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Making the Public Work: Geography, Externalities, and Preferences for Mass Transit

Published online by Cambridge University Press:  23 January 2023

Alisha C. Holland*
Affiliation:
Harvard University, Cambridge, MA, USA
*
*Corresponding author. Email: aholland@fas.harvard.edu
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Abstract

In much of the world, public transportation infrastructure is sorely needed. Political economy models suggest that provision lags because uneven access and use of public transit fragments political coalitions. Yet, traditional survey techniques tell us little about who supports valence issues, such as mass transit. I instead adopt a novel survey approach from economics designed to elicit preference intensity. I then sample households at different distances from a subway project in Bogotá, Colombia. Contra conventional expectations, I find little evidence that local geography shapes preferences. Those who use public transit the least and pay the most for its construction—the upper class—are its strongest supporters. An experiment and focus groups suggest that middle- and upper-class groups want others to take public transportation to reduce congestion and shorten their commutes. One implication is that a growing middle class might help to strengthen urban public goods provision.

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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press
Figure 0

Fig. 1. Quadratic voting platform.

Figure 1

Fig. 2. Comparing QV and Likert responses.

Figure 2

Fig. 3. Sampling around Bogotá's metro stations.

Figure 3

Table 1. The choice set for national and city issues

Figure 4

Fig. 4. Support for the metro by geography, property ownership, and class.Notes: The dots represent the estimated coefficients from an ordinary least squares (OLS) model with robust standard errors. The bars indicate 95 per cent confidence intervals. The dependent variable is the number of votes placed on the metro using QV (mean = 2.45; s.d. = 1.96). Each ordinal independent variable is rescaled from 0 to 1. The excluded categories are residents in the unaffected area (for geographic theories) and the lower class (for redistributive theories). The coefficients represent how a change from the lowest to the highest level of the covariate is associated with a change in the number of votes placed on the metro project.

Figure 5

Table 2. Comparing models of support for the metro

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Table 3. Effect of experimental treatments on support for the metro

Figure 7

Fig. 5. Conditional average treatment effects by class, proximity, and ownership.Notes: All treatment effects (dots) are calculated as the difference in mean votes placed on the metro between the treatment and control groups. Bars represent 95 per cent confidence intervals. Controls: proximity, class, ownership, age, and gender (excluding the conditioning variable).

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