Complementarity between Pricing and Land Use Planning: Variations in Gasoline Price Elasticity of Transit Ridership in U.S.

Urbanized Areas

Bumsoo Lee,* Assistant Professor Department of Urban and Regional Planning University of Illinois at Urbana-Champaign Yongsung Lee, PhD Student School of City and Regional Planning Georgia Institute of Technology

Conditionally accepted to the Journal of the American Planning Association

*Corresponding author: Bumsoo Lee Assistant Professor Department of Urban and Regional Planning University of Illinois at Urbana-Champaign Champaign, IL 61820 bumsoo@illinois.edu

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Problem, research strategy, and findings:

A shift toward more sustainable transportation requires both adequate pricing of externalities from driving and supportive land use policies. However, a wide gap exists between advocates of pricing and land use planning approaches. Proponents of each approach underestimate the potential and role of the other approach and often ignore the complementarity and potential synergy between the two. To fill this gap, this study investigates the interaction effects between gasoline prices and land use (policy) variables using a panel data set of transit ridership in 67 urbanized areas between 2002 and 2010.

We found that pricing and land use planning are complementary in promoting transit patronage. Estimated elasticities of key variables show that while doubling the average gasoline price would increase transit ridership by 17.6% in an urbanized area with the mean population weighted density and no regional containment policy, a one standard deviation higher density and containment policy would respectively boost the gasoline price effect by 4.8% and 16.6%. These interaction effects are additions to the ridership premiums of density and policy themselves. The interaction effects were 4.7% and 16.4% when an urban compactness index was used instead of the density variable. Findings were consistent across several different types of analyses.

Takeaway for practice:

As the study shows, pricing schemes will be more effective in places where alternatives to automobility and supportive land use policies are exist. The influences of urban form on travel behavior will be strengthened when driving is adequately priced. Planners and policymakers should take advantage of the complementarity between pricing and land use planning approaches by implementing policies in combination and in well-coordinated ways.

Keywords: transit ridership, land use, gasoline price, elasticity, complementarity

Research support: NA

About the authors:

Bumsoo Lee (Bumsoo@illinois.edu) is an assistant professor in the Department of Urban and Regional Planning at the University of Illinois, Urbana-Champaign. His research interests include urban spatial structure, travel behavior, and economic impact analysis. Yongsung Lee (yongsung.lee@gatech.edu) is a doctoral student in the School of City and Regional Planning at the Georgia Institute of Technology. His research focuses on land use and transportation interaction, polycentric urban form, and public transit.

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Introduction

Passenger transportation accounts for about 70 percent of U.S. greenhouse gas (GHG) emissions in the

transportation sector (one-third of total GHGs). Thus, reducing vehicle miles traveled (VMT) by shifting

to low-carbon transportation modes such as mass transit and non-motorized travel is critical to meet

GHG reduction goals (Chapman, 2007; Banister, 2008; Anable et al., 2012). Beyond environmental

benefits, public transit offers many additional benefits, including congestion reduction (Nelson et al.,

2007), enhanced mobility for low-income and disadvantaged individuals (Deka, 2002; Giuliano, 2005;

Lubin & Deka, 2012), and various health benefits from active travel typically accompanying transit use

(Litman, 2010; Lachapelle et al., 2011; Morency et al., 2011).

However, public transit has been losing market share to automobiles over the second half of the

last century. Even the substantial increase in public subsidies for and investments in public

transportation since the early 1990s have not appeared to have a major impact on transit ridership. Only

since 2004 has transit ridership begun to grow faster than both population and highway VMT, allegedly

due to an unprecedented increase in gasoline prices (American Public Transportation Association, 2012).

Recent ridership growth highlights the role of prices in transportation markets (Figure 1) and has

prompted many studies of the gasoline price elasticity of transit ridership (Maley & Weinberger, 2009;

Haire & Machemehl, 2010; Yanmaz-Tuzel & Ozbay, 2010; Stover & Bae, 2011; Lane, 2012). The

present research focuses on the interaction between price and land use planning. Specifically, it

investigates how compact urban form and urban containment policy reinforce the price effect to promote

transit patronage.

[Figure 1 about here]

Although a shift toward more sustainable transportation requires both correct pricing for driving

and transit-supportive urban forms, there remains a gap between advocates of pricing measures and

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proponents of land use policies. Advocates of each approach tend to underestimate the role of the other.

In particular, skepticism remains about the potential of a more sustainable urban form in reducing VMT

and carbon emissions (Moore et al., 2010; Echenique et al., 2012). Skeptics of recent planning initiatives

in line with smart growth principles typically emphasize the primacy of policies devoted to “getting the

price right.” However, land use planning and pricing approaches can be complementary and synergistic

rather than competitive and conflicting (Boarnet, 2010; Guo et al., 2011). Policy analysts and decision

makers should understand the complex interactions among various policy instruments to mitigate policy

conflicts and maximize synergistic effects (Stepp et al., 2009). Nonetheless, little empirical research has

been done on potential synergies among alternative approaches in transportation planning, pricing and

land use planning.

This study investigates the interaction effects of gasoline prices and urban form variables on

promotion of transit ridership in U.S. urbanized areas over a 108-month period from 2002 to 2010. It

employs a two-stage least square (2 SLS) regression analysis of transit ridership to take into account the

endogeneity of transit service supply. The interactions between gasoline price and urban form variables

are tested by including multiplicative interaction terms. Results show that transit ridership increases with

gasoline price and that the price effect is larger in those urban areas that have a more transit-supportive

urban form indicated by a higher population density or compactness index. The presence of a regional

level containment policy across jurisdictional boundaries is also associated with a larger elasticity of

transit ridership with respect to gasoline price. Policy implications of the findings are discussed in the

last section.

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Land Use versus Pricing Approaches to Sustainable Transportation

The debate between advocates and skeptics of land use planning approaches to sustainable

transportation has been one of the most contested in recent years. Central to the debate is a question

about the role of sustainable urban form in reducing VMT and mitigating climate change (Cervero &

Landis, 1995; Giuliano, 1995; Moore et al., 2010; Winkelman et al., 2010). The debate is intellectually

rooted in profound gaps between planners and economists in their beliefs regarding expected functions

and effects of different types of market interventions (Ewing, 1997; Gordon & Richardson, 1997;

Brueckner, 2000; Knaap, 2008). On the one hand, the debate has contributed to expanding our

knowledge about the connections between land use and travel by stimulating rigorous research; on the

other, it may carry a counterproductive message to practitioners and policy makers that land use and

pricing approaches are competing rather than complement each other.

Proponents of land use approach emphasize the role of smart growth policies in mitigating

climate change (Ewing et al., 2008a; Ewing et al., 2008b; Marshall, 2008; Winkelman et al., 2010; Dulal

et al., 2011). This approach aims to reduce the demand for driving and make alternatives to driving more

attractive (Handy, 2006; Salon et al., 2012) through a wide range of land use planning strategies that

improve development density, especially near transit stops; land use mix and jobs-housing balance;

regional accessibility; and network connectivity (Cervero, 2003; Cervero & Duncan, 2006; Salon et al.,

2012). These shifts toward a more sustainable urban form are believed to achieve, beyond their

environmental benefits, the additional benefit of creating more diverse, healthy, and livable communities

(Levine et al., 2005; Heath et al., 2006; Levine & Frank, 2007; Aytur et al., 2008; Boarnet, 2011). Most

professional planners and local public officials subscribe to this view.

Skeptics of smart growth policies challenge land use strategies on two primary grounds:

feasibility and cost (Litman & Steele, 2012). Although an increasing body of literature has cumulated

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evidence of significant links between compact urban developments and carbon-efficient lifestyles,

including less driving (Ewing & Cervero, 2001; Kuzmyak et al., 2003; Cao et al., 2009; Ewing &

Cervero, 2010), disagreement remains about the magnitude of urban form effects (Brownstone, 2008;

Transportation Research Board, 2009; Ewing et al., 2011; Heres-Del-Valle & Niemeier, 2011). Critics

of urban form strategies claim that the impacts are small compared with those of pricing and technology-

based policy measures (Brueckner, 2007; Staley, 2008; Mitchell et al., 2011; Echenique et al., 2012).

The weakening connection between land use and transportation has been attributed to ever-decreasing

transportation costs and the popularity of suburban lifestyles (Giuliano, 1995). Proponents of the market

approach also assert that compact development costs society by compromising housing affordability

(Phillips & Goodstein, 2000; Dawkins & Nelson, 2002) and worsening congestion (Sorensen, 2009).

Some authors argue that the welfare costs of anti-sprawl policies that may limit housing choices and

lifestyles are higher than policy benefits (Moore et al., 2010; Echenique et al., 2012) although smart

growth policies are likely to expand consumers’ choice sets in U.S. cities (Levine & Inam, 2004; Levine

et al., 2005; Handy et al., 2008).

Critics of land use planning approaches hold that correct pricing of transportation will be more

effective and efficient in inducing behavioral changes in travel (Anas & Rhee, 2006, 2007; Brueckner,

2007; Moore et al., 2010). In their view, many transportation problems—including excessive congestion,

pollution and GHG emissions—exist largely because individual drivers are not required to pay for the

negative externalities they impose on society (Small, 1997; Brueckner, 2005; Parry & Small, 2005).

Thus, pricing policies currently being implemented or under policy discussion, including congestion

pricing, carbon taxes, VMT taxes, and increased fuel taxes, aim to make driving relatively more

expensive and hence make alternative travel options more attractive although each of these pricing

instruments offers slightly different travel (dis)incentives (Greene & Plotkin, 2011).

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Many planners believe that pricing measures alone cannot effectively reduce VMT (Winkelman

et al., 2009) because the demands for driving and transit use are inelastic with respect to fuel price

(Goodwin et al., 2004; Small & Van Dender, 2008; Litman, 2012). Graham and Glaister (2004) add that

simply stabilizing VMT at its present level would require a price increase greater than income growth

because travel demands are more sensitive to income changes than fuel price changes. A primary reason

for the price inelasticity of VMT is the lack of alternatives to automobility (Cervero & Landis, 1995;

Winkelman et al., 2009). Thus, it may be naive to expect significant changes in travel behavior to be

caused by better pricing alone, given that driving is the only realistic travel option in many urban areas

in the U.S. (Boarnet, 2010). Further, there is a concern that mobility-based congestion pricing may

further accelerate metropolitan decentralization and dispersion when it is not well coordinated with

proper land use strategies, resulting in deterioration of overall accessibility and eventually increasing

VMT (Levine & Garb, 2002). Lastly and perhaps most importantly, an equity concern that price

increases will disproportionately constrain the mobility of low-income households often makes pricing

instruments like congestion pricing unpopular (Levinson, 2009).

In the recent search for effective policies to reduce VMT and mitigate GHG emissions in the

transportation sector, researchers began to emphasize the complementarity or potentially synergistic

effects of land use and pricing policies. Cervero (1998) explored a range of complementary land use

planning and pricing policy options to build a functional and sustainable “transit metropolis”. Shen

(1997) also emphasized the importance of combining various policy approaches to tackle ever

worsening transportation problems in mega cities like Shanghai, China. Now, there is an urgent need for

integrating different policy approaches to meet the great challenge of climate change mitigation (Handy,

2006; Boarnet, 2010, 2011; Guo et al., 2011). Stepp et al. (2009) indeed demonstrate, by applying

system dynamics tools, that the interaction of two or more GHG reduction policies can lead to either

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synergy or conflict effects and that so-called rebound effects can be effectively mitigated by

implementing synergistic policies in an integrated manner.1 However, despite the importance of

potential synergy between land use planning and pricing approaches, only a handful of studies on this

issue have been done to date.

Rodier et al. (2002), using two types of land use and travel demand models, show that pricing

measures (higher fuel tax and parking surcharges) are complementary to transit investments and transit-

oriented developments (TOD) in reducing VMT and emissions in the Sacramento region. Another

simulation study (Lautso et al., 2004) on seven European cities explicitly attempts to identify synergistic

effects and shows that the impact of combined car pricing and public transit policies is larger than the

sum of the effects of individual policies. A recent review (Rodier, 2009) of simulation results including

scenario planning studies of U.S. metropolitan areas also shows that land use, transit, and pricing

policies are mutually supportive when implemented in combination. Nevertheless, even state of the art

land use change and travel demand models have limited capacity to simulate the complex interactions of

combined policy instruments and hence are likely to underestimate synergy effects (Rodier, 2009).

Further, the result of a simulation study is largely determined by behavioral assumptions and parameters,

which can be studied only by empirical research.

In a comparison of travel mode choice in Boston, MA and Hong Kong, China, Zhang (2004)

shows that land use policies influence travel mode choice more effectively when complemented by

various pricing measures. More recently, Guo et al. (2011) tests whether compact land uses and

congestion pricing are mutually supportive in reducing VMT, using data collected from a pilot mileage

fee program in Portland, OR. This study shows that VMT reduction from congestion pricing is greater in

traditional neighborhoods, where alternative travel options are available, than in low density suburban

neighborhoods. It also reports that density and land use mix explain more of the variation in household

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VMT when the pilot program was implemented. However, it is difficult to generalize study findings

because, as the authors acknowledge, their sample size is small and the Portland metropolitan area is

unique in having an urban growth boundary. Clearly, more generalizable empirical research on the

interaction effects between planning and pricing approaches is necessary.

Research Approach

Research Hypotheses

The main hypothesis of this research is that land use planning and pricing approaches are

complementary in promoting alternative transportation modes. In particular, we hypothesize that the

cross-elasticity of transit ridership with respect to fuel price is greater in more compactly developed

urban areas. When gasoline prices increase, public transit and non-motorized travel can be reasonable

substitutes for driving in compact urban areas with high density, mixed land use, and better transit

accessibility. However, even when gasoline prices are very high, a shift from driving to transit riding

will not be easy in sprawling regions that are built around automobility. Thus, we expect greater transit

ridership gains from a gasoline price shock in urban areas that are more compact and are pursuing smart

growth policies more actively.

A second and potentially alternative hypothesis is that the effects of gasoline prices on transit

ridership are larger in urban areas with more choice riders, who can switch easily between driving and

transit, than in places with many dependent riders, who have no alternative but to travel by transit

(Stover & Bae, 2011). If this is the case, auto-centric urban areas may have larger gasoline price

elasticities, to the extent that choice riders outnumber dependent riders. A time-series analysis of

gasoline prices and transit ridership in 33 metropolitan areas (Lane, 2012) indeed shows relatively large

elasticities in traditionally thought auto-dependent cities in the Midwest and Sunbelt—for example,

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Indianapolis, IN; Atlanta, GA; Dallas, TX; Houston, TX; and Phoenix, AZ. The two hypotheses are not

necessarily mutually exclusive, and both are tested in the current research.

Research Methods

To test the hypotheses discussed above, this study examines how public transit ridership has

responded to gasoline price changes during the 2000s in the 67 largest urbanized areas (UAs) in the

United States. Our basic modeling strategy is a two-stage least squares (2 SLS) regression analysis with

multiplicative interaction terms between gasoline price and urban form variables. We employ the 2 SLS

regression analysis to take into account the endogeneity of transit service supply, reflecting the two-way

causal relationship between transit service supply and consumption. While the level of transit services,

including coverage and frequency, is an important determinant of transit ridership, the dependent

variable of our analysis, on the one hand, transit operators typically decide transit service level in

response to transit demand (ridership) on the other hand (Taylor et al., 2009). Thus, ordinary least

squares (OLS) regression models produce biased and inconsistent estimates of key coefficients because

of the two-way causal relationship.

As a solution, drawing from Taylor et al. (2009), we take a 2 SLS regression approach. We use

operating subsidies per capita from federal, state, and local governments to transit operators and UA

population as instrument variables (IVs) to predict transit vehicle revenue miles in the first-stage

regression model and use the predicted service supply variable as a key predictor of transit ridership in

the second stage. Ideally, IVs should be partially correlated with endogenous variables, transit service

supply in this study, but should not be correlated with the error term in the OLS model. Taylor et al.

(2009) use the percent of the UA population who voted for the Democrat in the 2000 presidential

election as an IV, expecting democratic-leaning UAs are more inclined to fund public transportation

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systems. We choose to use transit subsidy per 1,000 people as an IV which has more direct influence on

the level of transit services given population size.

First stage:

Sit = f (POPit, SBit, Pit, GPit, LUit, GPit × LUit, Xit, Tt, Mt) (1)

Second stage:

TRit = f ( it, Pit, GPit, LUit, GPit × LUit, Xit, Tt, Mt) (2)

, where Sit is transit service supply in UA i and in month t; TRit is transit ridership; POP is UA population size; SB is operation subsidy; S is predicted transit service supply; P is transit fare; GP is gasoline price; LU is land use variables, such as population density, urban sprawl index, and urban containment policy indicator; X is the vector of other UA characteristics, including percent college and graduate students, freeway lane miles, and unemployment rate; T is the vector of time-specific variables, such as trend and post-gasoline-price-peak period indicator; M is the vector of 11 month dummies;

As shown in equation (2), we test the complementary effects between fuel price and land use

planning variables by examining the coefficients of interaction terms between the two groups of

variables (GPit × LUit). Multiplicative interaction models are popular in testing a conditional hypothesis,

which typically assumes that X affects Y depending on the condition of Z (Wright, 1976; Friedrich,

1982; Brambor et al., 2006). A positive coefficient of the interaction term in our analysis will

corroborate the main research hypothesis: that increase in gasoline prices has a greater stimulating effect

on transit patronage in urban areas that have more transit-friendly land uses and policies. All interaction

variables are used after centering (subtracting the mean) so that we can interpret the coefficients of base

terms—gasoline price and land use—as the impact of each variable when the other variable takes on its

mean value.

This research analyzes a unique panel data set covering the 67 UAs for 108 time periods, which

will produce more generalizable findings than previous cross-sectional or time-series studies. Because

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longitudinal data may have area-specific and/or time-specific unobserved effects, OLS regression with

the pooled data (pooled-OLS) produces biased estimates. To control for the unobserved heterogeneity,

we employ a random effects model among available panel data analysis techniques. A fixed effects

model cannot be used because some of our key land use variables, the compactness index and

containment policy indicator, are time-invariant and hence are perfectly collinear with the fixed effects.

All regression models are estimated with heteroskedastcity consistent standard errors.

Variables and Data

The data set for this research covers the 67 largest urbanized areas (UAs) in the United States for the

108-month period between January 2002 and December 2010. Honolulu, HI; New Orleans, LA; Mission

Viejo, CA; and McAllen, TX, are excluded from the sample of UAs with more than 500,000 residents as

of 2000 for various reasons, such as missing values for key variables and disruptive events like

Hurricane Katrina. Table 1 summarizes descriptions and data sources of all dependent and independent

variables used in the analysis.

[Table 1 about here]

Natural logarithm of unlinked passenger travel (UPT) is used as the dependent variable of all

second-stage regression models. As a measure of transit service level, we use total vehicle revenue miles,

which we assume to be a function of UA population size, public subsidy for transit operation, and

seasonal effects. All transit-related data come from the National Transit Database (NTD), provided by

the Federal Transit Administration (FTA). We aggregate all transit-related variables across modes and

transit operators at the UA level. Monthly gasoline price data come from the consumer price index-

average price data, provided by the Bureau of Labor Statistics (BLS). Because the monthly average

price for unleaded regular gasoline is available only for 15 major metropolitan areas, average price by

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city size class and census region has been used for other smaller urbanized areas. All nominal gasoline

prices are converted to 1983 real prices, using the consumer price index for all urban consumers (CPI-U).

UA population density, an urban compactness index, and a containment policy indicator are used

as measures of land use characteristics and policy. We use a population-weighted density measure:

While a conventional density measure simply divides population by total land area, this alternative

measure weights densities of small geographical units (in this study, census blocks) by population to

obtain the urbanized area level weighted density. This population-weighted density measure captures the

density that average residents of an urban area experience in their daily lives better than a conventional

density measure does (Transportation Research Board, 2009). Further, the population-weighted density

measure is less affected by the change in boundary definition of each urbanized area between census

years.2 We estimate population-weighted densities using 2000 and 2010 census data and linearly

interpolate densities for 108 months in the study period for each UA instead of using annual American

Community Survey (ACS) data, which are tabulated using the 2000 census UA boundary definition.

We also use an urban compactness index as an alternative variable to measure land use

characteristics beyond population density. Ewing et al. (2002) developed a four-factor sprawl index

based on 22 variables that measure residential density, land use mix, strength of activity centers, and

accessibility of street network for 83 metropolitan regions. We rename this index an “urban compactness

index” because a higher index value indicates a region with less sprawl. A dummy variable indicating

the presence of a “non-local” containment policy, which goes beyond local government jurisdictional

boundaries (Wassmer, 2006), is also included in combination with either of the two land use variables.

Other factors considered in this study include freeway land miles per 100 people, percent of

residents who are college and graduate school students, and unemployment rate. Some other

demographic variables, such as percentage of immigrants and population in poverty, were initially

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considered but dropped after preliminary analyses. Monthly data are available for transit ridership,

service supply, gasoline price, and unemployment rate. However, annual data are used for freeway lane

miles and college students’ share because monthly data are not available. All independent variables

except dummy variables are used in natural logarithm so that we can interpret estimated coefficients as

transit ridership elasticity with respect to the corresponding variable.

Results

Elasticities of Transit Ridership

Table 2 presents the results of the 2 SLS regression analysis of the determinants of public transit

ridership. The first-stage regression analysis result is provided only for a pooled data model. The supply

of transit services measured in vehicle revenue miles is almost unit elastic with respect to the UA

population size (coefficient = 1.025). The pooled model result shows that a 10% increase in government

subsidies to transit operation per capita is associated with about 7% higher transit service level given

population size and, in turn, leads to about 8% additional transit passenger trips (0.6947 × 1.1697). This

combined elasticity of transit ridership with respect to operation subsidy is estimated to be smaller in a

fixed-effects model, 0.34.

[Table 2 about here]

Estimated cross-elasticities of transit patronage with respect to fuel price ranged from 0.245 in a

pooled data model to 0.082 in a random effects model. The results are comparable with elasticity

estimates from previous studies of U.S. transit systems, 0.08 – 0.42 (Mattson, 2008; Stover & Bae, 2011;

Litman, 2012). Whereas these estimates show that transit ridership is relatively inelastic to the change in

fuel prices, it should be noted that a large portion of the growth in public transit patronage in the U.S., a

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12.9% increase between 2002 and 2008, can be attributed to gasoline prices, which doubled in real

dollars for the period.

Population density shows a moderately larger elasticity, 0.3 in a pooled data model and 0.15 in a

fixed-effects model. These estimates are larger than the weighted average elasticity drawn from 10

previous studies examined in a meta-analysis (Ewing & Cervero, 2010), 0.07. Since our analysis does

not control for urban design and land use mix variables, the population density variable is expected to

capture the effects of these other urban form characteristics to the extent that they are correlated with

density. An alternative land use variable, the urban compactness index, is also found to have a

significant stimulating effect on transit patronage, with the elasticity ranging from 0.185 in a pooled data

model to 0.72 in a random effects model. It is also found that a non-local containment policy has an

additional stimulative effect on transit use. The results imply that transit ridership is 7 to 36% higher in

an urbanized area with a containment policy than in an identical urban region without such a policy.

Because we have already controlled for population density or other urban form characteristics, this

ridership increase may not be a direct effect of urban containment policy. Rather, we interpret this

containment policy dummy as a catchall variable of various smart growth policies, recognizing that

those regions with non-local containment policies (such as Portland, OR; Seattle, WA; and Minneapolis-

St. Paul, MN) have also been pursuing other transit-supportive policies, such as transit-oriented

development.

The results of other control variables are consistent with our expectations, except for freeway

land miles per 100 residents. Transit fare per ride is significant with a negative sign in two models and a

larger college student share of adult population is associated with higher ridership across the board. The

higher the unemployment rate, the fewer commute trips and hence less transit patronage. Another

interesting finding is a 4 to 9% higher transit ridership during the period following gasoline price peak

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($4.065 per gallon of unleaded regular gasoline) in June 2008, all else being equal. Although gasoline

prices have dropped after the peak, the unprecedented gasoline price shock in the 2000s seems to have

left a long-lasting, if not permanent, behavior change in the public transportation market. However, the

negative coefficient of the trend variable indicates that the underlying trend for the study period is still

downward. In other words, the uptick in transit ridership in the 2000s is more a result of the changes in

gasoline prices and other urban attributes than it is a long-term trend.

Variations in Gasoline Price Elasticity of Transit Ridership

Prior to examining the interaction effects between gasoline price and urban form variables, we conduct

an exploratory analysis to learn how the elasticity of transit ridership varies among urbanized areas with

regard to gasoline price. In this step, we estimate the elasticity in each UA by including interaction terms

between gasoline price and UA dummy variables in the pooled data model, without employing any

urban form or land use policy variables. The coefficient of gasoline price base term is, then, the

elasticity in the reference UA (Riverside-San Bernardino, CA), and we obtain the elasticities of all other

UAs by adding the coefficient of a corresponding UA interaction term to the reference elasticity.

Estimated elasticities range from -0.705 to 1.378 and are positive in 56 out of 67 UAs. The mean

and median elasticities of the sample are 0.284 and 0.262, respectively, falling within the range of

previous estimates. Estimated elasticities are plotted against the compactness index and the natural log

of per capita ridership, respectively, in Figure 2. The result confirms the main hypothesis: that cross

elasticity of transit ridership with respect to fuel price is greater in more compactly developed urbanized

areas such as Providence, RI; San Francisco, CA; and Portland, OR, than in places with more sprawl,

such as Riverside, CA; Atlanta, GA; and Raleigh, NC. The slope of the fitted line is about 0.002,

implying that a standard deviation higher compactness index is associated with about 0.05 point higher

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gasoline price elasticity. The second hypothesis, the negative association between the elasticity and per

capita transit ridership, seems also supported, although that association is not strong. The results of this

exploratory analysis suggest that we need to control for the effects of the current level transit patronage

per capita to tease out the effects of urban form variables on gasoline price elasticity.

[Figure 2 about here]

Interaction Effects between Gasoline Price and Land Use Variables

Table 3 presents the results of our main models, in which land use and containment policy variables are

directly interacted with gasoline price. Per capita ridership is also interacted with gasoline price to

control for the significance of choice versus dependent riders. As explained above, the coefficient of

gasoline price presents the elasticity of transit ridership with respect to gasoline price when the

interacted urban form variable has the sample mean value and there is no non-local containment policy.

This main effect is estimated to range between 0.09 and 0.22, a slightly smaller effect than estimates

from models without interaction terms, shown in Table 2. The coefficients of interaction terms are

positive and significant in all models except for the fixed effects model.

[Table 3 about here]

These results indicate that compact urban form and high gasoline price are complementary in

promoting transit ridership. A higher gasoline price has more stimulative effects on transit patronage in

urban areas where higher density, mixed land use, better connected street networks, and more clustered

development make public transportation a more viable travel option. It is also suggested that policies

aiming to create a more sustainable urban form will have greater effects on transit ridership when

gasoline prices are higher.

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Figures 3 and 4 summarize the magnitudes of the interaction effects. They show how gasoline

price elasticity changes depending on the level of density or compactness as well as the presence of

containment policy. Both graphs present the expected ridership changes in response to a 100% increase

in real gasoline price, projected by a study by the International Monetary Fund (Benes et al., 2012) to

occur over the next decade. Doubling gasoline price would increase transit patronage by 17.6% in an

urbanized area that has the mean population weighted density (10,964 persons per square mile) and no

regional level containment policy. However, ridership growth will increase to 22.4% and 25.7%,

respectively, in urban areas with higher densities by 1 and 2 standard deviations.3 It is also predicted that

adopting more aggressive land use and transportation policies, proxied by a regional containment policy,

would boost ridership gains by about 17% at all density levels. A similar analysis for the compactness

index is provided in Figure 4.

[Figures 3 and 4 about here]

It should be noted that the ridership gain premiums of a more compact urban form and policy

presented in Figures 3 and 4 present only interaction effects with pricing that occur in addition to their

own main effects. Elasticities of transit ridership with respect to density and the compactness index are

estimated at 0.26 and 0.17, respectively, in pooled models. Containment policy has an additional effect

of around 0.06.

Finally, we conducted an analysis with change variables to assess the robustness of the results

from analyses with level data discussed thus far. The dependent variable in models presented in Table 4

is the natural logarithm of ridership growth from the same month in the previous year, and most key

independent variables are also used in a form of logarithmic change. For the transit ridership per capita

variable, 1-year (12 periods) lagged value is used. We lose observations for 12 months in 2002 by

shifting to a growth model.

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[Table 4 about here]

The results corroborate the findings that compact development and higher gasoline prices are

mutually supportive in promoting transit patronage. Interaction terms between gasoline price change and

urban form change variables have positive and significant coefficients, except for the fixed-effects

model. The increase in gasoline price is associated with transit ridership growth, and this association is

much stronger in urban areas where population density has also increased. The effect of gasoline price

increase is also found to be larger in more compactly developed urban areas. In accordance with the

analyses above, both the increase in transit ridership and the impact of gasoline price increase are larger

in urban areas where the level of per capital transit ridership was lower in the previous year. The results

of the ridership change model also show correct coefficient signs of transit fare and highway lane miles

variables: A decrease in fare and highway supply is associated with higher transit patronage.

Conclusions

A shift toward more sustainable transportation requires both adequate pricing of externalities

from driving and supportive land use policies. However, there is a wide gap between advocates of

pricing and land use planning approaches. Proponents of each approach underestimate the potential and

role of the other approach. While some authors increasingly call attention to the complementarity or

potentially synergetic effects of pricing and land use planning, little empirical research has been done to

support the idea. To fill this gap, this study investigated the interaction effects between gasoline prices

and land use (planning) variables using transit ridership data in 67 urbanized areas between 2002 and

2010.

The results suggest that pricing and land use planning are indeed complementary. Estimated

elasticities of key variables show that while doubling the average gasoline price would increase transit

20

patronage by 17.6% in an urbanized area with the mean density and no regional containment policy, a

one standard deviation higher density and a regional containment policy boost the gasoline price effect

by 4.8% and 16.6%, respectively. These interaction effects were 4.7% and 16.4% when an urban

compactness index replaced the density variable.

Although the present analysis is focusing on transit ridership and gasoline price change

controlled by market forces and not as a result of policy interventions, the findings are generalizable at

the policy level. Pricing policies such as fuel tax, carbon pricing, VMT tax, and congestion pricing are

all designed ultimately to raise the costs of driving, just as the increase in oil prices does, although the

incentive systems and geographical scope of such pricing instruments are different. Skeptics of smart

growth policies typically claim that pricing policies will have larger effects on drivers’ behavior than

will land use planning approaches. It may be true that, assuming political barriers are removed, pricing

can produce strong outcomes in the short run. However, the present analysis has elucidated that pricing

strategies will be significantly more effective in places where alternatives to automobility exist and

supportive land use policies are implemented. Therefore, pricing schemes should be implemented in

combination with land use policies and transportation investments attuned to smart growth principles to

achieve the policy objectives most effectively.

Planners should also readily embrace pricing policies. Concerns in planning communities remain

regarding potentially negative effects of aggressive pricing measures on the mobility of low-income

households. However, the equity issues associated with pricing schemes can be effectively addressed by

active recycling of revenues to improve public transportation and incentivize accessibility-enhancing

developments (Levinson, 2009). The Transit-Oriented Development Program in the Portland, OR,

metropolitan area is a good example of flexible use of transportation funds that improves housing

affordability as well as transit accessibility for low-income households. The present analysis also

21

suggests that transit-supportive urban form and land use policies will have much larger impacts under

conditions where gasoline prices are higher or driving is more expensive. The use of behavior

parameters that ignore potential interaction effects in modeling future land use and transportation is

likely to underestimate the impact of sustainable urban development, given that the real oil price is

anticipated to double over the next decade (Benes et al., 2012). Interaction effects and potential policy

synergies between various pricing and land use planning strategies warrant research priority in the future.

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26

Tables and Figures

Table 1. Description of study variables.

Variable Description Data Source2)

Dependent Variable Transit ridership Natural logarithm of monthly unlinked passenger travel (UPT) NTD Independent Variables Transit service supply Natural logarithm of vehicle revenue miles NTD Public subsidy Natural logarithm of federal, state, and local subsidies for service

operation per 1,000 people in 1983 dollars NTD

Population Natural logarithm of urbanized area population Census Gasoline price1) Natural logarithm of monthly gasoline price in 1983 dollars CPI, BLS Population density 1) Natural logarithm of population density weighted by census block

level population Census

Compactness index1) Sprawl index based on residential density, land use mix, strength of activity centers, and accessibility of the street network

Ewing et al. (2002)

Containment policy dummy Dummy variable indicating the presence of a non-local urban containment policy: 1 for UAs with the policy; 0 for no policy

Wassmer (2006)

Freeway lane miles Natural logarithm of freeway lane miles per 100 people FHWA College students share Natural logarithm of share of residents who are college and graduate

school students Census & ACS

Unemployment rate Natural logarithm of unemployment rate LAUS, BLS Trend Continuous monthly series: 1 for Jan 2002, 2 for Feb 2002, … , and

108 for Dec 2010

Post-peak dummy

Dummy variable indicating the periods after the gasoline price peak in June 2008: 1 for June 2008 and after; 0 for months before the peak

Month dummies 11 dummy variables for each month: Reference= May Notes: 1) All interacted variables including ln (gasoline price) are used after centering for the ease of interpretation. 2) NTD is the National Transit Database, maintained by the Federal Transit Administration (FTA); CPI, BLS is the

Consumer Price Index managed by the Bureau of Labor Statistics; FHWA is the Highway Statistics Series published by the Federal Highway Administration; ACS is the American Community Survey by the Census Bureau; LAUS, BLS is the Local Area Unemployment Statistics maintained by the Bureau of Labor Statistics.

27

Table 2. Two-stage least squares regression analysis of transit ridership determinants.

  Pooled Model  Fixed effects model  Random effects model

Estimate  t  Estimate t  Estimate t  Estimate  t  Estimate t 

1st Stage    Subsidy per 1000  0.6947  89.24 ***       

   UA Population  1.0252  216.18 ***       

2nd Stage             

   Predicted VRM        1.1697 231.84*** 1.2079 292.80*** 0.7422  15.75 ***  0.7122 14.49***

   Gasoline price        0.2450 7.30*** 0.2785 7.73*** 0.0964  7.02 ***  0.0817 5.94***

   Population density        0.3026 21.14*** 0.1473  4.78 *** 

   Compactness index        0.1848 20.42***       0.7197 2.51 **

   Containment policy D        0.0683 7.23*** 0.0666 6.60***       0.3604 1.70 *

   Transit fare per ride        ‐0.0166 ‐1.58 0.0185 1.61 ‐0.1602 ‐10.24 ***  ‐0.1881 ‐11.53***

   Fwy lane miles/100        ‐0.0123 ‐0.97 ‐0.1871 ‐12.98*** 0.1196  3.28 ***  0.0959 3.48***

   % college students        0.5792 21.33*** 0.5645 16.33*** 0.1908  6.53 ***  0.0909 3.13***

   Unemployment rate        ‐0.0118 ‐0.66 ‐0.0497 ‐2.51 ** ‐0.0622  ‐6.47 ***  ‐0.0733 ‐7.97***

   Trend        ‐0.0040 ‐10.52*** ‐0.0047 ‐11.52*** ‐0.0008  ‐3.79 ***  ‐0.0007 ‐3.10***

   Post peak D        0.0525 2.42 ** 0.0920 3.89*** 0.0366  3.72 ***  0.0467 4.71***

January  ‐0.0064  ‐0.41    ‐0.0033 ‐0.18 0.0044 0.22 ‐0.0159  ‐2.33  **  ‐0.0166 ‐2.42 **

February  ‐0.0689  ‐4.45 ***  0.0440 2.39 ** 0.0532 2.67*** 0.0045  0.61    0.0015 0.20

March  0.0299  1.95  *  0.0126 0.69 0.0175 0.88 0.0195  2.78 ***  0.0218 3.06***

April  ‐0.0001  ‐0.01    0.0111 0.62 0.0128 0.66 0.0080  1.21    0.0089 1.36

June  ‐0.0158  ‐1.03    ‐0.0268 ‐1.53 ‐0.0205 ‐1.06 ‐0.0307  ‐4.69 ***  ‐0.0285 ‐4.40***

July  ‐0.0177  ‐1.14    ‐0.0336 ‐1.88 * ‐0.0311 ‐1.60 ‐0.0413  ‐5.83 ***  ‐0.0425 ‐5.92***

August  0.0096  0.62    ‐0.0173 ‐0.97 ‐0.0216 ‐1.13 ‐0.0162  ‐2.39  **  ‐0.0189 ‐2.84***

September  ‐0.0262  ‐1.67  *  0.0737 4.07*** 0.0747 3.85*** 0.0548  7.75 ***  0.0554 8.08***

Octorber  0.0199  1.26    0.0739 4.07*** 0.0742 3.81*** 0.0654  9.13 ***  0.0678 9.89***

November  ‐0.0479  ‐3.03 ***  0.0647 3.52*** 0.0678 3.44*** 0.0181  2.45  **  0.0154 2.17 **

December  ‐0.0253  ‐1.58    0.0017 0.09 0.0070 0.35 ‐0.0444  ‐6.16 ***  ‐0.0462 ‐6.74***

66 UA fixed effects        …     

Constant  ‐7.4794 ‐108.09 ***  ‐5.6364 ‐44.27*** ‐3.5204 ‐23.57*** 3.8434  4.34 ***  1.4875 1.05

N  7197  7197 6369 7197  6369

R‐Squared  0.950  0.957 0.957 0.993  0.371 Notes: 1) The dependent variable of the first stage regression is ln(vehicle revenue miles) and the dependent of all second stage

regression models is ln(unlinked passenger travel). 2) Parameter estimates of 66 UA dummy variables in fixed effects models are suppressed for reasons of space. * significant at 10%, ** significant at 5%, *** significant at 1%.

28

Table 3. Analysis of transit ridership with interaction effects between gasoline price and land use.

Pooled models  Fixed effects model  Random effects model

Estimate  t  Estimate t  Estimate t  Estimate  t 

Predicted vehicle revenue miles  1.1762  245.49 *** 1.2144 331.03 *** 0.7038 14.91 ***  0.6859 13.72 ***

Gasoline price  0.1763  5.21 *** 0.2215 6.10 *** 0.0925 6.62 ***  0.0880 6.12 ***

Population density  0.2612  19.21 *** 0.1444 4.80 *** 

   × Gasoline price  0.1082  2.09 ** ‐0.0237 ‐0.94 

Compactness index  0.1691 20.16 *** 0.7352 2.64 ***

   × Gasoline price  0.2137 5.38 *** 0.0800 3.99 ***

Containment policy D  0.0631  6.59 *** 0.0601 5.91 *** 0.3554 1.62

   × Gasoline price  0.1656  4.56 *** 0.1644 4.31 *** 0.0541 3.22 ***  0.0380 2.81 ***

Per capita ridership × Gas price  ‐0.0912  ‐3.33 *** ‐0.0736 ‐3.53 *** ‐0.0317 ‐2.07 **  ‐0.0208 ‐2.17 **

Fare per person mile traveled  0.1819  16.40 *** 0.1999 17.35 *** ‐0.1511 ‐10.33 ***  ‐0.1735 ‐11.73 ***

Freeway lane miles/100 people  ‐0.0252  ‐2.07 ** ‐0.1564 ‐11.12 *** 0.1139 3.22 ***  0.0839 3.03 ***

Percent college students  0.5525  21.59 *** 0.5856 18.12 *** 0.2119 7.06 ***  0.1369 4.65 ***

Unemployment rate  ‐0.0188  ‐1.06 ‐0.0450 ‐2.29 ** ‐0.0645 ‐6.66 ***  ‐0.0630 ‐6.60 ***

Trend  ‐0.0036  ‐9.45 *** ‐0.0044 ‐10.85 *** ‐0.0009 ‐4.41 ***  ‐0.0009 ‐4.20 ***

Post peak D  0.0335  1.54 0.0688 2.95 *** 0.0355 3.59 ***  0.0408 4.01 ***

11 Month dummies  …  … …    …

66 UA fixed effects    …   

Constant  ‐3.2616  ‐29.52 *** ‐3.2808 ‐24.32 *** 5.5414 7.05 ***  4.8163 6.33 ***

N  7197  6369 7197 6369

R‐Square  0.959  0.960 0.993 0.371

Notes: 1) In the first stage regression, ln(vehicle revenue miles) was regressed on ln(population), ln(operation subsidy), and 11

month dummies. The results are not presented for reasons of space. 2) The dependent variable of all second stage regression analyses is ln(unlinked passenger travel). 3) Parameter estimates of 11 month dummies for all models and 66 UA dummies in the fixed effects model are suppressed

for reasons of space. * significant at 10%, ** significant at 5%, *** significant at 1%.

29

Table 4. Analysis of transit ridership change.

  Pooled model Fixed effects model  Random effects model

  Estimate  t Estimate t Estimate t   Estimate  t

Predicted ln(VRM change)  0.6131  9.21 *** 0.5338 8.63 *** 0.5806 7.02 ***  0.3615  3.27 ***

ln(gasoline price change)  0.0538  7.02 *** 0.0499 6.91 *** 0.0399 5.83 ***  0.0367  5.82 ***

ln(population density change)  0.2348  3.39 *** 0.6870 1.77 *   

   × ln(gasoline price change)  0.4750  1.74 * 0.2280 0.92    

Compactness Index    0.0002 3.22 ***    0.0048  3.64 ***

   × ln(gasoline price change)    0.0009 3.07 ***    0.0006  2.71 ***

D Containmnet policy  0.0157  4.19 *** 0.0130 3.75 ***    0.1169  1.27

Lagged ln(ridership per capita)  ‐0.0119  ‐6.62 *** ‐0.0099 ‐5.42 *** ‐0.3561 ‐36.93 ***  ‐0.3121  ‐11.22 ***

   × ln(gasoline price change)  ‐0.0274  ‐3.50 *** ‐0.0322 ‐4.09 *** ‐0.0113 ‐1.62   ‐0.0207  ‐3.24 ***

ln(fare/PMT change)  ‐0.0767  ‐7.26 *** ‐0.0940 ‐9.45 *** ‐0.0817 ‐8.38 ***  ‐0.0959  ‐8.77 ***

ln(freeway/100 people change) ‐0.0564  ‐2.44 ** ‐0.0513 ‐2.18 ** ‐0.1348 ‐6.08 ***  ‐0.0871  ‐3.09 ***

Change in % college students  0.0093  2.96 *** 0.0036 1.22 0.0067 2.35 **  0.0012  0.52

Change in % unemployment 66 UA fixed effects 

‐0.0127  ‐10.44 *** ‐0.0109 ‐9.51 *** ‐0.0059…

‐5.22 ***  ‐0.0057  ‐5.53 ***

Constant  0.0077  3.07 *** 0.0090 3.78 *** 0.7394 29.46 ***  0.1664  3.87 ***

No. Obs.  6393  5661 6393     5661 

R‐square  0.086  0.089 0.285     0.220 

Notes: 1) In the first stage regression, ln(vehicle revenue miles growth) was regressed on ln(population growth), ln(operation

subsidy growth). The results are not presented for reasons of space. 2) The dependent variable of all second stage regression analyses is ln(unlinked passenger travel growth). 3) Parameter estimates of 66 UA dummies in the fixed effects model are suppressed for reasons of space. * significant at 10%, ** significant at 5%, *** significant at 1%.

30

Data sources: U.S. city average unleaded regular gasoline prices from Consumer Price Index data, U.S. Bureau of Labor Statistics; U.S. total unlinked passenger trips from the National Transit Database, Federal Transit Administration. Figure 1. Trends in gasoline price and transit ridership in the U.S., 2002-2012.

$0.00

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National Transit Ridership Gasoline Price per Gallon (1983 Real $)

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Figure 2. Elasticities of transit ridership with regard to gasoline price in U.S. urbanized areas.

Elasticity

-0.4

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0 20 40 60 80 100 120 140 160 180

Akron

Albany

Albuquerque

Allentown

Atlanta

Austin

Baltimore Birmingham

Bridgeport Buffalo

Chicago

Cincinnati

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Columbus

Dallas

Detroit Grand Rapids

Las Vegas

Los Angeles Memphis

Miami

New Haven New York

Oklahoma City

Phoenix

Portland Providence

Raleigh

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San Diego

San Francisco

Springfield

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Baltimore Birmingham

Boston Bridgeport

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Hartford

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Portland

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San Diego San Francisco

San Jose

Sarasota Tucson

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Washington

32

Figure 3. Transit ridership changes due to a 100% increase in fuel price by population density and

regional containment policy.

8.8%

17.6%

22.4%

25.7%25.4%

34.2%

39.0%

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Population density

Without containment policy

With containment policy

33

Figure 4. Transit ridership changes due to a 100% increase in fuel price by compactness index and

regional containment policy.

1. While complementarity and synergy effects in policy instruments can be defined distinctively, we use these terms loosely to denote any positive interactions of combined policies that reinforce the effects of one another (May et al., 2006). The complementarity in this research refers to the first two cases in the table below.

Interaction effects Definition

Synergy Additivity Decreasing returns to packaging Incompatibility

welfare gain [A + B] > welfare gain [A] + welfare gain [B] welfare gain [A + B] = welfare gain [A] + welfare gain [B] welfare gain [A + B] < welfare gain [A] + welfare gain [B] welfare gain [A + B] < welfare gain [A] and/or < welfare gain [B]

Sources: Timms et al. (2005), modified by the authors.

2. Between 2000 and 2010 census definitions, land areas of 67 urbanized areas in our sample expanded by 19% on average. Area expansion was particularly significant in Southern UAs such as Charlotte, NC-SC (70.5%), Austin, TX (64.4%), and Raleigh, NC (62.1%). These significant boundary expansions artificially lower the density when assessed by the conventional density measure, although the majority of population still live in areas of similar density.

3. Providence, RI-MA, and Fresno, CA, urbanized areas have population weighted densities that are close to the

sample mean (10,964 persons per square mile). Densities in Boston, MA-NH-RI, and Philadelphia, PA-NJ-DE-MD, are about one standard deviation (6,109 persons per square mile) above the mean. A change from the density level in Atlanta,

7.3%

16.0%

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26.9%

30.8%

23.8%

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38.6%

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Compactness index

Without containment policy

With containment policy

34

GA, to that of Dallas-Fort Worth-Arlington, TX, or San Diego, CA, would also approximate the density increase by a one standard deviation.