zolnierek, eisner, burton - an empirical examination of entry patterns in local telephone markets

8/13/2019 Zolnierek, Eisner, Burton - An Empirical Examination of Entry Patterns in Local Telephone Markets

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An Empirical Examination of Entry Patterns

in Local Telephone Markets

James Zolnierek, James Eisner, and Ellen Burton

Federal Communications Commission*

August 23, 1999

Abstract

In this analysis we examine the market entry patterns of new local telephone companies.We construct and estimate a multinomial logit model using information describing

numbering code distribution within local telephone markets and the associated income,density, and regulatory characteristics of these markets. Our findings support the

conventional wisdom that facilities-based entry by new local competitors is more likelyto occur in large urban telephone markets. In addition, we present evidence that, with

the exception of territories served by Ameritech, entry is more likely to occur in BellOperating Company service territories.

*The opinions expressed in this paper do not necessarily reflect the views of the Federal Communications

Commission (FCC) or its other staff members. Corresponding Author: James Zolnierek, FederalCommunications Commission, CCB/IAD, Room 6A-103, 445 12thStreet S.W., Washington D.C. 20554,

(202) 418-1020,[email protected].


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I. Introduction

A principal goal established by the Telecommunications Act of 1996 is the

opening of local telephone markets to competitive entry. This is a marked departure from

previous telecommunications policy that, while governing the pricing and practices of

local telephone companies, preserved their monopoly positions. The dramatic shift in

policy embodied in the 1996 Act affords us the opportunity to analyze the origins of

competition in the local telephone industry and the behavior of entrants into these

historically monopolized markets.

The primary focus of this analysis is a description of entry patterns in local

telephone markets following passage of the 1996 Act. The analysis we conduct below

examines entry in the three-year period following the Acts passage. Using data

measuring the emergence of facilities-based (and hybrid-facilities-based) competition, we

evaluate the relationships between entry in local telephone markets and the demographic

and regulatory factors that characterize these markets.1

There has been a great deal of conjecture surrounding the entry patterns of new

providers of local telephone service. Many characteristics of local telephone markets,

including both demographic and regulatory characteristics, have been identified as

important to firm entry decisions. In some cases, particularly regarding demographic

characteristics, there is virtual consensus as to how a particular characteristic will

influence entry decisions. In other cases, particularly regarding regulatory

characteristics, opinions as to how a characteristic will effect entry decisions are mixed.

Considering demographic characteristics for example, virtually all analysts claim

that local telephone competition is emerging most rapidly in urban business districts.

Huber [1997] states that In local markets, competition has developed rapidly but only

where competition makes strategic sense for new entrants. It makes sense in the business

markets of large cities. Cooper and Kimmelman [1999] claim that To the extent that

there is competition, it is almost entirely restricted to the large urban areas. Hubbard

1 Facilities-based carriers are those carriers that provide service to customers on their own network

using their own equipment (or plant). Hybrid-facilities-based carriers provide service to customers on theirown network using their own equipment in tandem with equipment leased from other telecommunications


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and Lehr [1998] assert that Such competition as the incumbents face is limited to

commercial customers in major metropolitan areas. Similarly, Gabel and Gabel [1997]

claim that Due to large sunk costs, as well as other barriers, replication of the loop

network has occurred in few places outside of central business districts.

These observations meet with prior expectations, which are based on historical

telephone cost and usage patterns. There is a large body of literature describing the

historical cost structure of the telephone network. As a body, the literature supports the

conclusion that local telephone companies have incurred greater costs by serving rural

customers than by serving urban customers.2 Furthermore, business customers, which are

often concentrated in urban areas, have historically used the network more intensively

than residential customers.3 Consequently, local telephone companies have historically

collected a disproportionate share of their local telephone revenue from business

customers. In concert these factors suggest that the high-volume, low-cost customers in

urban business districts should be more attractive to new entrants than either rural or

residential customers.

As indicated above, opinions regarding the influence of regulatory characteristics

of local telephone markets are more divided. For example, one aspect of the 1996 Act

that has drawn particular attention is the prohibition that prevents the largest incumbent

local telephone companies, the Bell Operating Companies (BOCs), from carrying long

distance traffic in their own local service territories. This prohibition, which applies to

BOC territories that fail to meet a number of competitive criteria outlined in the Act,

combats a two-fold problem in achieving the pro-competitive goals of the Act.4 First, in

order for a customer of a new local service provider to place calls to customers on an

incumbent providers network, and thereby receive the benefits of the existing telephone

subscribership base, the new local service provider must interconnect its network with

carriers. Hereafter, unless otherwise specified, we will use the term facilities-based carriers to refer to thecombination of both facilities-based and hybrid-facilities-based carriers.2 See Crandall [1995, Chapter 3] for a summary of the literature on telephone network costs.3 In 1996, 68% percent of local exchange carriers billable access lines reported to the FCC wereresidential lines (see FCC [1997, Table 2.19]). However, in 1996 only 51% of local revenue was collected

from residential customers (see U.S. Department of Commerce, U.S. Census Bureau [1998, Table 5]).4

A number of competitive requirements must be met in order for the BOCs to be permitted to

provide long distance service within their own local service territories. For detail on the specifics of theserequirements see Telecommunications Act of 1996, Pub. Law No. 104-104, 110 Stat. 56, codified 47

U.S.C. 151 et. seq.


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that of the incumbent.5 However, as noted by Crandall [1999], All carriers have an

interest in being able to connect with other carries, but an incumbent monopolist may

find that its optimal strategy is to refuse interconnection to new carriers, thereby making

it impossible for nascent carriers to survive. Second, absent competition in the local

service market, long distance carriers depend on the incumbent local telephone

companies for access to their customers. Under such circumstances, incumbent local

telephone companies that are able to provide long distance service can leverage their

monopoly power to gain competitive advantages in the provision of long distance

service.6 Consequently, the 1996 Act prohibits the BOCs from carrying long distance

traffic in their own local service territories until the existence of effective competition is

assured.

Opinions are mixed as to whether this prohibition is an effective means of

achieving the goals set forth in the Act. For example, Hubbard and Lehr [1998] maintain

that Allowing BOC entry into long distance while preserving the lack of choice in local

exchange markets will strengthen BOCs barriers to entry Huber [1997], however,

argues that the BOC prohibition has exactly the opposite effect. Huber argues that the

long distance carriers are not providing local telephone service in BOC territories in order

to block Bell Company entry into the residential long-distance markets by persuading

regulators that local competition has failed.

Another area of contention surrounds the regulations governing the leasing and

resale of incumbent facilities. The crafters of the 1996 Act recognized that fostering

access to reasonable interconnection terms alone might provide insufficient incentives for

firms to enter local telephone markets. In order to expedite the competitive process, the

Act opens up two additional avenues for entry into local telephone markets. Competitors

may purchase wholesale local service from incumbents or they may lease elements of the

incumbents telephone networks. Both strategies allow competitors to enter local

telephone markets by relying on the incumbents networks prior to the completion of

their own networks. Although designed to be catalysts in the competitive process, some

5 See Tirole [1990, Chapter 10] for a discussion of externalities (network externalities) that arise

when a good is more (or less) valuable to a user the more users adopt the same good or compatible ones.


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analysts believe these alternative entry vehicles may be creating unintended

consequences. For example, Crandall [1999] notes that by creating ample

opportunities for entrants to use incumbents network facilities, the act discourages

investment in new facilities.

In this analysis we examine the relationship between demographic and regulatory

characteristics of each U.S. local access and transport area (LATA) and the degree of

competition within each.7 While some of our results, particularly those concerning

demographic influences, merely confirm consensus opinion, they provide systematic

empirical support for observations that have heretofore been supported only by anecdotal

evidence. Other results, particularly those concerning regulatory influences, shed light on

those questions still being debated.

The remainder of this paper is organized as follows. In section two we explain

the data set used to analyze entry patterns in local telephone markets. We report the

statistical results of the empirical analysis in section three. In section four we present

conclusions and discuss possible extensions to our work.

II. The dataset

We examine the progress of competition at four points in time: at the conclusion

of the first quarter of 1996 (immediately following passage of the Act), at the conclusion

of the first quarter of 1997, at the conclusion of the first quarter of 1998, and at the

conclusion of the first quarter of 1999. The data set employed here contains, for each

time period, the number of carriers holding numbering code resources in each LATA

nationwide.

Telcordia Technologies (formerly Bellcore) maintains a database of numbering-

code information that telephone carriers rely on to route and rate ordinary telephone

traffic, the Local Exchange Routing Guide (LERG). A facilities-based provider of local

6 See Economides [1998] for an explanation of the means available to monopoly providers of local

telephone service, who also provide long distance service, to engage in anti-competitive actions against

their long distance rivals.7 LATAs delineate the geographical areas within which BOCs may offer telephone service. BOCs

are prohibited from carrying telephone traffic across LATA boundaries (inter-LATA traffic) in their own


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telephone service must acquire a numbering code in an area prior to commencing

operations in that area. Telephone numbering codes are currently assigned for use with

telephone lines located within a unique geographically defined rate exchange area. Rate

exchange areas form the building blocks of a LATA.8 The data set employed here

contains a count of the number of unique local service competitors that hold numbering

resources within each of 190 domestic U.S. LATAs.9 The data set also contains the

primary incumbent provider of local telephone service in all LATAs served primarily by

BOCs. The remaining LATAs are served primarily by independent (non-BOC) local

telephone companies.

The geographical boundaries of rate exchange areas and LATAs are inconsistent

with geographical boundaries typically used in demographic reporting such as county,

metropolitan statistical area, and zip-code boundaries. Therefore, in order to determine

the demographic characteristics of each LATA in our sample, we have identified the

longitude and latitude of each domestic U.S. census block group as reported in the 1990

Census. From this, and LATA boundary information, we identified the census block

groups contained within each LATA.10

Aggregating census block groups by LATA

provided us with an estimate of the demographic characteristics of each LATA.

Information summarizing state regulatory characteristics was collected from

NARUC [1995]. Each LATA was assigned a primary state. State regulatory information

was then assigned to each LATA according to the primary state designation.

A number of concerns should be acknowledged at the outset of this analysis.

Following passage of the 1996 Act, information reporting requirements imposed on new

providers of local telephone service by state and federal regulators were kept to a

service territories, but are allowed to carry telephone traffic, including toll calls, within LATA boundaries

(intra-LATA traffic). As used here long distance service refers to inter-LATA service.8 Although LATA boundaries were created in order to delineate the geographical area within which

BOCs could offer long distance services, other LATA boundaries have been created in order to segmentnon-BOC service territories. The LATA geography adopted here follows Telcordia conventions as

delineated in the LERG.9 There are 193 domestic U.S. LATAs defined in the LERG. Data limitations forced the removal ofthe Fishers Island (NY), Vermont, and Rhode Island LATAs from the data set.10 In order to uniquely assign census block groups to LATAs, we mapped the center of each census

block group (according to latitude and longitude data provided in Census information) into local telephone

company exchanges using exchange boundaries contained in MapInfos mapping software productExchangeInfo Plus. We then aggregated exchanges, and the corresponding Census information, into

LATAs according to MapInfo LATA assignments.


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minimum. What limited information was collected largely received proprietary

treatment.11 While many new providers report local service levels to share holders and

stock analysts, these reports are not comprehensive, systematic, or detailed enough to

allow one to address the questions examined here. Therefore, we have employed the

number of new carriers with numbering resources as a proxy for the number of new

carriers providing local telephone service on their own facilities.

While the counts of carriers holding numbering resources are consistently and

systematically collected in the LERG and can be determined at the LATA level, they may

not perfectly reflect the number of new carriers providing local telephone service on their

own facilities. Competitors that purchase telephone service from incumbents for resale,

and do not rely on their own facilities, may choose to either obtain their own numbering

resources for billing purposes or rely on the incumbents numbering resources.

Therefore, counts of new carriers with numbering resources may include some non-

facilities based providers. Furthermore, carriers acquire numbering resources prior to

providing service.12

Therefore, counts of new carriers with numbering resources may

exceed the number of firms actually providing local telephone services.

A second concern deserving attention pertains to the relationship between the

number of entrants in a market and the competitiveness of the market (measured by, for

example, price markups or quality of service). While, an extensive literature exists that

suggests that markets with more competitors are, ceteris paribus, more competitive, the

impact of entry by an additional competitor may not be continuously related to the

number of established entrants in the market.13

As a consequence, we have defined

discrete levels of competitive activity.

11 For example, the FCC, in order to administer both Universal Service and Telecommunications

Relay Service programs, collects revenue information from all providers of local telephone service.

Although the FCC publishes industry roll-ups, company detail receives proprietary treatment. See FCC

[1998].12 Reservation of numbering resources is permitted to accommodate technical and planning

constraints. However, absent special circumstances, reserved codes that are not activated will be released

from reservation after eighteen months.13 Although the number of competitors in a market may not in every case be positively correlated

with the degree of competition in the market, models that feature a positive correlation between the number

of competitors in a market and the degree of competition in the market are common. For example, see

Tirole [1990, Chapter 5.7] for an analysis of the traditional Cournot model. Phlips [1995, Chapter 2]however, demonstrates that the relationship between the effect of a new entrant and the number of existing

competitors in a market need not be continuous.


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Immediately after the 1996 Act was passed, very few local telephone markets had

any competitive activity. In fact only one market had more than four competitors with

assigned numbering resources. As a consequence, in 1996, we did not differentiate

between entered markets. Markets were defined as entered or not entered. In 1997,

more variation existed between entered markets. We characterized markets in 1997 as

not entered, entered with 1-4 carriers assigned numbering resources, or entered with 5 or

more carriers assigned numbering codes. In both 1998 and 1999, enough variation

existed in entered markets to define markets as not entered, as entered with 1-4 carriers

assigned numbering resources, as entered with 5-9 carriers assigned numbering codes, or

as entered with 10 or more carriers assigned numbering codes. Table 1 provides

distributions of firms across competitive categories for each of the four time periods

examined.

Because our classifications were dictated by entry behavior itself, and because the

variation in the number of competitors in each market has increased over time, our

analysis and subsequent results have become richer over time. As a corollary, however,

our ability to analyze some differences in markets will diminish over time. For example,

it is likely that in the near future virtually all markets will be entered. Relying on data at

that time, one will be unable to draw any conclusions regarding differences between

markets with and without entry. This further emphasizes the unique opportunity

available at this time to analyze the factors that contribute to the formation of local

competition in the telephone industry.

One further concern that arises relates to explanatory variables contained in our

data. Ideally, we would like to explicitly measure the business concentrations within

urban areas in each LATA. Unfortunately, we do not have at our disposal a measure of

the business concentration for each LATA. We do, however, have for each LATA the

percentage of households located in urban areas. To the degree that businesses locations

and urban areas are highly positively correlated, the percentage of households located in

urban areas will proxy for areas of high business concentration.

Table 2 provides a summary of all of the variables employed in the analysis.

Entry, demographic, and state regulatory variable averages are provided averaged across


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Table 1: Number of LATAs by Competitive Category

Period 0 1-4 5-9 10 or More All LATAs*

First Quarter 1996 155 34 1 0 190

First Quarter 1997 109 69 11 1 190

First Quarter 1998 50 103 24 13 190

First Quarter 1999 18 115 39 18 190

* The Fischers Island (NY), Rhode Island and Vermont LATAs are excluded due to missing data.

Table 2: LATA Characteristics by Competitive Category*

0 1 - 4 5 - 9 10 or More All LATAs

LERG Data

Numbering Codes 0.0 2.4 6.4 15.6 4.3

(0.0) (1.1) (1.5) (4.5) (4.5)

Percent Independent LATAs 66.7 11.3 5.1 5.6 14.7(48.5) (31.8) (22.3) (23.6) (35.5)

Percent Ameritech LATAs 22.2 (16.5) 12.8 5.6 15.3

(42.7) (37.3) (33.9) (23.6) (36.1)

Percent Non-Ameritech RBOCs 11.1 (72.2) 82.1 88.9 70.0

(32.3) (45.0) (38.9) (32.3) (45.9)

State Regulatory Data

Average PUC Expenditures Per HH*** (in $) 7.48 9.01 6.95 5.94 8.15

(4.15) (16.45) (4.81) (5.17) (1.42)

Percent Elected Commission 11.1 18.3 25.6 5.6 17.9

(32.3) (38.8) (44.2) (23.5) (38.4)

Data from the 1990 Census

Average Number of HH 88,118 261,853 698,161 1,769,640 477,795

(58,528) (149,761) (408,772) (1,170,962) (618,512)

Percent of HH in Urban Areas 13.6 41.3 57.6 82.1 45.9

(18.4) (20.2) (20.2) (11.0) (25.2)

Percent of HH With Income >= $45,000 17.5 22.5 26.6 34.1 23.9

(4.8) (5.9) (7.9) (7.8) (7.6)

Number of LATAs 18 115 39 18 190

* Standard errors are in parentheses

** Entrants at the end of the first quarter of 1999

*** HH is an abbreviation for household.

Number of Entrants

Number of Entrants in 1999**


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LATAs with 0 carriers assigned numbering resources, LATAs with 1-4 carriers assigned

numbering resources, LATAs with 5-9 carriers assigned numbering codes, and LATAs

with 10 or more carriers assigned numbering codes.

III. Empirical analysis

In each year examined, the relationship between competitiveness in LATAs and

demographic and regulatory characteristics of the LATAs is presumed to arise from a

logistic distribution with probabilities given by

=

+

=t

t

n

j

itj

j

itj

tji

x

xP

1

,

)1,min(

,

,,

)'exp(1

)'exp(

(j=0,,n), (i=1,,190), (t=1996,,1999),

where (nt+1) measures the number of competitive categories modeled at timet, jtindexes

these categories, i indexes LATAs,xiis the vector of LATA characteristics for LATA i,

and j,tare the parameters associated with these characteristics for each competitive

category and time period. In 1996, when only two competitive categories where

modeled (nt=1),Pi,0,1996andPi,1,1996 represent the probabilities that LATA iwill have no

entry and entry, respectively. In 1997,Pi,0,1997,Pi,1,1997, andPi,2,1997 represent theprobabilities that LATA iwill have no entry, 1-4 carriers, and 5 or more carriers,

respectively. In 1998 and 1999,Pi,0,1998 andPi,0,1999 represent the probabilities of no entry

in LATAi for the respective time periods,Pi,1,1998 andPi,1,1999 represent the probabilities

of entry by 1-4 carriers,Pi,2,1998 andPi,2,1999 represent the probabilities of entry by 5-9

carriers, and Pi,3,1998 andPi,3,1999 represent the probabilities of entry by 10 or more

carriers.

Table 3 contains the regression results for the multinomial logit models. In order

to simplify interpretation of these results, Tables 4-6 provide summary statistics and

predictions based on the most recent (1999) regression results for 15 LATAs. In order to

select the 15 LATAs, we calculated the probabilities, based on the regression results, that

each LATA would not be entered. We then selected LATAs in groups of three based on

quartile rankings of these probabilities.


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First Quarter 1999 First Quarter 1998 First Quarter 1997 First Quarter 1996

No Entry Relative to 1 -

4 Entrants


4 Entrants


4 Entrants

Constant -2.742 ** -5.797 ** -5.637 **

(1.084) (1.095) (1.210)

% of HHs in Urban Areas 3.205 ** 2.515 ** 0.734

(1.381) (1.213) (1.452)% of HHs with Income >= $45,000 4.548 11.253 ** 9.364 **

(4.674) (4.170) (4.472)

Number of HHs (In 10,000) ** 0.099 ** 0.061 ** 0.026 **

(0.025) (0.013) (0.077)

PUC Exp per HH -0.068 -0.020 -0.001

(0.044) (0.024) (0.022)

Elected Commission 0.950 -0.302 -0.943

(0.701) (0.562) (0.943)

Independent ** -1.680 ** -1.796 * -1.056

(0.660) (0.965) (0.996)

Ameritech * -1.397 ** -0.973 0.418

(0.644) (0.639) (0.633)


9 Entrants


9 Entrants

No Entry Relative to 5

or More Entrants

Constant ** -10.800 ** -14.761 **

(2.256) (4.027)

% of HHs in Urban Areas * 6.867 ** 9.451 *

(2.358) (4.982)

% of HHs with Income >= $45,000 16.587 ** 10.483

(7.854) (8.910)

Number of HHs (In 10,000) ** 0.146 ** 0.086 **

(0.028) (0.016)

PUC Exp per HH -0.196 ** -0.082

(0.090) (0.114)

Elected Commission 1.055 0.611

(1.162) (1.573)

Independent ** -3.710 ** -1.060

(1.586) (1.687)Ameritech ** -1.959 * -3.575 *

(1.121) (2.173)

No Entry Relative To

10 or More Entrants

No Entry Relative to

10 or More Entrants

Coefficient Coefficient

Constant ** -8.748 **

(3.771)

% of HHs in Urban Areas ** 5.505

(5.153)

% of HHs with Income >= $45,000 1.008

(15.037)

Number of HHs (In 10,000) ** 0.194 **

(0.034)

PUC Exp per HH -0.656 **

(0.331)

Elected Commission -0.346

(1.914)

Independent ** -36.114

7.318E+06

Ameritech ** -6.235 **

(2.760)

Number of Observations 190 190

Log Likelihood -88 -53

* Significantly different from zero at 90% level of confidence

** Significantly different from zero at 95% level of confidence

-9.0929

(2.960)

(3.765)

4.9842

(0.140)

-0.5093

(1.918)

-5.3243

(14.675)

190

-85

(Standard Errors in Parentheses)

Table 3: Logit and Multinomial Logit Regression Coefficients


Entry

-14.1615

-4.3361

(1.674)

-4.2703

(1.552)

0.5965

(1.414)

-101

190

0.2470

(0.060)

-0.1206

13.8514

(4.648)

(2.328)

0.2180

(0.059)

-0.0647

(0.117)

4.9743

(2.748)

14.3786

(12.809)

0.0386

(0.102)

-7.7102

(2.505)

-0.0517

(1.261)

-2.7670

(0.982)

-1.9704

(1.114)

-1.9006

(2.056)

2.9964

(2.282)

9.9514

(11.485)

0.1290

(0.057)


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Class 0 Class 1 Class 2 Class 3 Predicted Actual Matches**

Probability of Probability of Probability of Probability of Class Class

LATA State No Entrants 1-4 Entrants 5-9 Entrants 10+ Entrants

LOS ANGELES CA 0 0 0 100 3 3 Yes

NEW YORK NY 0 0 0 100 3 3 Yes

CHICAGO IL 0 0 19 81 3 3 Yes

MEMPHIS TN 0 27 70 3 2 2 Yes

CINCINNATI OH 0 42 49 9 2 2 Yes

LOUISVILLE KY 0 35 59 6 2 1 No

CHARLESTON SC 0 88 11 1 1 1 Yes

RENO NV 0 96 3 0 1 1 Yes

CEDAR RAPIDS IA 1 88 11 0 1 1 Yes

GREAT FALLS MT 4 91 4 0 1 1 Yes

AUBURN-HUNTINGTO IN 5 93 1 0 1 1 Yes

ROCKFORD IL 6 94 1 0 1 1 Yes

OLNEY IL 91 9 0 0 0 0 Yes

NAVAJO TERRITORY UT 97 3 0 0 0 0 Yes

NAVAJO TERRITORY AZ 97 3 0 0 0 0 Yes

% of HHs % of HHs # of HHs PUC Exp Elected Independen Ameritech

in with Income per HH PUC

LATA State Urban Areas >=$45,000 (in $)

LOS ANGELES CA 94 40 4,804,108 7.26 0 0 0

NEW YORK NY 97 40 4,092,749 8.18 0 0 0

CHICAGO IL 93 38 2,883,682 6.81 0 0 1

MEMPHIS TN 59 22 529,126 7.24 1 0 0

CINCINNATI OH 82 30 624,008 9.52 0 1 0

LOUISVILLE KY 56 22 528,424 4.26 0 0 0

CHARLESTON SC 66 24 204,358 4.38 0 0 0

RENO NV 48 30 173,154 13.36 0 0 0

CEDAR RAPIDS IA 50 23 242,623 4.12 0 0 0

GREAT FALLS MT 25 17 179,509 6.70 1 0 0

AUBURN-HUNTINGTO IN 49 27 194,842 2.06 0 0 1

ROCKFORD IL 62 29 132,278 6.81 0 0 1

OLNEY IL 0 14 55,206 6.81 0 1 0

NAVAJO TERRITORY UT 0 8 1,236 10.74 0 1 0

NAVAJO TERRITORY AZ 0 9 19,636 3.97 1 1 0

* The LATAs are presented, in descending order, according to predicted probability of entry. The LATAs were selected in groups of three based o

quartile rankings. Los Angeles, New York, and Chicago were the most likely to be entered in the first quarter of 1999, while Olney and the Navajo

Territories were the least likey to be entered at this time. The other quartile groups selected were Memphis, Cincinnati, and Louisville (the 25th

percentile), Charleston,Reno, and Cedar Rapids (the median), and Great Falls, Auburn-Huntington, and Rockford (the 75th percentile).

** 81% of the LATAs in the sample were predicted correctly. Of the remaining 19%, 12% were predicted to have less competition than actually

realized and 7% were predicted to have more competition than actually realized.

Table 4: Regression Based Predictions for Selected LATAs*

Table 5: Summary Statistics for Selected LATAs*

(First Quarter 1999)

Continuous Variables Indicator Variables

Predicted Probabilities by Class Classification


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Change in theChange in theChange in theChange in the Change in theChange in theChange in theChange in the

Probability of Probability ofProbability of Probability of Probability of Probability ofProbability of Probability of

LATA State No Entrants 1-4 Entrants 5-9 Entrants 10+ Entrants No Entrants 1-4 Entrants 5-9 Entrants 10+ Entrants

LOS ANGELES CA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

NEW YORK NY 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

CHICAGO IL 0.00 0.00 -9.74 9.74 0.00 0.00 -10.16 10.16

MEMPHIS TN 0.00 -8.37 7.58 0.79 0.00 -4.65 0.70 3.96

CINCINNATI OH 0.00 -13.34 9.49 3.85 0.00 -9.31 -2.37 11.68

LOUISVILLE KY 0.00 -10.05 8.06 2.00 0.00 -6.93 -1.49 8.43

CHARLESTON SC -0.12 -2.00 1.88 0.23 -0.13 -3.50 1.85 1.78

RENO NV -0.10 -0.45 0.54 0.00 -0.13 -0.61 0.71 0.03

CEDAR RAPIDS IA -0.14 -2.25 2.32 0.08 -0.14 -2.40 2.05 0.49

GREAT FALLS MT -0.88 0.10 0.78 0.00 -1.10 0.11 0.99 0.01

AUBURN-HUNTINGTO IN -1.18 0.96 0.22 0.00 -1.37 1.12 0.26 0.00

ROCKFORD IL -0.83 0.76 0.07 0.00 -1.38 1.25 0.13 0.00

OLNEY IL -0.62 0.62 0.00 0.00 -2.88 2.87 0.01 0.00

NAVAJO TERRITORY UT 0.00 0.00 0.00 0.00 -1.08 1.08 0.00 0.00NAVAJO TERRITORY AZ -0.08 0.08 0.00 0.00 -1.07 1.07 0.00 0.00

Change in theChange in theChange in theChange in the Change in theChange in theChange in theChange in the

Probability of Probability ofProbability of Probability of Probability of Probability ofProbability of Probability of

LATA State No Entrants 1-4 Entrants 5-9 Entrants 10+ Entrants No Entrants 1-4 Entrants 5-9 Entrants 10+ Entrants

LOS ANGELES CA 0.00 0.00 0.00 0.00 0.00 0.00 0.11 -0.11

NEW YORK NY 0.00 0.00 0.01 -0.01 0.00 0.00 0.63 -0.63

CHICAGO IL 0.00 0.00 19.62 -19.62 0.00 0.00 -18.75 18.75

MEMPHIS TN 0.11 37.48 -35.37 -2.22 0.06 52.22 -49.56 -2.72

CINCINNATI OH 0.00 -31.59 11.11 20.49 0.00 21.90 -13.13 -8.77

LOUISVILLE KY 0.15 38.26 -33.25 -5.15 0.07 50.65 -44.58 -6.15

CHARLESTON SC 7.11 2.35 -8.57 -0.89 3.15 7.53 -9.71 -0.96

RENO NV 6.80 -4.11 -2.68 -0.02 2.95 0.07 -3.01 -0.02

CEDAR RAPIDS IA 7.61 1.36 -8.73 -0.24 3.38 6.76 -9.87 -0.26

GREAT FALLS MT 38.18 -34.27 -3.90 0.00 20.79 -16.66 -4.12 0.00

AUBURN-HUNTINGTO IN 42.79 -41.82 -0.97 0.00 -4.77 -4.69 9.29 0.16

ROCKFORD IL 42.88 -42.38 -0.49 0.00 -4.78 -0.34 4.97 0.15

OLNEY IL -52.91 52.64 0.27 0.00 -9.28 9.28 0.00 0.00

NAVAJO TERRITORY UT -31.49 31.44 0.05 0.00 -3.66 3.66 0.00 0.00

NAVAJO TERRITORY AZ -31.36 31.13 0.23 0.00 -3.62 3.62 0.00 0.00

* The LATAs are presented, in descending order, according to predicted probability of entry. The LATAs were selected in groups of three based on

quartile rankings. Los Angeles, New York, and Chicago were the most likely to be entered in the first quarter of 1999, while Olney and the Navajo

Territories were the least likey to be entered at this time. The other quartile groups selected were Memphis, Cincinnati, and Louisville (the 25th

percentile), Charleston,Reno, and Cedar Rapids (the median), and Great Falls, Auburn-Huntington, and Rockford (the 75th percentile).

** For example, the table represents how the predicted probabilities would change if a LATA with New York's characteristics was served by an

independent, or if a LATA like Olney was served by a BOC.

*** For example, the table represents how the predicted probabilities would change if a LATA with Chicago's characteristics was served by aa non-Ameritech BOC, or if a LATA like Reno was served by Ameritech instead of SBC.

Table 6: Changes in Entry Predictions Resulting from Changes in LATA

Characteristics for Selected LATAs*

10% Increase in Households Increase in Urban Inside by 10 Percentage Points

(Change From Current Status) (Change From Current Status)

Change in Independent Status** Change in Ameritech Status***

(First Quarter 1999)


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Table 4 contains the probabilities predicted from the regression, based on the

LATA characteristics, that each LATA will have 0 entrants, 1-4 entrants, 5-9 entrants, or

10 or more entrants. For example, based upon the 1999 regression results the model

predicts that a LATA with the characteristics of the Los Angeles LATA will have 10 or

more competitors. The probability of such an outcome in Los Angeles is nearly 100%.

At the other extreme, the model predicts that a LATA with the characteristics of the

Navaho Territory Arizona LATA will likely have no competitive entrants, although there

is a small probability (approximately 3%) that this LATA will have 1-4 entrants. Table

4 also contains, for each of the LATAs, the predicted classification (the most likely

competitive outcome at the end of the first quarter of 1999 based on the models

predicted probabilities), the actual classification (based on the actual number of

competitive providers of local telephone service at the end of the first quarter of 1999),

and an indicator of whether the predicted classification matched the actual classification.

In the sample, 81% of LATAs were predicted correctly for the first quarter of 1999,

including 14 of the 15 LATAs reported in Table 4.

Demographic and regulatory characteristics of the 15 LATAs are reported in

Table 5. Casual examination indicates some rather obvious relationships between

demographic variables and predicted probabilities. For example, LATAs with a high

probability of entry typically contain more households than LATAs with a lower

probability of entry. Table 5 also demonstrates that many of the variables thought to

explain entry patterns are correlated. Therefore, caution should be exercised in

interpreting the regression results.

The regression results reported in Table 3 reveal the factors that influence

telephone company entry patterns and competitive classification probabilities. In Table 6

we provide interpretations of these coefficients. Table 6 contains changes in entry

predictions resulting from changes in demographics and local service provider

characteristics.

It is evident from examination of Table 3 that in each period examined there is a

statistically significant and positive relationship between the probability a market is


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Ameritech territories may indicate that, because of either efficient pricing by Ameritech

or successful rate rebalancing by regulators, profit opportunities in Ameritech territories

are insufficient to support competitive entry on the scale seen in other BOC territories.

Perhaps the most provocative explanation for this phenomenon is that competitors

in Ameritech territories are choosing resale-entry strategies as an alternative to facilities-

based entry strategies. The later explanation is further supported by responses to the

FCCs Local Competition Survey, where Ameritech reports a larger percentage of resold

lines than many of its BOC peers.18

Conclusion

The analysis presented here provides an initial inquiry into the determinants of

entry patterns in local telephone markets. As a preliminary assessment of competition

following the 1996 Telecommunications Act, two conclusions emerge. First, competitors

are more likely to enter highly populated urban areas. Second, all else equal, entry by

new facilities-based providers of local telephone service is more likely to occur in BOC

local service territories.

The conclusions reached here are based upon systematic and comprehensive data

covering all U.S. local telephone markets. Therefore, this study moves the analysis of

local telephone competition beyond speculation and anecdote. This work should,

however, be viewed as only an initial inquiry into the determinants of entry patterns in

local telephone markets. Viewed in this manner, the analysis illustrates the need for

further work in this area.

There are many aspects of local competition that warrant study. The conclusions

reached above suggest three in particular. First, our understanding of local competition

will improve as network traffic and usage data becomes available. Such data will provide

information that indicates whether the market presences measured in this work are, in

fact, meaningful. Similarly, while the data employed in this analysis measure

18

In year-end 1998 responses to the FCCs Local Competition Survey, Ameritech was toward the

high end of percentage of lines resold to competitors, on a company-wide basis. Of perhaps greatersignificance, Ameritech reported the highest percentage of resold lines in the earlier surveys, beginning at

year-end 1997.


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18

competition at the LATA level, further disaggregation that can separate urban and rural

markets may improve our ability to identify factors affecting firm entry patterns.

The third area of research suggested by this analysis stems from patterns observed

in Ameritech territories. Because entry patterns in Ameritech territories differ so

markedly from those observed in other BOC territories, and because Ameritech resells a

high percentage of their lines relative to the other BOCs, firms appear to be substituting

resale entry strategies for facilities based strategies in Ameritech territories. Such

questions of substitution may be answered by jointly examining data on resale, leasing,

and facilities-based activity as such information becomes available.


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19

Bibliography

Crandall, Robert W. 1999. Managed Competition in U.S.

Telecommunications. Working Paper 99-1. AEI-Brookings Joint Center for RegulatoryStudies.

Crandall, Robert W. and Leonard Waverman. 1995. Talk is Cheap: The Promise

of Regulatory Reform in North American Telecommunications. Washington, D.C.:Brookings Institution Press.

Cooper, Mark. and Gene Kimmelman. 1999. The Digital Divide Confronts theTelecommunications Act of 1996: Economic Reality Versus Public Policy. Mimeo.

Economides, Nicholas. 1998. The Telecommunications Act of 1996 and its

Impact. Japan and the World Economy. Forthcoming.

Federal Communications Commission. 1998. Telecommunications IndustryRevenue: 1997.

Federal Communications Commission. 1997. Statistics of Communications

Common Carriers: 1996/1997 Edition.

Gabel, Richard and David Gabel. 1997. The Application of Cost Data in theTelecommunications Industry. Presented at the 1997 Telecommunications Policy

Research Conference.

Huber, Peter W. 1997. Local Exchange Competition Under the 1996 Telecom

Act: Red-Lining the Local Residential Customer. Mimeo.

Hubbard, R. Glenn and William H. Lehr. 1998. Improving Local Exchange

Competition: Regulatory Crossroads. Mimeo.

National Association of Regulatory Utility Commissioners [NARUC]. 1995.Profiles of Regulatory Agencies of the United States and Canada: Yearbook 1994-1995.

Phlips, Louis. 1995. Competition Policy: A Game-Theoretic Perspective. New

York, New York: Cambridge University Press.

Tirole, Jean. 1990. The Theory of Industrial Organization. Cambridge,Massachusetts: The MIT Press.

U.S. Department of Commerce, U.S. Census Bureau. 1998. Annual Survey ofCommunication Services: 1996.


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Appendix

First Quarter 1999 First Quarter 1998 First Quarter 1997 First Quarter 1996

Constant -2.816 ** -5.824 ** -5.637 **

(1.081) (1.095) (1.210)

% of HHs in Urban Areas 3.294 ** 2.598 ** 0.734

(1.379) (1.211) (1.452)

% of HHs with Income >= $45,000 4.826 11.199 ** 9.364 **

4.654 4.163 4.472

Number of HHs (In 10,000) ** 0.100 ** 0.062 ** 0.026 **

(0.025) (0.013) (0.077)

PUC Exp per HH -0.070 -0.021 -0.001

0.044 0.025 0.022Elected Commission 0.955 -0.298 -0.943

(0.703) (0.561) (0.943)

Independent ** -1.713 ** -1.772 * -1.056

(0.661) (0.955) (0.996)

Ameritech * -1.418 ** -0.986 0.418

(0.644) (0.640) (0.633)

Number of Observations 190 190

Log Likelihood -24 -53

* Significantly different from zero at 90% level of confidence

** Significantly different from zero at 95% level of confidence

0.1305(0.057)

3.0131

(2.283)

9.996311.500

-69

(Standard Errors in Parentheses)

Table A: Binomial Logit Regression Coefficients


Entry

-57190

0.0388

0.102-0.0434

(1.260)


Entry


Entry


Entry

190

-2.7974(0.984)

-2.0071(1.115)

-1.8926(2.058)

zolnierek, eisner, burton - an empirical examination of entry patterns in local telephone markets

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