Transportation Spending by Low-Income California Households ...

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income, problems common to most household surveys, limit the usefulness of income as a classifying variable. . . . Also, some consumer units report income losses from a business they own, which may result in low or negative incomes, even if they report income from other sources. Because these consumer units may have expenditures levels that are more typical of higher-income consumers, the losses they report affect both the average income and the average expenditures in the income quintile and income level table. Specifically, their low or negative incomes may depress the average income level, while their higher expenditures raise the average expenditure level. These consumers are not what are considered to be typical low-income consumers. . . . Results from the CE Survey have typically shown that when the data are classified by income quintile, the expenditures-to-income ratio is quite high for the lowest income quintile. . . . That expenditures exceed income in these quintiles is not unreasonable, given consumers’ access to savings, borrowing, and credit, mentioned earlier. However, the degree by which expenditures exceed income—a factor greater than 2 for the lowest quintile—seems extreme. . . . Total outays were well above income for the two lowest income quintiles. This can be attributed to problems discussed earlier: underreporting income and consumers reporting income losses who have expenditures typical of higher-income consumers. 2 Because we wanted a categorization system that adjusted for household structure and that would be comparable to our other datasets, we created expenditure categories based on the federal poverty thresholds. We divided the household’s total annual expenditures by the official poverty threshold for that particular household based on family size, number of children, age of children, and family income. This method provides the variable that is used to rank the households— however, we then needed a way to identify cutoff points so that we would have groups that represented the same proportion of the total population as the income-based poverty thresholds do. With PUMS data, we determined that 11 percent of California households meet the official definition of poor, and 27 percent can be classified as lowincome. We then looked at the distribution of the ratio of total _____________ 2 Rogers and Gray (1994), pp. 32–33 and 35. 136
expenditures to the poverty threshold, and we calculated the 11th and 27th percentiles of that distribution, which were 1.15168 and 1.75536, respectively. We classified households with an expenditure-to-povertythreshold ratio less than 1.15168 as poor, those with a ratio below 1.75536 as low-income, and those with a ratio at or above 1.75536 as higher-income. Appendix C contains a sensitivity analysis that demonstrates how the results change under several different treatments of the data. Sample Sizes for the Datasets The following table summarizes the information on sample sizes for the datasets: Low- Income Higher- Income Poor Total PUMS Bay Area Households 8,850 22,267 98,845 121,112 Persons 26,314 64,474 259,665 324,139 BATSa Households 275 742 12,449 13,191 Persons 533 1,816 28,626 30,442 CES metropolitan California Consumer Units 1,215 2,984 8,069 11,053 CES metropolitan California vehicle users 561 1,867 7,170 9,037 CES metropolitan California vehicle purchasers 40 127 605 732 CES metropolitan California transit users 237 466 628 1,331 aBATS income data are missing for 1,873 households and 3,917 persons. Of those households with incomplete income data, 919 households reported that their income was $40,000 or higher, 153 reported that their income was below $40,000, and 801 were missing all income data. We did not include these incomplete income reporters in our analyses. Weights Sample weights are used with each of the datasets to make the data representative of the desired entity. In CES and PUMS data, the weights are designed to represent the entire United States, rather than California 137
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Transportation Spending by Low-Inco
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Foreword Mismatches between jobs an
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Summary Transportation is an import
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Table S.1 Median Annual Transportat
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about 5 to 10 percent of the median
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providing discounted transit fares
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5. COMMUTE BEHAVIOR IN THE BAY AREA
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Figures S.1. Vehicle Ownership and
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4.3. Costs of Illustrative Within-C
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1. Introduction Costs associated wi
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of “welfare-to-work” transporta
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lays out policy options for address
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2. Data and Methods In this report,
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advantage to this approach is that
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3. Transportation Expenditures in M
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Table 3.2 Vehicle Use, Vehicle Owne
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Components of Vehicle-Related Expen
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inspection, and auto club membershi
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income households and the higher-in
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eport looks primarily at cost and n
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The results from Table 3.5 indicate
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variation in transportation costs w
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overall household expenditures for
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31 Table 3.9 Transportation Expendi
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Table 3.10 Comparison of California
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Table 3.11 Comparison of the San Fr
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Table 3.10 have lower expenditure f
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4. Costs of Illustrative Bay Area C
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0-300 301-600 601-1100 1101-6813 Ba
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Table 4.2 Place of Work Comparison
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Concord CONTRA COSTA SAN FRANCISCO
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Administration (FHWA) mileage rate
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49 Table 4.3 Costs of Illustrative
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participants who took transit or ca
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Table 4.4 Costs of Illustrative Wit
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commutes is $4,240, whereas the hig
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The results from this chapter seem
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5. Commute Behavior in the Bay Area
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We also looked at mode choice using
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longer for higher-income workers if
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Table 5.3 (continued) Low- Income H
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Table 5.5 depicts the share of the
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73 Table 5.6 Mode Choice and Vehicl
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75 Table 5.8 Mode Choice and Vehicl
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households report having access to
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Table 5.9 (continued) % Using Each
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• Marin to San Francisco: As with
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Table 5.10 Median Commute Durations
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Page 109 and 110: Table 5.13 Mode Choice, Work Schedu
Page 111: trips, make more household-supporti
Page 114 and 115: only expenditures. Paying a given n
Page 116 and 117: improving service quality rather th
Page 119: Part II Policy Strategies and Areas
Page 122 and 123: potential social costs such as poll
Page 124 and 125: • Increasing income directly, to
Page 126 and 127: grants for developments that meet c
Page 128 and 129: make it easier for various entities
Page 130 and 131: pass.” 10 Perhaps discounts could
Page 132 and 133: free transit is most likely not app
Page 134 and 135: have fares based on the time of day
Page 136 and 137: hours for late night and graveyard
Page 138 and 139: credit ratings, assisting with insu
Page 140 and 141: would have paid with traditional au
Page 142 and 143: citizens. Rather, the question shou
Page 144 and 145: Facilitating Bicycling or Walking A
Page 146 and 147: transportation hubs may be another
Page 148 and 149: allowing flexibility at the local l
Page 150 and 151: services, health care, drug treatme
Page 152 and 153: costs. Efforts to incorporate infor
Page 154 and 155: are differences) and by looking at
Page 156 and 157: Dataset Poor Low-Income Higher-Inco
Page 160 and 161: or the Bay Area specifically. We us
Page 163 and 164: Appendix B Information on Specific
Page 165 and 166: We also excluded expenditures on bi
Page 167 and 168: Atlanta: Atlanta, GA MSA Miami: Mia
Page 169 and 170: 147 Table B.1 (continued) CES Metro
Page 171 and 172: Appendix C Sensitivity Analysis for
Page 173 and 174: 151 Table C.1 (continued) Poor Low-
Page 175 and 176: transportation expenditures constit
Page 177 and 178: Appendix D Methods Used for the Exa
Page 179 and 180: Bibliography American Automobile As
Page 181 and 182: Implications of Welfare Reform for
Page 183 and 184: Lucas, M., and C. Nicholson, “Sub
Page 185 and 186: Progressive Casualty Insurance Comp
Page 187: About the Author LORIEN RICE Lorien
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