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. Author manuscript; available in PMC: 2011 Aug 7.
Published in final edited form as: J Urban Aff. 2011 Aug 7;33(3):345–366. doi: 10.1111/j.1467-9906.2011.00551.x

Table 1.

Previous Studies’ Findings on Distributional Effects of Tolls in the United States

Study Geographic area Focal tolling regime Findings on distributional effects or effects on lowest income groups
Small (1983) San Francisco Bay Area Hypothetical toll of $1.25–$ 10.00

  • Lowest income group ($0-46,000 in 2005 dollars) has the largest absolute losses

  • Net benefits inversely related to income
Giuliano (1994) Los Angeles region Hypothetical toll of $0.15/mile

  • Low and middle income commuters would lose unless they could change their mode of travel to avoid a toll
Sullivan (2000, 2002) Orange County, CA Observation of SR 91 congestion tolling

  • Use of tolled facility is positively correlated with income

  • Work schedule flexibility appeared to be unrelated to use of I-15 tolled express lane
Supernak et al. (2002) San Diego area Observation of I-15 congestion tolling

  • Tolled express lane users are more likely to be from higher income households than non-users.
Safirova et al. (2003) Northern Virginia Hypothetical conversion of HOV lanes to tolled and HOT lanes (High Occupancy Transit)

  • All income groups would benefit from the conversion.

  • Wealthier drivers’ net benefits would be 27 times greater than those received by drivers from the poorest quartile, largely due to value of time
Burris and Hannay (2004) Houston area Observation of HOT lane users and non-users on Katy Freeway

  • Average usage of HOT lanes was not related to income among all users.

  • Insufficient sample size to compare low-income users to others
Safirova et al. (2005) Washington DC Hypothetical cordon or link-based tolls

  • Both tolls can provide a net benefit to all users as a whole

  • Without revenue recycling, both tolls create losses for the lower 3 income quartiles; losses are disproportionately high for lowest income quartile
Franklin (2007) Seattle area Hypothetical bridge toll

  • Toll is regressive

  • Toll more regressive when time taken into account
Puget Sound Regional Council (2008) Seattle area Experiment with variable charge for road use

  • Responsiveness to price is inversely related to income

  • Higher income household pay more in tolls, while lower-income households reduce trips, switch mode, or spend longer in travel