V. Empirical Strategy and Variables

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

Label	Definition
Commute	One-way commute length in miles (log)
lr_hcost	Monthly housing costs (constant $, log)
ly_per	Household income per person (log)
ly_per2	Household income squared (log)
married	Married = 1
educ	Years of education
latin	Hispanic = 1
black	Black = 1
asian	Asian, PI = 1
AGE	Age
age2	Age squared
male	Male = 1
pchild	Proportion of household members that are children
apart	Apartment dweller = 1
CARS	Number of cars
MOVED	Year last moved
ZADULT	Number of adults in household
renter	Renter = 1
pop	Population of metropolitan area
employ	Employment in metropolitan area
landarea	Land area of metropolitan area
cmsaflag	CMSA = 1
cbd	CBD = 1
ne	Northeast = 1
mw	Midwest = 1
west	West = 1
YEAR	Year
sub_emp	Total suburban employment share in metro
mfsub_emp	Manufacturing employment share in suburbs
srsubub_emp	Service employment share
cons_emp	Construction employment share
whole_emp	Wholesale employment share
retail_emp	Retail employment share
fire_emp	Finance, Real Estate, Insurance employment share
gov_emp	Government employment share