Transportation Spending by Low-Income California Households ...

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workers (with or without children) than it is to low-income workers without children. Families with children may choose to use automobiles because of the increased complexity of travel needs when children are present. Dropping off children at a school or day care center on the way to work can be time-consuming if one is commuting via public transit, particularly if transit service does not run frequently. Using two datasets, we arrive at different conclusions about whether commutes take longer for low-income households with children. (With both datasets, commute times appear to be the same for higher-income workers regardless of whether they have children.) The BATS data indicate that commutes for low-income workers with children take about five minutes longer than the commutes of those who do not have children. However, the PUMS data do not show any difference in commute times between low-income households with or without children. If commute times for households with children are in fact the same as for households without children, it would be likely that higher auto use rates for those with children are helping to mitigate the time cost burden of complex childrelated transportation needs. To isolate the effect of children from the effect of age, the bottom section of Table 5.13 looks at the percentage of households with access to a vehicle, by age categories and by the presence of children. Vehicle access is essentially the same for the low-income group, regardless of the age of the head of household. Put another way, the difference between households with and without children in the percentage with access to a vehicle still holds even after controlling for the age of the head of the household. This pattern strengthens the argument that auto use is higher for low-income households with children because of the transportation complications imposed when children are present. In a separate analysis using CTPP data, we investigated whether mode choice differed by the age of the children in the household. The results showed virtually no variation in mode choice by the age of the youngest child. Commuting behavior may differ by gender, particularly because women often bear the burden of transporting children to child care or to school. Blumenberg (2002) states that low-income women differ from low-income men in that they commute shorter distances, make more 88
trips, make more household-supporting trips, and chain trips together more often. However, our examination of PUMS data for the San Francisco Bay Area revealed median commute times of 20 minutes for both low-income women and low-income men, regardless of the presence of children. If women commute shorter distances, yet their commutes take as long as men’s commutes, this may be evidence that they are making extra stops along the way to transport children to school or child care. Using PUMS data, we find that employed women with children are more likely than employed women without children to commute by car, regardless of income level. However, the link between car use and the presence of children is even stronger for men. One main difference between the genders is the way car use plays out: Having children in the home is associated with higher rates of driving alone for men, but for women, the presence of children is associated with an increase in carpooling. These findings suggest that caretakers of both genders tend to accommodate their children’s transportation needs by taking advantage of the speed and flexibility of an automobile—in spite of the greater monetary costs associated with private vehicles. The results from Chapters 3 and 4 indicated that monetary transportation costs differ markedly by mode of transportation. In this chapter, we found that higher-income workers are less likely to walk or bike, and that higher-income workers who use public transit have relatively long commute times. These factors help explain why higherincome households have somewhat longer commute times than lowincome households. Although commute times are longer for higherincome commuters, the distance information from the MTC report indicates that higher-income commuters travel longer distances, which may compensate for the greater time costs by increasing access to key destinations. Policymakers must consider monetary costs in conjunction with time costs and access issues as they develop plans for addressing the transportation needs of low-income communities. 89
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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
Page 59 and 60: Table 3.10 have lower expenditure f
Page 61 and 62: 4. Costs of Illustrative Bay Area C
Page 63 and 64: 0-300 301-600 601-1100 1101-6813 Ba
Page 65 and 66: Table 4.2 Place of Work Comparison
Page 67 and 68: Concord CONTRA COSTA SAN FRANCISCO
Page 69 and 70: Administration (FHWA) mileage rate
Page 71 and 72: 49 Table 4.3 Costs of Illustrative
Page 73 and 74: participants who took transit or ca
Page 75 and 76: Table 4.4 Costs of Illustrative Wit
Page 81 and 82: commutes is $4,240, whereas the hig
Page 83 and 84: The results from this chapter seem
Page 85 and 86: 5. Commute Behavior in the Bay Area
Page 87 and 88: We also looked at mode choice using
Page 89 and 90: longer for higher-income workers if
Page 91 and 92: Table 5.3 (continued) Low- Income H
Page 93 and 94: Table 5.5 depicts the share of the
Page 95 and 96: 73 Table 5.6 Mode Choice and Vehicl
Page 97 and 98: 75 Table 5.8 Mode Choice and Vehicl
Page 99 and 100: households report having access to
Page 101 and 102: Table 5.9 (continued) % Using Each
Page 103 and 104: • Marin to San Francisco: As with
Page 105 and 106: Table 5.10 Median Commute Durations
Page 107 and 108: 85 Table 5.11 Differences in Mode C
Page 109: Table 5.13 Mode Choice, Work Schedu
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 158 and 159: income, problems common to most hou
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or the Bay Area specifically. We us
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Appendix B Information on Specific
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We also excluded expenditures on bi
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Atlanta: Atlanta, GA MSA Miami: Mia
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147 Table B.1 (continued) CES Metro
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Appendix C Sensitivity Analysis for
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151 Table C.1 (continued) Poor Low-
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transportation expenditures constit
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Appendix D Methods Used for the Exa
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Bibliography American Automobile As
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Implications of Welfare Reform for
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Lucas, M., and C. Nicholson, “Sub
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Progressive Casualty Insurance Comp
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About the Author LORIEN RICE Lorien
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Transportation Spending by Low-Income California Households ...

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