Transportation Spending by Low-Income California Households ...

advantage to this approach is that it allows us to create similar income
categories across the four datasets, although the income categories are not
exactly the same across the datasets. Some datasets reported only a range
for income, rather than an exact income amount, for example.
The most important difference between datasets in terms of incomegroup
categorization is that we created categories for the Consumer
Expenditure Survey based on expenditure data, whereas for the other
datasets, we used income data because consumption measures are not
available in those datasets. The CES analysis uses expenditures instead of
income for three main reasons:
1. Consumption is a better measure than annual income of
households’ long-term economic resources. 4
2. Income data are missing for almost a fifth of the California CES
sample; classifying based on expenditures allows us to keep these
households in the analysis. 5
3. For other households, income is not missing but it appears to be
underreported, biasing the expenditure results upward for lowincome
households and downward for other households.
These reasons are discussed further in the CES section of Appendix A.
(In addition, a sensitivity analysis showing how results change when
based on income data is presented in Appendix Table C.1 and discussed
in Appendix C.) The methods used for classifying households into
income categories for each of the four datasets are discussed in more
detail in Appendix A.
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4 From U.S. Census Bureau (2003): “A basic premise of this view is that families
and individuals derive material well-being from the actual consumption of goods and
services rather than from the receipt of income per se; hence, it is appropriate to estimate
their consumption directly. One argument that is often made for preferring
consumption as the resource definition rather than income is that consumption is a better
estimate of families’ long-term or ‘permanent’ income” (p. 3).
5 Because the households that have missing income data have lower total
expenditure levels than the average, dropping households with missing income data from
the analysis would probably eliminate proportionally more low-income households than
higher-income households.
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