The value of automobile travel time: implications for congestion policy

Calfee, John E.; Winston, Clifford

doi:10.1016/s0047-2727(97)00095-9

Cited by 176 publications

(87 citation statements)

References 10 publications

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“…They use stated preference data because they lacked revealed preference data for the choices involved with congestion pricing. Based on this approach, Calfee and Winston (1998) conclude that commuters have a lower valuation of time saving than previously thought.…”

Section: Introductionmentioning

confidence: 85%

“…It has been pointed out in the literature that value of time estimates from comparing auto travel to transit may be biased by the extent to which real differences between crowded public buses, light-rail, or private automobiles are not captured by the model. Calfee and Winston (1998) attempt to estimate willingness to pay to reduce travel time that is specific to automobile travel, thus excluding inter-modal biases. They use stated preference data because they lacked revealed preference data for the choices involved with congestion pricing.…”

Section: Introductionmentioning

confidence: 99%

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Drivers’ willingness-to-pay to reduce travel time: evidence from the San Diego I-15 congestion pricing project

Brownstone

Ghosh

Golob

et al. 2003

Transportation Research Part A: Policy and Practice

134

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The adoption of congestion pricing depends fundamentally upon drivers' willingness to pay to reduce travel time during the congested morning peak period. Using revealed preference data from a congestion pricing demonstration project in San Diego, we estimate that willingness to pay to reduce congested travel time is higher than previous stated preference results. Our estimate of median willingness to pay to reduce commute time is roughly $ 30 per hour, although this may be biased upward by drivers' perception that the toll facility provides safer driving conditions. Drivers also use the posted toll as an indicator of abnormal congestion and increase their usage of the toll facility when tolls are higher than normal.

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Section: Introductionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Drivers’ willingness-to-pay to reduce travel time: evidence from the San Diego I-15 congestion pricing project

Brownstone

Ghosh

Golob

et al. 2003

Transportation Research Part A: Policy and Practice

134

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“…Urban social problems derive more from the concentration of poor people in cities rather than anything intrinsic to cities themselves. 9 As such, urban social problems create a multiplier effect where an initial attraction of the poor to cities will then be greatly magnified to create significant poor/non-poor segregation. 10 Perhaps the poor just ended up in the city center by chance and the rest followed.…”

Section: Models Of Urban Povertymentioning

confidence: 99%

“…elasticities (see Small [34] and Calfee and Winston [9]). Our view is that the empirical literature is inconclusive, but that it is probably reasonable to assume that W Y ε equals .75…”

Section: If These Conditions Hold Andmentioning

confidence: 99%

Why do the poor live in cities? The role of public transportation

Glaeser

Kahn

Rappaport

2008

Journal of Urban Economics

655

361

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More than 19 percent of people in American central cities are poor. In suburbs, just 7.5 percent of people live in poverty. The income elasticity of demand for land is too low for urban poverty to come from wealthy individuals' wanting to live where land is cheap (the traditional explanation of urban poverty). A significant income elasticity for land exists only because the rich eschew apartment living, and that elasticity is still too low to explain the poor's urbanization. The urbanization of poverty comes mainly from better access to public transportation in central cities.

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“…Most empirical studies seem to conclude that the private value of time is on average somewhat lower than the wage; see e.g. Calfee and Winston (1998). In reality there are of course also many distortions such as income taxes that may cause deviations.…”

Section: The Modelmentioning

confidence: 99%