Tracking Changes in Ridehailing/Ride-sharing 

Expenditures

David A Poyer (Morehouse College) and Thomas S Stephens (Argonne National Lab)

2018 Consumer Expenditure Surveys Microdata Users' Workshop

Bureau of Labor Statistics

Washington, D.C.

July 20, 2018
Research Objectives

 Assess the relationship between “ride-hailing/ride-sharing” and 

other expenditure categories.

 How do other confounding factors (relative prices, income and 

demographic changes) affect the relationship?

 How may the use of Consumer Expenditure Survey data help inform 

these issues?

 What is the “ride-hailing/ride-sharing” expenditure share?

 How has it changed over time? 

 What type of good is it?

2
Consumer Expenditure Public Use 

Microdata Files: 2008 to 2016

 Consumer Unit (CU) Characteristics and Income File (FMLI)

1. Total Expenditures

2. Transportation Expenditures

3. Demographic Information 

 Monthly Expenditure File (MTBI) 

1. Detailed Public-Transportation Expenditures

3
Expenditure Categories/SubCategories

 Transportation Expenditures

1. New Vehicles

2. Used Vehicles

3. Other Vehicles

4. Gasoline and Motor Oil

5. Vehicle Finance Charges

6. Maintenance and Repair

7. Vehicle Insurance

8. Vehicle Rental

9. Public and Other Transportation

5
Public and Other Transportation 

Expenditures

1. Airline fares 

2. Intercity bus fares

3. Intracity mass transit fares

4. Local trans. on out-of-town trips 

5. Taxi fares and limousine services on trips 

6. Taxi fares and limousine services (ride-hailing/ride-sharing) 

7. Intercity train fares 

8. Ship fares 

9. School bus

6
Public Transportation Expenditure Share of Total 

Transportation Expenditures

7

Source: Various CE PUMD files, Bureau of Labor Statistics 

(these are unweighted shares for all Consumer Units) 

0.064

0.066

0.068

0.070

0.072

0.074

0.076

0.078

2006 2008 2010 2012 2014 2016 2018

Expenditure Share

Year

Expenditure Share of Total Transportation 

Expenditures

Public Transportation
Percent of Consumer Units Reporting “Taxi fares and 

Limousine services” Expenditures

8

Source: Various CE PUMD files, Bureau of Labor Statistics 

(these are unweighted shares for all Consumer Units) 

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

2006 2008 2010 2012 2014 2016 2018

Percent

Year

Percent of Consumer Units Reporting Ride 

Sharing Expenditures
“Intracity Mass Transit Fares,” and “Taxi Fares and 

Limousine Services” Shares of Total Public Transportation 

Expenditure 

9

Source: Various CE PUMD files, Bureau of Labor Statistics 

(these are unweighted shares for all Consumer Units) 

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

2006 2008 2010 2012 2014 2016 2018

Expenditure Share

Year

Mass Transit and Ride Share Shares of Total Public 

Transportation Expenditures

Mass Transit Ride Sharing
2016 Distribution of Ride-Sharing 

Expenditures

10

Variable Obs Mean Std. Dev. Min Max

Ride Sharing 1,413 $450.26 $715.43 $6.67 $8,000.00

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Expenditures per CU

Percent

Cumulative Distribution: Taxi fares and limousine 

services expenditures
Normalized Real Expenditures per CU: Total Expenditures, 

Transportation Expenditures, and Taxi Fares and Limousine 

Services (Ride Sharing)

11

Source: Derived from Various CE PUMD files, Bureau of 

Labor Statistics (unweighted values for all Consumer Units) 

0

0.5

1

1.5

2

2.5

2006 2008 2010 2012 2014 2016 2018

Normalized Expenditures

Year

Normalized Expenditures

Ride Sharing Transportation Total Expenditures
The Engle Curve

Properties

1. Satisfies the adding-up constraint (shares add up to one)

2. Individual share equations can be estimated using simple least squares

ww�ii = αα�ii + ββ̂

iillllll

Where ww�ii is the estimated share of the i

th good; llllll the natural log of total 

expenditures; where ∑ αα�ii = 1 and ∑ ββ�

ii = 0.

 ββ̂

ii > 0 : luxury good

 ββ̂

ii = 0 : necessity good

 ββ̂

ii < 0 : inferior good 

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Estimated Share Equations for “Taxi Fares 

and Limousine Services” for 2008 and 2016

2008

Robust

Taxi/Limousine Share Coef. Std. Err. t P>t [95% Conf. Interval]

Log of total Expenditures -8.8E-05 3.02E-05 -2.9 0.004 -0.00015 -2.9E-05

Rural x Log of total Expenditures

0.002455 0.001652 1.49 0.137 -0.00078 0.005693

Intercept

Rural -0.00022 0.000172 -1.3 0.193 -0.00056 0.000114

Constant 0.001083 0.000286 3.79 0 0.000524 0.001643

2016

Robust

Taxi/Limousine Share Coef. Std. Err. t P>t [95% Conf. Interval]

Log of total Expenditures -0.0002 5.38E-05 -3.8 0 -0.00031 -9.9E-05

Rural x Log of total Expenditures 0.002158 0.002587 0.83 0.404 -0.00291 0.007228

Intercept

Rural -0.00022 0.000276 -0.78 0.435 -0.00076 0.000326

Constant 0.002469 0.00052 4.75 0 0.001449 0.003488

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Predicted 

“Taxi Fares 

and Limousine 

Services” 

Shares versus 

Total 

Expenditures

14

0 .0005 .001 .0015 .002 .0025

predicted Taxi/Limo Share

0 50000 100000 150000 200000

Total Expenditures

Predicted Taxi/Limousine Share by Total Expenditures
15 Estimated Engle Curves: 2008 to 2016

Year Airline fares

(530110)

Intracity mass 

transit fares

(530311)

Taxi fares and 

limousine services

(530412)

2008 0.00403 -0.00188 -0.00009

(0.00020) (0.00042) (0.00003)

2010 0.00395 -0.00153 -0.00019

(0.00018) (0.00018) (0.00004)

2012 0.00411 -0.00132 -0.00021

(0.00019) (0.00015) (0.00004)

2014 0.00435 -0.00144 -0.00014

(0.00021) (0.00016) (0.00004)

2016 0.00411 -0.00125 -0.00020

(0.00020) (0.00025) (0.00005)

Standard errors are reported in the parenthesis
Findings and Preliminary Thoughts

 From 2008 to 2016, there has been a substantial increase in average “taxi 

fares and limousine service” expenditures.

 Driven in part by an increase in use by CUs

 Between 2008 and 2016, the “taxi fares and limousine service” expenditure 

share has increased substantially.

 The estimated Engle curves for “taxi fares and limousine service” indicate 

that it is an inferior good.

 Unanswered questions:

 Do demographic factors affect “taxi fares and limousine service” expenditures 

and patterns of transportation use in general?

 To what extent does urbanization, age composition and household debt 

changes influence ride sharing/ride hailing expenditures?

 Can we say more on if changes in the use of ride-sharing/ride-hailing services 

affect the overall pattern/composition in transportation expenditures?

16
Literature

 Deaton, Angus, and John Muellbauer. Economics and Consumer Behavior.

Cambridge: Cambridge University Press, 1988.

 Leser, C. E. V. “Forms of Engel functions." Econometrica, 1963: 694-703.

 Working, H. “Statistical laws of family expenditures." Journal of the American 

Statistical Association, 1943: 43-56.

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Acknowledgments

 This presentation and the work described were sponsored by the U.S. 

Department of Energy (DOE) Vehicle Technologies Office (VTO) under the 

Systems and Modeling for Accelerated Research in Transportation (SMART) 

Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility 

Systems (EEMS) Program managed by David Anderson. The authors 

acknowledge David Smith of Oak Ridge National Laboratory for leading the 

CAVs Pillar of the SMART Mobility Laboratory Consortium. 

 The views expressed in the article do not necessarily represent the views of the 

U.S. Department of Energy or the United States Government

 This presentation was created by DOE laboratories: Argonne National 

Laboratory, (Argonne ) managed by UChicago Argonne, LLC, under contract 

no. DE-AC02-06CH11357.The U.S. Government retains for itself, and others 

acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in 

said article to reproduce, prepare derivative works, distribute copies to the 

public, and perform publicly and display publicly, by or on behalf of the 

Government

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