regulation for AT&T in May 1989. Federal price-cap regulation of local telephone companies' interstate access rates was implementeO in January 1991. While price-eap regulation of local telephone companies has also been introduced by various 6
PUC~, these mechanisms typically contain explicit profit "sharing" provisions if the actual rate-of-return exceeds a predetermined limit (Braeutigam and Panzer (1993». Profit sharing mechanisms could also be implicit in the FCC form of price-eap regulation. In fact, high profits due to the increased efficiency of British Telecom under price-cap regulation in the U.K. and prices below the price-eap led to a revision of the price-eap formula toward a more binding cap (Kwoka (1993». The specific form of price-eap regulation adopted for AT&T divided services into three "baskets" depending on the perceived level of competition in the service. "Basket 1" includes residential and small business services, international services and operator assisted and calling card services; "Basket 2" is limited to 800 number services; and "Basket 3" contains all rem_:ning s:;"vices, prir;.~:;;::.!!y those offered to large businesses. Each basket has its own price-eap, and sometimes a floor, that increases with inflation and decreases with a productivity factor and "exogenous" changes in costs, mainly carrier access charges. As services have been shown to be competitive, they have been removed from price-cap regulation. In October 1991, the FCC permitted contract carriage for AT&T's offerings to large business customers. However, AT&T must publish a summary of the contract and offer the same price to similarly situated customers (Griboff (1992». Similarly, almost all 800 services were removed from Basket 2 in May 1993 with the introduction of 800 number portability? This study uses prices from Basket 1, thought to be less competitive than the other baskets. 2800 number portability allows a customer to keep its 800 telephone number when it changes long distance companies. Since companies make investments specific to a telephone number (e.g.,advertisements, printed material), switching costs arise if the number must be forfeited. See also Kaserman and Mayo (1991). 7
III. EmpiriCal Methodology This study attempts to me:lsure the degree of :narket power as the difference between price and marginal cost. In theory, a profit maximizing firm not facing price regulation will set prices such that the price markup over long-run marginal costs, its Lerner index, is equal to the absolute value of the inverse of the firm's own-price demand elasticity. Thus, the degree of market power can be inferred from estimates of this elasticity. However, the data and methodological requirements for the estimation of firm specific demand elasticities are extremely demanding. This section describes the methodology amployed to estimat~ demand elasticities for AT&T and the accs and to infer price markups over marginal cost. A. Two-level budgeting Long distance service can be somewhat differentiated across firms. For high-volume business customers and those who use data transmission services, there may be substantial variation in product attributes. Even for residential and small business customers, differences in, for instance, perceived quality, customer sel vice, and bill ing systems could render long distance services heterogeneous across firms. Finally, some carriers offer lower quality, lower priced, non-premium service, although its popularity has diminished considerably. Despite the foregoing differences, it is not unreasonable to aggregate different firms' long Glstance service intu a single market that is distinct from outside goods. In this way, it is possible to disentangle the decisions regarding the amount of long distance service to consume and the firm chosen to provide it. A two-level budgeting approach is used to estimate the demand system (Deaton and Muellbauer (1980)). The upper level determines the industry-wide demand for long distance, while the lower level determines how demand is allocated among the various tirms in the market. This approach is used 8
" because it accords with perceptions of the long distance market, and it partially separates the effects of general inaustry price reductions from price differences among firms within the industry. Real prices for long distance service fell by about 50% between 1984 and 1991. Differences among firms' prices were much smaller throughout this period. Two-level budgeting is also supported by the observation that, while real total expenditures on all toll service increased by only 3.5% between 1984 and 1991, the market shares of individual firms changed considerably. With two-level budgeting, consumers are assumed to allocate funds across different broad commodities (level one) and then distribute the allocated funds among the specific goods within the commodity group (level two). In the upper-level, demand for a!;gregate long-distance service is determined based on a price index for the industry, Q w = QW(pw pLoc pSer Y) D D ' , , where Y represents total income and pLD, pLoc, and Eft' represent the prices of long distance service, local telephone service and telephone sets respectively. The total expenditures on long distance service is determined as a function of price, y LD = pLDQLD(pW pLoc pser Y) D ' , , where ~ represents income allocated to the long distance commodity group in the upper level. In the lower level, consumers choose which long distance carrier's service to purchase based on their relative prices and t-D, the amount of income budgeted to long distance service, (1) Equation (1) looks like a traditional demand equation where quantity demanded is a function of prices and "income." Traditional demand elasticities are calculated by differentiating equation (1) with respect to firm l's price, pl. The partial derivative introduces two terms because firm l's price is implicit in long distance expenditures, 9
(, OQI oy~ OQll oyW opl ~ opl yW. This equation can be expressed in elasticity form by multiplying both sides by pi / (l, 11 ;: plOQI :: yWOQI p10YW ~ p1o<YI 11 Qlopl Ql oYW yWopl Q1apl yW. (2) The second term in equation (2) above is the elasticity conditional on the amount of income budgeted to long distance, r/fi' The first term represents the "income" effect due to changes in the amount of income allocated to long distance service from changes in the price of good I. The first part of the first term, ~ a(l / (l a~, is analogous to an "income" elasticity for the good, E7. If income elasticities do not differ much across long distance companies, the value of this lower-level income elasticity will be close to one. The second part of the first term, P' a~ / ~ api, can be modified as follows: where W is firm I' s share and 'T'Jw is the industry level demand elasticity. Putting the components together yields, 10
( LJ>..IC C '11\ = 1 + '1 }W E 1 + 'lll' (3) Equation (3) has an economically intuitive interpretation. TIle first term measures the "income" effect while the second measures the elasticity holding budgeted income constant. Further, the first term can be decomposed into three effects: pi on pLD, pLD on jW, and jW on (t. First, an increase in pi holding all other prices constant, will increase pLD by the share of the total market that good 1 represents, wi. Second, since jW is the product of pW and (,fD, a one percent increase in pW will increase jW one percent directly and decrease jW by the upper-level elasticity due to a movement along the upper-level demand curve. Finally, an increase in jW will increase the quantity of good 1 demanded according to the lower-level income elasticity. J. Upper Level Demand The industry demand elasticity for equation (3) is estimated from monthly time series data. Long distance quantity is estimated to be a function of its own price, the prices of local telephone service and telephone sets, income, a time trend and monthly seasonality dummy variables, 10gQ, W T)w logP, W + 'lI Loc logP,Loc + T)&ttogP, Sd + E loglru:ome, 12 + «0 time + L tXtmonth,' + W" • =1 (4) The quantity of long distance service demanded is expected to fall as price increases, with rF measuring the industry elasticity. Since local telephone service and telephone sets are complements to long distance service, increases in their prices are expected to lead to a fall in the demand for long distance service, implying that 'TlLoc and 'Tl Sd should both be negative. The coefficient on income, €, is intended to measure the income elasticity for long distance telephone service and is expected to be positive. The prices of PBXs, computers and modems, which can also be thought of as complements to long distance telephone 11
service, tended to decline over the sample period. Sirce price series are unavailable for these products, a time trend is Introduced to capture this shift in demand, and the coefficient on that time trend, (xo, should be positive. Month dummy variables are intended to account for the seasonality of the demand for telephone calling. 2. Lower Level Demand Budget share regression equations can incorporate the assumptions of two-level budgeting. If the lower-level demand elasticities are constant in the relevant range and only two carriers exist, then equation (1) becomes, Assuming E~ is one,3 then by adding log pl to and subtracting the last term from both sides, we get, Since yLD is total industry expenditure, this is the budget share equation. Firm level constrained elasticities are estimated by regressing a firm's revenue market shares against the price of its own service as well as that of its rivals, month seasonality dummy variables and state dummy variables, piQi k1 k1 log-- yLD k1 S 12 C . C . (l +11U> logP~ + 11ij logP~ + L A,:state~ + L Y,;month~ + 1-1&. K=I ,;=1 (5) Equation (5) is estimated for AT&T and an aggregation of MCl and Sprint. Changes in the relative prices within the industry are expected to induce brand switching causing 11:; to be negative and 71:j to be 3It is likely that income elasticities do not differ much across long distance companies, implying that the value of E: is one. Data limitations make its estimation problematic. Since quantity is the dependent variable and it is implicit in total long distance expenditures. errors in variables problems could render estimates biased. 12
positive. Long distance calling demand exhibits sensitivity to seasonal changes and including month dummy variables allows for differences in shifts in demand across brands. State dummy variables are intended to account for idiosyncratic differences in supply and demand across states. B. Econometric Issues One of the critical assumptions underlying regression analysis is that all of the regressors are uncorrelated with the error term. If the statistical independence assumption is violated, the parameter estimates are biased and inconsistent. In demand estimation, a frequent cause of statistical dependence is observed prices incorporating the influences of both supply and demand. That is, prices and quantities are determined at the intersection of supply and demand curves. For instance, industry price, one of the regressors in equation (4), and the firms' prices in equation (5), will depend on the level of output through supply relationships. The error terms, WI and Jl~, represent the effects on output that are not explained by the modeled demand relationship. Thus, because both the prices and the error terms depend on the output level, they are likely to be correlated. This problem, of endogenous explanatory variables, can also be thought of as part of the broader problem of "measurement error," that is, of the divergence between the data being observed and the variables being modeled. For example, in demand estimation, the relevant price variable is the price that would prevail if the demand curve did not shift. To the extent that the observed price is affected by a shifting demand curve, it is measured with error. Measurement error in explanatory variables implies correlation between the explanatory variables and the error term, resulting in biased coefficient estimates. 'The direction and magnitude of the bias is a function of various coefficients and correlations between variables. 4 In equation (5), coefficient bias can result from measurement error in both the own and 4Measurement error in a variable will tend to bias its coefficient toward zero. Measurement error in a different explanatory variable will tend to bias a coefficient in the direction of the product of the (continued ... ) 13
competitor prices. Measurement error in the own price will tend to bias the coefficient toward zero. Measurement error in competitor prices will also tend to bias own-price elasticity estimates toward zero (since the correlation between own and competitor prices and the cross-elasticity are both positive and own-price elasticity is negative). Likewise, measurement error in these prices will tend to bias cross- elasticities down (toward zero). Two general methods of dealing with measurement error are instrumental variables and reverse regressions. The instrumental variables method brings other information to bear in order to recover estimates that are consistent. This method attempts tJ purge endogenous explanatory variables of their correlation with the error term. Reverse regressions, on the other hand, simply attempt to put bounds on the magnitude of the bias. These regressions switch the dependent and independent variables to generate estimates biased above and below the true parameter value. 1. The Instrumental Variables Method Since price and quantity are jointly determined, observed prices represent a mixture of demand and supply relationships. Measures of the demand elasticity from direct regression techniques on observed prices will yield biased and incon3istent estimates because the price is endogenously determined. Disentangling demand relationships from supply relationships empirically requires a technique that can distinguish between shifts in the supply curve (movements along the demand curve) and movements along the supply curve (shifts in the demand curve). Price and quantity pairs associated solely with shifts in the supply curve, for instance, will trace out a demand curve whose slope (or elasticity) can be estimated without bias. One method for identifying shifts in the supply curve is to use variables that represent the 4( ... continued) correlation between the two variables and the coefficient of the variable with measurement error. The magnitude of the bias will be a function of these various correlations and coefficient values (Maddala (1988) pp. 388-391). 14
cost Jf prod""tion. The price level that is predictt'd by these variables would not depend on demand, but
instead would reflect only changes in the cost of prolluction. In this manner, the predicted price becomes
independent of the error term and the resulting demand estimates are unbiased. In such an application
of the instrumental variables technique, the variables representing the cost of production are called the
instrument set.
The ability of instrumental variable methods to obtain meaningful demand parameters depends
on the ability to find suitable instrumental variables for the endogenous price. The two general
requirements are that the instrumental variables be independent of the error term in the demand equation
and that they be correlated with the endogenous variable. First, instrumental variables wat are themselves
functions of the output level will create interdependence between the predicted price and the output level
and, thus, between the predicted price and the error term. This reintroduces the problem for which
instrumental variables were sought in the first place. Second, correlation between the instrumental
variables and price insures that they "explain" some ('f the variation in the price. That is, they must
represent enough of the shifting in the supply curve to provide significant movement along the demand
curve. Better predictions of the shifts in the supply curve provide more precise (i.e., smaller variance)
estimates of the shape of the demand curve.
A test of bias due to errors in variables can be conducted when instrumental variables are
employed. The Durbin-Wu-Hausman test (Hausman (1978» compares the parameter estimates from two
different specifications of a regression model. If the estimates are sufficiently different (in a statistical
sense), the specification that relies on the stronger assumptions regarding the data is rejected. In the
present context, the assumption that the errors in the variables do not lead to biased estimates (implicit
in ordinary least squares (OLS) results) is stronger than the assumption that they might (implicit in
instrumental variables results). A Durbin-Wu-Hausman test can also compare parameter results from two
different instrumental variable specifications where the instrument sets are different.
15
" 2. Reverse Regressions It is possible to place bounds on the true parameter by reversing the direction of the regression. Regressing Yon X when both are loeasured with error yields a coefficient biased toward zero. Similarly, regressing X on Y will also yield a coefficient biased toward zero. However, the reciprocal of the coefficient of Y from the second regression provides an alternative estimate of the coefficient of X from the first regression. This reciprocal will be biased upward and provides an upper bound on the true parameter, Iplim PI < 1131 < Iplim llY I A A where /3 is the estimated coefficient of X in the direct regression, 'Y is the estimated coefficient of Y in the reverse regression ard /3 is the true parameter value. This procedure generalizes to multivariate regressions and generates the set of estimates that bound the true parameter value (Klepper and Leamer (1984». Parameter estimates from reverse regressions are maximum likelihood estimates, and the set of parameter values bounded by these estimates contains the true parameters. As discussed in the previous section, instrumental variable methods, in principle, yield consistent parameter estimates. Yet, because instrumer. ',,} variable methods may still yiek biased coefficient estimates if the instruments themselves are functions of output, reverse regressions can provide additional information about the size of any remaining bias. C. The Lerner Index The reciprocal of the own-price elasticity, the Lerner index, provides an estimate of the percentage price markup over marginal cost for an unconstrained, profit maximizing firm: 16
"
P - Me 1
L=-'--'=
I P, I I I flu
This condition is derived from the first order conditions that equate marginal revenue to marginal cost.
For larger elasticities (in absolute terms), the marginal revenue curve is closer to the demand curve and
the profit maximizing price is closer to marginal cost. In this way, the demand elasticity indicates the
extent to which the firm can unilaterally raise prices without suffering a large loss in quantity. This is
the extent of the firm's market power. The application of the Lerner index to estimated demand
elasticities requires assumptions regarding: the degree to which competitors respond to price changes of
rivals, the effect of regulation on AT&T's ability to set prices above marginal costs; and tile bias in the
implied marginal cost due to the short-run, rather than long-run, nature of the estimated demand
elasticities.
The demand elasticity described in equation (3), used to infer the Lerner index, represents the
effect of a change in a firm's price on its quantity when competitors' prices are held constant. This is
the appropriate elasticity if the firm assumes that its competitors' prices will remain unchanged when it
changes its own price, as would be true with the Bertrand conjecture for a differentiated product industry
(Carlton and Perloff (1990), pp. 272-276, 308-310). If, however, the firm conjectures that changing its
price will induce rivals to adjust their prices (independent of a common change in costs), then this is not
the appropriate elasticity. In the extreme case of perfect collusion, price changes by one firm correspond
to equal price changes by all others, and the relevant firm-specific elasticity is the industry demand
elasticity .
We assume that AT&T will conjecture that its competitors' prices will not change in response to
its own price change. First, the non-AT&T carriers ("Other Common Carriers" or "OCCs") have ample
capacity with which to expand output. While AT&T's share of fiber optic capacity was 41 % in 1992
(Kraushaar (1993)) its share of output was 60% by 1992. This implies that the OCCs have even more
17
capacity for eXj.:-~.1sion than does AT&T. Second, the average acc customer is likely to be significantly more price elastic than the average AT&T customer. The average acc customer derr.:·..ds more than twice the calling volume as demanded by the average AT&T customer 5 Moreover, since most acc customers have switched from AT&T at some time, they have revealed themselves to consider acc service to be a closer substitute for AT&T service than do existing AT&T customers. Third, besides being more price sensitive, acc customers can choose among a large number of non-AT&T long distance companies whose services are likely to be perceived as good substitutes for each other. aver 500 accs other than MCI and Sprint compete in the interstate long distance market (Statistics of Communications Common Carriers (1992». While most of these firms' operations are confined to reselling service supplied over the facilities of other long distance carriers, nine firms operated facilities in more than 45 states by 1991 (Statistics of Communications Common Carriers (1992». Moreover, the combined market share of these other accs is greater than that of Sprint and the growth in their combined market share since 1988 was greater than that for MCI or Sprint. 6 This suggests that an acc faces the prospect of customers switching to any of a large number of potential competitors if it attempted to raise its price in response to an AT&T price change. Fourth, the telecommunications industry exhibits characteristics which tend to impede collusion, either tacit or explicit. In addition to the large number of firms, collusive behavior is more difficult to enforce in industries with rapidly changing technologies and, consequently, changing market shares (Stigler (1964». Long distance telecommunications has experienced an accelerating pace of innovation. Technology innovations include microwave and fiber 5The ratio of a firm's total minutes supplied to the number of its customers represents an index of calling volume per customer. The calculated calling volume index for acc customers is 2.7 times that for AT&T customers in December, 1987 and 2.0 in June, 1992 (Statistics of Communications Common Carriers (1992». 6Market shares in 1991 were 15.0%,9.7% and 13.1 % and 1988 market shares were 10.3%, 7.2% and 8.0% for MCL Sprint and all other accs respe:t;vely (Statistics of COlllmunications Common Carriers (1992)). 18
·. optic transmission, asynchronous transfer mode (ATM) and frame relay switching, and software defined network (SDN) and bandwidth-on-demand data communications. Consumer related innovations include magnetic strip calling cards, optional calling plans and "EasyReach 700" service. As long distance companies adopt these innovations to varying degrees, they become better able to serve different niches of the market and collusive arrangements become more difficult to enforce. Inferring a price-cost markup from a Lerner index requires the assumption that marginal revenue is equated with marginal cost. However, th is assumption may not hold for firms, such as AT&T, that face price regulation. With a constrained price, in the short-run the quantity demanded will exceed the uncomtrained profit maximizing level and marginal cost will exceed the unconstrained marginal revenue curve. Thus, the price-cost margin under regulation would be smaller than the price-cost margin derived from the profit maximizing assumption implicit in the Lerner index. However, since the firm may be able to respond to regulation, the divergence between marginal revenue and marginal cost will be diminished for two reasons. First, if the firm expects price to be constrained into the future, it can reduce marginal cost to the lower marginal revenue level by reducing investments that maintain qual ity. Second, if the firm expects price to be unconstrained for some amount of time in the future, the relevant marginal revenue is a weighted average of the constrained and the unconstrained marginal revenues, where the weight is the probability that the constraint will be binding. In the first case, a sub-optimal level of quality is chosen and in the second, the relevant constraint is the expected price-cap over periods when it is binding. In fact, regulation does not appear to have greatly constrained AT&T's prices, at least since price- caps have been in place. For Basket I services, AT&T's price was at its cap only about one-third of the time that price-cap regulation was in effect. Figure I shows that the price cap is more likely to be binding just after large changes in the cap brought about by large changes in regulated carrier access 19
·. prices. I am told that this result could be a reflection of regulatory delay in reviewing AT&T price changes. Thus, even one-third is likely to overstate the fraction oftime that the price-eap was binding. If the estimated elasticities represent less-elastic short-run demand, then the implied Lerner indices will overstate the actual long-run price-eost margin (see figure 2). There are two reasons to expect the estimates to represent short-run elasticities. First, the elasticity estimates are generated with monthly data on price and quantity. In this market, consumers are not likely to fully adjust to a price change until after a year or so. Short-run demand is less elastic because price information is conveyed imperfectly to consumers through advertisements and experiences with monthly bills. Consumers respond to new price information when they become aware of the price change, in some cases months after it actually occurred. Notably, studies estimating long-run industry demand curves for long distance telecommunication often allow current price changes to affect current quantity as well the quantity demanded for a year or so into the future (Taylor (1980), Taylor and Taylor (1993». Second, the acc Lerner index values, if construed as long-run estimates, would represent economic rents, i.e., profits from the exercise of market power. While it is possible that accs are earning rents, it is likely that most of their implied price-eost margin sterns from the upward bias due to the estimation of short-run, and not long-run, demand elasticities. A measure of the AT&T Lerner index bias generated by the estimation of short-run demand elasticities, instead of long-run elasticities, can be derived from the estimated acc Lerner index. Under the assumption that long-run acc firm-specific demand is nearly horizontal, (that is, the accs are virtually textbook competitive firms), their long-run price-eost margin is very small. The FCC considered "competition in business services to be thriving," frorP. which can be inferred that the FCC considered AT&T's ability, let alone the accs' ability, to set prices above marginal cost for business services to be negligible (Federal Communications Commission (1991». Since some of these business customers are long distance resellers, the ability of the accs to set prices significantly above marginal 20
·, costs to residential customers appears quite limited. In this case, almost all of the estimated acc Lerner index represents a measure of the bias. If the amount of the bias is the same for AT&T and the accs, then the actual price-cost margin for AT&T can be approximated by, -AIT -AIT -oce LLR = L SR - L SR 1 1 -- --' I_ AITI ,- ocelTlSR 'lSR the difference between the estimated price-eost margins for AT&T and the accs. IV. Data Description This section describes the basic data employed to estimate long distance telephone demand relationships described by equations (4) and (5). The upper level demand estimation, equation (4), uses national time series data for the period July 1986 to August 1991. The lower level demand estimations, equation (5), uses monthly data for the years 1988 to 1991 for five states. Instrumental variables are employed to identify structural demand parameters in both estimations. This section describes the data used, focusing primarily on the lower level estimation. A. Upper Level Estimation For the upper level demand estimates, equation (4), national data were collected from various sources on output, prices and income. The total number of minutes of interstate calling is used as the industry output. These data come from National Exchange Carrier Association (NECA) reports to the FCC. The CPJ prices for interstate long distance and local service, and the PPJ for telephone sets were used as price variables. Per capita personal income was used as the measure of income. All prices and income data are deflated by the CPI for all goods and services. 21
The price of long distance is treated as endogenous, so instrumental variable techniques are necessary. Instrumental variables that are available are the PPI indices for transmission and digital switching equipment and the wages of telecommunications workers. All of these represent prices of key inputs into the production of long distance services and, thus, should represent shifts in the supply curve. They will be correlated with the quantity demanded only to the extent that the long distance industry represents a significant portion of the total demand for the individual factors and these markets have upward (or downward) sloping supply curves. While the long distance industry does account for a large fraction of these equipment markets, there is no evidence on the slope of the supply curves. B. Lower Leve.' Estimation The principal data used in the estimation of lower level demand, equation (5), are interstate carrier access usage and expenditure information for AT&T and the Other Common Carriers (OCCs).? The carrier access usage information available is for sv,jtched and special access purchased by AT&T, MCI, Sprint and an aggregation of the other long distance companies. These data span five states and each month from January 1988 through December 1991 for a total of 240 observations. Interstate toll service is the focus of this study both because these data were the most accessible and because this is one of the most important segments of the market. I. Demand Variables A number of relevant variables are available. For each state, month and long distance company, the variables available are the number of minutes and dollar expenditure on interstate switched access and the number of lines and dollar expenditure on interstate special access. Since these data are obtained ?These data are used under a nondisclosure agreement with Southwestern Bell Telephone Co. and are not publicly available. 22
· , from billi~..., :_lformation, they should reflect actual purchases accurately. The number of switched access minutes measures the quantity of long distance service demanded. The quantity is the sum of both the number of outgoing and incoming minutes. Long distance cumpanies have increasingly moved their larger customers to special access and facilities bypass. If special access usage grew faster for the accs than for AT&T, market shares based on switched access alone would underestimate the acc market penetration and will tend to bias own-price elasticities downward. However, the market share based solely on switched access more accurately represents Basket I services. Dividing interstate switched access expense by interstate switched minutes yields an average price for switched access per minute. Dividing interstate special access expense by the interstate number of lines yields an average price per special access line. The price of long distance service for different firms is of key importance to the estimated results, and is the weakest data element. Long distance price variables were constructed from price infNmation in tariffs filed at the FCC. AT&T, MCI and Sprint su"'mit rate schedules to the FCC when they change their rates. These schedules list prices by time of day (day, evening and night), first or additional minute and distance of the call (there are twelve different mileage bands). Since the quantity variable aggregates calls over all of these dimensions, the relevant price is a weighted average over all of these dimensions. However, the appropriate weights can only be approximated and some assumptions must be made regarding the relative use along these dimensions. Time of day, duration and distance weights were computed using intraLATA toll information and some simplifying assumptions. The average duration and the fraction of calls by day, evening and night were available for local telephone toll service by state. Applying these weights to the interstate data assumes comparability between the shorter distance intraLATA and the longer distance interstate calling patterns. The relative weights for the separate mileage bands were computed in an admittedly ad hoc way. A so called "gravity" model of telephone traffic flows was employed to calculate the expected 23
number of calls flowing between two points as propJrtional to the product of ~:._ "mass" of the two locations divided by the square of the distance between the two locations. The expected flows were calculated for each of the 3187 cuumies in the U.S. to each county of the states in the dataset using the counties' geographic center to compute distances and its 1991 population for its mass. The individual county flows for a state are aggregated into the mileage bands. 8 The price is averaged over mileage bands using the aggregated state flows as weights. 2. Potential Measurement Errors in the Long Distance Prices Due to the ...a..i1y assumptions inherent in the construction of the price variable, it is appropriate to check it against other sources. One price series available for comparison is the AT&T price index reported to the FCC. As part of the til ing requirements for price-cap regulation, AT&T has reported price indices for different baskets of services since April of 1989. The price index for Basket 1 Services represents an independent measure of the long distance prices constructed above. Figure 3 displays the constructed long distance prices for AT&T and the OCCs in Texas and the Basket 1 index for AT&T. 9 (t appears as trough the constructed AT&T price closely follows the Basket 1 index. The gradual deviation between the two series may be accounted for by smaller price reductions in the international and calling card calls, which are included in the Basket 1 price index but excluded from the constructed price. Discounts from posted prices are another potential problem with the constructed prices. Programs like AT&T's Reach Out America, MCl's Friends and Family and Sprint's Most tend to offer a percentage discount off the posted prices. If discounted prices vary more than posted prices, then changes in posted 8Bob Evett and Equifax National Decision Systems are gratefully acknowledged for the use of these data. "The other states in the sample yield similar results. Texas was chosen because it represents about half of all the long distance in the sample. 24
prices will under~tate actual price changes and esti'11ated price elasticities will overstate true price elasticities. Figure 4 confirms that AT&T's Residential and Small Business and Reach aut America indices diverge slightly over time. However, the slight divergence suggests that this source of measurement error is small. The same is likely to be true for the acc price. Another measurement error in the acc price results from the exclusion of firms other than the two largest, MCI and Sprint. Price data were not collected for these other firms because the largest represents less than 1% of industry revenues and because quantity information was available only for the aggregation of these firms. The combined market share of these firms has grown from about 7 % to 13 % between 1988 and 1991. To the extent that these firms' services are substitutes for AT&T's and these smaller firms prices are uncorrelated with MCIs' and Sprints', AT&T's estimated own-price elasticity should be biased toward zero. However, the small differences between the MCI and Sprint prices relative to their difference from the AT&T price suggests that the accs' prices are highly correlated with each other and that this bias should be small. 3. Instrumental Variables As noted above, inconsistent coefficient estimates due to errors in variables can be overcome with the use of suitable instrumental variables. The constructed firm-specific long distance prices are likely to contain errors because of both supply and demand simultaneity and the assumptions inherent in their construction. Instrumental variables are required to predict supply prices and purge measurement errors. Moreover, in order to estimate firm-specific price elasticities, firm-specific instruments are needed so that shifts in the supply curve for a particular firm are independent of shifts in the supply curves for other firms. Factor prices could be suitable instrumental variables if they are unique to each firm. If, however, a factor represents a commodity good to all firms (e.g., raw materials), then changes in the factor price are likely to induce similar shifts in supply for all firms in the industry. 25
The avail:tble firm-specific factor prices include the cost of capital and the average prices of carrier access. These factor prices are added to the set of instrumental variables used in the upper level. Measures of the cost of debt are derived from Moody's yield to maturity calculations on outstanding debt for each of the largest three long distance companies. A bond's yield to maturity is deflated by the yield to maturity for a similarly lived government bond in order to adjust for changes in expected inflation. Finally, a firm's outstanding bonds are aggregated into a single yield to maturity using their face value as weights. The cost of capital can be expected to be unique to each firm, correlated with price and uncorrelated with the error term in the demand equation. First, the cost of capital depends largely on the riskiness of the firm borrowing the funds. In the long distance market, AT&T's capital costs reflect a relatively risk free firm, while MCI pays a relatively high interest rate on its junk bond debt. Second, since the firms in this industry are relatively capital intensive, changes in their cost of capital are likely to induce relatively large price changes. Third, the cost of capital is likely to be independent of the output level and, thus, the error term. While carrier access prices may be both unique for each long distance company and highly correlated with long distance price, they may also be correlated with output and, thus, the error term. Firms differ in their carrier access purchases mainly because of the degree to which they integrate into the distribution of telephone calls. When a sufficient volume of calling for a long distance company originates or terminates in a particular area, the long distance company will extend its network into the area and thus reduce its purchase of access from the local telephone company.1O Thus, average switched access prices will depend on the location and the calling patterns of a long distance company's customers. IOLong distance companies typically terminate their networks near the centers of metropolitan areas. Calls to and from outlying areas are transported to the long distance network by local telephone company at a charge that increases with distance. When the volume of traffic for the outlying area develops sufficiently, a long distance company will extend its network to the area. This occurs when the cost savings from reduced expenditures on local telephone company transport is greater than the cost of extending the network. 26
i' More geographically concentrated customers and calls will lead to more backward integration, lower expenditures for switched access and lower total costs. Likewise, average special access prices tend to be lower in areas of more densely located customers and calls because less costly, higher capacity lines can be used. Changes in carrier access rates are highly correlated with long distance prices, explaining much of the decreases in prices (Taylor (1991). However, there is evidence that carrier access prices fall as more is demanded (parsons and Ward (1993». Thus, using carrier access prices as instruments may reintroduce correlation between long distance prices and the error term in the demand equation. 4. Some Tests of the Instrumental Variables While the focus of this paper is on demand estimation, one way to evaluate the effectiveness of the demand instruments is to estimate supply relationships. This analysis can also provide an estimate of the effect of price-eap regulation on AT&T's costs. Price-eaps, which replaced rate-of-return as the regulatory scheme for AT&T midway through the sample, may provide AT&T stronger incentives to reduce costs (Liston (1993». In general, the supply curve is expected to be quite elastic given the relatively large fixed costs relative to variable costs in the industry.1I Supply relationships are estimated with the firm specific data using the equation, ~ 10gPb wcaPb + elogQb + L 4>llogw~ + L Pkstate~ + Vb· IEL k=1 (6) Cap is a dummy variable whose value is one during the time that AT&T was regulated under the price- cap regime as opposed to the rate-of-return regime. The ws, the factor input prices, are the PPI indices for transmission and digital switching equipment, the wages of telecommunications workers, the yield to IIIn fact, Huber, et al. (1993) contend that the technology is developing into that of a natural oligopoly due to scale economies in transmission. 27
, . maturity on corporate bonds, and the average prices far switched and special access. The instruments for quantity are income and month dummy variables. 12 Estimation results for equation (6) are reported in table I. First, as expected, supply curves appear to be flat. Still, coefficient estimates not significantly different from zero are likely due to the meager instruments available for the quantity demanded. Indeed, the negative coefficients for minutes of use could be a demand relationship picked up because both output and income, an instrumental variable, are correlated through a time trend in both variables. Second, there is evidence that the movement toward price-eap regulation lowered AT&T's costs. The price-eap coefficient is negative for both AT&T and the accs but is significant for AT&T only. This conforms to the hypothesis that price- cap regulation is a more efficient form of regulation for long distance telephone service ard with other empirical results (Mathios and Rogers (1989, 1990), Kaestner and Kahn (1990». Since the accs are not subject to regulation, the only effect of price-cap regulation on their prices would be through more vigorous competition with a more cost-efficient AT&T. The third result is that the industry-wide and firm-specific factor input prices are relatively good explanatory variables for the price of long distance service. Increases in the prices of inputs common to all firms -- switching equipment, transmission equipment and labor -- increase the output price for both AT&T and the accs, with coefficient magnitudes and confidence levels differing across firms. The yield to maturity on corporate bonds, a measure of the cost of capital, is significant only for the accs. This result is reasonable since much of MCl's debt is in the form of junk bonds whose prices are more variable than the bonds issued by AT&T. Note also that the estimated coefficients of switched and 12Price-cap regulation of AT&T could render its supply price censored at the price-eap, suggesting that Tobit estimates of equation (8) are more appropriate. As mentioned above, for Basket I services, AT&T's price was at its cap about one-third of the time that price-eap regulation was in effect. Also, periods in which the cap was binding 2Ie possibly the result of regulatory delay. Since, in these ca~es, the price cap may actually be a floor and not a ceiling, attempts to account for censoring are not reported. 28
, , special access prices are all positive and significant. The estimated standard errors are quite small, indicating that these input prices should be good proxies for firm-specific shifts in supply, which are needed in the demand estimation. V. Demand Estimation Results The results of the demand estimations are presented in this section. In the upper level regressions, the results are quite similar to estimates presented in other research. Specifically, the commonly accepted industry demand elasticity of -0.65 cannot be rejected. In the lower level regressions, firm-specific demand elasticities for both AT&T and the accs are found to be sensitive to assumptions regarding the errors in the variables. Carrier access prices can be rejected as suitable instrumental variables due to their endogeneity with output. The elasticity estimates resulting from the restricted instrument set (i.e., one without carrier access prices) provide a smaller range of estimates. Lower bound estimates of short-run own-price demand elasticities are -2.9 for AT&T and -6.6 for the accs. While consumers are not likely to fully adjust to price changes until after a year or so, these estimates indicate that demand is fairly elastic even in the short-run. Estimation results for the industry level demand estimation, equation (4), are reported in table 2. Columns (I) and (2) exclude the time trend and provide evidence that local service and telephone sets are strong complements with long distance services. Columns (3) and (4) include a time trend to capture an exogenous shift in demand (e.g., the growth of data and facsimile transmissions). The resulting cross- price elasticities are smaller and are now insignificant at standard levels. Where Hausman, Tardiff and Belefante (1993) find significant cross-elastic effects from long distance prices on local service penetration rates, the converse cross-elastic results are mixed here. Estimated own-price elasticities are significant 29
and similar to those reported elsewhere (faylor and Taylor (1993), Taylor (1980». In fact, the commonly accepted value of -0.65 can only be rejected in column (I). Table 3 reports two-stage least squares (2SLS) regression results for the lower level demand, equation (5), under various assumptions regarding the errors in variables. 13 Because Durbin-Wu- Hausman tests always reject aLS results in favor of instrumental variables results, only the latter are reported. The left panel uses all of the instruments and the right panel uses all instruments except the carrier access prices. Since carrier access prices can be correlated with the quantity demanded, results that rely on them may still be biased. In each panel, the results from the direct estimation of the market share regressions, as well as the results from the estima~ion of the reverse price regressions, are reported. Coefficient estimates from the reverse regressions are not directly reported, but are used to compute parameter e~timates comparable to those obtained from the direct regression. The top panel reports the results for AT&T demand and the bottom panel reports those for the accs. Coefficient estimates vary widely depending on the errors in variables assumptions made regarding the data. When all of the instruments are used, the estimate on AT&T's own-price elasticity varies from -I. 16 to -5.87. Bias toward zero is confirmed by reverse regression estimates that are larger (in absolute terms) than those from the direct regression. The range of coefficient estimates shrinks to - 2.15 to -4.68 when the instrument set excludes carrier access prices. Likewise, the range of own-price estimates for the accs collapses from between -3.09 and -13.95 using all instruments to between -5.73 and -9.27 using the restricted instrument set. fhese results are consistent with the conjecture that carrier access prices are unsuitable as instruments. Indeed, in all cases, Durbin-Wu-Hausman tests reject the hypotheses that the coefficient estimates are the same using either instrument set. 13While applying a Tobit regression to the first stage to account for possible censoring at the price-cap does change the estimated coefficients in the first stage, the results for the second stage are virtually unchanged. 30
, , UO<'<''1ditional own-price elasticities are now constructed using equation (3). These elasticities and their associated Lerner indices are reported in table 4 assuming an industry demand elasticity of -0.65 and revenue market shares of 0.647 for AT&T and 0.269 for t!}e accs. 14 For example, AT&T's own- price coefficient of -2.15 from the direct regression using the restricted instrument set yields an estimated own-price elasticity of -2.92 and a Lerner index of 0.337. Under the same assumptions, the acc own- price coefficient of -5.73 translates into an own-price elasticity of -6.64 and the Lerner index is 0.151. That is, AT&T's price markup over marginal cost is estimated to be about 33.7% of its price, while the ace's is 15.1 %. VI. Potential Deadweight Loss Calculations As noted above, firm-specific demand elasticities imply price markups over marginal cost via the Lerner index. Also noted above, the estimated elasticities and their associated Lerner indices are likely to represent short-run, and not long-run relationships. This implies that the estimated markup over marginal cost is biased upward. To calculate long-run elasticities and Lerner indices for AT&T, the size of the bias introduced by short-run demand elasticities must be accounted for. Under the assumption that the accs are earning no economic rents at all, their Lerner index represents a measure of this bias for the accs. 15 If the amount of the bias is the same for AT&T and the accs, then the actual price-eost margin for AT&T can be approximated by the difference between the estimated price-eost margins for AT&T and the accs. This provides an upper bound estimate of the price markup over long-run marginal cost of 18.6% (33.7% - 15.1 %) using the estimated results from the direct regression and the 14'[hese shares do not add up to one because the acc share excludes long distance companies other than MCI and Sprint. 151f the accs are in fact earning some economic rents, then this methodology will tend to overstate the Lerner index bias caused by the estimation of short-run demand elasticities. 31
·. restricted instrument set. The lower bound estimate from the AT&T price reverr~ :-egression is 8.5% (18.3% - 9.8%). The potential deadweight loss from supra-eompetitive pricing can be calculated as the area bordered by the demand curve, marginal cost curve and the current output (figure 2). The economic profits, output times the difference between price and long-run marginal cost, could also be considered losses to society if that amount is expended to perpetuate the entry barrier creating the rents (posner (1975». This is likely not the case here since regulators are taking significant steps to introduce competition to the long distance market. Given this regulatory predilection, the return to this sort of rent ~ek!ng by AT&T s!''''.!.!d be quite low. If so, the dead weight loss can be calculated simply as DWL = J~~ [Q(p) - Q(PJ] dp, assuming constant marginal costs. With constant elasticity, the inverse demand is given by Q(P) = Qo(PIPJ~. The resulting deadweight loss as a fraction of current revenue is DWLIREV = [l-(l-LY+~]I(l +17) - L where L is the Lerner index. While firm-specific elasticities. are appropriate for generating estimates of the Lerner index, the industry elasticity is the relevant elasticity for calculating the deadweight loss. If the accs were able to match the hypothetically lower AT&T prices set at long-run marginal cost, then the deadweight loss applies to the whole industry. This would be the case if AT&T were providing a price umbrella over an industry in which all firms had similar nondecreasing cost functions. If instead, AT&T can operate at lower long-run marginal cost than the aces, then it would capture the entire market with prices at long-run marginal cost. In this case the firm demand elasticity is the industry demand elasticity. Table 5 reports the ratio of potential deadweight loss to revenue for various price-marginal cost margins assuming a demand elasticity of -0.65. The relevant deadweight loss from table 5 depends on which values of long-run marginal cost are used. Reducing the largest estimate of AT&T's short-run price-eost margin (0.337) by the size of the bias derived from the acc Lerner index (0.151) yields a long-run Lerner index of 0.186 and a 32
, . deadweight loss of about 1.26% of total revenues. Rducing the smallest estimate of AT&T's short-run price-cost margin (0.183) by the measure of the bias derived from the acc Lerner index (0.098) yields a long-run Lerner index of 0.085 and a deadweight loss of about 0.25% of total revenues. These estimates compare favorably with the range of the current economy-wide estimates of deadweight loss due to market power of 0.5% to 2.0% of GNP (Scherer and Ross (1990), pp. 663-667). If this market is currently more competitive than during the 1988-1991 period, then these estimates will overstate the current potential deadweight loss due to supra-eompetitive pricing. The evidence of a more competitive market is: l) the fall in AT&T's market share from a national average of 67% during the sample period to 60% currently, 2) the introduction of 800 number portability, and 3) the increase in the number of foreign countries reached by acc networks. An alternative representation of the Lerner index is L-l TT = s"TTj [I TJLD I + (JOCC(1_SATT)J where s"TT is AT&T's market share and (Jocc is the acc supply elasticity (Landes and Posner (1981». With values of e TT , s"TT and TJLD of 0.186,0.67 and -0.65 respectively, (Jocc becomes 8.95. This implies that the accs would be willing to increase their output by almost 90% at prices 10% higher than current prices. Assuming an acc supply elasticity of 8.95, the reduction in AT&T's market share from 67% to 60% alone would reduce the Lerner index from 0.186 to 0.142 and the potential deadweight loss from 1.26% to 0.71 % of revenue. Portability of 800 numbers and increased international access by the accs will tend to increase (Jocc as the accs are better able to provide substitutes for AT&T's services. If, in addition to the fall in AT&T's market share, the acc supply elasticity has increased from 8.95 to 10.0, AT&T's Lerner index would decrease from 0.142 to 0.129 and the potential deadweight loss would decrease from 0.71 % to 0.58% of revenue. To be sure, even the upper-bound estimate of the potential deadweight loss from supra- competitive pricing, 1.26% of long distance industry revenue, represents $696 million in 1991, no small sum. The 0.25% estimate using the largest elasticities represents $138 million per year. However, a 33
t I I l c-;, sense of the ma:~itude of the deadweight loss can be discerned from the comparison between the benefits and costs of price regulation. Under perfect price regulation (an admittedly unattainable .:::;al), AT&T's prices would be equal to marginal costs and the elimination of the entire deadweight loss would be the benefit. Imperfect regulation that allowed AT&T to set prices midway between marginal cost and the profit maximizing price level would eliminate about three-quarters of the potential deadweight loss.16 The welfare costs due to price regulation of AT&T can only be gauged in reference to studies of past deregulatory actions. Math ios and Rogers (1989, 1990) and Kaestner and Kahn (1990) found that AT&T prices were 7 % lower in states that use price-eap incentive regulation compared to traditional rate- of-return regulation. This effect presumably occurred, at least in part, because'the less restrictive regulatory structure induced cost reductions. Supply estimates reported above provide some evidence that the movement to price-eaps from rate-of-return regulation reduced AT&T's long distance prices by approximately 1.6%. Olley and Pakes (1992) find that competition spurs telecommunications manufacturing plants to become more efficient. Ying and Shin (1993) found that the local telephone companies' costs fell due to the divestiture of AT&T. Crandall (1991) estimates that telephone industry costs would be $3.5 billion (10%) higher in 1988 than they would have been without the introduction of competition in telecommunications. Kwoka (1993) estimates that each percentage point decrease in AT&T's market share has led to a more than one-third percent (0.36%) improvement in productivity. Since these productivity increases provide continuing benefits into the future, seemingly small improvements quickly become substantial cost savings. 16'Jbe resulting upper-bound estimate of the benefit from regulation would be 0.96% of industry revenues or $530 million per year (the lower-bound estimate would be 0.20% cf industry revenues or $104 million per year). 34
1 1 ," VII. Conclusion This paper estimates supply and demand relationships in the long distance telecommunications market over the 1988 to 1991 period and interprets them in terms of potential welfare losses due to supra- competitive pricing. The estimates of industry demand elasticity are similar to those reported elsewhere. The prices of key inputs explain much of the variation in output prices and these are taken to be valid instruments for demand estimation. Estimates of firm specific demand elasticities are fairly high; lower bounds are -2.9 for AT&T and -6.6 for the accs. These elasticities are best interpreted as short-run elasti.::ities. Long-run price-eost margins are inferred from these estimates via the Lerner index from which the potential deadweight loss due to supra-eompetitive pricing is calculated. Estimates of this potential loss appear to vary between 0.25% and 1.26% of industry revenue ($138 million to $696 million per year). The above analysis brings new information to the question of whether further deregulation of AT&T is likely to be efficient. As stated in the introduction, the benefits of regulation are the possible limiting of the deadweight loss due to supra-competitive pricing while the costs are inefficient production due to regulatory distortion of profit inCl~ntives. In the early 1980s, competition may have been insufficient to constrain AT&T prices to long-run marginal cost. In the intervening decade, competitive pressures on AT&T increased substantially. This paper estimates that, for the 1988 to 1991 period, competition constrained the potential deadweight loss from supra-eompetitive prices to between 0.25% and 1.26% of total revenues. Competitive pressures have continued to mount and it is likely that the potential deadweight loss currently is smaller. 35
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Parsons, Steven G. and Michael R. Ward, "Telecommunications Bypass and the 'Brandon Effect, '" FTC Bureau of Economics Working Paper No. 199, 1993. Posner, Richard A., "The Social Costs of Monopoly and Regulation," Journal of Political Economy (1975) 807-27. Scherer, F. M. and David Ross, Industrial Market Structure and Economic Performance, Third Edition (Houghton Mifflin Company: Boston, MA 1990). Selwyn, Peter, Nina Cornell, Martin G. Taschdjian and John R. Woodbury, "The State of Competition in Telecommunications," in Barry G. Cole, ed., After the Breakup (Columbia University Press, New York, NY, 1991). Shin, Richard, "Cro~s-subsidies in Telephone Pricing," unpublished mimeo, 1993. Statistics of Communications Common Carriers, Industry Analysis Division, FCC, various years. Stigler, George J., "A Theory of Oligopoly," Journal of Political Economy (1964) 44-61. Taylor, Lester D., Telecommunications Demand: A '\urvey and Critique (Balinger Publishing Co., Cambridge MA 1980). Taylor, William, "Effects of Competitive Entry in the U.S. Interstate Toll Markets: An Update," filed with the Federal Communications Commission in CC Docket No. 91-141 (1991). Taylor, William and Lester Taylor, "Post-Divestiture Competition in the United States," American Economics Review 83 (May 1993) 185-190. Temin, Peter, The Fall of the BeLL System (Cambridge University Press, New York, NY 1987). Temin, Peter and Geoffrey Peters, "Cross-Subsidies in the Telephone Network," WiLLamete Law Review 21 (l985a). Temin, Peter and Geoffrey Peters, "Is History Stranger than Theory? The Origins of Telephone Separations," Economic History 75 (l985b). 38
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, , , Figure 1 AT&T's Basket 1 Price Cap & Price Index (Nominal Prices) 100 ,c----------------------------- 99 ~ I 98 f- 97 t- ,, 96 l- Price Cap - Price Index 94 e- 93 ~ 92 ~ 91 "-- 90 -------- -------------------------------------- May-R9 lul-89 OCl-R9 Dec-89 Feb-90 May-90 lul-90 Sep-90 Nov-90 Feb-9! Apr-91 Jun-91 Sep-91 Nov-91 40
· , t Figure 2 Deadweight Loss from Supra-competitive Pricing LR SR Est D D = D p ----------------------- LR MC MC Implied _ Q 41 Deadweight Loss LR MR SR MR
Figure 3 Long Distance Price Variables (Real Prices) Price 120 -.--------- 110 i 100 '-.----------------:--~~ 90 - - - - - -. - - - -. - - - - - - .. - .' - -. - - _.- - .- - '. -. - .- -. '- -. 80 ,-----------.---.--.--.---... 1988 1989 1990 Year 42 - Constructed AT&T Price Constructed OCC Price • Basket 1 AT&T Price 1991
, . Figure 4 Long Distance Discounts (Real Prices) Price 110 ~-----'-------------.,-------- - Res. & Small Bus. Reach Out America 100, 90 i- - - - - - - - - - - - - - - - - - - - - -:.- - - - - --------------------------, ........ . . . '. -.- , 80i------------------------------------------------------I I , 70 ,------------------ --.--. -- 1989 1990 Year 43 1991
Table 1 Supply Price F.quations 2SLS Estimates - 240 Observations January 1988 - December 1991 AT&T OCC Variable Price Price Price Cap -0.016 1 -0.007 (0.006) (0.007) Minutes of Use -0.048 -0.040 (0.039) (0.037) Price of Switching 0.189 3 0.117 Equipment (0.087) (0.093) Price of Transmission 0.368 1 0.717 1 Equipment (0.112) (0.094) Telecommunications 0.30J5 0.054 Workers Wage (0.151) (0.129) Yield to Maturity on 0.048 0.503 5 Corporate Bonds (0.404) (0.256) Price of Switched Access 0.131 1 0.194 1 (0.022) (0.021) Price of Special Access 0.081 1 0.0491 (0.020) (0.008) First-Order 0.7891 0.762 1 Autocorrelation (0.040) (0.042) Adjusted R2 .970 .977 Standard errors are in parentheses and superscripts denote significance levels for a two-tailed test if less than 10%. Not reported are coefficients of state dummy variables. 44
Table 2 Industry Quantity Demanded 2SLS Estimates - 63 Observations June 1986 - August 1991 National National National National Variable Quantity Quantity Quantity Quantity Constant -1. 127 -I1.78P -2.256 -4.737 (4.387) (5.533) (4.071) (4.652) Long Distance Price -0.899 1 -0.569 1 -0.622 1 -0.603 1 (0.085) (0.137) (0.133) (0.127) Local Service Price -0.849 1 -1.024 1 -0.221 -0.373 (0.242) (0.247) (0.281) (0.323) Telephone Set -0.946 1 -0.295 Price (0.293) (0.302) Income 1.703 1 2.661 1 1.499 1 1.797 1 (0.272) (0.409) (0.243) (0.383) Time Trend 0.042 1 0.032 7 (0.014) (0.018) Durbin-Watson 2.092 2.273 2.367 2.398 Adjusted R2 .984 .985 .987 .986 Standard errors are in parentheses and superscripts denote significance levels for a two- tailed test if less than 10%. Not reported are coefficients of month dummy variables. 45
, , , T2.ble 3 Lower Level Demand Equations 2SLS Estimates - 240 Observations January 1988 - December 1991 i I I I AT&T All Instruments I Instruments Exclude I Demand I Carrier Access Prices 1 I OCC I Market AT&T Market AT&T I OCC I Variable Share Price Price i Share Price Price ,I Regression Regression Regression I Regression Regression Regression 1 I I I , Own-Price -1.161 -5.87- -4.23- I -2.15 1 -4.68- -3.78- I (0.31 ) I (0.49) I I Cross-Price 1.44 1 6.09- S.lo- I 2.42 1 4.91- 4.61- I (0.31 ) I (0.49) 1 I Auto- 0.391 0.25\ 0.25 1 I 0.31 1 0.25 1 0.26 1 1 correlation (0.06) (0.06) (0.06) (0.06) (0.06) (0.06) Adjusted R2 .774 .987 .988 .644 .986 .988 OCC Demand Variable Cross-Price Own-Price Auto- correlation Adjusted R2 All Instruments Instruments Exclude Carrier Access Prices Market AT&T OCC: Market AT&T OCC 1 Share Price Price i Share Price Price Regression Regression Regression : Regression Regression Regression I 2.47 1 13.47- 7.97- S.141 8.73- 6.99- (0.72) (1.19) -3.091 -13.9S- -I!. 72- -5.731 -9.27- -8.87- (0.71) (1.18) 0.61 1 0.31 1 0.33 1 0.481 0.361 0.371 (O.OS) (0.06) (0.06) (0.06) (0.06) (0.06) .SSO .987 .988 .227 .988 .986 Standard errors are in parentheses and superscripts denote significance levels for a two-tailed t~st if less than 10%. Estimates with asterisks are the implied elasticities from the coefficients of reverse regressions. The underlying coefficients are always significant at the one percent level. Not reported are coefficients of state and month dummy variables. 46
Tnble 4 Unconditional Own-Price Elastl~cities and Lerner Indices Elasticities Lerner Indices AT&T OCC AT&T OCC All Instrumental Variables Market Share -1.97 -4.00 0.508 0.250 Regression AT&T Price -6.65 -14.86 0.150 0.067 Regression OCC Price -5.00 -12.63 0.200 0.079 Regression Restricted Instrument Set Market Share -2.92 -6.64 0.337 0.151 Regression AT&T Price -5.45 -10.16 0.183 0.098 Regression ace Price -4.55 -9.78 0.220 0.102 Regression 47
Table .'; Potential Deadweight Loss as a Percent of Current Revenues under Various Price Markup Assumptions Assuming a -6.65 Industry Demand Elasticity P - Me DWL 0 -- Po REV 0.350 4.99% 0.325 4.22% 0.300 3.53% 0.275 2.91 % 0.250 2.37% 0.225 1.89% 0.200 1.47% 0.175 1.10% 0.150 0.80% 0.125 0.55% 0.100 0.34% 0.075 0.19% 0.050 0.08% 0.025 0.02% 48
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