Transportation Spending by Low-Income California Households ...

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Consumer Expenditure Survey 1999–2001 The CES, maintained by the Bureau of Labor Statistics (BLS), collects information on household spending behavior. Most of the analysis in this report uses the household microdata for the years 1999–2001. We adjusted 1999 and 2001 expenditures to 2000 dollars, using the Consumer Price Index (CPI) for All Urban Consumers produced by the Bureau of Labor Statistics. The unit of analysis is the “consumer unit,” or CU, which represents an independent financial decisionmaking unit within the household. In over 95 percent of cases, there is only one CU per household, and therefore we stick with the more user-friendly term “household” throughout this report rather than referring to consumer units. CES data include civilian, noninstitutionalized persons. The CES microdata allow us to examine expenditure patterns for approximately 11,000 consumer units living in metropolitan areas of California. With the public use microdata, we are able to ascertain whether the household resides in California only if it is within an MSA. There are nonmetropolitan California households in the CES national sample, but in the public-use microdata, BLS has set the state identifier on these nonmetropolitan observations to missing. As a result, all households in CES that are associated with the California state identifier reside in an MSA and are defined as “urban.” We also cannot identify where within metropolitan California each household lives (e.g., whether it is in the Bay Area, or Los Angeles, or Bakersfield), because BLS removes the MSA identifiers from the public use microdata. To compare San Francisco to other metropolitan areas, we also made use of the CES MSA tabulated data for 1999–2000. However, the MSA-level data do not allow us to look at expenditures by income level. We exclude some items from our measure of transportation expenditures that CES includes in its summary measures of transportation expenditures, usually because these expenditures do not represent regular daily travel patterns. We excluded all costs associated with out-of-town travel because we wanted to concentrate on issues of access to jobs and services rather than occasional trips and vacations. 142
We also excluded expenditures on bicycle purchases because we had no way of differentiating between bicycles bought for transportation and bicycles bought for recreation. (CES lists bicycle expenditures under “mini-appliances” rather than “transportation.”) Not very many households purchased bicycles, and because the dollar amounts spent on bicycles were small enough that they would barely affect our estimates, we chose to exclude bicycle purchases from the analysis. We did include the purchase prices of motorcycles, scooters, and mopeds in the purchase price and capital cost calculations for vehicles. However, we excluded expenditures on automobile leases and rentals because very few (about 1%) of the low-income households had any expenditures reported for leased or rental vehicles. All items that CES includes as transportation expenditures but which we exclude from our numbers were put into “other expenditures” instead. To calculate the capital cost of the vehicles in a household, we took the purchase prices of all vehicles purchased by the household within the last five years, divided those purchase prices by five, and summed the capital costs for the individual vehicles to the household level. This calculation uses five-year straight-line depreciation, which is one method of depreciation allowable by the IRS for tax-reporting purposes. Because we had actual purchase price information only on vehicles that were either purchased during the interview period or still had money owed on them, we imputed values for missing purchase prices based on the following consumer and vehicle characteristics: • Vehicle new or used when purchased; • Race, age, and gender of the householder; • Size of the household; • Number of earners; • Before-tax income; and • Dummy indicator for the state of California. Using straight-line depreciation, we then took the imputed price, divided by five, and allocated that amount to the five years following the purchase of the vehicle. If the vehicle was purchased more than five years ago, we assigned a value of zero for the capital cost. This process 143
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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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85 Table 5.11 Differences in Mode C
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Table 5.13 Mode Choice, Work Schedu
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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 158 and 159: income, problems common to most hou
Page 160 and 161: or the Bay Area specifically. We us
Page 163: Appendix B Information on Specific
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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Transportation Spending by Low-Income California Households ...

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