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THREE ESSAYS ON THE IMPACT OF MARKET STRUCTURE ON NETWORK INDUSTRIES

A dissertation presented

by

Laura Wholley

to

The Department of Economics

In partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in the field of

Economics

Northeastern University Boston, MA

December 2013

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THREE ESSAYS ON THE IMPACT OF MARKET STRUCTURE ON NETWORK INDUSTRIES

by

Laura Wholley

ABSTRACT OF DISSERTATION

Submitted in partial fulfillment of the requirements

for the degree of Doctor of Philosophy in Economics

in the College of Social Sciences and Humanities of Northeastern University

December 2013

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Abstracts

Chapter 1: How did the Telecommunications Act of 1996 Impact Quality of Basic

Local Telephone Service?

The Telecommunications Act of 1996 changed the rules of competition in the

telecommunications industry. Regional Bell Operating Companies (RBOCs), otherwise known

as Baby Bells after the breakup of AT&T in 1984, were given the right to branch into new lines of

business, such as long-distance service. The Act also created new rules for firms to make their

networks accessible to new competitors but were also given the right to merge with each other

(subject to government approval). The years immediately after the Act saw much merger activity

which we might expect could impact service quality significantly. In this paper, I build a model

of service quality for residential and business customers using data from the FCC’s ARMIS

database to determine the impact of these mergers on service quality as measured by trouble

reports. I find that mergers tend to improve service quality while one divestiture actually may

reduce service quality.

Chapter 2: The Effect of Market Structure on Prices and Quantities in Freight Rail

Shipments

The impact of competition on a market is an issue that regulators in many industries face in the

policy making process. In the freight railroad industry, trackage rights are one of the tools used

to inject competition into a market. Trackage rights allow a firm to use another firm’s rail

infrastructure to transport goods without building its own infrastructure at a particular location.

The regulator can publicly order these trackage rights, but more often they are privately agreed

upon between firms. Often firms enter markets solely through trackage rights and this practice

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has become increasingly prevalent in recent years. This essay estimates the impact of market

structure and trackage rights on market outcomes in freight railroad markets. Using

instrumental variables, I find that voluntary trackage rights competition tends to raise prices in

certain subpopulations of shipments.

Chapter 3: The Impact of Competition on Price Dispersion between Rail Routes

Variation in prices across firms, geography, distance, and market structure is a phenomenon

that has been widely observed and examined by economic literature. There are many theoretical

papers that point to the causes of price dispersion including price discrimination, cost variation,

search costs, and demand uncertainty. Numerous empirical studies have used these theoretical

models to determine the causes of price dispersion in industries such as airlines, life insurance,

and retail gasoline. This essay examines price discrimination as the main cause of price

dispersion across freight shipment markets. I find that there is evidence of competitive-type

price discrimination in these markets, showing that dispersion increases as concentration

decreases.

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Dedicated to my family

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Acknowledgments

First, I would like to extend my gratitude to my committee: John Kwoka, James Dana,

and Gustavo Vicentini. Without your tireless efforts on my behalf, I would not have been able to

accomplish this task. Thank you for all of your comments, suggestions, and critiques of all three

of my dissertation chapters. I would like to especially thank my chair, John Kwoka for getting

me in contact with several key individuals in both the telecommunications and railroads

industries to whom I also extend my thanks.

Without my parents, Maryann and Joe, none of this would have been possible. Thank

you for being there to support me without fail, cheer me on (“Yabadabadoo!”), and always wish

me “Good luck, good fortune, and break a leg!” I would also like to thank my brother, Joseph B.

Wholley III (a.k.a. Jay), for always being there for moral support and comic relief. To my aunts,

Gin and France, thank you for your advice, laughter and endless excitement.

To my husband, Brian, I cannot express my thanks enough. From the moment we met,

you have been there for me. Thank you for listening to my worrying and complaining and

assuring me that it will all turn out okay. Thank you for believing in me.

I would especially like to thank Sean Isakower. Without you, this journey would have

been very different and I am so glad that we were able to go through it together (Pretty easy,

right?). And I would also like to thank Sean Isakower and Shaun O’Brien, without whom

studying for comps would have been much more difficult and much less entertaining.

In addition, I would like to thank Neil Alper for his support and advice, especially during

my time teaching at Northeastern; for all of the moral and administrative support from Cheryl

Fonville, Kathy Downey, and Will Dirtion; and, all of the suggestions and comments from the

weekly I.O. Lunch participants.

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TABLE OF CONTENTS

Abstract 2

Dedication 5

Acknowledgments 6

Table of Contents 7

Chapter 1: How did the Telecommunications Act of 1996 Impact Quality of Basic

Local Telephone Service?

Introduction 9

Section I: Background, Hypothesis, Literature Review 11

Section II: Data, Model & Results 22

Section III: Merger Endogeneity 41

Section IV: Conclusions 53

References 56

Chapter 2: The Effect of Market Structure on Prices and Quantities in Freight Rail

Shipments

Introduction 59

Section I: Background 60

Section II: Literature Review 62

Section III: Theoretical Model 64

Section IV: Data 70

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Section V: First Estimation Model 74

Section VI: Second Estimation Model 85

Section VII: Conclusions 98

References 100

Chapter 3: The Impact of Competition on Price Dispersion between Rail Routes

Introduction 102

Section I: Background 103

Section II: Literature Review 106

Section III: Model 115

Section IV: Conclusions 132

References 133

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Chapter 1: How did the Telecommunications Act of 1996 Impact Quality of Basic

Local Telephone Service?

In the telecommunications industry, other than price, quality of service is probably the

most important factor that consumers consider when evaluating a provider. Other factors such

as availability of new products and price are also extremely important, but if the phone

connection is constantly full of static, or installation wait-times are prohibitively long, then a

consumer might choose to change providers quite quickly.

Service quality in an experience good such as telephone access is a very interesting area

to study. In the realm of telecommunications, it is particularly interesting because of the myriad

of variables and constant change that may have an impact on service quality. Those variables

include innovation and changes in regulation. In addition to both of these variables, we also see

constant change in market structure due to merger activity that has been rampant in the last 15

years. The industry has been regulated partially to ensure that consumers receive fair prices and

that firms are able to maintain provision of service through adequate revenues. However, in

addition to the maintenance of fair prices, the industry has been regulated to ensure that

consumers receive the best possible level of service.

In this paper firstly, I document that service quality as measured by trouble reports has

increased since the Telecommunications Act of 1996 was implemented. Secondly, I construct a

model of service quality in local telephone service to measure the mechanism through which

quality changed due to the Act. This change in the federal regulatory structure is discussed in

detail in the Background section of the paper but in short, the Act allowed the Baby Bells to

branch out into businesses other than local telephone service including Internet provision and

video transmission. Because of this new ability to enter different lines of business, it is possible

that the level of service quality for telephone service declined in the years after the Act was

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implemented. The 1996 Act also allowed new entrants into the local telephone service market

and as such it is possible that service quality increased after the Act due to increased

competition. Another important aspect of deregulation is that it allowed the Baby Bells to merge

with each other and other firms. Diversification of the product line by firms could be a means to

take advantage of economies of scope, while mergers in the industry may have enabled firms to

utilize cost efficiencies, know-how of other firms, and economies of scale. These three

mechanisms: competition, diversification, and mergers, will be the focus of the service quality

model. This paper focuses on residential service quality following the literature on this topic

(Clements, 2004; Roycroft & Garcia-Murillo, 2000; Banerjee, 2003; Ai & Sappington, 2002)

separately from business customer service quality. There is evidence that residential and

business customers were treated differently by firms and also, that the Telecommunications Act

of 1996 impacted the two types of consumers differently.

Section I of the paper provides a discussion of the major provisions of the

Telecommunications Act of 1996, documentation of the increase in service quality over the

sample period, a detailed description of the hypothesis on the importance mechanisms through

which the increase in quality happened and a discussion of the literature relevant to this topic.

Section II describes the methodology, data, model and, the results of regressions used to

examine the data. Section III delves more deeply into the issues of merger endogeneity and an

extension of the model to look at the indirect impact of mergers on quality via firm

expenditures. Finally, Section IV provides the economic implications of these results and a

discussion of future research and conclusions.

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Section I

Background: The Telecommunications Act of 1996

This industry has been heavily regulated in the past in order to ensure many aspects of

consumer experience are held to a certain standard. However, in the last 20 years, the industry

has experienced a loosening of that heavy-handed regulation. The single most significant change

that the industry has seen in the last 20 years is the Telecommunications Act of 1996, which was

designed to allow the original Baby Bells much more freedom. The preamble of the 1996 Act

states the goal of deregulation:

To promote competition and reduce regulation in order to secure lower

prices and higher quality services for American telecommunications

consumers and encourage the rapid deployment of new

telecommunications technologies.

The Telecommunications Act was signed into law in February 1996 after much dispute

over the restrictions created by the break-up of AT&T. For a discussion of the history of the

regulatory environment of AT&T and its status as a natural monopoly, as well as the break-up of

the firm, please see Noll and Owen (1994) or Rubin (2005). The Telecommunications Act of

1996 created several means through which the original Baby Bells or Regional Bell Operating

Companies (RBOCs), as well as the other Incumbent Local Exchange Carriers (ILECs) could

enter markets in addition to local service. The Act also has provisions for entry of long distance

carriers to enter local markets and cable companies into the telecommunications market. The

major provisions of the Act are discussed next.

First, the Act allowed the Baby Bells to enter into the long distance market after they

proved to the state and federal authorities that their local exchange markets were now open to

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competition. This provision of the Act is very important to the question examined in this paper

because other ILECs such as GTE and Sprint were never restricted from long distance and thus

should have determined if entering long distance would be profitable prior to the 1996 Act

(Crandall & Hausman 2000).

Second, the Act allowed long distance carriers to enter the local exchange markets. Local

carriers were required to allow for interconnection between all other carriers using several

different schemes. These schemes included selling Unbundled Network Elements (UNEs)

whereby the different elements of what it takes to provide telephone services are priced

separately based on cost and sold to entrants. Entrants could also build their own new

infrastructure and request interconnection with a local exchange carrier. And finally, entrants

could simply buy local service from the incumbent and resell it to consumers (Rubin 2005).

Third, the Act of 1996 deregulated cable company rates. In exchange for lifting this

regulation on cable companies, these companies agreed to allow ILECs to carry video

programming (Rubin 2005). Creating this bridge between local service and provision of video is

what led to the entrance of companies like Verizon and AT&T into television service. This

section of the Act is important for this paper because after 1996, RBOCs and ILECs could branch

out into services other than traditional phone service which may have led these companies to

focus less on the quality of local service calls as the share of total revenue from local calls

declines.

Last, the Telecommunications Act of 1996 allowed the Baby Bells to merge with each

other and with other ILECs, pending government approval. Firms quickly took advantage of this

newfound ability once the Act was passed with Bell Atlantic and NYNEX applying for merger

approval. Mergers are important for analyzing service quality because firms may be able to take

advantage of various efficiencies in both cost and ability to fix problems in the network.

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The Increase in Service Quality over the Sample Period

Over the last eighteen years, there has been an increase in service quality as measured by

several different characteristics. This paper focuses on two measures of service quality: initial

trouble reports and repeat trouble reports from 1994 to 2008.

Initial trouble reports are those complaints that the customer files with the carrier about

a lack of service, static, and interruptions during calls. Initial trouble reports are a measure of

the infrastructure quality of the system. A high quality system will have fewer out-of-service

reports and static or call interruptions. A low quality firm will receive more reports of no service

or static-filled calls. Figure 1 depicts the decrease in average initial trouble reports per thousand

access lines. As is clear from the figure, initial trouble reports have fallen over the time period.

The causes of this increase in infrastructure service quality are the subject of the rest of this

paper.

Figure 1: Annual Average Initial Trouble Reports per Thousand Access Lines from

1994 – 2008

5010

0150

200

250

1994 1996 1998 2000 2002 2004 2006 2008Year

Residential Customers Business Customers

Average Initial Trouble Reports

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Repeat trouble reports are those complaints lodged with the telephone service provider

within 30 days of the initial report of the same type of trouble: out-of-service, static, or call

interruption. Repeat trouble reports represent the ability of the firm to fix problems within a

certain amount of time. This measure is more along the lines of a customer-service quality

measure. Figure 2 depicts the annual average repeat trouble reports per thousand residential

access lines from 1994 to 2008. Repeat trouble is also declining over the sample period.

Figure 2: Annual Average Repeat Trouble Reports per Thousand Access Lines from

1994 – 2008

In Figures 2 and 3, I have separated residential and business customers rather than

pooling the two groups together. There are a few reasons for this method. First, related

literature discussed later such as Clements (2004) and Roycroft and Garcia-Murrilo (2000) who

both focus solely on residential customers. To keep my results comparable to theirs, I decided to

keep residential customers separate from business customers. Second, Kahn, et al (1999) finds

1020

3040

5060

1994 1996 1998 2000 2002 2004 2006 2008Year

Residential Customers Business Customers

Average Repeat Trouble Reports

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that business customers were impacted more by the entrance of CLECs into markets post-

Telecommunications Act of 1996 and Kittl, et al (2006) find evidence that infrastructure-based

competition is more important for business customers than for residential customers. Third, I

have conducted means testing to find out if for every year and every firm, there is a statistically

significant difference between residential and business customers. The results of these tests

have indicated that there is indeed a difference. Finally, I will test the difference between the

coefficients in the model, which is discussed later.

Hypothesis

This paper is designed to explore the mechanisms through which changes in federal

regulation impact service quality in the telecommunications industry. Other research on service

quality in the telecommunications industry has found that competition, mergers, technology,

and state retail-rate regulation are all factors that influence quality of service.

I believe that the impact of changes allowed in the industry after the 1996 Act were

substantial for the industry for three reasons. First, competitors entered an industry once

characterized as a natural monopoly. Second, RBOC firms were allowed to merge with each

other. Third, those RBOCs were allowed to expand business operations from basic local service

for residence and business to various other communication methods. During the same time

period covered, technology was changing rapidly causing further development in the industry.

All three of these changes and technology have the potential to impact the level of service quality

provided to customers.

Firms want to differentiate themselves to increase profit margins by providing service to

a certain niche in the market. Undifferentiated firms (differentiated in quality for this market)

make no profit. Thus, quality differentiation becomes important and choosing quality is a key to

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increasing profit. Additionally, free entry of firms into the natural monopolist’s market pushes

profits to zero. When there is more differentiation, potential entrants see the opportunity to

earn profits in the market and enter the market increasing competition (Tirole 1988).

As discussed in the background section, competition was implemented in three distinct

ways and two of those ways may have a detrimental impact on Baby Bell service quality. If

competitors enter simply though resale or UNE, the Baby Bell will still be responsible for

maintaining the network and therefore dealing with trouble reports from both their own

customers and the customers of the entrants. This responsibility could cause a decline in service

quality (Roycroft and Garcia-Murrilo 2000). There is also evidence that entrants focused on

serving business customers rather than residential customers so we expect competition to

impact each group separately.

As soon as the 1996 Act passed, mergers started occurring in the industry. One very

important reason for merging, especially in an industry with high fixed costs, is to take

advantage of economies of scale. Economies of scale were an important issue during the

antitrust trial of AT&T in the 1970s. Evans and Heckman (1984) tested data for AT&T from

1958-1977 and found that the cost function was not subadditive while a subsequent study by

Chang and Mashruwala (2006) finds that the company was a natural monopoly. However, it was

always maintained that the local exchange market experienced economies of scale and that the

RBOCs should be heavily regulated by the government. It was determined that the industry

should be deregulated in the 1990s and the Telecommunications Act of 1996 did that. Several

mergers between RBOCs (and non-RBOCs) have occurred since the Act was passed, providing

some possible evidence that this industry may work better with fewer firms. There are many

reasons why this might be true which includes but is not limited to economies of scale. If

increased efficiencies in cost and in the ability to maintain the network are present, then

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mergers should improve service quality. I use dummies for major mergers between the RBOCs

and in one case an incumbent local exchange carrier (GTE) to measure the impact of mergers on

service quality. I also include a dummy for Verizon’s divestiture of service in Maine, New

Hampshire, and Vermont to FairPoint Communications in 2007 to provide a contrasting case to

mergers.

Diversification of product offerings is also an important issue with possible effects for

quality of service. Firms may diversify to create new revenue streams that are inversely

correlated with current revenue streams to shield investors and the firm from risk (Aron 1998).

Firms might also diversify to take advantage of economies of scope. Prior studies have found

that the telephone service experiences economies of scope between different types of switch

technologies (Banker, et. al. 1998, Gabel and Kennet 1994).

As time goes on and firms enter various industries, we would expect that service quality

could go either up or down. Service quality could be harmed if firms pay less attention

traditional service and focus their energies on another line of products. However if there are

economies of scope, producing two or more goods together could be less costly than producing

them separately. Take for example a firm selling residential local service. Post-1996, that firm

has the ability to also sell DSL (Digital Subscriber Line) for internet access. The average cost of

the firm for selling both goods would be lower because they already have the infrastructure in

place. The costs of maintaining one line just for telephone service are the same as keeping the

line active for both services. Diversification might be beneficial because average cost will be

lower and a new revenue stream is created. If this scenario is the case, it is possible for service

quality to increase with diversification because the company now has the incentive to maintain a

high level of quality for both products.

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A major market that has been impacted by deregulation is wireless telephone service.

The primary reason for investing in wireless communication services is the advancement of

technology. Consumer demand is moving away from traditional telephone service to cellular

service and companies must keep up with their customers. Firms want to be at the forefront of

new technologies to avoid the loss of consumers to competitors. Communication services all

have network effects and the firm can take advantage of these in one market when branching

into another one. Service quality could be harmed if there is less attention paid to a dying

segment of the business. For these reasons, addition of new products is a key component of

service quality in local telephone service.

Literature Review

The literature on service quality in telecommunications approaches the question in

different ways. A brief discussion of the literature relevant to this paper is presented now.

Sappington (2005) provides insight into the theoretical reasons for regulating service

quality in public utility industries. Specifically important for this paper is Sappington’s

discussion of the theoretical reasons why the federal government might find it advisable to

regulate service quality after the Telecommunications Act of 1996 was passed. One of the

sections of the Act required that incumbents unbundle their services and sell them to entrants,

thus making the incumbent the upstream firm selling downstream directly to competitors. It is

possible that the incumbent telephone company could reduce the quality of the UNEs that it

sells downstream. This possibility may have been part of the rationale for requiring the Regional

Bell Operating Companies (RBOCs) and Incumbent Local Exchange Carriers (ILECs) to file the

Service Quality report from which much of the data for this paper is collected.

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It is important to develop a clear definition of what “quality of service” means in

telecommunications. In Kidokoro (2002), two types of service quality are defined. The first

“investment-related service quality” addresses the problem of congestion and is dealt with

through investing to increase capacity. The second type is that of “effort-related service quality”

which is also called “soft quality.” This type of quality addresses the speed of serving new

customers and the response to customer complaints. Initial trouble reports is used to account

for investment or infrastructure related quality because it is the total number of reports filed by

customers about static or other such troubles. Repeat trouble reports show how many of those

initial reports are complained about again within 30 days of the initial report. This captures the

eff0rt of the firm in fixing existing troubles within a reasonable period of time.

Kidokoro (2002) also discusses how different the different types of service quality are

affected by price cap regulation. He finds that for investment-related service quality, price-cap

regulation lowers the level of service quality. However, for effort-related service quality, price-

cap regulation raises the level of service quality. These findings are extremely important and

testing them empirically would provide great insight into how these types of regulation effect

service quality. In a model of service quality, the type of incentive regulations in each state

clearly should be included. However, while a majority of the data on state retail regulation is

available from the National Regulatory Research Institute, several years (1999-2003) are not

collected by the NRRI. For this research question in particular, these years are vital and having

missing values for these years would not be an appropriate approach. To capture state-specific

effects on quality of service, I have opted to use state-level fixed effects.

Clements (2004) builds a model of service quality and tests several measures of service

quality in telecommunications by state from 1992-1998. Clements, like Kidokoro defines two

types of service quality. Clements defines equipment and system related quality as those

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activities directly related to the network. This subset of service quality includes investment in

capacity and reliability as well as in new, advanced services. People and process oriented quality

is defined as activities that support services provided over the network. These activities include

all services provided to consumers that require staff interaction with consumers from pre-

service to post-service including the assignment of phone numbers and account maintenance.

The author of this paper builds and tests a comprehensive model of the determinants of service

quality. I have reconstructed his model with some modifications. I test a longer period of time

and leave out two of his explanatory variables due to an inability to collect them completely. I

will test the impact of the Telecommunications Act of 1996 on only 2 of the 12 dependent

variables distinguished in Clements’ paper.

Sappington (2003) provides a review of four studies that address the effects of incentive

regulation by telecommunications regulators on service quality. The studies critiqued in this

paper include Michael Clements’ Ph. D. dissertation (2001), a larger study than Clements

(2004), as well as Roycroft & Garcia-Murrilo (2000), Ai & Sappington (2002), and Banerjee

(2003). In general, all four of these papers address the impact of state-level shifts from

traditional rate-of-return regulation to incentive regulation on service quality. These studies use

much of the same data as this paper utilizes. Sappington’s critique of these studies provides

insight into the advantages and disadvantages of each study. The aspects from the papers

reviewed by Sappington that are significant to this research are discussed next.

Roycroft and Garcia-Murrilo (2000) examine Ameritech, Bell Atlantic, NYNEX, and SBC

and the effects of various changes in the industry on service quality proxied by the number of

trouble reports. Trouble reports provide a measure of both how well the infrastructure works

and the ability of customer service to fix a problem. The number of initial trouble reports tells us

how many times consumers complain about various problems including static and out-of-

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service reports. If the same trouble is reported within 30 days of the initial report, it is called a

repeat trouble report and indicates the ability of the provider to fix problems on the network.

Using these measures of service quality as the dependent variable, the authors build a model

that tests the impact of mergers, competition, state-level regulation, and new technologies. This

paper provides evidence that these factors do in fact influence quality of service. Roycroft and

Garcia-Murrilo’s paper along with Clements (2004) provide the basis for my model.

Banerjee (2003) asks whether or not incentive regulation causes degradations of service

quality. The author uses 12 measures of service quality to test for Granger causality from

incentive regulation to retail service quality. Using 49 companies, one in each state except

Alaska and Hawaii but including Washington, D.C., Banerjee finds that over the time period

from 1991-1999, average service quality has not declined with shifts towards incentive

regulation.

Ai & Sappington (2002) examines the effects of incentive regulation on costs,

investment, revenue, profit, and local service rates, in addition to service quality as measured by

network modernization. Their study provides the evidence that state-specific demographic

variables are multicollinear with state and time dummies and thus are not used in this study as

well as Clements (2004). Ai & Sappington find that incentive regulation increases network

modernization, which in this study is defined as infrastructure oriented measures of service

quality.

Norsworthy and Tsai (1999) build a model to describe demand for telephone services

and the success of incentive regulation while including measures of service quality, switching

technology, and capital productivity. These authors create separate demand equations for

residential and business customers because they find it unlikely that one single demand

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equation could accurately describe both customer groups simultaneously. I follow that same

logic in my model.

Further justification for separating residential and business customers comes from

Kahn, et al (1999). The authors of this paper find that only two years after the Telecom Act was

passed, CLECs were taking over a large share of the growth in business lines. They conclude that

the markets for business customers in large cities are competitive by 1998. Business customers

are the more profitable side of the business and therefore competition was initially attracted to

this group. Telephone service for businesses traditionally cross-subsidized residential service

rates that were heavily regulated by the states. CLECs were able to easily enter and undercut

prices to steal business from the Baby Bells and other ILECs. Not only do these results create

justification for splitting the two groups into separate models, but they also mean that we should

expect competition to have a different impact on each group.

Kittl, et al (2006) find that competition that is infrastructure based is more important for

business customers than it is for residential customers. Service-based competition appears to be

more important for residential customers. The competition allowed by the Telecommunications

Act was threefold as described above and most CLECs used the resale or UNE methods rather

than building their own infrastructure. Therefore, competition may not have a significant impact

on business customers because new infrastructure was not the primary method of entry.

Section II

Data

The majority of the data used in this study is collected from the Federal Communications

Commission using the Automated Reporting Management Information System (ARMIS)

database. The panel data set covers the period from 1994 to 2008 and uses RBOC in each state,

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which is the largest company by number of lines except in one instance. The largest company in

Nevada is not a RBOC, so in this case I have used Nevada Bell instead of the largest company.

The state of Alaska is excluded due to its telecommunications structure’s historical link to the

military. Hawaii and Connecticut are also not included in the sample because during some

years, each company was not owned by a Baby Bell. Hawaiian Telecommunications changed

hands several times over the course of the sample period. Southern New England Telephone,

Connecticut’s phone company has traditionally been considered an independent carrier due to

the original AT&T’s minority stake in the company. The exact ARMIS reports where the data

comes from is recorded in the table of summary statistics below.

Holding company revenue data is collected from annual reports that are publically

available from the Securities and Exchange Commission. Public companies are required to file

quarterly and annual reports with the SEC and these reports are available on their Electronic

Data Gathering, Analysis, and Retrieval (EDGAR) system. This information was used to

construct one of the explanatory variables in the model.

Model

Service Quality = β0 + β1PCMarginit + β2Percent Business or Residential Linesit + β3Sizeit +

β4lnCLECst + β5Percent Fiberit + β6BLS Shareht + β7TimeTrend + β8

∑MergerDummies + θi + εit

i = State-Company; t = Year; h = Holding Company; where θi is a set of state-company fixed

effects

Following along with Roycroft and Garcia-Murrilo (2000), I use trouble reports as the

measure of service quality. For this study, measures are separated by customer group and

reported per 1000 residential or business access lines. Total Initial Trouble Reports include both

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reports for a complete lack of service (out-of-service report) and reports of static or interrupted

calls (all-other). This type of report signals the reliability of a firm’s network. Repeat trouble

reports are those same types of trouble complaints that are lodged within 30 days of the initial

report, revealing the ability of a firm to fix a problem in a reasonable amount of time. Therefore,

Initial Trouble Reports will be interpreted as the measure of infrastructure quality, while Repeat

Trouble Reports will show the ability of the firm to provide customer-oriented quality

(Clements, 2004; Roycroft and Garcia-Murrilo, 2000; Kidokoro, 2002).

In addition to the dependent variables just described, it would also be interesting to

know how many initial trouble reports become repeat trouble reports. As the trouble reports

cannot be individually tracked, one way to measure this is the ratio of repeat trouble reports to

initial trouble reports. For both residential and business customers, on average approximately

19% of initial trouble reports become repeat trouble reports over the course of the sample

period. This form of the dependent variable has been tried, but the model specification as

defined here does not explain any of the variation in the ratio, and thus is not included in the

results section of this paper.

PCMargin is a proxy for the monthly price-cost margin per line for each state-company

in a given year. Margins can be positive or negative. To construct this variable, I subtract Total

Operating Costs from Total Operating Revenue. The difference is then divided by the total

number of access lines to get the annual margin per line. Finally, the result is divided by 12 to

give us the margin on a monthly bill for telephone service. The expected sign of PCMargin is

ambiguous because a firm with a larger margin may provide higher quality products, but a high-

quality firm could also have a smaller margin because the costs of supplying that level of quality

are high.

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To separate residential (business) service quality from the other segment of customers

the percentage of total access lines that is used by business (residential) customers is provided

in Percent Business (Residential) Lines (Clements, 2004). If business customers demand high

quality products, you might expect more business lines to have a positive effect on residential

service quality when they both utilize the same network. There would be a positive impact on

both Initial Trouble Reports and Repeat Trouble Reports. However, if the company in question

favors business customers only, you could expect that the firm would be less responsive to

residential complaints and Repeat Trouble Reports could increase. The expected sign for this

variable is ambiguous as well.

Size measures the number of residential or business access lines in a particular year. It is

possible that a larger company could have high or low service quality, thus the expected sign of

this variable is ambiguous. An alternative measure for this variable would be the growth of

customer group access lines from year t to year t+1. However, this variable is likely endogenous

to quality of a particular firm than Size. In this sample, each firm is restricted to operations at

the state-level. State boundaries are exogenous to quality of telephone service.

Competition is measured here as the number of Competitive Local Exchange Carriers

(CLECs) in a state during a particular year. I have chosen this measure of competition because it

is the only one available for the entire time from 1994-2008. This measure is also used by

Roycroft and Garcia-Murrilo (2000). I utilize the natural log form of the variable because it is

unlikely that competition has a perfectly linear effect on service quality. It seems much more

plausible that the first firms to enter the market will have a larger impact on service quality and

as the number of CLECs rises, the marginal impact of an additional entrant tapers off. Because

prices are regulated, I expect service quality to increase with the number of competitors; without

regulation of prices, the theoretical effect of competition on quality is ambiguous. After the

26

Telecommunications Act of 1996, when the local exchange market was being opened to

competition, incumbents were required to provide unbundled network elements (UNEs) for

competitors to resell communication services. The UNE scheme meant that competitors were

allowed into central offices. Serving both incumbent customers and competitors could have a

negative impact on service quality (Roycroft and Garcia-Murrilo 2000). There is also evidence

that CLECs entered the market for business customers rather than residential, and therefore we

expect that there may be a significant impact on business customers and possibly not for

residential customers (Kahn, et al. 1999). Unfortunately, the data does not provide information

about the route through which the CLEC entered the market. This means that the impact of

resale-entry versus UNE-entry cannot be separated.

To show the impact of technology on service quality, I have used Percent Fiber, which is

the percent of all cable that is fiber-optic. Fiber-optic cable is able to transmit voice, data, and

video content at high speeds. It represents the newest technology available in the

telecommunications industry. The replacement of old technology with new technology could

reduce the number of trouble reports by improving the reliability of the network. But, there

might also be a learning curve related to use and maintenance of new technology, which would

be harmful to service quality. Economies of scope could also be used to explain investment in

fiber-optic technology because the technology is able to transmit multiple methods of

communication. Without investment in this technology, telecoms would be unable to provide

video services. The expected sign of this variable is uncertain.

BLS Share is the percent of total revenue from Basic Local Service. This variable is

included as a measure of a company’s product diversification. Basic Local Service only includes

local service within the service area. After the Telecommunications Act of 1996, RBOCs were

allowed to enter other lines of business including data, voice, video, long-distance service, and

27

cellular service. I have constructed BLS Share in order to capture the effects of this

diversification. RBOCs are required to report revenue information to the FCC for Plain Old

Telephone Service (POTS) but not for other products. Thus, Basic Local Service Revenue is

available at both the holding company and state-company levels. However, total revenue data

was collected from firm annual reports publically available from the SEC at the holding

company level. Due to these discrepancies in cross-sectional unit, I have included BLS Share at

the holding company level only. Economies of scope are the driving factor behind diversification

in this model (see Section I). The relative importance of economies of scope and entrance into

wireless service will determine the sign of BLS Share on service quality.

As soon as the Telecommunications Act of 1996 was passed, firms began lining up to

merge with each other. NYNEX and Bell Atlantic were the first to request approval of a merger

followed closely by Pacific Telesis and Southwestern Bell (SBC). Between 1996 and 2008, there

were seven mergers in the industry. Six of those mergers were RBOC to RBOC while only one

was between an RBOC and an ILEC – Bell Atlantic and GTE to create Verizon. In 1994, there

were seven RBOCs in the industry. After a plethora of mergers, only three of them are left now

(AT&T – formerly SBC, Verizon, and Qwest). I have included a dummy variable for each merger

equal to 1 in the year that it happened and thereafter, and equal to zero otherwise. It is

important to note that these mergers are all different in terms of the companies involved, the

territories covered, the type of services offered, and the resulting situations post-merger.

Dummy variables do not capture these differences and it is important to keep that in mind when

interpreting the results.

Presumably, mergers will have cost efficiencies if firms take advantage of economies of

scale, and improvements in quality if firms are able to take advantage of each other’s better

infrastructure, know-how and the ability to fix problems in the network. I also include a dummy

28

variable of the same form to capture the impact of Verizon’s divestiture of service in Maine, New

Hampshire, and Vermont to FairPoint Communications in 2007. If we assume that there is an

increase in service quality from a merger, we should see a decrease in service quality from this

divestiture.

A time trend is included in the model to control for the impact of time on trouble reports.

State-company fixed effects are used to capture state-specific circumstances including factors

such as regulations, population density, and economic activity in addition to others. The model

used in this paper does not include a control variable for the type of state-level regulation

applied to firms. While this aspect of regulation is certainly very important, the data for all of the

years covered in this sample is not available. Information for the years 1984-1998 was compiled

in Abel & Clements (1998) for the National Regulatory Research Institute (NRRI). The report

providing this information was created through a series of surveys sent to state regulatory

commissions. This same method was used to create similar reports in 2004, 2005, and 2006 by

the NRRI, but not from 1998-2004 or any time after 2006 (Pérez-Chavolla 2004, 2006, 2007).

As I am looking at a change in federal regulation, I have decided to use a fixed-effects method to

capture state-specific differences. Summary statistics for all of the variables are provided below.

29

Table 1: Summary StatisticsVariable Definition N Mean Std. Dev. Min Max

Y ear 7 20 1994 2008

COSA State-Company ID 7 20 48

Residential Initial

T rouble

The number of residential service complaints – trouble reports -

made to the company regarding residential service quality , per

1000 residential access lines (FCC 43-05II, 43-05V)

7 17 235 83.2 86.59 592.28

Business Initial T rouble

The number of business service complaints – trouble reports - made

to the company regarding residential serv ice quality , per 1000

residential access lines (FCC 43-05II, 43-05V)

7 17 104.32 46.5 21.01 282.43

Residential Repeat

T rouble

The number of residential service complaints receiv ed within 30

day s of the initial trouble report per 1000 residential access lines

(FCC 43-05II, 43-05V)

7 17 46.56 21 7 .7 7 153.49

Business Repeat T rouble

The number of business serv ice complaints received within 30 day s

of the initial trouble report per 1000 residential access lines (FCC 43-

05II, 43-05V)

19.68 11.94 1 .7 6 7 2.01

P-C Margin

A prox y for monthly price-cost margins using (Total Operating

Revenues minus Total Operating Expenses) per total access lines

div ided by 12 (FCC 43-01I, 43-05V)

67 2 15.05 6.52 -17 .68 40.2

Percent Business LinesThe percent of total access lines that are business access lines (FCC

43-05V)7 17 33.6 7 .25 23 7 7

Percent Residential

Lines

The percent of total access lines that are residential access lines

(FCC 43-05V)7 17 66.4 7 .25 23 7 7

Size (Residential) The number of residential access lines in thousands (FCC 43-05V) 7 17 1641.17 1860.7 9 59.685 11167 .37

Size (Business) The number of business access lines in thousands (FCC 43-05V) 87 7 .14 1151 .87 42.64 1057 4

CLECsThe total number of competitiv e local exchange carriers (CLECs) in

the state in a given year (FCC Local Competition Reports)7 19 19.62 27 .33 0 196

Percent Fiber A measure of technology : The percent of all loop and interoffice

cable that is fiber (FCC 43-07 II)67 2 11 .56 3.8 4.89 28.32

BLS Share

A measure of diversification: The percent of total operating revenue

that is from basic local serv ice at the holding company level (SEC

EDGAR Annual Report Filings, FCC 43-01)

67 5 38.51 10.47 14.3 57 .3

T im e T rendA variable to control for changes due to time: 1994=1 , 1995=2…

2008=15.7 20 1 15

PacT el/SBCA dummy variable equal to 1 if the firm is Pacific Telesis or SBC and

the year is 1997 or thereafter when the two firms had merged.7 20 0 1

NYNEX/BAA dummy v ariable equal to 1 if the firm is NY NEX or Bell Atlantic

and the year is 1997 or thereafter when the two firms had merged.7 20 0 1

Am eritech/SBCA dummy variable equal to 1 if the firm is Ameritech or SBC and the

year is 1999 or thereafter when the two firms had merged.7 20 0 1

BA/GT E

A dummy variable equal to 1 if the firm is Bell Atlantic or GTE and

the y ear is 2000 or thereafter when the two firms had merged to

create Verizon.

7 20 0 1

USWest/QwestA dummy variable equal to 1 if the firm is USWest or Qwest and the

year is 2000 or thereafter when the two firms had merged.7 20 0 1

SBC/AT T

A dummy variable equal to 1 if the firm is SBC or AT&T and the y ear

is 2005 or thereafter when the two firms had merged to create the

new AT&T.

7 20 0 1

BellSouth/AT TA dummy v ariable equal to 1 if the firm is Bell South or AT&T and

the year is 2006 or thereafter when the two firms had merged.7 20 0 1

VZ/FairPoint

A dummy variable equal to 1 if the firm is Verizon or FairPoint and

the year is 2008 or thereafter when Verizon div ested itself of

service in ME, NH, and VT to FairPoint Communications.

7 20 0 1

NOT E: All variables are reported at the COSA (state-company ) lev el ex cept BLS Share which is reported at the holding company level.

Explanatory Variables

Service Quality Measures

30

Econometric Issues

In this model, four econometric issues are present. Using the Time Trend variable as

described above mitigates autocorrelation across the time-series of data. The problem of

heteroskedasticiy present in cross-sectional data is controlled for with robust standard errors.

The problem of multicollinearity is much more difficult with which to deal. While

bivariate correlations don’t reveal very high coefficients, the Variance Inflation Factor (VIF) and

Tolerance (1/VIF) tests report that there is multivariate correlation. Unfortunately, this problem

crops up with variables that cannot and should not be taken out of the model. Nor can these

variables be transformed in any effective or logical manner. I fully acknowledge this problem

and alert the reader to the fact that multicollinearity may be causing larger standard errors.

Lastly, the problem of omitted variable bias may also be present. The model as presented

here does not control for cell phone subscribership or penetration. While data on mobile

subscribers at the state level is available after and including the year 1999, it is not available in

the years 1994-1998. Therefore, the number of cell phone subscribers prior to 1999 is missing

from the dataset. These missing years are perfectly correlated with three of the seven mergers

that occurred prior to or in 1999. For the purposes of this paper, I have chosen to leave out this

imperfect measure of cellular phone usage in favor of measuring the impact of mergers on

service quality.

Econometric Results

Table 2 presents the regression coefficients for all of the explanatory variables except the

merger dummies for both consumer groups and both measures of quality. Table 3 presents the

results for the merger dummies for both groups and both measures of quality.

31

(Standard Errors in parentheses); ****Significant at 1%; ***Significant at 5%; **Significant at 10%; *Significant at 15%

Table 2: Results

32

Table 3: Mergers & Divestiture Results

(Standard Errors in parentheses); ****Significant at 1%; ***Significant at 5%; **Significant at

10%; *Significant at 15%

Price – Cost Margin

The average margin on a monthly bill as proxied in this model is about $15.05. The

results for the Price-Cost Margin variable are only significant for residential customers

regressions in both Initial and Repeat Trouble. Business customers are not impacted by the size

of the margin on a monthly bill for basic local service.

Specifically, for the initial trouble, increasing the difference between the price and cost

on a customer’s monthly bill by one dollar decreases the number of initial trouble reports per

thousand lines by 0.53 reports, ceteris paribus. This result implies a decrease of about 500

Resident ia l

Cu st om ers

Bu siness

Cu st om ers

Resident ia l

Cu st om ers

Bu siness

Cu st om ers

Pa cT el/SBC -2 2 .6 8** -2 5 .2 8 **** -2 .7 2 -4 .5 2 ****

(1 2 .3 3 ) (7 .5 6 ) (3 .7 3 ) (1 .03 )

NYNEX/BA -9 3 .5 1 **** -3 2 .5 2 *** -1 1 .6 7 **** -5 .9 9 ****

(2 8 .7 0) (1 2 .9 2 ) (3 .4 8 ) (1 .7 6 )

A m erit ech /SBC -3 5 .2 4 **** -6 .1 4 * 2 .03 1 .2 7

(7 .4 3 ) (3 .7 9 ) (2 .9 5 ) (1 .07 )

BA /GT E -1 8 .06 *** -1 4 .9 2 **** -2 .3 6 1 .02

(7 .1 3 ) (3 .5 1 ) (3 .2 0) (1 .1 8 )

USWest /Qwest -7 4 .1 3 **** -2 9 .5 **** -3 4 .9 4 **** -1 8 .5 6 ****

(6 .2 3 ) (2 .6 0) (3 .6 4 ) (1 .5 0)

SBC/A T T -3 4 .3 **** -2 0.6 9 **** -4 .2 3 0.9 2

(8 .5 5 ) (3 .7 3 ) (3 .6 7 ) (1 .3 9 )

BellSou t h /A T T -4 9 .8 3 **** -1 9 .3 8 **** -8 .5 9 *** -1 .4

(9 .7 1 ) (4 .9 4 ) (3 .8 4 ) (1 .4 6 )

V Z/Fa irPoint 3 0.4 4 **** 4 .8 9 9 .05 **** 3 .5 0****

(6 .03 ) (4 .2 9 ) (2 .4 1 ) (1 .3 2 )

Initial Trouble Repeat Trouble

33

reports a year for a given company. The coefficient means that an increase in the margin

increases infrastructure-related service quality. The result is significant at the 10% level. The

negative sign indicates that a larger margin leads to higher service quality for residential

customers. We see the same sign for the price-cost margin on Repeat Trouble, though significant

at the 15% level. This result implies that residential customers gain quality when paying a higher

price for telephone service. It makes sense that residential customers should pay more for

higher quality.

For business customers, the results are not statistically significant for either

infrastructure-related quality or customer service-oriented quality. Business customers are not

significantly impacted by a higher or lower margin.

Percent Business (Residential) Lines

This variable is included to control for the other set of customers to which the Baby Bells

provided basic local service. For residential customers, the percent of total access lines that are

designated for business customers is included. For the business customer group, the percent of

total access lines that are designated for residential customers is used. On average, 33% of total

access lines are for business use and therefore 67% are for residential customers.

The results are statistically significant for the both consumer groups in the initial trouble

reports regressions at the 1% level. A one percent increase in the amount of business customers

will increase the number of initial trouble reports by 150.61 per thousand residential access

lines. For business customers, an increase of 1% in residential customers increases the number

of initial trouble reports by 80.36 initial trouble reports per thousand access lines. The positive

and statistically significant results indicate that when the opposite consumer group becomes

bigger the service quality for the first consumer group actually declines. When the company’s

34

allocation of lines grows in one direction, the other side becomes slightly less important and

therefore may receive lower quality. While the infrastructure for both customer groups must be

similar if not exactly the same, firms may do a better job maintaining quality for their primary

group.

For customer-service oriented quality, the coefficient on percent business lines for the

residential customer group is positive and statistically significant at 5%. However for business

customers, the result is both negative and not statistically significant. Customer-service quality

for residential customers is harmed by a larger percentage of business customers. As business

customers do bring in more revenue, a firm may concentrate customer service on fixing

problems for business customers rather than for residential customers who may have fewer

options and less power in demanding better quality.

Size

Size is measured as thousands of either residential or business access lines for a

particular company in a given year. I include this measure to help get at the possibility that firms

may experience economies of scale when they have a larger percentage of access lines for a

consumer group. The average number of residential access lines is 1, 641, 170 while the average

number of business access lines is 877, 138.

The results for Size are statistically significant for both consumer groups in the

infrastructure-quality regressions. In the residential customer regression for initial trouble

reports, an increase of 1000 residential access lines will increase the number of initial trouble

reports by 0.011 per thousand access lines or a total of 11 reports, holding all else constant. The

result is significant at the 1% level. This result indicates that residential customers receive lower

infrastructure quality when the firm is larger.

35

Business customers also experience a statistically significant impact from the size of the

company but the impact is the opposite of that for residential customers. For business

customers, an increase of 1000 business access lines decreases the number of initial trouble

reports by 0.013 per thousand lines or by about 13 reports all others equal. The statistically

significant and negative result indicates that business customers are better off when the number

of business access lines increases.

For repeat trouble reports, the same signs and statistically significant results appear for

both residential and business customers. Residential customers see lower customer service-

oriented quality when the firm gains residential access lines but business customers see higher

quality. The difference in the signs may indicate that the RBOCs treated residential and business

customers differently.

For residential customers, economies of scale for quality are not present. Quality of

infrastructure and customer service both decline when the firm serves more customers.

However, the results provide evidence that business customers do benefit from the Baby Bell’s

gains in access lines.

Competition

This variable is intended to measure the level of competition faced by the RBOCs in the

form of competitive local exchange carriers. I have transformed the variable using the natural

log of the number of CLECs because I do not believe there to be a linear relationship. That is, I

do not think that the entrance of the first CLEC will necessarily have the same impact as the

entrance of the hundredth CLEC. The impact of each new competitor is assumed to be

diminishing as entry occurs.

36

The results for competition are not statistically significant in any of the four regressions.

Residential and business customers do not appear to have been impacted in a significant way in

terms of quality by the Telecommunications Act of 1996’s methods of injecting competition. The

Act clearly did not alter firm behavior in terms of infrastructure or customer service quality.

Forcing the Baby Bells to unbundle their services and sell them to competitors at fair prices did

not create significant effects on service quality for customers of Baby Bells. Clearly these

methods of competition did not accomplish the goal set out by Congress. There are many

reasons for which this might have happened including increased use of wireless service over

traditional landline telephony. Unfortunately, this model does not control for wireless use

because the data on cellular phone penetration is not freely available at the state level for all

years.

Percent Fiber

Percent fiber is included to capture the impact of advancing technology on service

quality. I expect an impact on both infrastructure and customer service oriented quality.

Infrastructure is clearly being updated and should improve reliability and therefore initial

trouble reports should decline with increasing technology. In terms of repeat trouble reports,

the expected sign is ambiguous. If the new technology is easily repaired, we should see

increasing service quality with implementation of fiber-optic cable. However, it is possible for

there to be a learning curve in maintenance of the new infrastructure that would make the

possibility of repeat reports more likely (Roycroft & Garcia-Murrilo, 2000). In a given year, the

average state’s percent of total cable that is fiber optic is 11.56%.

The results for both consumer groups and measure of service quality regressions indicate

that investment in fiber-optic technology increases service quality. The results are only

37

statistically significant for business customers. It is possible that fiber-optic technology was

targeted first to the business customer segment of consumers as that group is more likely to

demand the benefits of improved technology sooner than residential customers. If this is true, it

is not surprising that business customers would see improved quality from fiber technology

while residential customers are not impacted.

For the business customers, a one percent increase in the percentage of all cable that is

fiber-optic decreases initial trouble reports per thousand lines by 1.42, ceteris paribus.

Investment in new technology improves infrastructure service quality for local service business

customers but has no statistically significant impact on residential customers in this model.

It should also be noted that investment in fiber is not solely to improve telephone call

quality. Fiber is a useful technology because it is able to efficiently transmit many modes of

communication including voice, data, and video. It may be that firms invest in fiber-optic cable

so that they are able to diversify their product offerings. Business customers are more able to

take advantage of the new services provided by fiber optic cable and providers may be more

inclined to maintain high quality for these lucrative customers.

The results for business customers in repeat trouble reports regressions show that

technology also improves the ability of a firm to repair the network without the customer being

compelled to file another trouble report. The coefficient on Percent Fiber is statistically

significant at the 5% level. A one percent increase in the percentage of all cable that is fiber-optic

decreases repeat trouble reports per thousand lines by 0.44, ceteris paribus. Again, residential

customers do not benefit in any statistically significant way from the improved technology

provided through more fiber optic cable in the system.

38

BLS Share

This variable is intended to capture the effects of diversification on service quality. In

this case, a negative coefficient implies that a less diversified company (or a more specialized

company) has better residential telephone service quality either in infrastructure or customer

service. Most likely, diversification and technology move together. Investment in new

technology comes first and then a firm markets and earns revenue from that new infrastructure.

The reason behind upgrading to fiber technology was not necessarily only for the improvement

of traditional telephone service. The infrastructure for that was in place and working. Firms

invested to be able to diversify into data and video transmission. The result of this coefficient

means that a more specialized company will have a higher level of service quality and

diversifying actually harmed the quality of residential telephone service. The results for this

variable should all be analyzed at the holding-company level.

The results for this variable are statistically significant for Initial Trouble reports. The

signs for Initial and Repeat trouble are all negative implying specialized firms have better

quality. Both infrastructure and customer-oriented quality are impacted in the same way by

diversification. Both consumer groups are impacted in the same way as well. However, the

results for Repeat Trouble are not statistically significant.

For initial trouble reports, holding all else constant, a one percent increase in the percent

of total operating revenue that is from basic local service decreases the number of initial trouble

reports per thousand access lines by 0.88 for residential customers. Holding all else constant, a

one percent increase in the percent of total operating revenue that is from basic local service

decreases the number of initial trouble reports per thousand access lines by 0.65 for business

customers. Both consumer groups receive better quality from a firm whose focus is primarily on

basic local service in terms of the quality of the infrastructure in the network.

39

The regression for repeat trouble reports exhibits analogous results in terms of sign, but

specialization is not statistically significant for customer service quality. Customer service

quality does not appear to be impacted by diversification, at least using the measure that I have

created to capture this effect.

Time Trend

The time trend is included to control for the possibility that service quality is simply

increasing or decreasing over time as well as the problem of autocorrelation of the errors over

time. The coefficient on TimeTrend is only statistically significant for the business customer

group for both measures of service quality. The negative signs on the coefficients imply that

service quality has increased over time both in the infrastructure dimension and in the

customer-service oriented measure, verifying what is seen in Figures 1 and 2. Other

specifications that include quadratic- or log-form time trends are not statistically significant.

Mergers and Divestiture

Mergers are a very important aspect of this industry and since deregulation in 1996;

there have been so many mergers that the industry looks completely different from the first year

of this study to the last. The model includes eight dummy variables to control for the impact of

seven mergers in the industry over the time period and one divestiture. The results of the

merger dummies are discussed below for each regression.

All of the merger dummies have negative coefficients for the initial trouble regressions

for both consumer groups indicating that the merger actually decreased the number of initial

trouble reports per thousand lines thus, increasing quality. All of the coefficients are statistically

significant. The signs and significance of these variables provide evidence that mergers have a

very important impact on service quality of basic local service. These coefficients have the

40

largest magnitudes and are the most consistent of all the effects of the Telecommunications Act

of 1996 laid out in the Background section.

The coefficient for the dummy variable indicating Verizon’s divestiture of Maine, New

Hampshire, and Vermont services to FairPoint is positive. This gives further evidence that

consumers benefit from mergers in terms of better infrastructure quality maintenance and are

harmed by divestiture. For residential customers his result is statistically significant but, not for

business customers.

Regressions for repeat trouble reports provide similar but weaker evidence that mergers

increase customer service quality as well for both consumer groups. The merger dummies do not

always have negative signs, nor are they always statistically significant. For both consumer

groups, the divestiture of service in three states from Verizon to FairPoint has positive and

statistically significant coefficients.

Mergers and divestiture are a major part of this industry and it is no surprise that this

activity would have significant impacts on consumers in terms of quality. When firms propose to

merge, often times it is said to be for economies of scale. Other reasons for merging beyond cost

efficiencies include being able to test new technology more easily or to roll out new products

more quickly. Firms also argue that mergers will allow them to gain a national presence and

improve the network. The purpose of this model and the merger and divestiture dummies is to

determine if and how much the mergers impacted service quality, not the reasons behind

merging. For initial trouble reports, the results indicate that mergers improve quality while

divestiture harms quality. The result for both consumer groups for repeat trouble reports are not

as consistent not significant and therefore we cannot definitively say how each merger impacts

customer-service quality, but a Wald test does indicate that the coefficients of the mergers

41

together are jointly significant. However, divestiture clearly decreases customer-service oriented

quality.

Chow Test for Statistical Difference between Residential and Business Customers

To confirm that there is a statistical difference between the coefficients on the variables

for residential and business customers, I use a Chow test. The results of the Chow test for both

Initial Trouble and Repeat Trouble have mixed results. For P-C Margin, Size, ln (CLECs), BLS

Share, and the merger dummies for Bell Atlantic/GTE, Bell South/AT&T, and the divestiture of

Verizon’s service in Maine, New Hampshire, and Vermont to FairPoint, the Chow test indicates

that there is a statistically significant difference between the coefficients. For the remaining

variables, the Chow test indicates that there is no statistically significant difference between the

two consumer groups. While these results are mixed, the results that do indicate a difference

between the two groups coupled with the evidence described in the Background section provides

justification for continuing to separate residential and business customers.

Section III: Endogeneity of Mergers

Informal Analysis of a Merger

Mergers are clearly the most important variable that influenced the increase in

infrastructure quality of service for residential and business customers. The coefficients on the

merger dummies in the Initial Trouble Reports regressions are the largest in magnitude and

provide the most consistent results in both measures of service quality. Firms in the

telecommunications industry have very high fixed costs and very low marginal costs. The local

service system was always considered a natural monopoly by the government and after the

break-up of AT&T; the Baby Bells were regulated as such. The 1996 Act removed much of the

42

national regulation of this industry, but states still retained the power to regulate prices. As soon

as deregulation was approved, Bell Atlantic and NYNEX, two firms that operated adjacent to

each other, proposed a merger. This first merger created a precedent that has lead to several

mergers in the industry. As of today, the Baby Bells have merged together and now there are

only three major firms left: AT&T (formerly SBC), Verizon, and Qwest.

If we look at the signs of the coefficients on the merger dummies, for the most part they

are all negative and statistically significant. When firms merge, the quality of service provided

increases both for infrastructure and customer service quality. While it is possible that mergers

are endogenous to quality and a low quality firm will merge with a high quality firm leading to

better quality overall, I do not believe this reason is always the case.

Figure 3 is a map that shows the seven baby bells as they were divided by the

government in 1984. Notice mergers tend to take place between firms that operate in adjacent

areas. The pattern of mergers seems to indicate that mergers take place regardless of quality

level and most likely to take advantage of economies of scale in having an easily connected

system and the ability to take advantage of the know-how of a firm that is close by and a network

that is easily connected. The proximity of firms may make it easier to test and roll out new

services to a larger area at one time.

43

Figure 3: Map of Baby Bell Operating Territories before Deregulation1

The data on infrastructure quality for all of the firms that now make AT&T shows that

firms do not merge only with other firms of either high or low quality. Figure 4 shows the levels

of average initial trouble reports for all of the companies that now make up AT&T.

1 Map from http://www.porticus.org/bell/images/rboc_map.gif

44

Figure 4: Mergers to Create the New AT&T v. Average Annual Infrastructure

Quality

If mergers are endogenous to service quality, we would expect that either the merger is

always characterized by a high quality firm buying up a lower quality firm or a low quality firm

buying a high quality firm. But, that pattern does not appear in this dataset. Instead, the quality

level of the purchasing firm may be high or low. This occurrence suggests that quality is not the

driving factor in mergers and that there is another factor influencing the pattern of mergers.

In 1997, Pacific Telesis became part of SBC. As shown in Figure 4, in 1996 Pacific Telesis

was a low quality firm (high average initial trouble reports) and SBC was of significantly higher

quality. It is unlikely this merger was endogenous to quality. Additionally, if we look at the next

merger, when Ameritech joined SBC, the same situation is occurring. Ameritech is a slightly

lower quality carrier than SBC. Ameritech’s territory was adjacent to SBC’s operating territory. It

is possible this merger is not endogenous to quality. In 2005, SBC purchased AT&T, which at the

time was selling long-distance and wireless service, along with a small number of markets in

which it was a CLEC. SBC then changed its name to AT&T. The most plausible reason SBC chose

SBC buys Pacific Telesis

SBC buys AmeritechSBC buys AT&T

AT&T buys Bell South

020

0400

600

800

1000120

014

00

Res

iden

tial a

nd B

usines

s In

itialTro

uble p

er 1

000

Acc

ess Line

s

1994 1996 1998 2000 2002 2004 2006 2008Year

Pacific Telesis AmeritechSBC AT&TBell South

45

to change its name was because of the significant history and reputation of AT&T. Finally in

2006, Bell South becomes part of the new AT&T. Again, the dominant party in this merger is the

one with higher quality.

The geographical evidence suggests that in a market like this one with high

infrastructure costs and certainly network externalities, the mechanism behind merger decisions

is not necessarily to buy up firms that will improve quality. The results of this paper show that

mergers do increase quality, particularly for infrastructure measures. A final result of this paper

that corroborates this idea is that the divestiture of Maine, New Hampshire, and Vermont

services from Verizon to Fairpoint actually ends up decreasing service quality in both

infrastructure and customer-oriented measures. The implication of this result is that a smaller

firm cannot maintain high quality like a bigger firm can in this industry.

For policy makers, these results provide evidence that mergers may have both price and

quality benefits for consumers. The ability to merge was clearly the most important change that

the Act brought about to improve quality of basic local telephone service.

The industry has changed significantly over the last twenty years and much of that has to

do with new technology. Mobile, Internet, and television services are very similar to local

telephone service and thus may also be natural monopolies. Therefore, quality of service should

be a key factor in merger regulation of these companies, especially as they leave local fixed-line

service behind.

Separating the Relationship between Mergers and Quality

The previous section addressed the possibility that mergers are endogenous to quality by

looking at a particular company over time while this section uses econometric modeling to

46

separate the relationship between mergers and quality using expenditures as the intermediate

stage.

It is possible that firms post-merger, may reduce expenditures to increase profits which

could reduce quality of basic local telephone service. Due to this issue, the relationship between

mergers and quality can be broken up into two separate relationships. Ter-Martirosyan and

Kwoka (2010) model this two-stage causation idea to separate the effect of incentive regulation

in electricity distribution from expenditures and then quality measures. In this section, I follow

their method in separating the impact of mergers on maintenance and operating expenditures

and expenditure’s impact on Initial Trouble and Repeat Trouble.

Ter-Martirosyan and Kwoka (2010) use maintenance and operating expenditures (O&M)

of the utility as the specific costs that would be related to quality. In this case, maintenance

expenditures are those costs that are associated with fixing problems with the network’s

infrastructure (titled Plant Specific by the Federal Code of Regulations Title 47) 2. Total

2 (3) Accounts shall be maintained as prescribed in this section subject to the conditions described in § 32.13 in subpart B.

Subsidiary record categories may be required below the account level by this system of accounts or by Commission order. (b) Plant

Specific Operations Expense. (1) The Plant Specific Operations Expense Accounts, 6110 through 6441, are used to record costs

related to specific kinds of telecommunications plant. (2) The Plant Specific Operations Expense accounts predominantly mirror the

telecommunications plant in service detail accounts and are numbered consistently with them; the first digit of the expense account

being six (6) and the remaining digits being the same as the last three numbers of the related plant account. In classifying Plant

Specific Operations expenses, the text of the corresponding plant account should be consulted to ensure appropriateness. (3) The

Plant Specific Operations Expense accounts shall include the costs of inspecting, testing (except as specified in Account 6533,

Testing Expense) and reporting on the condition of telecommunications plant to determine the need for repairs, replacements,

rearrangements and changes; performing routine work to prevent trouble (except as specified in Account 6533), replacing items of

plant other than retirement units; rearranging and changing the location of plant not retired; repairing material for reuse; restoring

the condition of plant damaged by storms, floods, fire or other casualties (other than the cost of replacing retirement units);

inspecting after repairs have been made; and receiving training to perform these kinds of work. Also included are the costs of direct

supervision (immediate of first-level) and office support of this work. (4) In addition to the activities specified in paragraph (b)(3) of

this section, the appropriate Plant Specific Operations Expense accounts shall include the cost of personnel whose principal job is

the operation of plant equipment, such as general purpose computer operators, aircraft pilots, chauffeurs and shuttle bus drivers.

However, when the operation of equipment is performed as part of other identifiable functions (such as the use of office equipment,

capital tools or motor vehicles), the operators' cost shall be charged to accounts appropriate for those functions. (For costs of

operator services personnel, see Accounts 6621, Call completion services, and 6622, Number services, and for costs of test board

47

operating expenditures are all expenditures including Plant Specific, Plant Non-Specific,

Customer Operations, Corporate Operations, and Reimbursements.3 I have subtracted Plant-

Specific costs from Total Operating Costs to obtain Operating Costs similar to those used by Ter-

Martirosyan and Kwoka. Maintenance (MAINEX) and Operating expenditures (OPEX) are in

per total access lines format and I will continue to separate customer groups to see if residential

and business customers are impacted differently.

In the first stage, the following models are estimated:

MAINEXit= MAINEX(Mergersit, Xit) and OPEXit=OPEX(Mergersit, Xit)

Where Mergers are the same dummies used in the original model and X is a vector that

includes all of the other explanatory variables in the original model. From these estimations, we

can obtain predicted maintenance and operating expenditures as a function of Mergers and X.

In the second stage, predicted MAINEX and OPEX from the first stage regressions are

used as explanatory variables and the following model is estimated separately for residential and

business customers:

personnel see Account 6533.) From: http://www.gpo.gov/fdsys/pkg/CFR-2006-title47-vol2/xml/CFR-2006-title47-vol2-sec32-

5999.xml

3 (c) Plant nonspecific operations expense. The Plant Nonspecific Operations Expense accounts shall include expenses related to

property held for future telecommunications use, provisioning expenses, network operations expenses, and depreciation and

amortization expenses. Accounts in this group (except for Account 6540, Access expense, and Accounts 6560 through 6565) shall

include the costs of performing activities described in narratives for individual accounts. These costs shall also include the costs of

supervision and office support of these activities.(d) Customer Operations Expense. The Customer Operations Expense accounts

shall include the cost of performing customer related marketing and services activities described in narratives for individual

accounts. These costs shall also include the costs of supervision, office support and training for these activities.(e) Corporate

Operations Expense. The Corporate Operations Expense accounts shall include the costs of performing executive and planning

activities and general and administrative activities described in narratives for individual accounts. These costs shall also include the

costs of supervision, office support and training for these activities.(f) Reimbursements. Reimbursements of actual costs incurred in

connection with joint operations or projects repairing plant due to damages by others, and obligations to make changes in

telecommunications plant (such as highway relocations), shall be credited to the accounts originally charged. .) From:

http://www.gpo.gov/fdsys/pkg/CFR-2006-title47-vol2/xml/CFR-2006-title47-vol2-sec32-5999.xml

48

QUALITYit = QUALITY(MAINEXit, OPEXit, Xit)

Including current period Maintenance and Operating expenditures presents an

additional endogeneity issue. Therefore, lagged expenditures are included in the estimation of

the model. Table 4 presents the regression results for the first stage and Table 5 provides the

results for the second stage.

49

Table 4: Maintenance and Operating Expenditures as a Function of Model

Variables

(Standard Errors in parentheses); ****Significant at 1%; ***Significant at 5%; **Significant at

10%; *Significant at 15%

Residen t ial

Cu st om er s

Bu siness

Cu st om ers

Resident ia l

Cu st om ers

Bu siness

Cu st om ers

Const a nt 0.1 5 4 **** 0.2 8 2 **** 0.4 86 **** 0.4 2 9 ****

(0.03 4 ) (0.05 7 ) (0 .05 3 ) (0.09 8 )

P-C Ma r gin -0.0008 2 2 **** -0.0009 1 9 **** -0.006 7 1 **** -0.006 7 5 ****

(0.000) (0.000) (0.001 ) (0 .000)

Percent Bu siness Lines 0.0004 5 3 -0.001 4 5

(0.001 ) (0.001 )

Percent Resident ial Lines -0 .001 7 1 *** 0.0001 7 5

(0.001 ) (0 .001 )

Size -0 .00001 2 9 *** -0.00002 4 0**** -0.00001 01 -0.00002 5 8 ***

(0.000) (0.000) (0.000) (0.000)

ln (CLECs) 0.004 5 2 ** 0.004 5 9 *** -0.005 6 0** -0.004 8 3 *

(0.002 ) (0.002 ) (0.003 ) (0.003 )

Percent Fiber 0.0002 1 7 0.0003 7 9 0.003 6 3 * 0.003 7 5 *

(0.001 ) (0 .001 ) (0 .002 ) (0.002 )

BLS Sh a re -0 .0009 1 3 **** -0.0008 4 5 **** -0.001 09 **** -0.0009 7 0****

(0.000) (0.000) (0.000) (0.000)

T im eT ren d 0.002 9 7 *** 0.002 7 5 *** 0.01 4 4 **** 0.01 3 9 ****

(0.001 ) (0 .001 ) (0 .002 ) (0.002 )

Pa cT el2SBC 0.01 4 5 -0.01 1 5 -0.02 84 *** -0.05 5 2 ****

(0.01 6 ) (0 .01 4 ) (0.01 0) (0.01 4 )

NYNEX2BA -0 .01 5 5 -0.01 1 8 -0.07 6 9 **** -0.07 2 0****

(0.01 3 ) (0 .01 4 ) (0.01 0) (0.009 )

A m erit ech 2SBC -0 .001 3 7 -0.001 01 -0.02 9 8 **** -0.02 8 9 ****

(0.005 ) (0.005 ) (0.01 0) (0.01 0)

BA 2GT E -0 .03 9 4 **** -0.03 8 6 **** -0.04 4 8 **** -0.04 2 4 ****

(0.005 ) (0.004 ) (0.01 0) (0.008 )

USWest 2Qwest 0.0004 5 6 -0.0001 7 8 0.003 3 1 0.003 3 9

(0.005 ) (0.005 ) (0.01 1 ) (0 .01 1 )

SBC2A T T -0.02 81 **** -0.02 6 0**** 0.01 1 8 0.01 3 9

(0.007 ) (0.008 ) (0.01 4 ) (0 .01 4 )

BellSou t h 2A T T -0 .02 7 9 *** -0.02 5 8*** 0.01 01 0.01 3 2

(0.01 1 ) (0.01 1 ) (0.01 3 ) (0 .01 3 )

V Z2Fa irPoint 0.02 8 6 *** 0.02 8 3 *** 0.02 1 9 ** 0.02 1 9 **

(0.01 2 ) (0.01 1 ) (0.01 2 ) (0 .01 1 )

N 5 80 5 8 0 5 7 3 5 7 3

R-squ a red 0.01 4 4 0.01 9 4 0.4 7 3 0.4 7 1

Maintenance

Expenditures

Operating

Expenditures

50

The focus here will be concentrated primarily on the impact of mergers on expenditures.

From the results above, we see that for three of the mergers and the divestiture, expenditures

are affected at statistically significant levels. For the merger of Bell Atlantic and GTE to form

Verizon, all four regressions show negative and statistically significant coefficients. For both

residential and business customers, maintenance and operating expenditures decline post-

merger. This result (and the other negative and statistically significant results) means that

quality-related expenditures seem to decline with a merger. For Verizon’s divestiture of service

in Maine, New Hampshire, and Vermont to FairPoint the opposite is true. Positive and

statistically significant coefficients meant that post-merger maintenance and operating

expenditures are higher for residential and business customers. These results are not

unexpected as a merger could create some efficiency that lowers costs and divestiture could have

the opposite effect. Clearly, not all of the mergers had significant impact on expenditures but

there are a few examples of cases where the merger was very important for spending.

Table 5 presents the results of the second stage model using a 1-year lag of Predicted

MAINEX and OPEX. Lags are used to avoid the issues of simultaneity when using the same

period for expenditures and quality.

51

Table 5: Quality of Service as a function of 1-year Lagged, Predicted Maintenance

and Operating Expenditures

(Standard Errors in parentheses); ****Significant at 1%; ***Significant at 5%; **Significant at

10%; *Significant at 15%

Residen t ia l

Cu st om ers

Bu siness

Cu st om ers

Residen t ia l

Cu st om ers

Bu siness

Cu st om ers

Const a nt 2 5 3 .4 **** 9 8 .1 2 **** 7 1 .2 5 **** 4 1 .7 3 ****

(4 5 .87 0) (3 1 .4 1 0) (2 1 .7 3 0) (1 1 .4 1 0)

Predict ed MA INEX (1-y ear la g) 5 9 .8 7 2 3 1 .6 **** 4 4 .8 2 4 7 .3 5 **

(1 7 4 .000) (7 4 .2 5 0) (7 1 .4 9 0) (2 4 .7 2 0)

Predict ed OPEX (1-y ea r la g) -9 3 .9 7 * -8 0.3 4 **** -7 6 .9 3 **** -3 2 .4 1 ****

(6 0.7 4 0) (2 5 .4 2 0) (2 6 .3 8 0) (9 .06 1 )

P-C Ma rgin -0 .8 7 3 *** -0.1 81 -0.4 2 6 **** -0.1 3 8 ***

(0.3 8 3 ) (0.1 9 8) (0.1 4 2 ) (0.06 2 )

Percen t Bu siness Lines 1 .6 7 3 *** 0.4 7 4

(0.7 2 5 ) (0.3 3 8 )

Percen t Residen t ia l Lines 1 .2 1 5 **** 0.05 03

(0.3 6 7 ) (0.1 2 9 )

Size 0.009 6 0* -0.01 3 6 *** 0.004 5 3 -0.001 3 1

(0.006 ) (0.006 ) (0.003 ) (0.002 )

ln (CLECs) -0 .7 02 2 .4 2 0*** -0.4 8 4 0.1 5 1

(2 .1 8 7 ) (1 .1 5 8) (0.9 2 6 ) (0.3 8 0)

Percen t Fiber 1 .4 1 2 -0.4 81 0.09 3 8 -0.1 9 5

(1 .4 1 8 ) (0.6 7 4 ) (0.5 8 8 ) (0.2 1 8 )

BLS Sh a re -0.9 2 5 **** -0.5 4 1 **** -0.2 04 -0.07 04

(0.3 1 9 ) (0.1 3 8) (0.1 6 5 ) (0.05 6 )

T im eT rend -0 .5 7 9 -2 .6 9 3 **** 0.1 1 9 -0.7 8 0****

(1 .1 9 8 ) (0.5 7 9 ) (0.5 7 0) (0.2 2 9 )

Pa cT el2SBC 2 .2 7 9 -4 9 .3 8 *** 0.1 3 7 -3 .2 7 6

(1 3 .9 6 0) (2 4 .1 3 0) (6 .04 2 ) (9 .2 9 9 )

NYNEX2BA -9 8 .3 2 **** -3 5 .7 5 **** -1 8 .2 4 **** -9 .09 9 ****

(2 8 .4 8 0) (6 .3 4 3 ) (3 .6 3 5 ) (1 .3 4 9 )

A m erit ech 2SBC -3 4 .3 4 **** -7 .9 5 4 0.9 1 3 0.4 1 2

(8 .9 4 1 ) (5 .7 6 1 ) (3 .1 9 8 ) (1 .2 2 1 )

BA 2GT E -1 3 .4 0* -7 .9 01 ** -2 .3 5 9 1 .3 2 7

(8 .08 7 ) (4 .3 6 7 ) (4 .000) (1 .6 9 7 )

USWest 2Qwest -7 4 .4 0**** -3 0.1 1 **** -3 6 .4 9 **** -1 8 .9 8 ****

(6 .3 1 1 ) (3 .04 9 ) (3 .09 1 ) (1 .5 9 6 )

SBC2A T T -3 1 .6 3 **** -1 7 .2 1 **** -4 .9 5 3 0.9 1 5

(1 1 .1 4 0) (4 .6 9 8 ) (6 .08 3 ) (2 .1 1 0)

BellSou t h 2A T T -4 9 .4 5 **** -1 6 .4 8 **** -1 0.05 *** -1 .7 6 5

(1 0.84 0) (5 .07 2 ) (4 .9 6 5 ) (1 .89 5 )

V Z2Fa irPoint 3 2 .2 2 **** 2 .6 4 4 1 1 .6 4 **** 3 .9 1 3 ****

(5 .7 6 8 ) (3 .4 84 ) (2 .4 7 5 ) (1 .04 1 )

N 5 3 0 5 3 0 5 3 0 5 3 0

R-squ a red 0.1 8 8 0.2 1 6 0.1 06 0.1 1 1

Initial Trouble Repeat Trouble

52

The second stage results provide a somewhat odd result for MAINEX. Maintenance

expenditures in time t-1 have a positive (only statistically significant for business customers)

impact on both Initial and Repeat Trouble reports. For business customers, this significant

coefficient implies that when we increase maintenance expenditures by $1 per line, the number

of initial trouble reports increases by about 232 holding all else constant. I believe this to be an

endogeneity issue, and therefore using a one-year lag of maintenance expenditures is

inappropriate in the case of basic local telephone service.

For operating expenditures in both customer groups and each measure of quality, the

resulting coefficient is negative and statistically significant. Therefore, operating expenditures in

time t-1 improve quality in time t.

This exercise has provided some evidence that mergers impact both maintenance and

operating expenditure for firms. However, the results of the regressions showing the impact of

those maintenance and operating expenditures on quality is somewhat mixed. The second stage

regressions show that last year’s operating expenditures have a significant impact on this year’s

quality except for in the case of business customers. Despite some ambiguity in the results, there

does appear to be this two-stage relationship in the impact of mergers on quality.

53

Section IV

Economic and Policy Implications

The Telecom Act of 1996 was intended to deregulate the industry from a federal

standpoint. States still retained control over pricing regulation. However, the federal standpoint

on entry, diversification, and mergers changed with the passing of the Act. The goal of this paper

is to measure how those three aspects of the industry impacted residential service quality.

According to the results, the ease of entry created by the Telecommunications Act did not

significantly change service quality in either direction for basic local service. The

Telecommunications Act was intended to allow and encourage competition into the local service

market. Prior to 1996, CLECs were merely a fringe segment of the market, but the number of

competitors skyrockets after 1996. Unfortunately, this competition had not real impact on either

the infrastructure quality of the local telephone system, nor the customer service that a firm

provided consumers. Clearly, there were much more important factors than competition that

influenced the increase in service quality for this industry.

The implication of this result is not that competition does not matter for improving

quality. The implication is that this regulation did not influence competition perhaps in the way

in which it might be expected for quality. Regulators may have only been attempting to create

competition in terms of prices. While price and quality can be thought of as two sides of the

same coin, in this case we do not see an impact on the quality provided by firms.

Diversification should increase quality of service if there are economies of scope amongst

different products. The telecommunications system can support several different product

offerings using the same infrastructure. Traditional copper lines to provide phone service can

54

also provide DSL for Internet access. Fiber optic cables use light to transmit voice, data, and

video information. For example in the 1996 Act, the government required that firms prove that

their local service markets were competitive before being allowed to diversify into the long-

distance market. Diversification into other lines of business such as Internet and wireless service

started becoming popular around the same time. At the very least, there should be some

economies of scope in terms of managing customers and their various product purchases from

diversification. However, the results indicate that basic local service does not benefit from

diversification of products. A more specialized firm will provide higher quality infrastructure

and customer service. The argument for economies of scope between various product offerings

does not hold water in this case. While firms may decide to diversify, the results of this paper

imply that the reason to become less specialized has virtually nothing to do with improving the

quality of the baseline product offering.

Conclusions and Future Research

The most important mechanism through which the Telecommunications Act of 1996

impacted service quality in basic local service is the ability for firms to merge. Competition from

CLECs had almost no impact on quality of infrastructure or customer-oriented service for either

consumer group. The results of this paper find that a more specialized firm will have better

quality infrastructure, but the magnitude of the coefficients is relatively small compared to the

coefficients on merger activity. Converting to fiber optic technology does not appear to improve

or reduce the quality of basic local service, which is unsurprising as this technology is more

important for broadening the range of products provided through one system.

Mergers are clearly the most important factor influencing quality of service for the Baby

Bells. The main impact of the Telecommunications Act of 1996 is the newfound ability of firms

55

to merge with each other to take advantage of economies of scale, other firm’s know how, and

better ability to test and implement new strategies and technologies. Mergers do not seem to be

based on an endogenous quality consideration, but instead are based on proximity of service

territories. The question of what factors influence the decision to merge with another firm is

something that should be addressed in future research.

Future research may also want to examine why there is a difference of service quality

between business and residential customers in the first place. Another question that should be

addressed by future research is why are firms merging so much in this industry? A final question

that I find to be compelling is to see ask how the Telecommunications Act of 1996 impacted

well-established Incumbent Local Exchange Carriers that were not part of the Bell System, such

as GTE and Southern New England Telephone.

56

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59

Chapter 2: The Effect of Market Structure on Prices and Quantities in Freight Rail

Shipments

The impact of competition on a market is an issue that regulators in many industries face

in the policy making process. Regulators often create policies to foster competition in markets so

that consumers can reap the benefits in the form of lower prices. In the freight railroad industry,

trackage rights are one of the tools that can be used to inject competition into a market.

Trackage rights allow a firm to use another firm’s rail infrastructure to transport goods without

building its own infrastructure at a particular location. The regulator can publicly order these

trackage rights or they can be privately agreed upon between firms. Often firms enter markets

solely through trackage rights and this practice has become increasingly prevalent in recent

years. The question that this paper aims to answer is whether or not entry through trackage

rights provides meaningful competition in a market.

Section I provides a brief background of the freight railroad industry in the United States

as it stands today. Section II is a review of the literature relevant to this research while Section

III presents the model used to examine the research question and Section IV identifies the data

used. Section V presents an estimation model used to examine the impact of market structure in

markets that do not have firms competing through trackage rights as well as the results of that

estimation. Section VI presents the model and results of the main research question, looking at

various subsets of freight shipments to examine the impact of trackage rights competition on

market outcomes. Section VII concludes.

60

Section I: Background

The means by which goods are transported to various locations is a very important issue,

especially for a country as large as the United States. Commodities are typically transported via

truck or rail, though some may be transported via air. About forty percent of all freight is

transported by railroads. In particular commodities such as minerals, metallic ores, and

petroleum are large portions of the total shipped. Seventy percent of all coal is transported by

railroads and coal makes up more than forty percent of all rail shipments (AAR 2010b).

In 2010, there were 566 freight railroads in the United States with mileage of 138,623.

Freight railroads are divided up into different classes based on the amount of annual revenue

that each firm makes. Defined by having annual revenue of $250 million or more, in 2010 Class

I railroads were actually characterized by revenue greater than $398.7 million (AAR 2010b).

There are currently seven Class I railroads operating in the United States: Burlington National

Santa Fe (BNSF), Canadian National (CN), Canadian Pacific (CP), Chessie and Seaboard System

Railroad (CSX), Kansas City Southern (KCS), Norfolk Southern (NS), and Union Pacific (UP).

The Surface Transportation Board (STB), an agency that is part of the Department of

Transportation (DOT), regulates freight railroads. The STB is in charge of resolving any rate and

service disputes between railroads as well as between railroads and shippers. It is also charged

with the responsibility to approve or deny railroad mergers. The STB has not approved a merger

between any Class I firms since the 1996 merger of Union Pacific and Southern Pacific.

Additionally, there has been no entry to or exit from the Class I firm classification since this

merger. Market structure as described in this manner has remained constant for the last 17

years.

61

Figure 1 above shows a map depicting the Class I firms’ ownership of track throughout

the United States. Regional and short-line railroads are indicated in grey. These smaller carriers

make up the vast majority of the number of railroads in the United States, but account for very

little of the trackage ownership and revenue. Class I firms account for about 69% of mileage,

94% of revenue, and 90% of employees in the freight rail industry (AAR 2010b). Most areas of

the country are served by only one or two Class I carriers while there are some markets that are

served by three or more of these firms. However, there are no markets that are served by all

seven Class I firms through track ownership alone. In those cases where 7 Class I railroads are

present, at least one is present due to trackage rights.

Figure 3: Association of American Railroads Map of Class I Track Ownership, 2010

62

Section II: Literature Review

In 1976, the Railroad Revitalization and Regulatory Reform Act was enacted to

encourage competition and reliance on cost-based rate making. And then in 1980, the Staggers

Act deregulated railroad rates in an effort to help railroads increase profitability. In their 1987

study, Lee, Baumel, & Harris use a structure, conduct, performance analysis of Class I railroads

from 1971 to 1984 finding that these changes in regulation had no long-term impact on the trend

for larger firms seen in the Class I railroad group. The biggest impact of the Staggers Act was

further concentration as measured by the four-firm concentration ratio. One limitation of this

study is that it uses aggregate Class I industry data and due to this constraint, cannot look at the

impact on specific commodities, geographies or regional railroads. Using data envelopment

analysis to answer a similar question, Chapin & Schmidt (1993) try to determine if there has

been an increase in efficiency since the Staggers Act and if so, if that efficiency increases can be

attributed to the mergers since deregulation. The authors find that deregulation did in fact

increase efficiency in the market but that many firms are larger than the efficient scale. The

authors conclude that mergers have reduced scale economies and any efficiency gains cannot be

attributed to mergers.

The last major merger in the railroad industry was between two Class I firms: Union

Pacific and Southern Pacific in 1996. Kwoka & White (2004) and Breen (2004) both provide

case study analyses of the UP/SP merger. The initial years after the merger were very difficult

for the newly integrated firm and shippers were subjected to poor service quality as the firms

tried to coordinate activities. Due to the extreme difficulties of the merger, the Surface

Transportation Board has essentially placed a moratorium on mergers and there have not been

any allowed between Class I firms since 1996.

63

To allay some of the fears and anxiety of shippers prior to the merger of UP and SP, the

Surface Transportation Board created some conditions with which the companies needed to

comply in order for the merger to be consummated. The most important condition of the merger

between UP and SP was the trackage rights granted to BNSF – the only other Class I track-

owner in the west. This merger significantly impacted many markets in the west because many

of those markets went from having two competitors to only one competitor (2-to-1 markets) or

from three competitors to only two competitors (3-to-2 markets). Those shippers who only have

access to one carrier are called captive shippers and as Pittman (2010) argues, need to be

protected by the government due to this status.

Pittman (2010) discusses a few means by which captive shippers may be protected. The

most direct path, Pittman argues, would be tighter regulation of rates charged to captive

shippers, though this would require significant time and effort in streamlining the process of

rate setting and regulation. Another route would be to introduce legislation that would place

more railroad behavior under the jurisdiction of the antitrust laws (there is currently an

exemption for railroads), which could limit the ability of a railroad to exploit its market power at

the expense of captive shippers. Mandatory switching, which would need to be regulated or

legislated, is a third way of protecting captive shippers from high rail rates (Pittman 2010).

Trackage rights are another way to try to accomplish this task.

While Christensen (2009) finds that trackage rights may be difficult to implement and

sustain because of coordination issues with track-owners, studies of the trackage rights imposed

in the west find that they can be effective. Karikari, Brown, & Nadji (2002) look specifically at

the impact of the merger on rail rates for those shippers in potential 2-to-1 markets after the

merger of UP and SP. They find that the competition of BNSF through government imposed

64

trackage rights was more effective in a specific economic area– kept downward pressure on

prices – than SP’s pressure on UP prices prior to the merger. However, these results were only

relevant to the Salt Lake City economic area and varied by commodity type, direction of traffic

and shipper type. Winston, Maheshri, & Dennis (2011) examine the long-run effect of the

mergers of UP/SP and BN/SF, focusing specifically on grain shipments. The authors find that in

the long run, these mergers have not raised rates and have had negligible impact on consumer

welfare.

The impact of market structure on prices and quantities is an important question both

theoretically and practically. Schmidt (2001) attempts to answer this question for the freight

railroad shipment market. His paper provides the theoretical background and method for

measuring market structure for this research. The theoretical model is presented next and the

method of measurement is discussed in Section V.

Section III: Theoretical Model

The theoretical model is based on the one presented in Schmidt (2001). Schmidt

estimates the reduced form of a structural model of supply and demand for railway shipment of

a commodity. The definition of the market in this model is a shipment of a particular commodity

from an origin location to a destination location. Schmidt is attempting to answer the following

question: is shipment using only one firm (a single line shipment) cheaper than shipping a

commodity using two or more carriers (an interline shipment)? To answer this question,

Schmidt analyzes markets using both regional and Class I firms that own trackage in the origin

and/or destination location. While Schmidt does not include firms with trackage rights as

competitors in the market, the theoretical model remains the same.

65

On the demand side of the market, there are assumed to be a large number of shippers

who are price takers with the options of shipping via rail or truck:

Qr = fr (Pr, Pt, X, b) (1)

Qt= ft (Pr, Pt, X, b) (2)

where Qr is the quantity of the good shipped via rail, Qt is the quantity of the good shipped via

truck, Pr is the price of the shipment via rail, Pt is the price of the shipment by truck, X is a

vector of exogenous variables that impact demand for shipment, and b is a vector of structural

parameters.

The trucking sector is competitive and therefore behavior of suppliers can be described

as:

Qt= gt (Pt, Zt, g) (3)

where Zt is a vector of exogenous variables that affect the cost and supply of transportation via

truck, and g is a vector of parameters.

Equilibrium in the trucking sector can therefore be solved by setting supply equal to

demand:

ft (Pr, Pt, X, b)= Qt= gt (Pt, Zt, g) (4)

and when solving for equilibrium price, Pt, we find:

Pt=h (Pr, X, Zt, b, g) (5)

Substitution of the price of shipments by truck into the demand equation for railroad

shipments provides the inverse demand function for freight as a function of its own prices:

66

Pr=fr’ (Pr, X, Zt, b, g) (6)

Due to the fact that the rail industry has a small number of firms, solving for price and

quantity is more difficult because the market is not competitive. Schmidt (2001) follows

Bresnahan (1989) by assuming that the ith rail firm maximizes profit where marginal revenue

equals marginal cost:

MC (Qri, Zri) = Pr + Qri*θi(N)* dfr/dPr (7)

which can be rewritten as:

MC (Qri, Zri) = Pr*(1 + Sri(N)* θi(N)/εr) (8)

where Qri is the quantity of output produced by the ith rail firm, MC (Qri) is the marginal cost of

the ith rail firm of carrying another unit of this commodity from the origin location to the

destination location, Sri is the market share of the ith rail firm, Zri is a vector of exogenous

variables that impact firm i’s costs of providing rail freight transport, εr is the elasticity of market

demand, and θi is a parameter to describe the competitiveness of firm behavior.

Equation (8) implies that a firm’s demand elasticity is equal to its market share divided

by the market’s demand elasticity. This result may not always come about. For instance, suppose

a firm has a very high market share and the demand elasticity of the market is very elastic. This

would imply a high elasticity of demand for that firm which in turn implies that there are a

significant amount of close substitutes for the good. Therefore, the market power of the firm

may be overestimated by this model. It is important to keep this in mind when interpreting the

67

results of this exercise as the impact of another competitor on market outcomes may be

overestimated by the model.

θi may depend on the number of rail competitors serving the market. θi may also vary

between firms because some firms may be more aggressive than others in competition for

shippers’ products. If we hold demand constant, Qri will change as the number of firms changes

and MC(Qri) may also change with the number of firms.

Since the sum of the outputs of individual firms equals the market output, we have the

following identity:

Qr = ∑ Qri (9)

If the market has N firms, there are therefore N + 2 endogenous variables: Qri for each of

the N firms, Qr, and Pr; and, there are N + 2 equations relating those variables (equation (8) for

each of the N firms, equation (9), and the demand curve equation (6)). If we assume that the

functional forms of these equations are well behaved, we can solve the system for each of the

endogenous variables as a function of only the exogenous variables and parameters:

Pr = k(X, Zt, Zr, b, g, θ, N) (10)

Qr = m(X, Zt, Zr, b, g, θ, N) (11)

Qri = m(X, Zt, Zr, b, g, θ, N) (12)

where Zr and θ are vectors composed of the individual Zri and θi for each firm.

Schmidt (2001) does not estimate the full structural model for three reasons. First, firm-

specific information on the exogenous variables is needed to be able to identify the model.

68

Second, data about firm-specific quantities is needed and is something that is not publicly

available. And third, one must make an assumption about the behavior of the firms, but there is

little empirical evidence to provide guidance. For the same reasons, this paper will first estimate

a reduced form model of the market to look at the impact of market structure on prices and

quantities.

To analyze the results, Schmidt considers four theories of market structure which have

implications for the magnitudes and significance of the coefficients in the model:

(1) Constant returns to scale and competitive (Bertrand) behavior. If this is

the case, then firm marginal cost does not depend on the number of firms

in the market and therefore θ, the parameter to describe the level of

competitiveness of firms will be equal to zero. Therefore, price will be

equal to marginal cost, quantity and price in the market will not depend

on the number of firms and will not be statistically significant in the

estimation.

(2) Decreasing returns to scale and competitive (Bertrand) behavior. In this

situation, as the number of firms in the market increases, quantity per

firm falls and therefore firm costs fall which would cause increasing

market quantities and falling prices.

(3) Increasing returns to scale and competitive (Bertrand) behavior. Here,

as firms enter, quantity per firm would decline and therefore costs of

individual firms will rise. We would expect to see falling market quantities

and increasing prices as the number of firms goes up.

69

(4) Constant returns to scale and imperfect competition between firms. As

the number of firms increases, market shares of individual firms fall. θ,

the parameter to describe the level of competitiveness of firms may also

fall if competition is characterized as Cournot. As market share falls, each

firm has less ability to hold up price and increases quantity. If θ falls,

increasing the number of firms reduces a single firm’s ability to raise

price.

An additional theory of industry structure is described below:

(5) N is correlated with unobserved factors in the vector of exogenous

variables that influence demand for shipment of products. If this case is

true, a market would exhibit higher prices and quantities.

In this model, the number of firms is taken to be exogenous but it is possible (if not

likely) that the number of firms is dependent on unobservable demand characteristics. If firms

are more likely to enter markets that have high demand for transportation of a specific

commodity, then market structure would be dependent on N. The results in Schmidt (2001) are

therefore short-run results because of this characteristic as well as the fact that entry is costly in

this industry and exit requires a firm to abandon assets which are sunk. Exit of the market also

requires regulatory approval. The assumption means that the number of firms is fixed in the

short-run.

For the purposes of this paper, this assumption only holds true if we do not include firms

with trackage rights as competitors. As discussed earlier, market structure for the Class I firms

has remained constant since 1996. However, it is very likely that trackage rights are not created

70

randomly or exogenously. Therefore, this possibility will become very important for my

estimation methodology. The next sections describe the data used to estimate the impact of

market structure and trackage rights on prices and quantities in these markets.

Section IV: Data

The main source of data used in this paper is the 2010 Public Use Waybill Sample from

the STB. This dataset is a sample of waybills from Class I, II, and III railroads in the United

States in the year 2010. An observation is defined as a shipment of commodity i from origin

Bureau of Economic Analysis (BEA) economic area to destination BEA economic area. Figure 2

provides a map depicting the definitions of BEA economic areas used by the STB in the Public

Use Waybill Sample. Note from Figure 2 that a BEA economic area can be a fairly large

geographic area. The use of BEA economic areas as the geographic unit is not ideal because a

firm present at the north end of an economic area may have little influence on a firm at the

southern tip of that same area if the area is particularly large. This may lead to the belief that

shippers have more options in choosing a carrier than they do in actuality. Additionally, a firm

in one BEA economic area may have a significant impact on prices in its neighboring economic

area, which by construct cannot be captured in this model. However despite these potential

issues, this research is constrained to using BEA economic areas because that is what is

provided in the STB’s Public Use Waybill Sample. The Confidential Waybill Sample provides

much more detailed geographic information and would provide a more detailed analysis that fits

the reality of railroad location better than the Public Use Waybill Sample.

71

Figure 4: Map of BEA Economic Areas (1995)

The sample provides information on many aspects of a shipment including but not

limited to the weight of the shipment in tons, the shortest distance via rail track from origin to

destination, and the revenue of the shipment. A market is defined as a commodity shipped

within an origin-destination pair. Data will be aggregated to the origin-destination market level

for each type of good.

The Public Waybill Sample does not identify the carrier of the shipment, this is only

included in the Confidential Waybill Sample. When looking at pure market structure, the

identity of the firm may not matter. However, to determine the impact of a particular firm on

prices and quantities in a market, identity must be known. Data has been collected from the

Association of American Railroad’s publication Railroads and States to supplement the Waybill

72

sample with carrier identification. From the information provided in Railroads and States, I

was able to match railroad locations as identified by the AAR with BEA economic areas across

the country for each of the seven Class I railroads and 17 regional railroads. With this

information, it is not only possible to count the number of firms at an origin and destination, but

to also identify track owners at both ends of a shipment.

Table 1 below describes the market structure in the 2010 Public Use Waybill Sample. The

first column indicates the number of single-line carriers on a route (firm is located at both ends

of a shipment). The second column indicates the percent of shipments in the 2010 Public Use

Waybill Sample that exhibit this market structure. As discussed above, it is important to note

that the data used in this research does not identify carrier location beyond BEA economic area.

The reader should keep in mind that the results may not be capturing the exact market structure

at the station level. However, the BEA economic area provides an approximation of location

which is correlated with the presence of a carrier at a particular station; if a carrier is not located

in the BEA economic area, it cannot be present at a particular station in that BEA economic

area.

73

Table 6: Market Structure of Single Line Firms by Number of Shipments in the

2010 Public Use Waybill Sample

This table includes all seven Class I railroads as well as the 17 largest regional railroads

as defined by the Association of American Railroads Railroads and States publication. A vast

majority of the shipments in the sample are shipped along a duopoly route (73.53%). In second

place are monopoly routes with 14.55% of the shipments. A little over five percent of the

shipments in the sample do not have any single line firms on the route. These shipments are

either interline shipments, where the shipment must be transferred to another carrier mid-

route, or they are carried by a railroad not included in this research.

Additional supplementary data sources for control variables include the U.S. Census

Bureau, the Bureau of Economic Analysis, the United States Geological Sample, the Annual

Survey for Manufactures, and the United States Department of Agriculture. The variables

obtained from these resources are discussed in the next section and outlined in Table 2.

Number of

Single Line

Firms

Percent of

Shipments

None 5.40%

One 14.55%

Two 73.53%

Three 3.82%

Four 1.65%

Five 0.21%

Six

Seven 0.84%

Total 100.00%

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Table 7: Variable Definitions, Sources, & Units

Section V: First Estimation Model

The first estimation model looks only at shipments between markets where firms do not

compete through trackage rights in which the only competitors are trackage owners. The market

structure of trackage-owners for Class I firms has remained unchanged for the last seventeen

years (since the merger of UP and SP in 1996). Thus, the rationale for discarding trackage rights

competitors is to attempt to only look at the effects of what may be called “exogenous” market

structure on market outcomes in the short run. Section VI will address the approach to and

results of the second estimation model dealing with the impact of trackage rights on market

Variable Source Description/Units

Shipm ent STB Public Carload Way bill Sample Shipment from j to k of commodity i

Price Created from STB Carload Waybill

Av erage Rev enue per ton-mile,

weighted by the number of carloads

in the market for commodity i

Quantity Created from STB Carload WaybillNumber of carloads in a market for

commodity i

Structure AAR - Railroads and StatesNumber of class I RR operating in

BEA Economic Area

Origin T rackage Rights Firm s DOT RITA NTAD DatabaseNumber of Class I RR with trackage

rights in Origin BEA

Destination T rackage Rights Firms DOT RITA NTAD DatabaseNumber of Class I RR with trackage

rights in Destination BEA

State Fuel Price (FORG,FDEST )

EIA 2010 Transportation Sector

Distillate Fuel Prices, 2010 Dollars per million BTU

City Population (PORG, PDEST ) U.S. Census Bureau 2010, Number of persons

State GDP (GDPORG, GDPDEST ) Bureau of Economic Analy sis 2010, Millions of current dollars

State T otal Value of Agri. Sector (AGORG,

AGDEST )

USDA Economic Research Serv ice:

U.S. and State Farm Income and

Wealth Statistics

2010, Thousands of dollars

State Manufacturing Value Added

(MANUFORG, MANUFDEST )Annual Surv ey of Manufactures 2010, Thousands of dollars

State T otal Value of Minerals Extracted

(MINORG, MINDEST )USGS Minerals Handbook 2010, Thousands of dollars

Short Line Distance (SHIP) STB Public Carload Way bill SampleShortest distance in miles of track

from Origin BEA to Dest. BEA

WAT ER STB Public Carload Way bill SampleDummy =1 if shipment travels v ia

water, zero otherwise

Comm odity STB Public Carload Way bill SampleDummy =1 for commodity i , zero

otherwise

Unit of Observation

Independent Variables

Dependent Variables

75

outcomes using instrumental variable regression analysis. One important limitation of this first

exercise is that the markets examined may not include firms with trackage rights because they

are not as profitable and thus endogeneity may still be present. All of the models presented are

necessarily static models and do not account for any dynamic interactions.

However, it is important to note that track-ownership could be endogenous to the

market itself as well, particularly if there are bottleneck issues present. If it is very difficult for a

new track-owner to build into a market simply due to space constraints, then the number of

competitors is restricted. Therefore, it is important to keep in mind that the results presented

may face some issues of endogeneity and may only be relevant in the short-run as discussed in

Section III.

A very general functional from is used by Schmidt (2001) and the same technique is

adopted here for markets that do not have trackage rights competitors. For a particular

shipment, a firm will be designated as “single-line” firm if it serves both ends of a particular

route. A firm will be considered an “origin-only” firm if it only serves the origin BEA economic

area and a firm will be designated “destination-only” if is only serves the destination BEA

economic area. A dummy is created for each possible combination of the numbers of each type

of firm. For example, the base case will be a monopoly single-line firm with no origin-only or

destination-only firms in the market (1-0-0). When there are two single-line firms, the dummy

for (2-0-0) will be equal to 1, zero otherwise. For a market with a single-line firm, an origin-only

firm, and a destination-only firm, the dummy for (1-1-1) will be equal to 1, zero otherwise. There

are over one hundred different combinations of single-line, origin-only, and destination-only

firms in the data.

76

Market price will be measured as the weighted average revenue per ton-mile in the

market of a particular commodity. Using price per ton-mile purges the effects of weight and

distance from the price as these characteristics clearly influence how much a shipment will cost

to transport. The average price in a market is weighted by the number of carloads on a

shipment. Quantity will be measured as the number carloads of commodity i in an origin-

destination pair. I will include variables to control for fuel prices, population, and economic

well-being in the origin and destination cities or states. I also include dummies for each

commodity type in addition to the market structure dummies. The base commodity will be STCC

1132, which is the category for barley shipments. The estimation model is defined as follows:

log Price | log Quantity = β0 + β1 log FORG+ β2 (log FORG)2 + β3 log FDEST + + β4 (log FDEST)2

+β5 (log FORG * log FDEST) + β6 log PORG + β7 (log PORG)2 + β8 log PDEST + β9

(log PDEST)2 + β10 log (GDPORG) + β11 (log GDPPORG)2 +β12 log (GDPDEST) +

β13 (log GDPDEST)2 + β14 (log AGORG) +β15 (log AGORG)2 +β16 (log AGDEST) +β17

(log AGDEST)2+ β18 (log MANUFORG) +β19 (log MANUFORG)2+β20 (log

MANUFDEST) +β21 (log MANUFDEST)2+β22 (log MINORG) +β23 (log

MINORG)2+ β24 (log MINDEST ) + β25(log MINDEST)2+β26 (log SHIP) +β27 (log

SHIP)2+β28 WATER +βijk Structureijk +βi Commodityi

The expected impact on shipment prices for fuel costs in the origin is positive. An

increase in fuel costs should increase the cost of a shipment as it originates. The impact of fuel

costs in the destination on the cost of shipping a good is less clear in direction, but should be

lesser in magnitude – have less impact – on the price of a shipment because the shipper is

unlikely to be willing to pay for the cost of returning the cars and the carrier can push the fuel

costs onto the shippers who load in the destination.

The variables that control for demand characteristics are population, GDP, agricultural

sector value, mineral sector value, and value added to the manufacturing sector in the origin and

destination BEA of the shipment. It is expected that prices will generally be higher in origin

77

locations with larger populations and GDP. These may be larger cities where there more demand

for shipment and there is also congestion that increases the cost of transport. It is expected that

origin locations with large agricultural sectors may have lower prices because these commodities

are generally shipped by truck, not rail. On the other hand, prices may be higher because these

goods may require special cars to prevent spoilage. Commodities from the mineral or

manufacturing sector are more likely to be shipped via railroad and therefore, shipment prices

may increase if the value of these sectors is large in the origin. The impact of these demand

characteristic variables from the destination standpoint is less clear. Shipment of agricultural,

mineral, or manufacturing commodities to locations with large respective sectors of their own

may raise or lower the average price of a shipment, depending on the commodity and if that

commodity is produced locally in the destination BEA economic area.

The distance of a shipment (SHIP) as well as its weight (TONS) are included despite

measuring prices as average price per ton-mile because there still may be some leftover impact

of distance and weight on market outcomes.

The impact of all 300 commodities is not enumerated here, but it is expected that some

commodities will be more costly to ship than others and thus dummies for the commodity on

each shipment are included in this model.

Multicollinearity

The model above exhibits significant multicollinearity issues. While can be hypothesized

that the relationship of fuel costs and demand characteristics may not be linearly related to price

and quantity, including squared or inverse terms in this estimation leads to severe

multicollinearity in the results. Even after removing these terms, multicollinearity still exists

78

primarily for the terms measuring GDP and value-added to the manufacturing sector. These

explanatory variables have also been dropped from the estimation.

The remaining variables exhibit a small amount of multicollinearity (variance inflation

factor of 6.08) which is attributed to multicollinearity in 10 of the market structure dummies. I

have elected to keep those market structure dummies in the estimation as they are integral to

the estimation. The impact of this multicollinearity is most prevalent for the quantity

regressions and the reader should keep this in mind when interpreting the results presented

next.

Results

Table 3 below presents the regression results for the control variables used in the first

estimation model. Table 4 and Table 5 present the results for the market structure dummies in

this estimation. These coefficients should be interpreted in relation to the price in a monopoly

single-line market, which is the market structure 1-0-0.

79

Table 8: Control Variable Estimated Coefficients

(Standard errors in parentheses) ***p<0.001; **p<0.01; *p<0.05

ln (Price) ln (Quantity) ln (Price) ln (Quantity)

ln (FORG) 1 .51 6*** 7 .682*** ln (MINORG) -0.0290*** 0.0239**

(0.06) (0.1 4) (0.004) (0.01 )

ln (FDEST) -0.1 1 4* -2 .055*** ln (MINDEST) 0.1 01 *** -0.1 53***

(0.05) (0.1 4) (0.004) (0.01 )

ln (PORG) 0.0609*** 0.1 86*** ln (SHIP) -0.47 4*** 0.0404***

(0.002) (0.005) (0.003 ) (0.01 )

ln (PDEST) -0.0432*** 0.266*** ln (TONS) -0.41 4*** 0.387 ***

(0.002) (0.004) (0.003 ) (0.005)

ln (AGORG) 0.037 2*** 0.222*** WATER 0.1 20* -1 .260***

(0.003) (0.01 ) (0.05) (0.20)

ln (AGDEST) -0.1 47 *** 0.224*** Constant 1 9.54*** 69.91 ***

(0.003) (0.01 ) (1 .08) (2.58)

Commodity Dummies? YES YES

N 241 ,7 1 8 241 ,7 1 8

Adj. R-sq 0.93 0.82

80

Table 9: Market Structure Coefficients – Price

0 1 2 3 4 5 6 7 0 1 2 3 4 5 6

0 0.326*** 0 0.266*** 0.21 6*** 0.1 07 * 0.1 33* 0.001 4

(0.06) (0.05) (0.06) (0.05) (0.06) (0.05)

1 0.326*** 0.200*** 0.1 7 3** 0.322*** 0.404*** 0.07 5 1 0.31 3*** 0.1 96*** -0.0261 0.1 04 0.499*** 0.007 6

(0.07 ) (0.06) (0.06) (0.07 ) (0.1 1 ) (0.06) (0.06) (0.05) (0.06) (0.06) (0.06) (0.05)

2 0.47 1 *** 0.352*** 0.1 24* 0.444*** 0.67 6*** 2 -0.581 *** 0.0297 -0.1 01 0.0353 -0.1 50* 1 .005***

(0.06) (0.05) (0.06) (0.06) (0.09) (0.07 ) (0.06) (0.06) (0.05) (0.07 ) (0.06)

3 0.364*** 0.37 6*** 0.21 3*** 0.57 2*** 0.580*** 3 -0.0334 -0.1 44* -0.1 26* -0.37 3*** 0.386***

(0.08) (0.05) (0.06) (0.08) (0.1 7 ) (0.06) (0.06) (0.05) (0.06) (0.1 2)

4 -0.207 0.1 41 * -0.1 96* 0.436 4 0.241 ** -0.0922 0.465*** -0.1 06 0.806***

(0.1 1 ) (0.06) (0.09) (0.24) (0.08) (0.05) (0.06) (0.06) (0.05)

5 0.599** 0.433*** 5

(0.20) (0.09)

6 6 0.0327 0.1 34*

(0.05) (0.05)

7 0.57 4***

(0.08)

0 1 2 3 4 5 0 1 2 3 4 5

0 0.21 4*** 0.1 7 7 *** 0.21 8*** 0.27 1 *** -0.0229 0 0.361 *** 0.368*** 0.584*** -0.1 52** 0.7 48***

(0.05) (0.05) (0.05) (0.05) (0.05) (0.06) (0.05) (0.06) (0.06) (0.1 0)

1 0.1 94*** -0.047 3 0.236*** 0.342*** 0.97 6*** 0.00564 1 0.01 01 0.235*** 0.430*** 0.47 8*** 0.07 7 7

(0.05) (0.06) (0.05) (0.06) (0.05) (0.05) (0.06) (0.06) (0.07 ) (0.1 3) (0.06)

2 -0.0437 -0.0267 0.0304 0.001 42 -0.627 *** 2 -0.31 2*** -0.1 54* -0.485*** -0.1 7 8**

(0.05) (0.06) (0.07 ) (0.07 ) (0.05) (0.06) (0.06) (0.1 3) (0.06)

3 -0.234*** -0.0599 -0.454*** -0.507 *** 3

(0.05) (0.06) (0.08) (0.07 )

4 4 0.305*** 0.251 *** 0.41 4***

(0.06) (0.06) (0.07 )

5 0.220*** -0.201 *** 0.434***

(0.05) (0.05) (0.05)

0 1 2 3 4 0

0 0.7 68*** -0.0904 0.1 03 0.31 2*** 0 1 .063***

(0.06) (0.06) (0.07 ) (0.06) (0.08)

1 0.620*** -0.495*** 0.255***

(0.06) (0.1 4) (0.06) Origin Firm s

2 0

0 0.695***

3 -0.285*** -0.225*** (0.06)

(0.06) (0.06)

0 Single Line Firms 1 Single Line Firm

Origin Firm s Origin Firm s

Destination Firm s

Destination Firm s

2 Single Line Firms 3 Single Line Firms

Origin Firms Origin Firm s

Destination Firm s

Destination Firm s

4 Single Line Firms 5 Single Line Firms

Origin Firm s Origin Firm s

Destination Firm s

Destination Firm s

7 Single Line Firms

Destination Firm s

81

Table 10: Market Structure Coefficients – Quantity

0 1 2 3 4 5 6 7 0 1 2 3 4 5 6

0 -1 .97 6*** 0 -0.958*** -1 .507 *** -0.1 83* -1 .005*** -0.27 6**

(0.2 4) (0.1 0) (0.1 1 ) (0.09) (0.1 1 ) (0.09)

1 -1 .31 1 *** 0.1 53 -1 .649*** -1 .228*** -1 .7 64*** -1 .62 8*** 1 -1 .803*** -1 .1 40*** -1 .354*** -1 .398*** -1 .463*** -1 .1 00***

(0.1 7 ) (0.1 0) (0.1 2 ) (0.1 2) (0.28) (0.1 1 ) (0.09) (0.09) (0.1 1 ) (0.1 0) (0.1 0) (0.09)

2 -2.7 66*** -1 .537 *** -3.068*** -2.1 92 *** -2 .338*** 2 -0.92 3*** -1 .668*** -0.960*** -0.7 90*** -0.0493 -3 .281 ***

(0.1 5) (0.09) (0.1 0) (0.1 1 ) (0.1 5) (0.1 1 ) (0.1 2) (0.1 1 ) (0.09) (0.1 2) (0.1 5)

3 -2.1 28*** -2 .426*** -3.1 55*** -2.400*** -2.361 *** 3 -0.67 7 *** -1 .355*** -0.42 3*** 0.21 9 -2 .480***

(0.1 9) (0.1 0) (0.1 0) (0.1 4) (0.23) (0.1 0) (0.1 0) (0.1 0) (0.1 3) (0.1 8)

4 -0.440** -1 .544*** -2.7 3 7 *** -2.563 *** 4 -1 .983*** -0.537 *** -0.960*** -1 .447 *** -4.225***

(0.1 5) (0.1 3) (0.1 6) (0.26) (0.1 8) (0.1 0) (0.1 0) (0.1 3) (0.09)

5 -2.608*** -2.7 7 1 *** 5

(0.23) (0.1 9)

6 6 -0.7 7 3*** -1 .01 3***

(0.09) (0.09)

7 -0.7 1 3 ***

(0.1 9)

0 1 2 3 4 5 0 1 2 3 4 5

0 -0.7 03*** -0.91 9*** -1 .1 61 *** -0.440*** -0.1 90* 0 -0.87 2*** -0.7 31 *** -0.850*** -1 .202*** -2.400***

(0.09) (0.09) (0.09) (0.09) (0.09) (0.1 0) (0.1 0) (0.1 2 ) (0.1 0) (0.1 7 )

1 -0.51 9*** -1 .641 *** -1 .337 *** -0.951 *** -3.07 5*** -0.7 23*** 1 -0.87 1 *** -1 .405*** -0.969*** -1 .81 7 *** -0.929***

(0.09) (0.1 0) (0.1 0) (0.1 1 ) (0.09) (0.09) (0.1 1 ) (0.1 1 ) (0.1 2 ) (0.21 ) (0.1 1 )

2 -0.224* -1 .646*** -1 .802*** -0.61 9*** -3.005*** 2 -0.846*** 0.7 39*** -0.7 7 5*** -1 .200***

(0.09) (0.1 0) (0.1 2) (0.1 2 ) (0.1 0) (0.1 2 ) (0.1 2) (0.21 ) (0.1 3)

3 0.204* -1 .41 4*** -1 .046*** -0.239 3

(0.09) (0.1 1 ) (0.1 3) (0.1 4)

4 4 0.1 7 8 -1 .427 *** -2.955***

(0.1 0) (0.1 0) (0.1 6)

5 0.322*** -0.506*** -4.091 ***

(0.09) (0.09) (0.09)

0 1 2 3 4 0

0 -0.47 7 *** 0.1 69 -2 .391 *** -1 .289*** 0 -1 .02 6***

(0.1 0) (0.1 0) (0.1 4) (0.1 1 ) (0.1 2 )

1 -0.950*** -0.480*** -2 .065***

(0.1 0) (0.1 3) (0.1 0) Origin Firm s

2 0

0 -1 .23 5***

3 -0.308** 0.849*** (0.1 0)

(0.1 0) (0.1 1 )

4 Single Line Firms 5 Single Line Firms

Origin Firms Origin Firms

Destination Firms

Destination Firms

7 Single Line Firms

Destination Firms

2 Single Line Firms 3 Single Line Firms

Origin Firms Origin Firms

Destination Firms

Destination Firms

0 Single Line Firms 1 Single Line Firm

Origin Firm s Origin Firms

Destination Firms

Destination Firms

82

From Table 4 we see that the coefficients on all of the control variables are statistically

significant. The signs of the control variable coefficients are fairly consistent with the results

presented in Schmidt (2001), however the statistical significance is different in some cases. The

magnitude of the results presented here is larger than those of the results presented by Schmidt.

The results show that fuel cost increases prices and quantities at the origin. Fuel costs at

the destination point tend to decrease prices and quantities. Fuel costs are also large in

magnitude as compared to the other variables indicating, as one might expect, that fuel costs are

a large component of rail costs. The extremely large coefficients on fuel costs imply that there

may be some residual multicollinearity in the estimation. However, at the very least it can be

said that fuel costs are a significant component of price and the cost of fuel at the origin has a

much larger impact than the price of fuel at the destination point of a shipment. Other cost

characteristics such as the distance of the shipment (SHIP) and the weight of the shipment

(TONS) are also all statistically significant at the 0.001 level, even when using price per ton-mile

as is used here. The negative signs of these coefficients indicate that heavier shipments or those

that must travel further have lower average price per ton-mile. A shipment traveling via water

has prices approximately 12% higher than those that do not.

Included demand characteristics are also statistically significant at least at the 0.001

level. The results for population show that a one percent increase in the population at the origin

is associated with a 6.09% increase in prices and an 18.6% increase in quantity. As population at

the destination point increase by one percent, there will be a 4.32% decrease in price and a

26.6% increase in quantity holding all else equal. Population at both ends of a route is clearly a

significant factor in determining shipment prices. Prices to ship goods tend to be higher at the

originating location if there is a larger population. It may be more difficult to run track or

83

schedule times to run trains in very populated areas than in less densely populated areas, thus

increasing the price of shipping from these locations.

The total agricultural value of the origin and the destination states also has significant

impacts on the price of shipments. Holding all else equal, a one percent increase in the total

value of the agricultural sector in the origin state will increase the price of the shipment by

3.72%. A one percent increase in the value of the agricultural sector in the destination state

decreases the price of shipments by 14.7% and increases quantities by about 22.4% holding all

else equal. The agricultural value of an origin or destination is important because those areas

whose business is primarily farming will be shipping products to areas where there is less

agricultural activity. Agricultural goods are less likely to be shipped by rail because they typically

are goods with limited shelf life, thus the minimal (though statistically significant) impact on

price.

Minerals are a group of products more likely to be transported via rail because they

typically do not have a limited shelf life and are often very heavy. A one percent increase in the

value of the mineral sector in the origin state decreases shipment price by about 2.9% and

increases quantity by about 2.4% holding all else equal. A one percent increase in the value of

the mineral sector in the destination state will increase prices by about 10% and decrease

quantity by about 15.3% holding all else equal. Again, there is a small but statistically significant

impact on prices from this variable. From the results, it appears that if a destination starts to

produce more minerals itself, prices will increase significantly.

The coefficients for the market structure dummies are presented in Tables 4 and 5. These

coefficients should be interpreted in relation to the price in a monopoly single-line market, that

is the market structure 1-0-0. This type of market would occur when the only option for freight

84

shipment via rail is service from one Class I or regional carrier. It is important for the reader to

note again that there is a high likelihood of market structure endogeneity and that the results

presented in this section must be considered to be short-run with no change in market structure.

These results are static rather than dynamic.

As can be seen in the results, prices (Table 4) are generally higher and quantities (Table

5) lower when there is no firm that serves both BEA economic areas in the market pair directly

(0 single line firms). This result, consistent with Schmidt (2001), indicates that interline service

is more costly than shipping a good using only one carrier for the duration. For example, if we

compare the situation where there is no single-line carrier and exactly one carrier in the origin

and one carrier in the destination (0-1-1), we see that prices are 32.6% higher and quantities

131% lower than the base case of the monopoly single-line shipper. The coefficient on quantity (-

1.31) is clearly confusing because it implies negative shipment, so clearly the multicollinearity or

some other issue is present in this estimate. However, we can say that prices are higher but

cannot definitively state if quantities are lower than the base case when interline shipment is the

only option. Schmidt (2001) finds that interline shipments tend to be more expensive than

single-line shipments and the results presented here coincide with that finding. Those markets

without any single-line firms have generally higher prices – and coefficients of large magnitude

– as well as significantly lower quantities.

Analysis of the coefficients presented in Tables 4 and 5 reveals some strange results.

While the results for markets without any single line carriers seem to make some sense

(interline shipment is more costly than single-line shipment), as the number of single line firms

increases, the results show that prices continue to increase and quantities fall. Unfortunately,

these results do not coincide with those found by Schmidt (2001) nor do they follow traditional

85

economic theory. However, there are a couple of possible explanations. The first is that there is

still an endogeneity problem present. Track ownership and the number of firms is endogenous

to the prices and quantities of shipments (and therefore profitability) in a market. The other

possibility is that there is some oligopolistic behavior occurring that the model is capturing.

However, there is no way to tease this out of the model with the available data.

It is important to note that the results presented in this section do not consider the

impacts of firms competing through trackage rights on market outcomes. Competition via

trackage rights is a very important aspect of this industry that should not be excluded from a

contemporary exploration of the freight railroad shipment market. Section VI presents the

methodology and results of such an exploration.

Section VI: Second Estimation Model

A firm may enter a market without building its own infrastructure by obtaining the right

to use the track of existing firms. These trackage rights may be negotiated between firms using

contracts or may be imposed by the regulator, the Surface Transportation Board. As noted by

Kwoka & White (2004):

Sometimes a railroad will extend “trackage rights” to a second railroad, so that the latter can run its trains over the former’s track (typically for a relatively short distance) and thereby connect shippers/recipients to the second railroad’s track network. In principle this can result in competition between the two carriers despite the single track. However, the relationship between the two railroads is that of landlord and renter, and there are many ways that the landlord can use its position to mute the competitive threat from the tenant. For example, the fee for use of the tracks may be set so high that the second railroad has to price its service noncompetitively. In addition, the landlord railroad can use its train scheduling (“dispatching”) prerogatives, track maintenance routines, and longer-run investments affecting the route to favor itself and raise its rival’s costs or degrade the latter’s service. And the extensive and close contact between the two railroads, especially on routes where they are the only providers of rail service, may provide the basis for oligopolistic coordination.

86

If railroads are voluntarily extending trackage rights to competitors, there must be some

benefit to doing so. Therefore, these voluntary trackage rights may not provide the expected

downward pressure on prices that should emerge from meaningful competition. This caveat is

very important to keep in mind, as the data used in this estimation uses trackage rights that are

voluntarily agreed upon as well as those mandated by the government.

As discussed earlier, the regulator may impose trackage rights as part of the conditions

of a merger approval as was done in the UP/SP merger in 1996. The STB made these trackage

rights the centerpiece of the merger agreement and awarded approximately 4,000 track-miles of

rights (Kwoka & White 2004). These trackage rights were seen by the industry as likely

ineffective because these they were imposed only in places where the shipper and the recipient

were directly connected to the UP and SP (and only UP and SP) track. In doing so, the STB

neglected to include instances where either UP or SP might be close enough to the shipper or

recipient such that the threat of using another means of shipment or the possibility of build out

to the competitor led to effective competition between UP and SP. These instances were counted

as pre-merger monopoly that would not change post-merger. Furthermore, the STB ignored

instances where UP or SP may be a monopoly at one end of a shipment, but the other carrier

was competing at the opposite end of a shipment. Merging would create a single-line service

monopoly. It was estimated that there would be freight price increases of about 20% post-

merger (Kwoka & White 2004).

Some additional data is required to obtain the identities of firms with trackage rights in

particular BEA economic areas. The Department of Transportation’s Research and Innovative

Technology Administration (RITA) publishes the National Transportation Atlas Databases

(NTAD) on an annual basis. Using 2010’s NTAD, I am able to obtain information on the

87

identities of firms with trackage rights (voluntary or mandated) in an economic area. Even

though I do know the identity of each firm in each BEA economic area, I do not present results

looking at the impact of a particular firm’s use of trackage rights because of endogeneity issues.

The presence of a particular competitor may be endogenous and I do not have a good

instrument to mitigate the issue at this time. As noted, the trackage rights included in this

dataset include both those that have been mandated by the STB through merger conditions as

well as trackage rights privately agreed upon between firms themselves. Mixing of these

trackage rights is not strictly appropriate as they differ in fundamental ways, but the data does

not provide information on the reason that trackage rights exist on a particular route. In one of

the estimations, I attempt to separate the two types of trackage rights by looking only at those

routes for which trackage rights were granted in the UP/SP merger approval. However, the

other estimations do not differentiate between voluntary and mandated trackage rights.

About 52% of the shipments in the sample are not served by any firms that are present

through trackage rights only. About three percent of the sample is comprised of shipments for

which one trackage rights competitor is present at both the origin and the destination point,

while about 0.14% of shipments are in markets where two trackage rights competitors are

present at both the origin and the destination point of a shipment. There are, however, many

routes where there may not be a single-line trackage rights competitor, but there are trackage

rights competitors at either the origin or destination point of a shipment. Table 6 below

describes how trackage rights are mixed in with the market structure of single-line track-

owners.

88

Table 11: Shipments Distribution of Trackage Rights by Track Ownership Market

Structure in the 2010 Public Use Waybill Sample

From Table 6 it can be seen that when there is a duopoly in track-ownership, most of the

time there will not be any trackage rights competitors but it is more likely that there will be one

trackage rights competitor at both ends of a shipment than two. Additionally, it is far more

likely for there to be just one trackage rights competitor at one end of a shipment when the

track-ownership market structure is duopolistic than two or more trackage rights competitors.

These tables provide some insight into the distribution of trackage rights across market

structures in the sample which leads to the question of how these trackage right competitors

impact market outcomes.

Table 7 below describes the number of markets (origin, destination, commodity

combination) in which firms have trackage rights in the sample. Trackage rights are typically

granted in miles along a route. Table 8 simply reports the number of markets not the number of

miles for which a firm holds trackage rights.

Number of Track Owners at

Both Ends of a Shipment

Zero

Firms

One

Firm

Two

Firms

Zero

Firms

One

Firm

Two

Firms

Three

Firms

Zero

Firms

One

Firm

Two

Firms

Three

Firms

Zero 5.41% 0.12% 0.00% 3.50% 1.82% 0.17% 0.04% 2.29% 2.24% 0.76% 0.23%

One 14.10% 0.81% 0.06% 6.96% 3.79% 3.69% 0.55% 7.58% 3.23% 3.62% 0.55%

Two 71.79% 1.77% 0.01% 59.25% 13.50% 0.79% 0.03% 59.12% 12.48% 1.92% 0.04%

Three 3.70% 0.10% 0.00% 2.44% 0.98% 0.38% 0.00% 2.73% 0.91% 0.16% 0.00%

Four 1.05% 0.08% 0.07% 0.67% 0.41% 0.12% 0.00% 1.01% 0.05% 0.14% 0.00%

Five 0.02% 0.07% 0.00% 0.09% 0.00% 0.00% 0.00% 0.09% 0.00% 0.00% 0.00%

Six 0.84% 0.00% 0.00% 0.84% 0.00% 0.00% 0.00% 0.84% 0.00% 0.00% 0.00%

Total 96.91% 2.95% 0.14% 73.75% 20.49% 5.15% 0.62% 73.66% 18.91% 6.60% 0.83%

Percent of Shipments by

Number of Firms with Trackage

Rights Only at the Destination of

the Shipment

Percent of Shipments by

Number of Firms with

Trackage Rights at Both

Ends of a Shipment

Percent of Shipments by

Number of Firms with Trackage

Rights Only at the Origin of the

Shipment

89

Table 12: Number of Markets in which Firms have Trackage Rights

Of the almost 8,000 markets in the sample, less than 350 markets have firms competing

through trackage rights. CP – at 130 markets – is by far the largest trackage rights competitor in

terms of its presence in the most markets. KCS follows with 71 markets and next are CN and

BNSF with 36 and 32 markets respectively. CSX with 25 markets, UP with 13 markets, and NS

with 6 markets rounds out the competition from Class I railroads in terms of trackage rights.

FEC and WE are regional carriers but both have a fair presence in trackage rights competition.

The existence of trackage rights in a particular market is most likely not exogenous.

Firms are probably much more likely to negotiate trackage rights in a market that would be

profitable than one that is not. Additionally, the regulator is much more likely to try to impose

competition in a market where either there are already high prices or a merger will reduce

competition. To deal with the issue of endogeneity, I employ two-stage least-squares.

To instrument for the number of single-line track owners and the number of single-line

trackage rights competitors, I use county-level data on agricultural production, manufacturing,

Firm Number of Markets

BNSF 32

CN 36

CP 130

CSX 25

FEC 22

KCS 71

NS 6

UP 13

WE 8

Total 343

Total Markets 7,991

90

and population from the 1900 U.S. Census of Population and Housing. These variables were

successfully used as instruments for the number of firms at the origin and destination of a

shipment in Hughes (2011). Because total size of the U.S. railroad network peaked in the early

1900s and existing track operates on historical right-of-way, it is expected that historical rail

service will be correlated with present railroad participation. As actual historical railroad

participation is not observed, county level population, value of agricultural goods, livestock, and

manufacturing at the origin and destination are used to predict railroad participation along a

route (Hughes 2011).

Tests for relevance in the following regressions show that these instruments are valid.

The model is over-identified, in that there are more instruments than endogenous regressors. As

such, exogeneity can be tested statistically. Unfortunately, the results of these tests reject the

null hypothesis indicating that they are in fact endogenous. However, the qualitative reasoning

above and the successful use of these instruments by Hughes (2011) inspires some confidence in

their usefulness for this exercise. First stage results and test statistics are available from the

author upon request.

The rest of this section discusses the second-stage results of four explorations of the

impact of trackage rights on freight shipment prices and quantities. I have chosen these four

ways to examine the data because of their relevance to the sector. The first set of results

presented look at all shipments whether they occur in markets with or without trackage rights.

The second results presented look specifically at the impact of government imposed trackage

rights. Third, shipments of coal are examined as they make up a large portion of freight rail

traffic. The fourth and fifth regressions look at the impact of trackage rights on short-haul versus

long-haul shipments. A market continues to be defined as a shipment from origin j to

destination k of commodity i.

91

Table 13: All Shipments

(Standard errors in parentheses) ***p<0.001; **p<0.01; *p<0.05

Table 8 presents the results using the instruments from the 1900 U.S. Census of

Population and Housing described above for all of the shipments in the 2010 Public Use Waybill

Sample. The results show that increasing the number of single-line track owners along a route

will decrease prices by about 30% and increase quantities by about 179%, holding all else equal.

The statistical significance of this sign indicates, as would be expected, that the addition of an

additional competitor – that owns track – will create downward pressure on market prices.

A negative sign is also present for Single TR, indicating that the addition of a trackage

rights competitor will also exert downward price pressure. However, the coefficient of 323% is

very large in magnitude making the result and interpretation questionable. The railroad must be

paying the shipper to use its services in order for this result to hold. This result suggests that

ln (Price) ln (Quantity)

Single Lines -0.305*** 1 .7 87 ***

(0.01 ) (0.01 )

Single TR -3 .230*** 3.47 5***

(0.07 ) (0.1 5)

ln (SHIP) -0.821 *** 1 .1 23***

(0.01 ) (0.01 )

ln (TONS) -0.423*** 0.31 6***

(0.002) (0.005)

WATER -0.325 -1 .041 **

(0.1 7 ) (0.35)

Constant 3.37 4*** -8.055***

(0.09) (0.1 9)

Commodity Dummies? YES YES

N 257 ,840 257 ,840

92

trackage rights are still endogenous, which is confirmed with statistical testing. It is also

important to remember that this dataset includes voluntary trackage rights which are clearly

endogenous.

Overall, shipments that weigh more and are transported over longer distances have

lower prices and higher quantities. Long-distance shipments tend to be less costly because they

involve fewer switches and since only single-line carriers are included here, the negative sign is

expected. The dummy for water transport has no significant effect on price. The next set of

results looks only at routes on which BNSF was granted trackage rights by the merger of UP and

SP.

Table 14: UP/SP Merger Condition Routes - Government Mandated Trackage

Rights

(Standard errors in parentheses) ***p<0.001; **p<0.01; *p<0.05

Table 9 presents the results exclusively for those markets in which trackage rights were

government imposed in the UP/SP merger approval in 1996. There are 16 routes of this list in

ln (Price) ln (Quantity)

Single Lines 0.552*** 0.648***

(0.02) (0.03)

Single TR 0.292*** 0.7 97 ***

(0.03) (0.05)

ln (SHIP) -0.1 62*** -0.1 7 3***

(0.01 ) (0.01 )

ln (TONS) -0.1 83*** 0.1 48***

(0.01 ) (0.02)

Constant -0.927 * -1 .1 47

(0.37 ) (0.62)

Commodity Dummies? YES YES

N 4,985 4,985

93

the 2010 Public Waybill Sample and 74 markets. The government imposed trackage rights at the

route level. The Surface Transportation Board mandated trackage rights along these routes to

mitigate the impact of a lost competitor due to the merging of UP and SP in 1996. The routes

identified by the STB are primarily in Arkansas, California, Colorado, Louisiana, Nevada, Texas,

and Utah with the majority of routes in the state of Texas. These trackage rights may be

considered as exogenous (as in comparison to voluntary trackage rights agreements) because

they were imposed in an effort to mitigate predicted effects of the loss of a competitor along

these routes.

As discussed above, these trackage rights were seen by the industry as likely ineffective

because these they were imposed only in places where the shipper and the recipient were

directly connected to the UP and SP (and only UP and SP) track. The STB did not include

situations in which either UP or SP might be close enough to the shipper or recipient such that

the threat potential entry of the other or shipper build out to the competitor led to effective

competition. Additionally, the STB ignored instances where UP or SP may be a monopoly at one

end of a shipment, but the other carrier was competing at the opposite end of a shipment, such

that interline alternatives had previously been available to shippers. 20 percent price increases

were estimated (Kwoka & White 2004). Karikari, Brown, & Nadji (2002) found that these

trackage rights were effective in decreasing prices in the Salt Lake City economic area.

These results look only at the routes on which the STB imposed trackage rights in 1996.

Holding all else equal, the addition of one more single line track owner would increase prices by

55.2% and increase quantities by 64.8% in these markets. In contrast, to the results obtained by

Karikari, Brown, & Nadji (2002) trackage rights competition has a positive and statistically

significant impact on price in the results presented in Table 9. The addition of a trackage rights

competitor increases price by 29.2% and increases quantity by about 80%, ceteris paribus.

94

While the endogeneity problem may not be entirely mitigated by only looking at government

mandated trackage rights (the sign and magnitude of the coefficient on Single TR for quantity is

positive and fairly large), the sign and coefficient of Single TR in the price regression coincides

with the estimated impact described in the case study by Kwoka & White (2004). Even

seventeen years after the merger was approved and trackage rights were mandated, higher

prices are still present. The next section exclusively examines coal shipments in the 2010 Public

Use Waybill Sample.

Table 15: Shipments of Coal, STCC 11

(Standard errors in parentheses) ***p<0.001; **p<0.01; *p<0.05

ln (Price) ln (Quantity)

Single Lines 0.662*** -0.824***

(0.03) (0.06)

Single TR 1 .960*** -5.468***

(0.38) (0.69)

ln (SHIP) -0.339*** -0.347 ***

(0.02) (0.03)

WATER 0.1 85 -1 .835

(1 .65) (2.96)

Constant -6.7 7 1 *** 1 3.37 ***

(0.1 9) (0.34)

N 25,866 25,866

95

The results presented in Table 8 are exclusively for shipments of coal – Standard

Transportation Commodity Code (STCC) 11. Coal has been singled out for two reasons. The first

reason to look at coal separately from other commodities is that it makes up about 40% of all rail

shipments. For this reason, coal is clearly an important product for railroads and thus may be

impacted by trackage rights differently from other commodities. The second reason coal has

been singled out is the captive shipper problem. As discussed in Pittman (2010), captive

shippers are those shippers that are served by only one firm and have very little potential to

attract an additional carrier. Coalmines may be particularly subject to these restrictions if they

are located in remote areas to which it may be very difficult and costly to build additional track

to serve. It is important to note that coal is not the only product for which the captive shipper

problem is present. Other shippers such as petrochemical plants among others may be in similar

situations. Coal was primarily chosen because of its prevalence as a commodity shipped via rail.

The results in Table 8 indicate that adding an additional track owner at both ends of a

coal shipment will increase prices by about 66%. And, holding all else equal, one more single-

line trackage rights competitor will increase the price of a coal shipment by 196%.

The positive sign on these coefficients could be for a few reasons. It could be that firms

are making agreements with each other for trackage rights because they provide some benefit –

profit or otherwise – and this is at the expense of the shipper. Thus, voluntary trackage rights do

not benefit shippers.

Econometrically, there may still be some endogeneity that is not being taken care of by

the instrumental variable method. Though there is statistical evidence that the instruments are

endogenous, though valid, I do not believe this to be the primary issue. Additionally, the

unavoidable use of BEA economic area as origin and destination location is correlated with the

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existence of the railroad in a place where the shipper may use its services, but it is not the best

measure. A coal mine may be located in an area with only one carrier choice (captive shipper)

but the data (wrongly) categorizes that shipment in a market that has more than one shipper.

However, it is impossible to avoid this problem with the available data.

As the first estimation results show, the distance of a shipment has a statistically

significant impact on both prices and quantities. It is possible that the impact of a trackage

rights competitor may be different depending on the distances that the shipment must travel.

The results presented in Table 11 are for short haul shipments (traveling less than 250 miles),

while Table 12 presents the results for long haul shipments (a distance greater than 250 miles).

Table 16: Short Haul Shipments (Less than 250 Miles)

ln (Price) ln (Quantity)

Single Lines 0.01 25* 0.07 41 ***

(0.01 ) (0.01 )

Single TR 0.41 1 *** 0.464***

(0.03) (0.05)

ln (SHIP) -0.3 7 5*** -0.229***

(0.01 ) (0.01 )

ln (TONS) -0.57 9*** 0.427 ***

(0.01 ) (0.01 )

Constant 0.31 8* 3.91 5***

(0.1 4) (0.25)

Commodity Dummies? YES YES

N 21 ,449 21 ,449

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Table 17: Long Haul Shipments (Greater than 250 Miles)

(Standard errors in parentheses) ***p<0.001; **p<0.01; *p<0.05

Short-haul and shipments experience higher prices and higher quantities when an

additional single-line track owner enters the market. Holding all else equal, short haul

shipments experience 1.25% higher prices and 7.4% higher quantities. In contrast, an additional

single-line track owner for long-haul shipments decreases prices 46.7% and increases quantities

by 211%. The impact of a firm entering through trackage rights has a statistically significant and

positive impact on price. For example, a competitor entering along an entire route will increase

prices by 41.1% and increase quantity by 46.4% holding all else equal.

In contrast, a firm competing through track ownership and trackage rights at both ends

of a long haul shipment will decrease price and increase quantity. This sign is consistent with

the results in the first regression (Table 8), where long-haul shipments are less costly than

short-haul shipments. The magnitude of the impact on price and quantity for both track owners

ln (Price) ln (Quantity)

Single Lines -0.467 *** 2 .1 1 5***

(0.01 ) (0.02)

Single TR -3 .359*** 1 .57 3***

(0.08) (0.1 8)

ln (SHIP) -0.625*** 1 .1 91 ***

(0.004) (0.01 )

ln (TONS) -0.396*** 0.281 ***

(0.002) (0.004)

WATER -0.524*** -0.447

(0.1 5) (0.3 2)

Constant 2.37 5*** -8.950***

(0.08) (0.1 8)

Commodity Dummies? YES YES

N 236,391 236,391

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and trackage rights competitors is much larger than the magnitude of the coefficients for short-

haul shipments. As the number of observations used in this regression is almost the entirety of

the shipment data, the same issues of endogeneity discussed above likely apply. However, the

results do provide some evidence that long-distance shipments tend to be less costly because

they involve fewer switches and since only single-line carriers are included here, the negative

sign is expected.

Section VII: Conclusion

This paper has used publically available cross-sectional data to examine the impact of

market structure on market outcomes in the freight railroad shipment industry in the United

States. It has confirmed previous findings that prices and quantities do vary with the number of

firms competing through track-ownership.

This paper has also taken the analysis one step further by investigating the impact of

firms competing through trackage rights without building their own infrastructure. The trackage

rights used here are both voluntarily agreed upon between firms and imposed by the

government. Trackage rights are clearly not exogenous and while instrumental variables have

been employed, there is still some level of endogeneity present.

If firms agree to grant trackage rights to each other, there must be some positive benefit

to both parties. As Kwoka & White (2004) points out, in principle this should provide

competition and result in lower prices for shippers. However, the “landlord” is easily able to

impose costs on the renting railroad and use its position to mitigate the competitive threat from

the renter. These techniques could be used to raise rivals costs, thus increasing the costs of a

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shipment along a route where trackage rights are present. Additionally, close contact between

railroads may facilitate collusive behavior.

The results of this exercise imply that these voluntary trackage rights are actually not

beneficial to shippers and in fact do create higher market prices per ton-mile. Even looking

specifically at trackage rights imposed by the government in the UP/SP merger approval, prices

increase with the entrance of a trackage rights competitor. This result is likely due to the way in

which the Surface Transportation Board applied trackage rights.

Trackage rights may be a viable way to create competition in a market, but only through

careful economic analysis and appropriate application will this result in lower prices. Further

research into this topic is clearly needed, and this paper is just a first step. In future, the

Confidential Waybill Sample should be used to provide a more detailed analysis with better

geographic and price data. Additionally, instruments that can be proven to be both statistically

valid and exogenous would also help to improve this analysis. Furthermore, it is imperative to

be able to separate voluntary and government imposed trackage rights to determine empirically

if trackage rights are a good policy alternative to increase consumer welfare.

100

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Chapter 3: The Impact of Competition on Price Dispersion between Rail Routes

Variation in prices across firms, geography, distance, and market structure is a

phenomenon that has been widely observed and examined in economic literature by authors

such as Pratt, Wise, & Zeckhauser (1979), Carlson & McAfee (1983), Stahl (1989), Shepard

(1991), Borenstein & Rose (1994), Dana (1999), Gerardi & Shapiro (2009), and Orlov (2011)

among others . There are many theoretical papers that point to the causes of price dispersion

including price discrimination (Borenstein & Rose 1994; Gerardi & Shapiro 2009), cost

variation (Carlson & McAfee 1983; Chandra & Tappata 2011), search costs (Pratt, Wise, and

Zeckhauser 1979; Stahl 1989) and demand uncertainty (Dana 1999). Numerous empirical

studies have used these theoretical models to determine the causes of price dispersion in

industries such as airlines (Borenstein & Rose 1994; Gerardi & Shapiro 2009; Cornia, Gerardi &

Shapiro 2012), life insurance (Brown & Goolsbee 2002), and retail gasoline (Shepard 1991). The

market for airline tickets in particular has been used numerous times because of dynamics of the

market and the richness of the available data. The literature on this market in particular as well

as the existing literature on price dispersion in other markets will help shed light on the

dynamics of variation in prices in the freight railroad industry examined here.

Chapter two of this dissertation looked at the impact of competition through track-

ownership and trackage rights on prices and quantities of freight railroad shipments. As

documented in that paper there are significant variations in price across routes as well as by

commodity type and distance of the shipment.

This paper attempts to answer a related but different question: what are the potential

causes of price variation in this market? To examine this question, I look at three possible

explanations for price dispersion in economic theory and the empirical literature: price

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discrimination, cost variation, and search costs. I describe the predictions of these different

theories apply them to the freight railroad shipment market. Next, I empirically test how well

the price discrimination explanation fits the freight railroad shipment data using a methodology

similar to that used by Borenstein & Rose (1994) for the airline industry.

Section I provides a brief discussion of the freight railroad industry in the United States

and the results of chapter two of this dissertation on variation in prices. Section II discusses the

economic literature on price dispersion, highlighting three possible explanations: price

discrimination, cost variation, and search costs. Section III tests the predictions of the price

discrimination explanation for the railroad shipment market. Section IV concludes and

discusses avenues for future research.

Section I: Background

The means by which goods are transported to various locations is a very important issue,

especially for a country as large as the United States. Commodities are typically transported via

truck or rail, though some may be transported via air. About forty percent of all freight is

transported by railroads. In particular commodities such as minerals, metallic ores, and

petroleum are large portions of the total shipped. Seventy percent of all coal is transported by

railroads and coal makes up more than forty percent of all rail shipments (AAR 2010b).

In 2010, there were 566 freight railroads in the United States with mileage of 138, 623.

Freight railroads are divided up into different classes based on the amount of annual revenue

that each firm makes. Defined by having annual revenue of $250 million or more, in 2010 Class

I railroads were characterized by revenue greater than $398.7 million (AAR 2010b). There are

currently seven Class I railroads operating in the United States: Burlington National Santa Fe

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(BNSF), Canadian National (CN), Canadian Pacific (CP), Chessie and Seaboard System Railroad

(CSX), Kansas City Southern (KCS), Norfolk Southern (NS), and Union Pacific (UP).

The Surface Transportation Board (STB), an agency that is part of the Department of

Transportation (DOT), regulates freight railroads. The STB is in charge of resolving any rate and

service disputes between railroads as well as between railroads and shippers. It is also charged

with the responsibility to approve or deny railroad mergers. The STB has not approved a merger

between any Class I firms since the 1996 merger of Union Pacific and Southern Pacific.

Additionally, there has been no entry to or exit from the Class I firm classification since this

merger. Market structure as described in this manner has remained constant for the last 17

years.

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Figure 1 above shows a map depicting the Class I firms’ ownership of track throughout

the United States. Regional and short-line railroads are indicated in grey. These smaller carriers

make up the vast majority of the number of railroads in the United States, but account for very

little of the trackage ownership and revenue. Class I firms account for about 69% of mileage,

94% of revenue, and 90% of employees in the freight rail industry (AAR 2010b). Most areas of

the country are served by only one or two Class I carriers while there are some markets that are

served by three or more of these firms. However, there are no markets that are served by all

seven firms through track ownership alone; this only comes about when a Class I firm enters the

market through trackage rights.

Figure 5: Association of American Railroads Map of Class I Track Ownership

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Chapter two of this dissertation confirmed previous findings published in Schmidt

(2001) that found that market structure does have a statistically significant impact on prices and

quantities. Using a reduced form model and cross-sectional data, both papers find that prices

vary across routes for particular commodities depending on the number of firms that are at both

ends of a shipment. Chapter two takes the analysis one step further than Schmidt (2001) by

including firms competing through trackage rights without building their own infrastructure.

Overall, chapter two finds that there is significant price variation in this market and while the

results imply that competition influences price, it does not explain why there are these

significant variations in price. That question is the subject of this analysis.

Section II: Literature Review

This section provides a review of the economic literature on three possible explanations

for price dispersion: price discrimination, cost variation, and search costs. These three

explanations are by no means the only causes of price dispersion. However, they were chosen for

this analysis because of their particular relevance for the freight railroad shipment market.

Price Discrimination

Price discrimination is the explanation for price dispersion most examined by economic

literature. In particular, Shepard (1991) and Borenstein & Rose (1994) are seminal works on the

topic. Economic theory tells us that as a market becomes more competitive, it becomes more

difficult for firms to price discriminate and variation in prices disappears. However, the existing

literature indicates that under certain circumstances the outcome predicted by theory may not

result.

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Shepard (1991) examines the retail gasoline market in a small geographic area where

costs are the same across firms but there is still price dispersion. This market was once seen as

fairly competitive, however Shepard finds evidence that gasoline stations have enough local

market power to strategically price discriminate. Shepard’s work confirms that hypothesis that

price discrimination based on consumer willingness to pay for quality occurs in the market for

full-service or self-service gasoline using micro data on gas stations in the Boston area. This

paper provides evidence that price discrimination is a strategy not exclusive to monopoly

markets.

As noted by Borenstein & Rose (1994), price discrimination in monopoly markets takes

place solely due to variations in the characteristics of consumers and the ability of the firm to

segment demand based on the resulting differences in demand elasticity. At the other extreme,

perfectly competitive markets cannot sustain price discrimination – therefore, for imperfectly

competitive markets we would expect that there may be less price discrimination than monopoly

but more than in perfectly competitive markets. As more firms enter a market, it becomes more

difficult to maintain markup over marginal cost.

Borenstein & Rose (1994) distinguish between “monopoly-type” discrimination and

“competitive-type” discrimination. “Monopoly-type” price discrimination occurs when the firm

sorts customers based on their price elasticity of demand at the industry-level – i.e. air travel in

their example, freight-transport for this paper. The “competitive-type” of price discrimination

occurs when consumers are grouped based on their cross-elasticity of demand among certain

brands. For example, if consumers of Coca Cola have a more inelastic demand for Coke than

consumers of Pepsi, Coca Cola will be able to charge a higher price than Pepsi, and therefore

108

there is price dispersion. For air travel, brand could mean flight-times or airlines and for this

work, it would mean train-times or railroads.

To examine the possibility of price discrimination as a cause of price dispersion,

Borenstein & Rose include three groups of factors: market structure, consumer attributes, and

product characteristics. Market structure can be measured in various ways including the

Herfindahl-Herschman Index (HHI), continuous variables indicating the number of firms, and

simply with dummy variables indicating if the market is monopoly, duopoly, or competitive. The

expected impact of market structure depends on the type of price discrimination that is more

prevalent – monopoly-type or competitive-type.

The authors argue that price discrimination may increase with the variance in

characteristics of the consumer population that reflect buyers’ industry elasticities or cross-

elasticities between brands. Borenstein & Rose use the example of business travelers having a

lower industry demand elasticity than tourists and use a dummy variable to identify high-

tourism routes. In addition, to measure the ability to substitute between competing flights,

Borenstein & Rose use a measure of market density as measured by the total number of flights

on the route. If competitive-type discrimination is the more prevalent cause of price dispersion

then dispersion will decline as density increases because the cost of switching becomes smaller.

It is expected that the effect on price dispersion of shipment density is positive if there is

“monopoly-type” price discrimination on a monopoly route because the monopolist has market

power and there is high demand. In competitive markets, the impact of density on price

dispersion is expected to be negative. The more shipments and competitors there are, the less is

the ability of firms to price discriminate.

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In addition to market structure and consumer characteristics, product characteristics

may also be indicative of price dispersion. For instance, contracts between shippers and

railroads are likely a major source of price discrepancies in this market. Unfortunately, the data

used in this work does not provide information about the existence of a contract for a particular

shipment so it cannot be measured here.

It is important to note that Borenstein & Rose’s seminal paper does have an issue of

omitted variable bias. The results of their work indicated that price dispersion increased with

increased competition. The authors conclude that this result implies that there is a significant

amount of brand loyalty in the airline industry. However, in their 2009 paper, Gerardi &

Shapiro replicate the results of Borenstein & Rose and find the same results using cross-

sectional data but the results of panel data analysis confirm the predictions of microeconomic

theory – price dispersion decreases with increased competition. Gerardi & Shapiro argue that

Borenstein & Rose’s use of cross-sectional data created an omitted-variable bias by not being

able to include route-specific fixed effects. Omitted variable bias can be mitigated with the use of

a valid instrument and without such an instrument, the empirical results of a work can only

claim association rather than causality.

Dana (1999) shows that in industries with price rigidity, demand uncertainty, and goods

that are not easily stored there may be significant inter-firm and intra-firm price dispersion.

Dana finds that under these conditions, price dispersion actually increases with the number of

firms, a result that is in contrast with traditional models of price dispersion. Dana notes that this

result is useful in that it helps to explain the intra-firm price dispersion that has been attributed

to price discrimination.

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Gaggero & Piga (2011) find similar results to Gerardi & Shapiro (2009) using data for

airline fares between the U.K. and Republic of Ireland. They find evidence of monopoly-type

price discrimination – where consumers are segregated by price elasticities. Gaggero & Piga also

find evidence of inter-temporal price discrimination where there is less price dispersion during

peak-periods of the year such as Christmas and Easter.

Most recently, Cornia, Gerardi & Shapiro (2012) examine the possibility that price

dispersion is pro-cyclical and find that the results of their study are consistent with a discrete-

choice model of second-degree price discrimination. The authors argue that this interpretation

of the results is more likely than stochastic demand pricing or pro-cyclical variation in costs but

they cannot rule out the possibility that consumer behavior changes over the business cycle.

As discussed above, chapter two of this dissertation concluded that market structure has

a significant impact on prices. The natural question to investigate next is why market structure

creates differences in price across markets. And one plausible explanation is the ability of firms

to price discriminate. The market is defined as a unique origin-destination-commodity

combination and prices vary both within and between markets. Price discrimination based on

consumer and product characteristics could explain some of this variation. In this paper, I will

use variables similar to those used by Borenstein & Rose (1994) to measure the impact of market

structure on price dispersion to shed light on the possibility of price discrimination based on the

type of market.

Cost Variation

Cost variation is a key component of price variation. Some firms may be more efficient

than others or have differing input costs. This variation in costs is likely to reveal itself in

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variation in price across firms. In the freight railroad market, as in the airline industry, there is

also variation in costs across routes. Major expenses for railroads include wages, fuel, materials,

equipment rental, maintenance, interest, depreciation, and interest.

Carlson & McAfee (1983) present a theoretical model that provides a few predictions

concerning the impact of firm costs on price dispersion. The model first predicts first that lower-

cost firms tend to set lower prices and second, that the variance of price varies directly with the

variance of costs. Lastly, Carlson & McAfee’s model implies that the number of firms is

influenced by the distribution of cost functions of potential firms.

While Shepard (1991) uses data in which there is little cost variation to look at the

possibility of discrimination as a cause of price variation in retail gasoline markets, Chandra &

Tappata (2011) explicitly examine the impact of cost on price dispersion in retail gasoline.

Chandra & Tappata take production costs to be exogenous and the results show that the impact

of marginal cost on price dispersion is negative. This result implies that increasing marginal

costs reduce price dispersion and push firms to a more homogenous price distribution.

Cornia, Gerardi & Shapiro (2012) build a model of price dispersion for airline tickets to

study variation over the business cycle. In order to do so, the authors note that it is important to

have some measure of airlines’ marginal cost. Cornia, Gerardi & Shapiro use total aircraft

operating costs as a measure of variable costs of airlines, which includes wages, fuel,

maintenance, leasing, and depreciation. They find that cost is a statistically significant portion of

price dispersion, but it does not explain all of the price variation seen in the airline industry.

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While it would be ideal to be able to explicitly test for cost variation as a cause of price

dispersion in this market, data availability and econometric issues make this examination

difficult. These issues will be discussed in Section III.

Search Costs

The third and final component of price dispersion discussed is that of consumer search

costs. Consumers do not always know the distribution of price of a product and therefore must

spend some time researching prices to determine where to buy a product. There are several

studies in the literature that examine the impact of search costs on variation in prices. These

studies find that as the range of search costs increases, the more firms there will be in a market

(Carlson & McAfee 1983) and that these asymmetric search costs will lead to variation in prices

in equilibrium (Stahl 1989).

In one of the first empirical papers describing price dispersion due to search costs, Pratt,

Wise, & Zeckhauser (1979) develop a model of search costs which assumes that buyers have

exact knowledge of the probability distribution of sellers’ prices and respond to it by searching

and buying optimally given search costs. Under this assumption, there is a resulting distribution

of buyers’ strategies and sellers’ respond with a distribution of prices. Secondarily, the authors

develop a model where buyers do not know the distribution of prices and through prior beliefs

and search learn the distribution of seller price. Pratt, Wise, & Zeckhauser apply their model to a

sample of thirty-nine relatively standardized products and obtain price quotes from various

retailers in the Boston area. While the authors admit that there may be some factors for which

they have been unable to control, they conclude that these standardized products do in fact have

wide price distributions that can be explained by search costs for consumers.

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Brown & Goolsbee (2002) examines the impact of decreasing search costs due to the

Internet on price dispersion in the life insurance industry, finding that the initial introduction of

the Internet is associated with increases in price dispersion across demographic groups.

However, as use spreads price dispersion falls. Similarly, Sengupta & Wiggins (2012) look at the

impact of the Internet on airline ticket prices. They find that while positive price dispersion still

exists, it is less prevalent on the Internet than in traditional offline markets. The authors of both

studies follow the model of search costs presented by Stahl (1989).

In Stahl’s model 1-µ customers pay a search cost for each price quote that they obtain.

The rest of the customers,µ, do not need to search and thus incur no search costs. Customers

search sequentially and in Nash equilibrium, stores choose prices from a distribution.

Customers with search costs have an endogenously determined reservation price and stop

searching when they fine a price below it. Customers with no search costs know the prices from

all of the firms and purchase only from the lowest-priced store.

Stahl’s model predicts that when there are asymmetric search costs across customers,

firms draw equilibrium prices from a distribution rather than charging a single market price.

Therefore, price dispersion should be found in equilibrium. Additionally, as the share of

customers with complete information increases, the price distribution shifts downward – as the

share of customers without search costs increases, average prices fall. Lastly, the result of Stahl

(1989) implies that when all customers have to search (µ=0), the price distribution reduces to

just the monopoly price; similarly, when µ=1 and all customers have no search costs, all prices

fall to the competitive equilibrium. The relationship between search costs and price dispersion is

not monotonic – increasing the share of people without search costs will increase price

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dispersion for low starting levels of µ. However, if µ is large to start with then increasing the

share without search costs will decrease price dispersion.

Sengupta & Wiggins (2012) and Orlov (2011) both look at the impact of the Internet on

price dispersion in airline markets. Sengupta & Wiggins (2012) find that the online transactions

tend to have less dispersion as compared to offline transactions for airline tickets. More

specifically, Orlov (2011) finds that the decrease in search costs associated with the Internet – as

measured by the percent of consumers with Internet access – may increase intra-firm price

dispersion but lower prices and reduce inter-firm price variation.

Chandra & Tappata (2011) develop their own theoretical model and applying it to the

retail gasoline market, find that search costs are a very important driver of price dispersion.

Their results show that search costs deter customers from price shopping and that consumers

could save as much as 5% by searching within a one mile radius. The authors also find that

higher grades of gasoline have higher search costs, greater price dispersion, and are less

competitive. Additionally, when the aggregate level of prices increases price dispersion

decreases. Chandra & Tappata argue that this result implies that there is less to gain from

searching during these periods. The results of this study suggest that an increase in search

intensity will decrease price dispersion.

Search costs are most likely an important factor for shippers in areas where there are

several carriers available. However, railroad markets also have a “captive shipper” problem

where the shipper has no ability to search because there is only one option available. While

search costs may be a factor causing price dispersion in railroad markets, this paper does not

attempt to test for its existence.

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Section III: Testing for Price Discrimination as a Cause of Price Dispersion

Data

The main source of data used in this paper is the 2010 Public Use Waybill Sample

provided by the STB. This dataset is a sample of waybills from Class I, II, and III railroads in the

United States in the year 2010. An observation is defined as a shipment of commodity i from

origin Bureau of Economic Analysis (BEA) economic area to destination BEA economic area.

Figure 2 provides a map depicting the definitions of BEA economic areas used by the STB in the

Public Use Waybill Sample.

Figure 6: Map of BEA Economic Areas (1995)

116

BEA economic area is clearly not the ideal variable for location. For example, a carrier

may be present in one small area of an economic area but not anywhere else. If this is the case,

this carrier may not provide meaningful competition to other carriers because there is no way

for shippers to switch to this remote carrier. Unfortunately, the data does not allow for more

precise determination of a carrier’s presence than at the BEA economic area. The reader should

keep this caveat in mind when analyzing the results of this model.

In addition to origin and destination of a shipment, the waybill sample provides

information on many aspects of a shipment including but not limited to the weight of the

shipment in tons, the shortest distance via rail track from origin to destination, and the revenue

of the shipment. A market is defined as a commodity shipped within an origin-destination pair.

The Public Waybill Sample does not identify the carrier of the shipment, this is only

included in the Confidential Waybill Sample. When looking at pure market structure, the

identity of the firm may not matter, but the number of firms along a route does. To determine

the number of track owners along a route, data has been collected from the Association of

American Railroad’s publication Railroads and States (AAR 2010a) to supplement the Waybill

sample with carrier identification. From this, I was able to match railroads with BEA economic

areas and now have the identity of the Class I and regional firms in each market. To identify

trackage rights competitors, an additional data source is required. The Department of

Transportation’s Research and Innovative Technology Administration (RITA) publishes the

National Transportation Atlas Databases (NTAD) on an annual basis. Using 2010’s NTAD, I am

able to obtain information on the identities of firms with trackage rights (voluntary or

mandated) in an economic area. The number of competitors in a market will include any Class I

or regional track owners and any firms with trackage rights at both ends of that route.

117

Measuring Price Dispersion

The Public Use Waybill Sample provides total revenue of a shipment. To obtain a proxy

for measuring price, I calculate the average revenue per ton-mile. Using price per ton-mile

purges the effects of weight and distance from the price as these characteristics clearly influence

how much a shipment will cost to transport. Otherwise, price variation may be purely for cost-

based reasons.

Price dispersion is a topic most often explored in the airline industry and is typically

measured by a Gini coefficient. The Gini coefficient is a desirable measure for two reasons. First,

it is scale invariant and therefore it is easy to compare coefficients across different distributions

for example in different years or subsets of the data by distance or geography. Second, the Gini

coefficient uses the entire distribution of prices whereas other measures such as the ratio of the

highest to lowest price or the range only uses two observations (Gaggero & Piga 2011). Those

observations may be outliers and therefore not representative of the sample. A larger Gini

coefficient indicates more price dispersion. The Gini coefficient will look at the inequality across

the range of prices paid per ton-mile. Multiplying the Gini coefficient by two results in the

expected absolute difference in prices as a proportion of the mean price for two shipments

drawn from the population at random.

In addition, two other statistics will be used to examine price dispersion: the coefficient

of variation and kurtosis. The coefficient of variation – calculated as the standard deviation over

the mean – is useful for measuring the degree of variation across datasets. Because it is

dimensionless, the coefficient of variation allows for comparison between datasets with very

different means. As the coefficient of variation increases, so does the amount of price dispersion.

118

Kurtosis is a statistic that describes the shape of a distribution. More specifically,

kurtosis captures the “peakedness” and the “heaviness” of the tails of a distribution, regardless

of the variance of that distribution. A positive kurtosis is characterized by a higher peak and

heavier tails than the normal distribution, while a negative kurtosis indicates a distribution that

is flatter and has lighter tails than the normal distribution (DeCarlo 1997). Kurtosis is not

strictly a measure of price dispersion because it is calculated in such a way that the overall

variance of the distribution remains the same. However, it is included here to provide some

insight into the distribution of prices across routes. In this paper, kurtosis is calculated using the

following equation, where a perfect normal distribution has a kurtosis equal to 3:

22

4

)(

)(

−−−−

−−−−

====∑∑∑∑

∑∑∑∑

n

xx

n

xx

kurtosis

119

Table 1 below presents summary statistics for the Gini coefficient, the coefficient of

variation, and kurtosis, as well as the other variables used in the model estimation.

Table 18: Summary Statistics

As can be seen from the summary statistics in Table 1, overall there is clearly inequality

in price per ton-mile across routes as measured by the Gini coefficient. If we look at the Gini for

monopoly, duopoly, and competitive routes separately, we see that on average inequality

increases as the number of firms increase. For competitive routes, a mean Gini of 0.39 implies

an expected absolute price per ton-mile difference of 78% of the mean price.

Variable Mean Std. Dev. Min Max

GINI 0.33 0.1 2 0.00 0.92

COEFFICIENT OF VARIATION 0.85 0.91 0.00 1 3.61

KURTOSIS 42.1 7 7 7 .69 1 .00 7 1 5.81

MONOPOLY 0.1 5 0.35 0.00 1 .00

DUOPOLY 0.7 7 0.42 0.00 1 .00

COMPETITIVE 0.08 0.28 0.00 1 .00

DENSITY 57 55.1 0 7 251 .41 1 .00 21 87 7 .00

GINI 0.27 0.1 4 0.00 0.88

COEFFICIENT OF V A RIA T ION 0.63 0.45 0.00 5.1 7

KURTOSIS 25.7 0 37 .28 1 .00 1 81 .82

ln (DENSITY)*MONOPOLY 5.49 1 .87 0.00 7 .7 8

GINI 0.33 0.1 0 0.00 0.92

COEFFICIENT OF VARIATION 0.84 0.94 0.00 1 3.61

KURTOSIS 38.66 46.59 1 .00 441 .95

ln (DENSITY)*DUOPOLY 7 .7 6 2.08 0.00 9.99

GINI 0.39 0.1 7 0.00 0.7 1

COEFFICIENT OF VARIATION 1 .26 1 .07 0.00 3.89

KURTOSIS 1 02.56 21 2.37 1 .00 7 1 5.81

ln (DENSITY)*COMPETITION 5.87 1 .53 0.00 7 .63

Monopoly (1081 Routes)

Duopoly (1622 Routes)

Competitive (250 Routes)

120

Similarly, as the number of firms increases we see increasing variation as measured by

the coefficient of variation. For kurtosis, the same result is present. As the number of firms

increases, we see that the tails become “heavier” i.e. there are more shipments with price per

ton-mile that are located at the extremes of the distribution.

Table 2 below provides correlation coefficients of the three dependent variables and the

variables used to examine price discrimination as a cause of price dispersion.

Table 19: Correlation Coefficients

As shown above, the three measures of price dispersion are highly correlated with each

other. The Gini coefficient and coefficient of variation have a correlation coefficient of about

0.61 indicating positive correlation. While not as large in magnitude, there is correlation

between Gini and kurtosis with a coefficient of about 0.36. Kurtosis and the coefficient of

variation are also highly positively correlated with a coefficient of 0.58. These correlation

coefficients are expected as we are using the same variable to calculate all three – price per ton-

mile. These results imply that we should expect similar signs in all three regressions with these

dependent variables.

GINI

COEFFICIENT OF

VARIATION KURTOSIS

GINI 1 .000

COEFFICIENT OF VARIATION 0.608 1 .000

KURTOSIS 0.355 0.580 1 .000

MONOPOLY -0.1 97 -0.098 -0.088

DUOPOLY 0.060 -0.009 -0.083

COMPETITIVE 0.1 59 0.1 37 0.236

DENSITY 0.305 0.042 0.034

121

From these correlation coefficients we see very little correlation between monopoly or

duopoly markets and price dispersion. However, as the number of firms increases and we move

into competitive markets, there is more correlation with the dependent variables. These

correlation coefficients provide similar evidence to that presented in Table 1’s summary

statistics – as the number of firms increase so does price dispersion.

In terms of density, we see little correlation with the coefficient of variation indicating

little variation in price per-ton mile across routes due to the number of shipments on that route.

Nor do we see significant correlation between density and kurtosis meaning the density of the

route does not contribute to the heaviness of the tails in the price per ton-mile distribution.

However, there is some correlation between the Gini and density indicating that a route with

more shipments may have more price inequality.

Model

ijijkijij

ijijij

kECOMPETITIVDENSITYDUOPOLYDENSITY

DENSITYECOMPETITIVDUOPOLYDisperson

εεεεββββββββββββ

ββββββββββββββββ

++++++++++++

++++++++++++++++====

** 54

3210

Where i denotes origin, j denotes destination, and k is a dummy variable equal to 1 when a

commodity is present on a route, zero otherwise.

The measures of price dispersion discussed above – the Gini coefficient, coefficient of

variation, and kurtosis – are all measures of dispersion in price per ton-mile. The focus is on

across-route price variation rather than within-route price dispersion because market structure

122

does not vary within-route at the BEA economic area level and therefore the model could not be

identified given the available data.

MONOPOLY, DUOPOLY, and COMPETITIVE are dummy variables equal to one if the

route’s market structure falls into that category and zero otherwise (Borenstein & Rose 1994).

All types of competitors are included in this analysis – whether they are track-owners or

compete in a market solely through trackage rights. A firm must be present at both ends of a

shipment to be counted as a competitor. MONOPOLY will be left out of the regressions and

therefore will constitute the base case to which the coefficients should be compared.

The impact of market structure on price dispersion can depend on the type of price

discrimination that is most prevalent: monopoly-type or competitive-type. If monopoly-type

price discrimination dominates, where consumers are segmented based on price elasticity, then

price dispersion should increase as concentration increases. If differences in cross-price

elasticity across railroads are the source of price differences, dispersion should decrease with

concentration. Under monopoly-type price discrimination, price dispersion will decrease as the

market moves towards competition and vice-versa under competitive-type price discrimination.

From the summary statistics and correlation coefficients above, we expect price dispersion to

increase as concentration decreases. That is, competitive-type price discrimination is more

prevalent for this market and markets with more firms will have more price dispersion. Without

looking at any econometric results, we expect that heterogeneities in cross-elasticity are

associated with price dispersion in this dataset.

The Public Use Waybill Sample provides shipments without carrier identification, so it is

not possible to calculate a more in-depth measure of market concentration such as the HHI.

However, it is possible to create a variable similar to that used by Borenstein & Rose (1994) to

123

measure density – the number of shipments on a route. This measure is included here as

DENSITY and is included in the analysis to examine price discrimination as a cause of price

discrimination further. Under monopoly-type price discrimination, the impact of DENSITY on

price dispersion is expected to be positive. That is, the more shipments on a route, the greater

the price dispersion if the route is served by a monopolist. This result is expected because the

monopolist has more market power as the only carrier on a highly active route. Under

competitive-type price discrimination, the impact of DENSITY is expected to be negative.

Meaning that when there are a large amount of carriers and shipments, firms have a more

difficult time competing over price because shippers have so many options and are more price

sensitive. As indicated by Borenstein & Rose (1994), interaction terms between DENSITY and

MONOPOLY, DUOPOLY, and COMPETITIVE are included because the impact of DENSITY

varies by market structure.

In this model, errors are clustered by route due to the expectation of unobserved

variation within route. Not including clustered errors will underestimate the standard errors of

the coefficients resulting in coefficients appearing to be statistically significant when they should

not be.

Econometric Issues

This model is based primarily on that presented by Borenstein & Rose (1994). The data

used in this paper is also cross-sectional and thus some of the same problems identified by

Gerardi & Shapiro (2009) and discussed above for Borenstein & Rose (1994) may be present

here. The reader should be aware of the issues of omitted variable bias due to cross-sectional

data when interpreting the results of this model. Future work should address those issues

directly by using panel data or instrumental variable estimation.

124

Inclusion of variables such as fuel costs to further control for cost variation results in

severe multicollinearity. This issue is likely due to the aggregated level of the fuel cost data

(origin/destination state level distillate fuel price). Unfortunately, the available data does not

provide for firm-specific fuel costs. Prices are calculated as the average revenue per ton-mile to

help control for variations across shipment and route due to the costs associated with

transportation of a shipment of a certain weight over a certain distance.

It is also important to remember the issue with identifying a route based on origin and

destination locations only at the (relatively large) BEA economic area, as discussed above.

Furthermore, as there is no firm level data, these results may only be interpreted as across-route

variation in prices. The use of price per ton-mile and control variables such as commodity

dummies aid in making the comparison across routes relevant. However, the more interesting

question is why there is price dispersion within routes, which is not and cannot be examined

here due to data constraints.

Results

The results of OLS regressions as defined by the model above are presented in Tables 3

and 4 below. Table 3 presents a log-log model specification while Table 4 presents the model in

the linear-linear functional form. As stated above, errors are clustered by route in both

specifications to control for unobserved variation within route. Again, the coefficients should be

interpreted relative to the monopoly case.

125

Table 20: Log-Log Regression Results

(Standard errors in parentheses) ***p<0.001; **p<0.01; *p<0.05

ln (Gini)ln (Coefficient

of Variation)ln (Kurtosis)

[1] [2] [3]

DUOPOLY 1 .545*** 0.639*** 0.191

(0.21) (0.14) (0.47 )

COMPETITIVE 1 .652*** 0.27 9 -1.448

(0.27 ) (0.41) (1 .08)

ln(DENSITY) 0.467 *** 0.244*** 0.338**

(0.04) (0.03) (0.12)

ln (DENSITY)*DUOP -0.27 7 *** -0.106*** -0.0208

(0.04) (0.03) (0.12)

ln (DENSITY)*COMP -0.223*** 0.034 0.365

(0.05) (0.09) (0.24)

CONSTANT -4.27 4*** -2.7 40*** 0.134

(0.26) (0.21) (0.41)

Commodity Dummies? YES YES YES

Clustered Errors? YES YES YES

N 232010 2317 12 2317 12

Adj. R-sq 0.17 0.42 0.45

126

Table 21: Linear-Linear Regression Results

(Standard errors in parentheses) ***p<0.001; **p<0.01; *p<0.05

Overall Results

The results of this exercise are consistent across model specifications and two of three

tested measures of price dispersion. The Gini coefficient appears to be the best measure of price

dispersion for this data in terms of the statistical significance of the coefficients in both

specifications. The results of the regressions using the coefficient of variation are consistent with

GiniCoefficient of

VariationKurtosis

[1] [2] [3]

DUOPOLY 0.07 36*** 0.258* 6.526

(0.01) (0.10) (6.98)

COMPETITIVE 0.0942*** 0.217 -43.36

(0.02) (0.18) (32.61)

DENSITY 0.00007 83*** 0.000164** -0.00051

(0.00) (0.00) (0.01)

DENSITY*DUOPOLY -0.00007 44*** -0.000159** 0.0007 8

(0.00) (0.00) (0.01)

DENSITY*COMPETITVE 0.0000311 0.000462 0.161

(0.00004) (0.00040) (0.10)

CONSTANT 0.0690** 0.125 14.41

(0.02) (0.08) (10.7 3)

Commodity Dummies? YES YES YES

Clustered Errors? YES YES YES

N 232445 2317 67 2317 12

Adj. R-sq 0.38 0.13 0.31

127

those using the Gini coefficient, though the significance of the coefficients varies. The results

using kurtosis as the measure of price dispersion provide little evidence of price discrimination

as the reason for price dispersion for these markets.

Using the Gini coefficient or the coefficient of variation, the signs of the coefficients for

DUOPOLY and COMPETITIVE both imply that as concentration decreases, price dispersion

increases. This result is seen in both the log-log and linear-linear specifications of the model.

When the variance in prices increases as the number of firms increases, competitive-type price

discrimination is said to be present. This type of price discrimination implies that consumers are

grouped based on cross-price elasticity between products. In this case, consumers may become

less cross-price inelastic because there are so many substitutes in terms of shipment times or

opportunities.

The same implication is observed when looking at the coefficients of the interaction

terms of DENSITY and market structure. Competitive-type price discrimination as defined by

Borenstein & Rose (1994) is evident in the coefficients for these variables as well. Analysis of

each regression is discussed next.

Gini Coefficient

The estimation using the Gini coefficient as the measure of price (per ton-mile)

dispersion exhibits the expected results for both specifications: log-log and linear-linear.

Comparing to the case of a monopoly market in the log-log regression and holding all else equal,

duopoly markets have 154% more unequal prices and competitive markets about 165%. To see

the result of these percentage increases, we can take the mean Gini coefficient for monopoly

markets (0.27) and increase it by 154% which results in a Gini coefficient of 0.4158 for duopoly

128

markets. A Gini coefficient of 0.4158 implies an expected absolute price difference of 81.2% of

the mean price per ton-mile. A similar exercise for the competitive markets results in an

expected absolute price difference of about 89.1% of the mean price per ton-mile. These results,

which are statistically significant at the 0.001 level, indicate that as the number of firms

increases the inequality of prices per ton-mile also increases which according to Borenstein &

Rose (1990) implies that competitive-type price discrimination is more important in this

market.

The signs and significance persist when a linear-linear specification is employed, as

presented in Table 4. Holding all else equal, a market with two firms is associated with a Gini

coefficient 0.0736 greater than that of a market with only one firm. This result matches very

closely with the mean difference between a monopoly (mean of 0.27) and duopoly (mean of

0.33) market presented in Table 1. Similarly, when a market is classified as competitive, the

associated Gini coefficient is approximately 0.0942 greater than that of a monopoly market,

ceteris paribus. This result does not match up with the means as well as the coefficient of

DUOPOLY, but it is fairly close. The results for DUOPOLY and COMPETITIVE are both

consistent with the expected results based on the patterns in the data presented in Table 1. The

consistency between model specifications also elicits confidence in the model.

The coefficient on the natural log of the number of shipments on a route (DENSITY) is

also positive and statistically significant at the 0.001 level. This result indicates that as the

number of shipments on a route increases, price dispersion as measured by the Gini coefficient

also increases. However, if we look at the interaction terms ln(DENSITY)*DUOP and

ln(DENSITY)*COMP the signs reverse which implies that density actually does not increase

price dispersion by as much as ln(DENSITY) would imply in these markets. The expected result

129

depends upon which type of price discrimination is believed to be present: monopoly-type or

competitive-type. These results, like those for the market structure indicators, imply that

competitive-type price discrimination is more prevalent. As the number of firms increases (from

one, to two, to more than two), the ability to price discriminate decreases as consumers may

become less cross-price inelastic because there are so many substitutes in terms of shipment

times or opportunities. The coefficient of ln(DENSITY)*DUOP implies a Gini coefficient 27.7%

lower than that for the average monopoly market and the coefficient of ln(DENSITY)*COMP

implies a Gini coefficient 22.3% lower than that for the average monopoly market, holding all

else equal.

Again, the same signs and significance persist in the linear specification, with the

exception of the interaction between DENSITY and COMPETITIVE. DENSITY on its own

indicates that as the number of shipments increases by 1000, the Gini coefficient will increase

by 0.0783 for markets overall, holding all else equal. However if the market is characterized by

two firms, increasing the number of shipments by 1000 will actually decrease the Gini

coefficient by 0.0744 holding all else equal. Thus, the two taken in conjunction almost cancel

each other out. The difference in signs indicates that the impact of density depends on type of

market structure. These results also imply that competitive-type price discrimination is present

in these markets.

Coefficient of Variation

Using the coefficient of variation (standard deviation divided by the mean) provides

similar results in terms of sign but not statistical significance to those results using the Gini

coefficient in both model specifications presented in Tables 3 and 4. Moving from a monopoly

market to a duopoly market increases the coefficient of variation by 63.9% holding all else equal.

130

This result indicates more price dispersion (and competitive-type price discrimination) as the

number of firms increase. Similarly, moving from a market with two firms to one with more

than two firms increases the coefficient of variation by about 28% - but this result is not

statistically significant. Therefore, the evidence for competitive-type (or monopoly-type, for that

matter) price discrimination is much less compelling when using the coefficient of variation

instead of the Gini coefficient in the log-log model specification.

The conclusions are even less compelling for the linear-linear model specification using

the coefficient of variation, as the statistical significance of DUOPOLY drops to the 5% level. As

compared to a monopoly, a market classified as a duopoly will have a coefficient of variation that

is 0.258 greater, holding all else equal. This increase in the coefficient of variation implies

increased price dispersion when moving from monopoly to duopoly, but the result does not

follow as concentration decreases further as the coefficient on COMPETITIVE is not statistically

significant.

The variable DENSITY and its interactions with DUOPOLY and COMPETITIVE follow

the same pattern when using the coefficient of variation rather than the Gini coefficient.

DENSITY on its own increases price dispersion – which could imply monopoly-type price

discrimination – but the interaction with DUOPOLY reverses the sign. In the case of

ln(DENSITY)*DUOPOLY, increasing the number of shipments by one percent decreases the

coefficient of variation by about 10.6% as compared to a monopoly market, ceteris paribus.

However, the coefficient for ln(DENSITY)*COMP is not statistically significant.

Looking at the results in Table 4 yields similar conclusions. Holding all else constant,

increasing the number of shipments by 1,000 for all markets regardless of structure will increase

the coefficient of variation by 0.164. However increasing the number of shipments in a duopoly

131

market by 1000 will decrease the coefficient of variation by 0.159, ceteris paribus. Again, we see

the two coefficients almost cancelling each other out. The difference in signs again indicates that

competitive-type price discrimination may be present in duopoly markets. However, the results

for markets with more than two firms are not statistically significant and thus the conclusions

cannot be extended beyond duopoly markets.

Kurtosis

The third measure of price variation, kurtosis, measures the heaviness of the tails of the

price per ton-mile distribution. The coefficients of this regression are by and large not

statistically significant. One exception is ln(DENSITY), which is positive and statistically

significant at 1%. The coefficient implies that an increase in the number of shipments along a

route of one percent is associated with an increase in the kurtosis score of approximately 34%,

holding all else equal. This result implies that as the number of shipments increases, the

heaviness of the tails increases. When the heaviness of the tails increases, we see more skewness

in the distribution. The positive sign on this coefficient would imply that monopoly-type price

discrimination is more prevalent than competitive-type.

Unfortunately, none of the other coefficients in the log-log regression are statistically

significant. Additionally, none of the signs in the linear-linear specification are statistically

significant. While kurtosis is clearly an interesting measure of dispersion because it looks

specifically at the tails of a distribution, it does not appear to work well for this model. To

examine the question posed here – if price discrimination is a viable explanation for price

dispersion – kurtosis does not provide as much insight as the Gini coefficient or the coefficient

of variation.

132

Section IV: Conclusions and Future Research

This paper has used publically available cross-sectional data to examine the potential

causes of price dispersion in the railroad shipment market. The paper tests for the possibility of

price discrimination as the cause of price dispersion.

The results imply that price discrimination is a likely cause of price dispersion when

using the Gini coefficient or the coefficient of variation. Competitive-type price discrimination is

more likely to be the cause of price dispersion than monopoly-type based on this analysis. The

Gini coefficient appears to be the best measure of price dispersion for this data, however the

coefficient of variation provides similar if less compelling results. Regressions using kurtosis as

the measure of variation in prices yield little insight into the explanation of price discrimination

as the reason for price dispersion for these markets.

There may also be other causes such as search costs, cost variation and other sources not

discussed in this work such as demand uncertainty. Furthermore, there may be a problem of

omitted variable bias resulting from the use of cross-sectional data. Future work should use a

panel dataset or instrumental variables to alleviate this problem. Furthermore, future research

would benefit from the use of the Confidential Waybill Sample, which provides much more

detailed and precise information on the origin and destination stations of a shipment as well as

carrier identity and prices. This information would allow for the calculation of HHIs so that

within-route price discrimination can be examined. Contracts are also an important part of

pricing in this industry that is not identified in this paper but should be included in future work.

While this study provides a first look at the causes of price dispersion in freight railroad

shipment markets, there is much work to be done on the topic.

133

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