Jasmine Boatner

Howard University

Costs and Benefits of WMATA Metrorail for D.C. and Suburban Residents

Simpson-Curtin rule-of-thumb claims each 3 percent fare increase reduces ridership by 1 percent (Litman 2004)

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)

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)

Literature

WMATA Metrorail SubsidySource: WMATA

Do Washington D.C. residents receive fewer WMATA Metrorail benefits while paying more of the costs for the system?

Central Research Question

Most data comes from WMATA’s websites

OLS and fixed effect regressions used to calculate average price elasticity.

6 months of Metrorail ride time data to see if performance could have impact on price elasticity.

Data and Methodology

Elasticity

Coefficient

P>|t|

1.22 .000

Absorbing

Elasticity

Coefficient

P>|t|

Region .616 .000

Year 3.589 .000

Fixed Effects RegressionAbsorbing Region

Log Ridership= - 4.795 - .616(Log Real Price) + 1.06 (Log Population) + .0439(Number of Stations) + .024(Real Gas Prices) +.120(Log Bus Ridership)

Absorbing YearLog Ridership= 7.937 - 3.589(Log Real Price) + .532

(Log Population) + .0099(Number of Stations)

Compared to less accurate

OLS Regression of

Panel Data with same variables OLS

Log Ridership = 10.487 - 1.22(Log Real Price) + .115 (Log Population) - .0159(Log Bus Ridership) + .0459(Number of Stations) + .0982(Real Gas Prices)

Region

Elasticity

Coefficient P-Value

R squared

of Model

Whole System .401 .056 .9496

D.C. 1.214 .000 .9151

Maryland .4501 .163 .9498

Virginia .7665 .000 .9586

OLS Regression ResultsWhole System

Log Ridership= .337 - .401 (Log Real Price) + .695 (Log Population) + .0077 (Miles of Metro) + .036 (Real Gas Prices) + .159 (Log Bus Ridership)

D.C. Log Ridership = - 15.45 – 1.214 (Log Real Price) + .043 (Number of

Stations) + 2.266 (Log Population) + .0266 (Real Gas Prices) -.205 (Log Bus Ridership)

Maryland Log Ridership = - 4.269 – .450 (Log Real Price) + .054 (Number of

Stations) + .812 (Log Population) - .0179 (Real Gas Prices) + .2788 (Log Bus Ridership)

Virginia Log Ridership = .999 – .766 (Log Real Price) + .0495 (Number of Stations)

+ .698 (Log Population) + .0782 (Real Gas Prices) + .0555 (Log Bus Ridership)

OLS Regional Equations

D.C. exhibits price elastic demand

Both Maryland and Virginia exhibit price inelastic demand

On the whole, demand is price inelastic

Why is demand in D.C. price elastic?

OLS Regression Results

Metrorail Performance Data

0.0

05.0

1.0

15.0

2D

ensi

ty

0 100 200 300Percent Deviation

Metrorail Ride Time Histogram

Percent Deviation

Coefficient

P>t

Red Line -13.96 0.199

Green Line -58.95 0.015

Green/Yellow Line

-62.09 0.001

Orange/Silver Line

-29.73 0.044

Orange/Silver/Blue

Line

-56.51 0.008

Morning Peak -8.52 0.437

Evening Peak 9.55 0.470

Weekend 46.51 0.000

Mileage Shortest Route

-5.72 0.000

Constant 106.73 0.000

Shorter routes have longer delays

Orange Line appears to be slowest

Riding during peak times does not significantly reduce deviations

Metrorail Performance Regression

Fare does seem to have an impact on ridership

On average, demand is most price elastic in Washington D.C.

Significant deviations between WMATA quoted ride time and actual ride time

Suburban commuters do seem to benefit more from Metrorail

Key Findings