To examine the issue of how sprawl influences the commute, we estimate commute length as a function of employment dispersal, individual occupation, and life-cycle factors. Other independent variables include demographic and economic characteristics of the individuals and households, such as income, the presence of dual earners, sex, race/ethnicity (African Americans, Asian Americans, and Latinos are represented with indicator variables), and educational attainment.8 In addition to the respondent's age (and a squared age term to reflect nonlinearity in the relationship between age and travel), life cycle characteristics are included (whether the respondent is married and the number of children in the household).
Household characteristics that are expected to affect chosen commute length include housing tenure (renter or owner) and the number of automobiles owned by the household. Housing costs and income are included as explanatory variables. These are instrumented in later regressions.
Finally, in addition to variables representing both total employment decentralization and decentralization of employment by industry (percentages represented as whole numbers), we include several other measures of urban form: population decentralization (using the same county-based Census definition as for employment decentralization), total population and total employment, and land area of the counties in the metropolitan area. A single intra-metropolitan variable is included: whether the housing unit of the respondent is located in the central business district. Indicator variables for the Census regions are included to reflect differences in weather and transportation infrastructure that might vary by U.S. region. A variable representing an independent time trend (YEAR) is included. Variables and labels are listed in Table 4.
Several estimation strategies are used to investigate the hypothesized relationships between commute distance and employment decentralization. The initial analysis is carried out using a single ordinary least squares regression, and all observations are treated as independent despite the repeated nature of the panel. Because income and housing cost are used as explanatory variables in this model, subsequent models correct for the potential endogeneity of these variables by using a two-stage instrumental variables technique. In later models we use panel regression techniques that separately account for cross-sectional variation (between the workers in different housing units) and variation over time (for each housing unit, for all workers living in the unit over the twelve-year period).
TABLE 4. Variable Labels