Thesis for Masters - Boatner

HOWARD UNIVERSITY

Costs and Benefits of WMATA

Metrorail for D.C. and Suburban Residents

A Thesis

Submitted to the Faculty of the

Graduate School

Of

HOWARD UNIVERSITY

in partial fulfillment of

requirements for the

degree of

MASTER OF ARTS

Department of Economics

by

Jasmine Simone Boatner

Washington, D.C.

May 2016

ii

HOWARD UNIVERSITY

GRADUATE SCHOOL

DEPARTMENT OF ECONOMICS

THESIS COMMITTEE

____________________________

Rodney D. Green, Ph.D.

Chairperson

____________________________

Haydar Kurban, Ph.D.

____________________________

Benoit Schmutz, Ph.D.

_______________________

Haydar Kurban, Ph.D.

Thesis Advisor

Candidate: Jasmine Simone Boatner

Date of Thesis Defense: March 31, 2016

iii

ACKNOWLEDGEMENTS

I would like to thank Dr. Haydar Kurban, my thesis advisor, for invaluable support

throughout the thesis process and for always being available to answer any questions that I have.

I would like to thank Dr. Benoit Schmutz, a thesis committee member, for assistance with

some of the statistical analysis and suggestions/feedback on earlier drafts.

I would like to thank Dr. Omari Swinton for helping me to refine my research question

and for providing me with valuable comments and contacts that provided me with additional

research assistance.

I would like to thank Dr. Tom Zimmermann for always being willing to meet with me to

discuss my research and for helping me find appropriate STATA models for the data.

I would like to thank Dr. Rodney Green, a thesis committee member, for serving on my

thesis committee.

I would like to thank the WMATA Bus Planning Division for providing me with annual

bus ridership counts.

Lastly, I would like to thank the Economics Faculty at Howard University and the

Economics Faculty at Duke University for the outstanding education I have received. I would

also like to thank my Howard Cohort for support throughout the Masters process.

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ABSTRACT

WMATA Metrorail ridership has been steadily declining in recent years. Although ridership has

decreased, operating costs have not. This budget imbalance has led to increased jurisdictional

subsidy support, with Washington D.C. paying the brunt. With D.C. Residents paying more taxes

for the Metrorail, do they also receive more benefits from the Metrorail system? The data reveals

WMATA Metrorail demand does not exhibit perfect price inelasticity, indicating Metrorail rides

are not essential goods. Furthermore, Metrorail ride time data reveals significant deviations

between expected ride time and actual ride time, with the percent deviation decreasing with

miles. This indicates Metrorail time savings could be overstated for short trips. For District

residents who commute short distances, alternative modes of transportation could be more cost

effective and faster than WMATA Metrorail.

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TABLE OF CONTENTS

THESIS COMMITTEE ............................................................................................................................. ii

ACKNOWLEDGEMENTS ...................................................................................................................... iii

ABSTRACT ................................................................................................................................................ iv

LIST OF TABLES ..................................................................................................................................... vi

LIST OF FIGURES .................................................................................................................................. vii

CHAPTER 1: INTRODUCTION .............................................................................................................. 1

1.1. Overview............................................................................................................................................ 2

1.2. Results ............................................................................................................................................... 3

1.3. The Washington Metropolitan Area Transit Authority ................................................................... 4

CHAPTER 2: THE LITERATURE AND QUANTITATIVE ANALYSIS ........................................... 9

CHAPTER 3: METHODOLOGY AND ECONOMIC ANALYSIS OF PRICE ELASTICITY....... 15

3.1. Data Collection ................................................................................................................................ 15

3.2. Elasticity Results .............................................................................................................................. 19

3.3. Fixed Effects ..................................................................................................................................... 25

3.4. Government Subsidy ....................................................................................................................... 27

3.5. Metrorail Ride Time Data................................................................................................................ 28

CHAPTER 4: CONCLUSION ................................................................................................................. 32

4.1 Possible Problems with Approach ................................................................................................... 32

APPENDIX ................................................................................................................................................ 35

REFERENCES .......................................................................................................................................... 40

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LIST OF TABLES

Tables Page

3.1. Correlation……………………………………………………………………………………………………………………18

3.2. Whole System Results…………………………………………………………………………………………………..19

3.3. Whole Systems Results with Weighted Demographic Data…….……………………………………..21

3.4. D.C. Regression Results…..…………………………………………………………………………………………….23

3.5. MD Metro Regression Results...........................................................................................24

3.6. VA Metro Regression Results..………………………………………………………………………………………25

3.7. Fixed Effects Absorbing Region……………………………………………………………………………………..26

3.8. Fixed Effect Absorbing Year….……………………………………………………………………………………….27

3.9. Metrorail Ride Time Data Regression……………………………………………………………………………30

5.1. Whole System Results Basing Fares on Boarding Fares………………………………….……………..39

5.2. Whole System Results Basing Fares on Boarding Fares (Dropped Variables)………….……..39

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LIST OF FIGURES

Figures Page

1.1. Metrorail Map…………………………………………………………………………………………..……………4

1.2. Subsidy Allocation…………………………………………….………………………..………………………....7

3.1. Regional Subsidy Breakdown, 2005……….……………………………………………………………….22

3.2. Hausman Test………..…………………………………..………………………………….………………………25

5.1. Google Map Estimation 1……………………………….………………………………………………………35

5.2. Google Map Estimation 2………………………………….……………………………………………………36

5.3. Google Map Estimation 3……………………………….………………………………………………………37

5.4. Google Map Estimation 4…………………………………….…………………………………………………38

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CHAPTER 1: INTRODUCTION

An essential good is an item required by a consumer in order to sustain health or life. In

many cases, public transportation qualifies as an essential good. Despite alternatives “the opinion

is that the demand for services of different modes of transportation is typically inelastic,

[because] transportation costs are relatively small in comparison with the value or utility of these

services” (Bekő 2004, 64). Many residents of large cities with robust public transportation

systems do not own cars. “Between 2010 and 2012, the number of car-free households in in the

District of Columbia grew by 12,612—fully 88% of new households citywide” (Chung 2014).

Of all D.C. households, 37.9% are car free (Chung 2014). These car-free residents are largely

dependent on the Washington Metropolitan Area Transit Authority (WMATA) public

transportation system. Despite this large market of potential consumers, it appears many District

residents do not consistently utilize WMATA Metrorail. This aversion to Metrorail could be a

consequence of high fares and inefficient service that make alternative transportation methods,

like buses, bikes, and car shares, more attractive to someone with a short commute.

An efficient public transportation system is a very important element of a city’s economic

well-being. “The exchange of goods and services entails the movement of goods and people.

Thus, the size, structure, and efficiency of an urban area are influenced by the transportation

system on which goods and people are moved” (Mills and Hamilton 1994, 278). WMATA

Metrorail appears to be economically inefficient and unreliable, demonstrated by its record of

82% on time rail performance, which is well below its own goal (“Scorecard”). If Washington

D.C.’s public transportation system is inefficient, by extension Washington D.C. itself is not as

functional as it could be. One of the main advantages to living in or near a city is that there

should be a reduction in transportation costs, both in monetary terms and in time savings.

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Transportation cost savings are an example of a pecuniary agglomeration effect. Pecuniary

agglomeration effects are what drive firms to large cities when both its market and suppliers are

located there (Brueckner 2011, 5-7). However, if WMATA cannot effectively move people then

this pecuniary agglomeration effect will be diminished. After years of Metrorail delays, fatalities,

and price hikes, it appears that many D.C. residents have switched to alternative modes of

transportation based on the steady decline in ridership (Duggan 2015a). Suburban commuters do

not have as many alternatives to Metrorail as District riders, but data points to decreasing

suburban ridership as well.

1.1. Overview

The central research question of this thesis is: Do Washington D.C. residents receive

fewer WMATA Metrorail benefits while paying more of the costs for the system? This thesis

assumes benefits can be measured from price elasticity of demand for Metrorail rides. Using

OLS regressions, average price elasticities for the period ranging from 1981-2015 are calculated

for the entire Metrorail system, Washington D.C., the Maryland Washington area suburbs, and

the Virginia Washington area suburbs. Fixed effect regressions are also run. This thesis also aims

to explain why Washington D.C. has higher price elasticity based on Metrorail performance data

collected over a six month period.

Chapter 1 serves as an introduction to this thesis, covering the highlights of my results

and a brief history of the Washington Metropolitan Area Transit Authority. Chapter 2

summarizes some of the previous economic literature relevant to price elasticity of public

transportation. Chapter 3 contains the economic analysis, with explanations of data collection

methods, OLS regressions, fixed effect regressions, the impact government subsidy could play in

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the results, and Metrorail ride time data results. Chapter 4 concludes the thesis, and lists possible

problems with the approach that could bias my results.

1.2. Results

When regressions are run using Metrorail ridership as the response variable and Metrorail

real fare as a predictor (along with controlling variables such as real gas prices) the coefficients

indicate fare does have an effect on ridership. On average, from 1981-2015 Metrorail demand

exhibits price inelasticity, but recent years have higher elasticity. The elasticity coefficient shows

the impact the natural log of Metrorail real fare has on the natural log of Metrorail ridership.

Washington D.C. exhibits a higher elasticity coefficient than Maryland or Virginia in

regressions, indicating D.C. residents are not as dependent on the Metrorail. This reduced

dependence on the system indicates D.C. residents might not benefit from the Metrorail as much

as their more dependent suburban counterparts. Six months of data on WMATA Metrorail rides

reveals significant timeliness issues that might have an impact on Metrorail demand in the

District. From June 1st, 2015 to December 15th, 2015, I visited twenty-nine Metrorail stations in

the D.C. Metropolitan area and covered a large portion of the Metrorail track (see Figure 1.1.). In

total, I spent 4,256 minutes (70.9 hours) to travel 922.4 miles. My average fare was $3.06, which

is slightly higher than the $2.48 2015 average fare for commuters entering the Metrorail system

at D.C. stations. My average trip was 7.62 miles. Most importantly, the average percent deviation

between expected, or WMATA quoted, ride time and actual ride time was 49.04%. Intriguingly,

it appears shorter commutes have higher deviations than longer commutes, indicating those who

live in the suburbs and commute into the city using Metrorail likely save more commuting time

than those close to the core. If suburban commuters benefit more from the Metrorail, their

jurisdictions should be covering a larger portion of the WMATA Metrorail subsidy.

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Figure 1.1. Metrorail Map

1.3. The Washington Metropolitan Area Transit Authority

Washington D.C. was not designed for heavy automobile use, so by the 1950s

commuters were plagued by the city’s inability to handle the heavy traffic.

All could agree that the spread of automobiles threatened the health of a city

designed for pedestrians and carriages. Traffic engineers watched streets become

even more congested, politicians fretted, and commuters cursed. The Sunday Star

complained that “motorists today spend from one to three hours of their off-job

time in cars…the 189,000 metro residents who enter the core of the city daily by

public transit…are plagued by slow movement and transfers. (Schrag 2006, 32)

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By 1955, plans were being laid out for what would become Metrorail. However, it would take

more than 20 years, and billions of dollars, for Metrorail to actually start running. (Schrag 2006).

The total cost… [for] the 103 mile system [of] January 2001 was around $10

billion in nominal dollars. This enormous sum has earned Metro its most enduring

criticism, as well as such epithets as ‘the Solid-Gold Cadillac of Mass Transit’

and ‘the biggest boondoggle in the history of all mankind’. (Schrag 2006, 172)

Part of the reason WMATA Metrorail was so timely and expensive to build is because of the

jurisdictional nature of the system. Washington D.C., Virginia, Maryland, and the federal

government are all involved with Metrorail and all have their own agenda. “Suburbanization

made mass transit a matter of interstate concern” (Schrag 2006, 96). Suburbanization raises

complex question for an interstate mass transit system like WMATA Metrorail. The main

question being, who ultimately should shoulder the financial responsibility for the system?

Washington Metropolitan population trends reveal years of District of Columbia population

declines as public transit makes suburban living more attractive. Consequently, D.C.’s tax base

was shrinking while Metrorail needed more and more funding.

“The erosion of city tax bases occasioned by departure of commerce and industry has

intensified urban problems; ironically, at the very time suburban growth has sapped much

of the financial strength of the central cities, the rapid growth of suburbs is increasing the

demands for certain urban amenities normally furnished by central cities.” (Meyer, Kain,

and Wohl 1965, 2)

Recently, D.C. has seen population increases for the first time in many years, largely as an effect

of the attractiveness of federal jobs during the recession. However, growth has slowed in more

recent years. Generally, Metrorail seems to benefit suburban commuters more, but because

Metrorail funding is based on a complicated 33/33/33 formula partly weighted by number of

stations in a jurisdiction, The District of Columbia pays more than any other (see Figure 1.2.). It

seems inequitable that D.C. has to devote more of its tax revenue to Metrorail, considering

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WMATA’s main function is to bring people from the suburbs into the District of Columbia, both

for work and for pleasure. According to Mills and Hamilton,

Commuting constitutes about 25 percent of personal urban travel… [but]

commuting is [probably the most] important [aspect of urban

transportation]…because it is concentrated during the morning and evening rush

hours. It is commuting that strains the capacity of the transport network, and in

part, it is commuting needs that dictate the extent of road and public-transit

capacity. (279)

Peak commuting hours do strain the Metrorail system, which led WMATA to adopt peak pricing.

During peak commuting hours, which WMATA has defined to be 5:00 AM – 9:30 AM; 3:00 to

7:00 PM on weekdays, and from midnight until 3:00 AM on Friday and Saturday nights

(“Metrorail Fares”), riders are charged a premium fare ranging from $2.15 to $5.90. Off peak,

fares range from $1.75 to $3.60 (“Metrorail Fares”). However, even surcharging peak

commuting hours has not resulted in WMATA Metrorail covering its operating costs so

Washington area taxpayers end up subsidizing WMATA Metrorail’s deficit. The subsidization

is based on the following formula:

The base subsidy allocation for Metrorail service is based on three elements in equal

proportions:

1. Density weighted population 33.3%

2. Number of rail stations 33.3%

3. Average weekly ridership 33.3%

Density weighted population is determined by taking the urbanized area population

distribution for the compact area (50 percent weighted) and combining that with the

weighted population density (urbanized population divided by area). The rail stations

factor is calculated by taking the number of stations, or portions of stations, assigned to

each jurisdiction, divided by the total number of stations in the system. Ridership is

calculated by taking the system average weekday ridership (month of May sample) times

the jurisdictional ridership distribution, as determined by the rail passenger survey. Only

persons who reside in the compact area are included in the distribution. (WMATA 2012a)

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Based on this formula, Washington D.C.’s share of the subsidy in 2016 will be 33.4%, compared

to 19.1% for Montgomery County, 16.4% for Prince George’s County, 4.7% for the City of

Alexandria, 9.6% for Arlington County, .3% for the City of Fairfax, 16.1% for Fairfax County,

and .3% for the City of Falls Church (WMATA 2015a). If WMATA is able to reach forecasted

fares and doesn’t require additional subsidy, Washington D.C. taxpayers will be responsible for

Figure 1.2. Subsidy Allocation. Source: WMATA

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$96,957,323 in Metrorail subsidy this year. The subsidization formula has changed slightly over

the years, but from the very beginning it has ensured that the District of Columbia will pay more

than any other jurisdiction.

In the fall of 1967 WMATA’s consultants proposed the 40/30/15/15 formula. Each

jurisdiction’s capital contribution would be based 40 percent on the construction cost of

the lines in its territory, 30 percent on the operating cost of those lines, 15 percent on the

number of its citizens projected to ride the system in 1990, and 15 percent on its

projected 1990 population. A strict application of this formula would have swamped the

District of Columbia, so the consultants designated downtown – with the most stations,

the most underground construction, and the most suburbanites getting off their trains in

the morning as “Sector Zero,” removed from D.C.’s responsibility and instead blended

into general system costs. As one critic later pointed out, neither this scheme not the

financing formula fully apportioned costs according to benefits; both Maryland and

Virginia, whose citizens would save the most time and whose developers would get the

largest windfall, would pay less than their share…The 40/30/15/15 formula passed the

board with little discussion. (Schrag 2006, 114)

WMATA depends on the jurisdictions to find funding to keep the Metrorail running, but public

funding is limited and therefore WMATA Metrorail often has to increase the already high fares.

In its current form, the Washington Metrorail system does not seem economically viable.

With steadily declining ridership, continuing fare hikes to address the budget deficit is bad

economic policy. The fare hikes could simply push more riders to alternative modes of

transportation. The declining ridership shows demand is not perfectly inelastic. Depending on

just how elastic demand becomes, WMATA needs to find a way to lower its’ costs, not raise

fares, to address profitability.

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CHAPTER 2: THE LITERATURE AND QUANTITATIVE ANALYSIS

Chapter 2 of this thesis provides a brief overview of the literature in the economics field

about public transportation. When possible, the literature is connected to the current situation

facing the Washington Metropolitan Area Transit Authority.

Public transportation is an extremely relevant topic in urban economics, and there is a

plethora of literature about the economic efficiency of public transportation. Currently, much of

the debate has turned to whether privatization is feasible and more economically efficient.

Outcomes in privatized and deregulated venues seem to demonstrate economic and social

benefits of privatization, but there are political and institutional constraints that prevent

privatization from becoming widespread (Castleman 2011, 939). It is important to note that most

transportation systems were first delivered by private firms, underscoring that “the … case for

[the twentieth-century] public takeover of [infrastructure and systems] was weak and …

evidence of government failure has been overwhelming” (Winston 2010, 125). Winston asserts

government failure is worse than market failure. WMATA Metrorail’s failure to turn a profit, as

well as its failure to provide reliable, safe, and affordable transportation illustrates Winston’s

point. Additionally, Winston opposes public private partnerships, similar to the relationship

WMATA has with D.C. Circulator buses. Winston’s “pure privatization policy” allows “no

economic role for the public sector other than to enforce antitrust, antifraud, and other general

business laws and to set standards for safety and environmental regulations” (2010, 126). Public

private partnerships (PPPs) have been shown to be more efficient than public systems, but they

do not “showcase the benefits of a full-fledged privatization program” (Winston 2010, 146).

Although complete privatization is not feasible for WMATA, some private oversight might

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increase economic efficiency. Specifically, there should be WMATA board members who

represent private industry. Currently, there are eight voting board members representing the

District of Columbia, Maryland, Virginia, and the Federal Government (“Board of Directors”).

Considering how much private development is spurred by the Metrorail system, adding two more

voting board members from private industry in the Washington Metropolitan area seems

beneficial for both private industry in the area and for WMATA. These private sector

representative board members could offer new perspectives and private sector experience that

prove invaluable to increasing WMATA’s profitability. In the long run, subsidization could cost

more than implementing necessary changes now. Castleman acknowledges that

[Winston’s] privatization proposal is a massive enterprise, but nonetheless it is the

book’s universe. Arguing that economic efficiency is the unifying justification,

Winston pegs the current cost of inefficient public ownership at more than $100

billion in welfare losses (in 2005 dollars), plus, importantly, a circumvention of

innovation. (940)

Private oversight might be able to help reduce this circumvention of innovation.

There are many economists who are completely against privatizing public transportation.

Public transportation is a public good, and the provision of public goods is not necessarily meant

to be economically efficient. However, public goods are supposed to be socially beneficial. “At

the national level, fewer than 5 percent of commuters use mass transit, but…ridership…is

relatively high among low-income commuters” (O’Sullivan 2007, 235). WMATA Metrorail is so

expensive that many of the low income commuters who heavily depend on public transportation

don’t seem to use it. 51% of Metrobus riders are low income, compared to only 12% of Metrorail

riders (WMATA 2015b). Many low income commuters are being pushed toward the more

affordable Metrobuses, raising concerns of a classist system where only the wealthy get to ride

the partly tax funded Metrorail system. In order for WMATA Metrorail to fulfill its role as a

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public good, it must find a way to be accessible for the public at large. This means finding a way

to address the deficit without raising already high fares and further alienating the low income

community.

Public transportation is important, so important that “it tends to be heavily subsidized

generally by a combination of local, state, and federal government” (Johnson 1995, 1010). The

subsidization also helps correct for externalities of private car usage. According to the third

axiom of urban economics, externalities cause inefficiency. (O’Sullivan 2007, 207). Private car

usage leads to increased pollution, added congestion, and more wear and tear on roads. Drivers

do not necessarily pay these societal costs. “The basic idea is to match the underpricing of car

travel with equivalent underpricing of buses, subways, commuter trains, and light rail”

(O’Sullivan 2007, 220). WMATA Metrorail needs to be subsidized, but because WMATA

covers two different states, the District, and has a federal component, it becomes a political issue.

D.C. taxpayers seem to bear an unfair portion of the tax burden considering that D.C. residents

depend on Metrorail the least. The subsidization formula places an unfair amount of the tax

burden on District residents (see Figure 1.2.). A D.C. resident can usually easily substitute

Metrobus for Metrorail. Johnson concludes that “public transportation links Americans to those

things we universally value - economic opportunity, environmental protection, social services,

education, health care, recreation, and time savings through reduced traffic congestion” (1014).

All these things are true. At its core, public transportation is supposed to be socially beneficial.

However, when Metrorail prices are so high that lower income communities cannot afford to use

it and the jurisdiction with the highest poverty level bears more cost than the others, social utility

is not being optimized.

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This thesis aims to determine whether Metrorail rides are an essential good by seeing if

demand is responsive to price changes. Demand for WMATA Metrorail rides is determined by

several factors. “Transportation studies indicate that travel time is a more significant demand

determinant than out-of-pocket costs, so that many transportation demand curves have travel

time as the independent variable” (Dodson 1975, 139). WMATA Metrorail consistently runs

late. Its website claims 82% on time performance, well below WMATA’s own goal of 91%

(“Scorecard). If WMATA is consistently late and travel time helps determine demand, this will

impact the demand for the service unless Metrorail rides are an essential good. Essentially, the

demand function or model is a behavioral description so “selection of the independent variables

(i.e., the demand determinants) is the heart of the problem for the behavioral scientist” (Dodson

1975, 145-146). In the case of WMATA, this thesis assumes the principal independent variable

is price, but the regressions also control for other macroeconomic conditions.

It is interesting to note that much of the economic literature on public transportation dates

from the mid-1960s to the early 1980s. Economists’ interest likely stemmed from all the new

mass transit systems being built in U.S. cities during this time, including Washington’s rail

transit system. There has been less interest in subsequent years. Recently, an article about

elasticity of railway transport demand in Slovenia was added to the economic literature. In

Slovenia, “according to the aggregate values of demand elasticities, the railway passenger

demand is price and income inelastic…For the average consumer, the services of railway

passenger transportation in Slovenia can be classified among essential consumer expenditures”

(Bekő 2004, 63). WMATA Metrorail rides are less likely to be essential goods. Consumers have

many alternatives, including Metrobus, car sharing services like Uber, biking, walking, and

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countless others. Bekő concludes that “recorded price inelasticity of demand [means] that

revenues…increase when the average real fare increases” (83).

Based on a simplistic price elasticity model based on maximum fare nominal values,

WMATA’s 2014 price hike resulted in a larger drop in ridership than increased fare revenue

among existing stations. According to WMATA,

Metro’s most recent system-wide fare changes were implemented in July 2014. The

changes included a $0.15 (9 percent) increase in bus fares; a $0.05 (2.3 percent)

increase in the base rail fare and a $0.15 (2.6 percent) increase in the maximum rail

fare; a $0.10 base parking fee increase; and other minor changes. As a result of the

fare changes, passenger revenues increased in FY2015 by approximately $39 million or

5 percent. However, revenues fell short of projections due to a decline in Metrorail and

Metrobus ridership from their forecasted levels. (2015b)

Five new stations were opened in July 2014, which increased ridership by making Metrorail

accessible to new markets. Subtracting ridership from new stations in 2015 and then calculating

elasticity for the entire Metrorail system shows price elastic demand, which is consistent with

WMATA’s claim of lower than forecasted ridership levels.

Many dismiss WMATA’s budget deficits as an unavoidable consequence of public

transportation being a public good. However, public goods do not necessarily have to be

unprofitable. This economic inefficiency could be a consequence of government having no real

incentive to make WMATA Metrorail more profitable. “Governments are not primarily

interested in maximizing a social welfare function, or the common good, or the public interest-

whatever these may be- but that instead they seek to maximize their own interest” (Breton 1966,

455). This hypothesis about government behavior was initially put forward by Schumpeter and

Downs and Breton tries to tie it together with the theory of public goods as formulated by

Lindahl, Bowen, Samuelson, and Musgrave (456). Breton feels it is “necessary to devise another

14

system of taxation to pay for public goods” (456) in order to actually reach a social Pareto

optimum.

Niskanen’s theory of government budgeting claims powerful agencies are largely

interested in maximizing their budgets through bargaining with a weak, poorly informed

governmental ‘sponsor’ (McGuire 1981, 313). McGuire performed empirical tests in order to test

Niskanen’s ideas. McGuire found that the behavior of state and local governments is inconsistent

with Niskanen’s model of supply by government agencies (318).

Bureaucrats in general have no reason to actively try to maximize their agency’s budget.

However, they also don’t have any reason to cost minimize. WMATA Metrorail could do several

things to reduce its costs. WMATA could fix elevators in a timely manner so expensive shuttles

for handicapped passengers do not have to run. WMATA could better plan and supervise

maintenance work so escalators and tracks get permanent repairs, not temporary fixes which

ultimately require more maintenance work. WMATA could base their pay on performance so

executives have incentive to ensure Metrorail runs properly. With no incentive to minimize

costs, WMATA Metrorail does not operate in an economically efficient fashion.

15

CHAPTER 3: METHODOLOGY AND ECONOMIC ANALYSIS OF PRICE

ELASTICITY

Chapter 3 of this thesis contains the economic analysis of price elasticity for WMATA

Metrorail. Data collection, methodology, and possible explanations for elastic demand are

discussed.

WMATA Metrorail officially opened in 1976, but these early years were characterized by

frequent price increases and decreases, as well as extensive track building. With all this change,

it is difficult to isolate the effect price has on ridership. Furthermore, Washingtonians and their

suburban counterparts needed time to become accustomed to public transportation. WMATA

Metrorail is one of the few post war systems, created “in an era when Americans passionately

embraced the automobile” (Schrag 2006, 1). For these reasons this thesis only looks at ridership

from 1981 onward, when prices had stabilized and the novelty of Washington rail transit had

likely run its course.

3.1. Data Collection

WMATA’s website provides ‘History of Fare Increases’ which documents all its price

changes and dates these changes went into effect. Metrorail has two different pricing systems,

peak and non-peak. This thesis uses peak pricing because most Metrorail usage occurs during

peak commuting hours. Fare is determined based on the peak boarding fare and distance

traveled, up to the maximum fare distance of 15.7 miles. The longest possible trip one can make

is 29.6 miles. The $2.15 boarding charge covers 0 to 3 miles of travel. 1st tier miles (3-6 miles)

are charged at $.326 per mile. 2nd tier miles (6 plus) are charged at $.288 per mile (WMATA

2015c). When prices are adjusted, WMATA might increase or decrease boarding fare, 1st tier

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miles, or 2nd tier miles, which means I had to decide which price to use in order to calculate price

elasticity. I used maximum fare instead of boarding fare because most riders are paying closer to

maximum fare than boarding fare. More about the calculation of prices will be discussed later in

this thesis. WMATA also annually publishes historical rail ridership, cataloging average

weekday daily passenger boardings for all WMATA Metrorail stations. The counts have been

taken annually in May every year since 1978, except in 1983. WMATA also keeps data on

historical bus ridership, which the Bus Planning Division provided when contacted. Non

WMATA data on macroeconomic conditions, like Mid Atlantic gas prices and population data,

were sourced from the Department of Labor, U.S. Census Bureau, Bureau of Economic Analysis,

and U.S. Energy Information Administration. All prices were converted from nominal to real

using 2015 as the base year with Department of Labor annual national CPI figures.

In order to determine optimal subsidy allocations, it is important to know whether or not

WMATA Metrorail benefits everyone equally. One way to try to determine this is to compare

price elasticities for different D.C. Metropolitan regions. Of course, price is not the only variable

that is going to impact Metrorail ridership. There are countless other macroeconomic factors that

will impact Metrorail ridership numbers. The regressions aim to control for as many external

macroeconomic conditions as possible without running into multicollinearity. Ultimately,

average real fare paid, weekday Metrorail ridership in May, population, number of stations,

miles of metro, real gas prices, real bus fares, weekday bus ridership in May, Black percentage of

D.C. population, and 25-34 year old percentage of D.C population were available predictors for

the regressions.

Instead of simply using real maximum fare for price, average fares from each station

were collected from PlanItMetro, WMATA’s Planning Blog site. These fares were then averaged

17

for regions and the entire system. Finally, because these average fares were from 2015 data, the

percentages of real maximum fare paid in the three regions for 2015 were found by dividing the

averages by $5.90, the current and 2015 maximum Metrorail fare. For the whole system, average

fare was 53% of maximum fare. In comparison, D.C. was 42%, Maryland 64%, and Virginia

59% of maximum fare. These respective percentages were then multiplied by the real maximum

fare of each year since 1981 for the whole system and each respective region. The averages were

not weighted by entries per station (with the exception of Arlington National Cemetery being

thrown out because of single digit entries) because the populations of neighborhoods has likely

changed significantly over 34 years. It is safer to assume commuting patterns have not changed

significantly. Using maximum fare should account for Metrorail system size, as the maximum

fare generally increases along with increasing miles of Metrorail track. Therefore, percentage of

maximum fare should control for the growing system and provide fairly accurate Metrorail fares

to calculate elasticity from.

After a test was run for correlation (see Table 3.1.), several variables had to be dropped

from the whole system regression because of high collinearity. We observe high correlation

between DC GDP and Population, Number of Stations and Miles of Metro, and Metro Real Fares

and Bus Real Fares. Essentially, these sets of variables are measuring the same thing.

18

Table 3.1. Correlation

Year Metro Real Fare

Metro Ridership

Population DC Metro

DC GDP Number of Stations

Miles of Metro

Year 1.0000

Metro Real Fare

-0.0989 1.0000

Metro Ridership

0.9575 -0.2614 1.0000

Population DC Metro

0.9931 -0.0704 0.9452 1.0000

DC GDP 0.9837 0.0218 0.9411 0.9741 1.0000

Number of Stations

0.9441 -0.3307 0.9454 0.9414 0.8816 1.0000

Miles of Metro

0.9403 -0.3415 0.9427 0.9374 0.8762 0.9985 1.0000

Gas Prices Real

0.5160 0.4714 0.4575 0.4954 0.6371 0.2605 0.2427

Bus Fares Real -0.4644 0.6668 -0.6054 -0.4139 -0.4296 -0.5078 -0.5136

Bus Ridership 0.1666 -0.2418 0.2748 0.1759 0.1981 0.1777 0.1746

Gas Prices Real

Bus Fares Real

Bus Ridership

Gas Prices Real

1.0000

Bus Fares Real -0.1447 1.0000

Bus Ridership 0.1528 -0.3466 1.0000

19

Total 2.38217109 33 .072187003 Root MSE = .06551

Adj R-squared = 0.9405

Residual .12016507 28 .00429161 R-squared = 0.9496

Model 2.26200602 5 .452401204 Prob > F = 0.0000

F( 5, 28) = 105.42

Source SS df MS Number of obs = 34

3.2. Elasticity Results

After dropping DC GDP, Number of Stations, and Bus Real Fares, a regression was run

on the whole Metrorail system data (see Table 3.2.).

Table 3.2. Whole System Results

According to this linear regression, on average from 1981-2015 demand for WMATA

Metrorail rides throughout the system is inelastic. For every one percent increase in price, we

would only expect ridership to decrease by .4%, indicating that WMATA can safely increase

fares because rides appear to be essential goods. This result is largely consistent with the

Simpson-Curtin rule-of-thumb, which claims each 3 percent fare increase reduces ridership by 1

percent (Litman 2004, 43). However, many sources seem to believe that demand in more recent

years has been more elastic. Despite the warnings against a price hike, with flat ridership

WMATA officials might consider a fare hike the only viable option to cover costs. WMATA

Log Metro Ridership

Coefficient P>|t|

Log Metro Real Fare

-.401 0.056

Miles of Metro

.00767 0.027

Log Population DC

Metro

.695 .289

Gas Prices Real

.0356 .094

Log Bus Ridership

.159 0.244

Constant .337 0.971

20

could avoid fare hikes by reducing service or with increased jurisdictional support, but the

jurisdictions are balking at increasing support from FY 2015 levels and reduced service is just as

unpopular. For 2016 “Metro wants Maryland, D.C. and Virginia to pay $882.8 million in

subsidies, or about $105 million more than in Fiscal Year 2015” (Ashe 2015). Montgomery

County officials do not want to pay $147.2 million in subsidy, but they also do not want service

cuts or price hikes (Ashe 2015). Council members sent a letter to WMATA that read:

Regrettably, we believe that the staff budget proposal threatens to create a ‘death spiral.’

If fares increase even further and service deteriorates, fewer people will opt to ride Metro.

Lower ridership in turn will inevitably translate into further service cuts, fare increases

and even lower ridership. This is most assuredly not a sustainable path forward. (Ashe

2015)

The empirical evidence seems to collaborate this idea that ridership is fairly sensitive to price.

Controlling for demographics, specifically percentage of young and black people in Washington

D.C. because they are the heaviest users of public transportation (Mckenzie 2015, Neff and

Pham 2007), shows stronger price sensitivity. Historical demographic data is only available for

census years, meaning 1980 (which was used with 1981 ridership data), 1990, and 2000. More

recent data is available for every year, so 2007-2014 data is also included. Ideally, it would have

been preferable to run a regression on just 2007-2014 data, but with this small sample size the

regression results were insignificant. In order to avoid this regression being weighted heavily by

more recent data, the 1980 and 1990 observations were included 8 times to serve as

representation for the entirety of the 80s and 90s. The 2000 data was weighted 5 times to be

representative of the early 2000s (see Table 3.3.). For every one percent increase in real price,

this regression estimates that ridership will decline by 1.52%, offsetting the increased revenue

from higher fares. Additionally, this regression provides empirical evidence to what one would

expect. More millennials moving into Washington D.C. increases Metrorail ridership, and the

21

Total 3.52834275 28 .126012241 Root MSE = .04094



Model 3.48644469 3 1.16214823 Prob > F = 0.0000

F( 3, 25) = 693.44


significant negative coefficient in front of black percentage of the population indicates that the

gentrification of D.C. increases Metrorail ridership. This is likely due to the socioeconomic

differences between those leaving D.C. and those moving into the city now as the cost of living

increases. The new residents tend to be much wealthier, and are more likely to ride Metrorail

than Metrobus. Nationally, black people are the racial group most likely to utilize public

transportation, but the regression results indicate that Metrorail’s high prices might have isolated

these potential users. The relatively small sample and high r squared value indicates these

regression results should be accepted with caution. However, because this thesis is mostly

concerned with the sign of the coefficients, not the actual values, these regression results should

suffice.

Table 3.3. Whole System Results with Weighted Demographic Data

In addition to looking at demand for the entire system, this thesis also looks at demand on

a regional basis. Ideally, miles of metro would be available annually for every region, but

unfortunately the only available data on WMATA’s website was total miles of Metro track

currently in each region. As of 2016, there are 38.3 miles of track in Washington D.C., 38.29

miles of track in Maryland, and 41.17 miles of track in Virginia, which WMATA rounds to a

system wide total of 117.7 miles. Because of the lack of annual data on miles, number of stations

Log Metro Ridership Coefficient P>|t|

Log Metro Real Fare -1.523964 0.000

25 to 34 years old % of D.C. Population

6.47906 0.000

Black % of D.C. population

-3.432161 0.000

Constant 15.47877 0.000

22

was used instead in regional analyses. Number of stations and miles of metro are correlated at

.9985, so they are essentially interchangeable in terms of their impact on ridership. Based on this

high correlation, it raises questions as to why WMATA uses number of stations instead of miles

of metro as the third weight in the subsidy formula. Washington D.C. is home to 39 out of 91

stations, (WMATA assigns D.C. 40 stations, splitting responsibility with Prince George’s and

Montgomery for two Maryland stations located very close to the D.C. border) or 43% of stations,

but D.C. only accounts for 32.5% of Metrorail track. The station weight is why D.C. ends up

paying so much of the subsidy. In the other two weights, population weighted by density and

average weekday ridership, which are probably much better indicators of the value of Metrorail

to a region, D.C. has the smallest share of the allocation (see Figure 3.1.).

Figure 3.1. Regional Subsidy Breakdown 2005, Source: WMATA

23

Total 1.42655439 33 .043228921 Root MSE = .06578



Model 1.30541141 5 .261082281 Prob > F = 0.0000

F( 5, 28) = 60.34


The regression results collaborate that D.C. should not be paying such a large share of the

subsidy. D.C. is the only region that exhibits price elastic demand for 1981 to 2015, with a

significant elasticity coefficient of 1.214 (see Table 3.4.). The regression estimates that every 1%

increase in price will result in a 1.214% decline in ridership in Washington D.C. Additionally,

the D.C. regression has a negative coefficient in front of log bus ridership, indicating D.C.

residents substitute Metrobus for Metrorail. This coefficient is not significant, but these results

are what one would expect. Within the District, it is relatively easy to ride the buses instead of

the Metrorail. In the suburbs, we do not see this same Metrobus effect.

Table 3.4. D.C. Regression Results

For the Maryland Metro region, Prince George’s County and Montgomery County,

demand for Metrorail exhibits price inelasticity from 1981 to 2015. For every 1% increase in

fare, ridership is only expected to drop by .45% (see Table 3.5.). Also, there is a positive

Log Metro Ridership

Coefficient P>|t|

Log Metro Real Fare

-1.214 0.000

Log D.C. Population

2.266 0.000

Number of Stations

.0432 0.000

Gas Prices Real .0266 0.142

Log Bus Ridership

-.205 0.210

Constant -15.453 0.017

24

Total 7.60906986 33 .230577875 Root MSE = .11685



Model 7.22677321 5 1.44535464 Prob > F = 0.0000

F( 5, 28) = 105.86


relationship between bus and Metrorail ridership, indicating that Maryland Metro residents might

use the bus as a complementary good instead of as a substitute good.

Table 3.5. MD Metro Regression Results

For the Virginia Metro region, we see similar results. For every 1% increase in fare, it is

expected that Metrorail ridership will decrease by .77% (see Table 3.6.). Like the Maryland

Metro suburbs, the Virginia Metro suburbs also exhibit price inelastic demand. The higher

elasticity coefficient than Maryland could be because of the higher income level in the Virginia

suburbs. It is possible Virginia residents substitute with cars more than Maryland residents do.

Additionally, Virginia also has a positive relationship between bus ridership and Metrorail

ridership, indicating complementary goods. The data suggests Maryland and Virginia residents

are more dependent on Metrorail and less sensitive to price changes. Therefore, it makes

economic sense for the subsidization formula to reflect this dependence by increasing the

financial support coming from Virginia and Maryland.

Log Metro Ridership

Coefficient P>|t|

Log Metro Real Fare

-.450 0.163

Log MD Metro

Population

.812 0.269

Number of Stations

.0540 0.001

Gas Prices Real

-.0179 0.583

Log Bus Ridership

.279 0.250

Constant -4.269 0.690

25

Total 2.83536931 33 .085920282 Root MSE = .06478



Model 2.71786802 5 .543573604 Prob > F = 0.0000

F( 5, 28) = 129.53


Table 3.6. VA Metro Regression Results

3.3. Fixed Effects

The fixed effect model controls for variations that do not change over time. Additionally,

a fixed effect model treats D.C., MD, and VA as separate panels instead of simply looking at the

Metrorail system as a whole year to year. After running a Hausman test on the data, the .0000

probability value confirms that the fixed effect model is appropriate (see Figure 3.2.).

Figure 3.2. Hausman Test

.

(V_b-V_B is not positive definite)

Prob>chi2 = 0.0000

= 80.45

chi2(3) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Test: Ho: difference in coefficients not systematic

B = inconsistent under Ha, efficient under Ho; obtained from xtreg

b = consistent under Ho and Ha; obtained from xtreg

gaspricesr~l .027327 .0942566 -.0669296 .

numberofst~s .0401658 .0459631 -.0057972 .

population 8.57e-07 4.95e-08 8.07e-07 5.72e-08

logrealprice -.6663169 -1.099631 .4333142 .

fixed random Difference S.E.

(b) (B) (b-B) sqrt(diag(V_b-V_B))

Coefficients

scaling your variables so that the coefficients are on a similar scale.

test. Examine the output of your estimators for anything unexpected and possibly consider

being tested (4); be sure this is what you expect, or there may be problems computing the

Note: the rank of the differenced variance matrix (3) does not equal the number of coefficients

. hausman fixed random

Log Metro Ridership

Coefficient P>|t|

Log Metro Real Fare

-.766 0.000

Log VA Metro Population

.698 0.004

Number of Stations

.0495 0.000

Gas Prices Real

.0782 0.001

Log Bus Ridership

.0555 0.677

Constant .999 0.786

26

Root MSE = 0.0895


R-squared = 0.9807

Prob > F = 0.0000

F( 5, 94) = 277.80

Linear regression, absorbing indicators Number of obs = 102

The fixed-effect regression estimates elasticity at .616 (see Table 3.7.), making demand

inelastic on average from 1981-2015. The fixed effect regression is absorbing regional

differences, which indicates that Metrorail could be an essential good for the Washington

Metropolitan region as a whole. Metrorail definitely provides a valuable benefit to the

Washington Metropolitan region as a whole, but the data seems to suggest it benefits some

regions more than others.

Table 3.7. Fixed Effects Absorbing Region

If another fixed effects regression is run absorbing year instead of region, price seems to

have a much larger impact on demand. By absorbing year fixed effects, the regression can

control for national or region-wide trends that could have impacted demand and prices. For

example, the drop in government subsidy that occurred in 2014. This was not an actual 2014

price change, but for government employees who were completely subsidized and now only had

a portion of their commute being covered, it felt like a large price increase. The fixed effects

regression absorbing year drops gas prices because this thesis uses average mid-Atlantic real gas

Log Metro Ridership

Coefficient P>|t|

Log Metro Real Fare

-.616 0.000

Log Population 1.060 0.000

Log Bus Ridership

.120 .254

Number of Stations

.0439 0.000


Constant -4.795 0.016

27

Root MSE = 0.1286


R-squared = 0.9724

Prob > F = 0.0000

F( 3, 65) = 551.70

Linear regression, absorbing indicators Number of obs = 102

prices, which are the same in Maryland, Virginia, and the District of Columbia. The regression

also drops log bus ridership because bus ridership data is for the entire system, not regional

subsets. This fixed effect regression finds over the years, demand has become more elastic. On

average, for every 1 percent increase in prices, the regression predicts a corresponding 3.589

percent decline in ridership (see Table 3.8.). This large coefficient indicates that when one

controls for year, Metrorail riders are fairly sensitive to price.

Table 3.8. Fixed Effects Absorbing Year

3.4. Government Subsidy

It is possible that part of the reason there now seems to be price elasticity in the Metrorail

system is attributable to the large drop in government Metrorail subsidy that occurred in 2014

(Duggan 2015b). Much lower 2014 numbers caused by the drop in subsidy could be biasing the

estimations. WMATA Metrorail is very dependent on government riders, which was evident

during the government shutdown. These government riders are heavily subsidized, so they do not

truly feel the effect of price hikes. This indicates that the fares are too high for ordinary riders.

“Metrorail’s farebox recovery rate of 66 percent is the highest in the nation. As a result of this

Log Metro Ridership

Coefficient P>|t|

Log Metro Real Fare

-3.589 0.000

Log Population .532 .001

Log Bus Ridership

(dropped)

Number of Stations

.00993 0.050

Gas Prices Real

(dropped)

Constant 7.937 0.000

28

farebox recovery rate, fares are already too high and budgets are extremely sensitive to

corresponding dips in ridership” (Ashe 2015). Recently, the subsidy has been brought back up to

$255 (Ross 2015). “A study by Metro estimated that the system lost 6,000 daily round trips as a

result of the January 2014 cut in the transit benefit from $230 to $130” (Ross 2015). This large

drop in ridership that resulted from the increase in commuting costs for federal workers shows

demand is in fact price elastic. Furthermore, having such a large portion of riders being heavily

subsidized is problematic for unsubsidized riders. The increase in revenue that will likely

accompany the restoration of federal Metrorail benefits to previous levels will make it appear

that Metrorail is economically sound, when in reality the deficit is simply being passed off to

federal tax payers. The fares are too high, and ultimately this needs to be addressed.

3.5. Metrorail Ride Time Data

WMATA recently released its third quarter ‘Vital Signs’, and Metrorail third quarter

2015 performance was disappointing. “On-Time Performance (OTP) fell below 80 percent…the

lowest rail OTP published in Vital Signs” (“Vital Signs”). Additionally, “the minimum car

requirement was only met 10 out of 64 weekdays…Average weekday service was run with a

shortage of about 50 cars” (“Vital Signs”) As a consequence of all the Metrorail issues, “[r]ail

customer satisfaction fell to the lowest since the reporting of the measure, 67 percent, attributable

almost entirely to the reliability of the service” (“Vital Signs”). With Metrorail satisfaction so

low, it seems likely that only those with few alternatives will continue to put up with the high

prices and unreliable service. As the Washington D.C. Metropolitan area grows, Metrorail has to

serve more and more riders in order to avoid the automobile congestion that plagued Washington

D.C. in the 1950s and 1960s. To do this, WMATA Metrorail must build new suburban lines and

add new stations. This requires a massive amount of capital, which is complicated by the fact

http://planitmetro.com/wp-content/uploads/2014/11/Recent-Trends-in-Metrorail-System-Ridership_V3.pdf

29

that city residents end up subsidizing the Metrorail system more than their suburban

counterparts. With Washington D.C. residents paying the brunt of Metrorail costs, it is important

to determine whether District residents are realizing significant time savings with Metrorail.

According to six months of Metrorail ride time data, actual Metrorail ride times deviate

significantly from WMATA estimated ride times, indicating that the Metrorail might not be

faster than alternative modes of transportation for D.C. residents.

The Metrorail ride time data OLS regression uses percent deviation as the response

variable. Independent variables include mileage (shortest route according to Google maps), and

dummy variables like on which Metrorail line the entry station is, Morning Peak, Evening Peak,

and Weekend (see Table 3.9.). When one runs the regression comparing the independent

variables to an orange line train ride during non-peak/non weekend, the formula is:

Percent Deviation = 106.73 – 13.96(Red Line) - 58.95(Green Line) - 62.09(Green/Yellow Line)

- 29.73(Orange/Silver Line) – 56.51(Orange/Silver/Blue) - 8.52(Morning Peak) + 9.55(Evening

Peak) + 46.51(Weekend) - 5.72(Mileage Shortest Route)

All the coefficients are significant, with the exception of Red Line, Morning Peak, and Evening

Peak. As expected, the stations with more Metro lines running through them experience less

deviation between quoted and realized ride time. Also unsurprisingly, Orange line trains seem to

perform the worst in terms of on-time trains. The addition of the Silver Line to the same track

from Stadium Armory to West Falls Church seems to be detrimental to those who live on the

Orange Line. One of the most interesting aspects of the formula is the significant -5.72 in front

of Mileage Shortest Route. This negative coefficient indicates there is less deviation as the miles

traveled increases. This means suburban commuters who travel further distances will on average

30

Total 307802.281 119 2586.57379 Root MSE = 40.8


Residual 183113.539 110 1664.66854 R-squared = 0.4051

Model 124688.742 9 13854.3047 Prob > F = 0.0000

F( 9, 110) = 8.32


face lesser percent deviations than commuters close to the core, the D.C. residents who are

paying higher taxes for the Metrorail. This seems to indicate that suburban residents do in fact

benefit more from Metrorail.

Table 3.9. Metrorail Ride Time Data Regression

For many suburban residents, their only options when commuting to the city are either

driving or taking the Metrorail. Because D.C. traffic is notoriously terrible, they are almost

always better off taking the Metrorail. Google map data estimated on February weekdays when

there was minimal snow (February 2nd to February 5th, 2016) confirms the benefits of Metrorail

for suburban commuters. When traveling into D.C. from the suburbs, or out of D.C. to the

suburbs, during peak commuting hours Metrorail is consistently faster than driving (see Figures

5.1. - 5.4. in appendix). Off peak when train service slows down, driving is usually faster for

Percent Deviation Coefficient P>t

Red Line -13.96136 0.199

Green Line -58.94761 0.015

Green/Yellow Line -62.09363 0.001

Orange/Silver Line -29.73228 0.044

Orange/Silver/Blue Line

-56.50586 0.008

Morning Peak -8.524948 0.437

Evening Peak 9.54592 0.470

Weekend 46.50956 0.000

Mileage Shortest Route

-5.715288 0.000

Constant 106.7284 0.000

31

suburban residents unless traveling a significant number of miles, like 29 miles from Shady

Grove to Farragut West (see Figure 5.4.). There are exceptions to this general rule. Orange line

suburban trips seem to take just as long, if not longer, than driving. A 7:15 AM trip from New

Carrolton to Farragut West was estimated at 44 minutes to drive, and 45 minutes on Metrorail

(see Figure 5.1.). At 6:19 PM, it was estimated to be a 33 minute drive and a 65 minute Metrorail

trip from Shaw-Howard Metrorail station to New Carrolton Metrorail Station (see Figure 5.2.).

For D.C. residents with the option of driving, Metrorail offers much less time savings than it

does for their suburban counterparts. A trip from Minnesota Avenue to Shaw-Howard at 9:35

AM is estimated to be a 21 minute drive but a 25 minute Metrorail trip (see Figure 5.3.). Off

peak, the time difference is even worse. Traveling from Friendship Heights to Minnesota Avenue

off peak is estimated at a 30 minute drive and a 52 minute Metrorail trip (see Figure 5.2.).

Intriguingly, although the Google Map Estimations almost always show that Metrorail will take

significantly longer than driving off peak, the true deviation might be larger if the Google

estimates are not accurate. For example, I actually made the off peak Metrorail trip from

Friendship Heights to Minnesota Avenue. Google Maps estimated the trip would take 52

minutes, or 73% longer than driving. In reality, it took 76 minutes for me to make this trip

because of train delays, 153% longer than Google Maps estimated the drive to be.

32

CHAPTER 4: CONCLUSION

In conclusion, all the evidence seems to suggest that suburban commuters do benefit

more from the Metrorail than D.C. residents. With suburban residents benefitting more from

Metrorail, the subsidization formula should reflect this by decreasing the amount D.C. has to pay

every year. The very simple change of making the third weight miles of metro instead of number

of stations could correct the current imbalance. Looking at the 2005 Base Rail Formula (see

Figure 3.1.), using 2015 miles of Metrorail (because 2015 is the only year with known number of

miles in each region) instead of number of stations as the third weight would have resulted in

D.C. paying 28.9% of the subsidy instead of the 34% they paid that year. If population weighted

by density and weekly ridership are similarly distributed to 2005 this year, this miles of metro

change would result in D.C. paying $83,835,022 in subsidy, a savings of $13,122,301 compared

to the $96,957,323 it is currently projected to pay. Additionally, as Metrorail continues to

expand further and further into the suburbs, increasing the benefits for suburban commuters, this

miles of metro weight will substantially lessen the tax impact on D.C.

4.1 Possible Problems with Approach

Although this thesis concludes that D.C. residents do benefit less from the Metrorail and

should pay less of the subsidy, there could be problems with the approach that bias the

conclusion.

One possible problem resulting from WMATA’s lack of flat rate pricing is determining

how to measure price changes. As previously explained this thesis uses real maximum fare and

then multiplies by a fraction to determine average fare paid. This thesis assumes maximum fare

will be a better base for calculating average real fare paid than boarding fare. However, instead

33

of simply assuming tests should be run to confirm. If real boarding fare is used instead of real

maximum fares, the results change. Using boarding fare, the system wide average fare paid for

2015 is 1.465 times boarding fare. For D.C., it is 1.153, Virginia 1.633, and for Maryland 1.767.

When an OLS regression is run using all the same independent variables that are used in Table

3.2., almost all the coefficients are found to be insignificant, including the elasticity coefficient

(see Table 5.1. in appendix). In Table 3.2 using maximum fare as the basis, the coefficient found

is -.393 which is fairly close to the -.0071 found in Table 5.1. When most of the variables are

dropped in order to obtain a significant elasticity coefficient, the results seem to improve (see

Table 5.2. in appendix). Ultimately, the fact that so many variables have to be dropped in order

to get a significant elasticity coefficient shows that using maximum fare is preferable. Very few

riders on Metrorail are paying close to the boarding fare; the vast majority is paying close to the

maximum fare. Consequently, using maximum fare does seem to be a better measure for price

elasticity. Of course, ultimately it would have been preferable to be able to measure price

elasticity at every distance/price point, but with limited time and resources this is unfeasible.

This thesis uses boardings in each region to measure ridership, but these numbers do not

accurately reflect the benefit of Metrorail to a region because people can board in a jurisdiction

they do not live in. WMATA knows this, which is why they multiply the boardings by the

jurisdictional residence as determined by the Metrorail survey when determining the subsidy.

However, the accuracy of this measure is influenced by the precision of WMATA’s Metrorail

ridership survey. In 2007, WMATA found that 27% of Metrorail passengers lived in D.C., 40%

lived in Maryland, 29% lived in Virginia, and 4% lived elsewhere (WMATA, 2012b).

Considering that 56% of the boardings were at D.C. stations in contrast to the 27% of D.C.

34

residents using Metrorail, this shows that using boardings as the measure for jurisdictional

ridership could make Metrorail seem more valuable to the District than it is.

Another possible problem with the thesis’ approach is omitted variables. Several

variables had to be dropped because of multicollinearity, and there are conditions I would have

liked to control for that were difficult to find data for. For example, I would have liked to include

a variable that modeled driving preference, or D.C. traffic. Traffic and consumer’s preference to

drive likely has a large impact on Metrorail ridership, but the thesis regressions cannot account

for this.

Lastly, the conclusions from the Metrorail ride time data are inconclusive because I could

only use my own Metrorail rides for data. My Metrorail trips might not be representative of

Metrorail service system wide. There are certain Metrorail lines I rarely use, and areas I never

visited in the six months I collected data, which could have biased my results. Despite these

potential research issues, ultimately this thesis’ conclusion fits the conventional wisdom that the

subsidization formula does undervalue Metrorail’s benefit to the suburbs. Maryland and Virginia

pay less than their fair share.

35

APPENDIX

Figure 5.1. Google Map Estimation 1

36


37


38


39

Total 2.38217109 33 .072187003 Root MSE = .07001



Model 2.24493913 5 .448987826 Prob > F = 0.0000

F( 5, 28) = 91.61


Total 2.38217109 33 .072187003 Root MSE = .08077



Model 2.17994512 2 1.08997256 Prob > F = 0.0000

F( 2, 31) = 167.09


Table 5.1. Whole System Results Basing Fares on Boarding Fare

Table 5.2. Whole Systems Results Based on Boarding Fares (Dropped Variables)

Log Metro Ridership

Coefficient P>|t|

Log Metro Real Fare (using

Boarding Fare)

-.00709 0.972

Miles of Metro .0115 0.002

Log Population DC Metro

.0544 0.941


Log Bus Ridership

.249 0.101

Constant 8.107 0.444

Log Metro Ridership

Coefficient P>|t|

Log Real Metro Fare (using

Boarding Fare)

-.461 0.003

Log Population DC Metro

2.545 0.000

Constant -24.685 0.000

40

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