Mileage-Based User Fee Winners and Losers - RAND Corporation

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particular, this study improves the methodology of the only prior national-level study of the distributional implications of MBUFs by strongly improving the estimated price elasticities of demand for VMT as described in Chapter 3 (Weatherford 2011). This study is also the first to directly compare the differences in the estimated collection and administration costs between fuel taxes and MBUFs or any other alternative to fuel taxes, a contribution made possible by recent research to estimate those costs by Balducci et al. (2011). Lastly, while most sophisticated distributional analyses of fuel taxes and their alternatives consider the distribution of changes in household welfare, these typically include only the change in consumer surplus and net revenue. This study is one of the first, if not the first, to add the net change in the externalities of driving and fuel consumption to the net change in revenue (Parry, Walls, and Harrington 2007). Page 24 of 131
3. DATA AND MODEL This chapter describes the 2001 and the 2009 National Household Travel Surveys (NHTS) which are the primary sources of data used in this study. After describing the data, the functional form of the linear regression model used to estimate changes in household demand for VMT in response to change in the cost of driving due to changes in tax policy is developed and explained. This model is fit using the NHTS data and the predictor variables are then described and discussed. DATA The NHTS is a nationally representative survey of US households. It provides detailed information on daily travel for each household member, household composition, income, geographic location, and vehicle ownership (US FHWA 2004a, 2011). 14 The data include variables for vehicle fuel economy and fuel prices estimated by the EIA and variables for vehicle annual VMT estimated by Oak Ridge National Laboratory (ORNL). The most recent survey, the 2009 NHTS, was conducted between 2008 and 2009. The version of the 2009 NHTS used in this analysis, Version 2.1, was released in February 2011 (US FHWA 2011). The 2009 NHTS data are pooled with data from a prior survey, the 2001 NHTS, that was conducted between 2001 and 2002 (US FHWA 2004b). Some data cleaning is necessary to ensure consistency between the variables and those procedures are detailed in the Technical Appendix. The naming convention used by FHWA to refer to the two surveys is confusing. The earlier survey is named the “2001 NHTS” for the year in which it commenced while the most recent survey is named the “2009 NHTS” for the year in which it concluded. The proper survey name is referred to when directly describing the data. However, inferences based on the survey data are more accurately referred to as “2001-02” and “2008-09.” The 2001 NHTS includes 26,038 households and the 2009 NHTS includes 150,147 households. 15 These surveys do not have, and are not intended to have, sufficient power to draw statistically significant inferences for some states (US FHWA 2004b, 2011). Twenty state and local governments purchased “add-on” samples for planning and modeling purposes in the 2009 NHTS. States that are oversampled are Arizona, California, Florida, Georgia, Indiana, Iowa, New York, North Carolina, South Carolina, South Dakota, Tennessee, Texas, Vermont, Virginia, and Wisconsin. 16 Summary statistics for the 2001 and 2009 NHTS are provided in Table 3.1. There are two columns for each survey; the first contains sample means and standard deviations calculated from the sample excluding households that do not own a vehicle. The second column contains statistics calculated from the entire sample. Five percent of the observations in the 2009 NHTS, corresponding to 8.6 percent of households in the population after adjusting for sampling errors, do not own a vehicle. Households that do not own vehicles have zero VMT and therefore lower the average VMT of the sample. These households also have missing information on relevant vehicle characteristics such as the cost of driving and fuel economy. 14 The publically available NHTS was used instead of the “confidential” NHTS. 15 The total sample size of the 2001 NHTS is 69,817 including nine state and local government “add-ons”. It is not possible to consider the “full” sample, however, because estimates for vehicle fuel economy and VMT were not added to the add-on observations. The “national” sample includes correct sampling weights and is most consistent with the 2009 NHTS, despite the large differences in sample size. 16 New York, Texas, and Wisconsin also purchased add-on samples in the 2001 NHTS. Page 25 of 131
Page 1 and 2: CHILDREN AND FAMILIES EDUCATION AND
Page 3 and 4: Mileage-Based User Fee Winners and
Page 5: ABSTRACT The mileage-based user fee
Page 8 and 9: Lastly, I would especially like to
Page 10 and 11: 6. Future Distributional Implicatio
Page 13 and 14: 1. MILEAGE-BASED USER FEES MOTOR FU
Page 15 and 16: Additionally, fully electric vehicl
Page 17 and 18: MOVING TOWARDS A TAX ON VEHICLE MIL
Page 19 and 20: THE EQUITY IMPLICATIONS OF MBUFS AR
Page 21 and 22: 2. THE EQUITY OF HIGHWAY USER FEES
Page 23: “regressive, progressive, or neut
Page 27 and 28: Table 3.1 Weighted Sample Means of
Page 29 and 30: values and their robust standard er
Page 31 and 32: Table 3.2 Table of Coefficients Lin
Page 33 and 34: Figure 3.1 Distribution of Price El
Page 35 and 36: This expression illustrates that th
Page 37 and 38: method used by FHWA to calculate th
Page 39 and 40: least fuel efficient vehicle is gre
Page 41: State fixed effects There are diffe
Page 44 and 45: gasoline tax with a flat per-mile t
Page 46 and 47: Alternative 3: Replace both the sta
Page 48 and 49: Alternative 8: Replace state and fe
Page 50 and 51: Disaggregating the change in taxes
Page 52 and 53: “relative share rate of return fr
Page 54 and 55: gasoline in the United States could
Page 56 and 57: The first method divides total fede
Page 58 and 59: Risk of global warming: The margina
Page 60 and 61: additional projections based on alt
Page 62 and 63: industry and public sector energy c
Page 64 and 65: FLAT-RATE MBUFS ARE NO MORE OR LESS
Page 66 and 67: Table 5.1 Percent Change in Average
Page 68 and 69: Table 5.3 Change in the Consumer Su
Page 70 and 71: weighting of the changes in revenue
Page 72 and 73: Table 5.6 Average Changes in Federa
Page 74 and 75:
Table 5.7 Components of Welfare Cha
Page 76 and 77:
The finding that increasing the rat
Page 78 and 79:
Table 5.10 Change in Consumer Surpl
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of adopting MBUFs. Specifically, th
Page 82 and 83:
Table 5.14 Percent Change in VMT an
Page 84 and 85:
side by side for each alternative.
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The findings for other groups are m
Page 88 and 89:
Table 5.17 Average Change in Consum
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6. FUTURE DISTRIBUTIONAL IMPLICATIO
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e enacted. Figure 6.4 completes the
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Table 6.1 Projections of Suits Indi
Page 97 and 98:
change in consumer surplus for key
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7. CONCLUSION The purpose of this d
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The marginal costs of congestion an
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Commercial trucks were explicitly n
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The variable name for Hispanic stat
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price series from the EIA. The “M
Page 110 and 111:
Table A.1 State Gasoline Excise Tax
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Table A.3 Household Type Categories
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CALCULATING THE CHANGE IN CONSUMER
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Revenue Revenue Revenue Method
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PROJECTIONS OF CHANGES IN CONSUMER
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Table B.3 Projections of Changes in
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Page 124 and 125:
Page 127 and 128:
LIST OF REFERENCES AAA. 2011. Your
Page 129 and 130:
Musgrave, Richard A., and Peggy B.
Page 131:
Wachs, Martin. 2003. Improving effi
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