The results of the first simple regression are shown in Table 5. The dispersal of employment metro-wide is represented as the percentage share of employment in outlying counties within the metropolitan area, a figure ranging between 1.2 and 38.4 percent in the pooled panel sample of individuals.
Commute length is shorter the more suburbanized all employment. The coefficient on SUB_EMP is -0.299, implying that a five percent increase in the amount of employment in a metropolitan area’s outlying counties will lead to a 1.5 percent reduction in the average commute distance, equivalent to a reduction of less than a tenth of a mile.
A separate time trend for increased commute length, as represented by the coefficient on YEAR, implies that despite the dampening effect of employment dispersal over this 12-year period, unexplained causes are lengthening commutes by about one percent per year.
TABLE 5. OLS Regression of Logged Commute Distance using Pooled Panel
We can decompose this result by breaking out employment by industry (see Table 6). The effect of suburban employment share varies by industry. Construction and wholesale employment dispersal are associated with shorter commutes, while manufacturing and government employment dispersal with longer commutes. Retail and service employment does not appear to be strongly associated with commute length.
These differences may be due to the pattern of clustering characteristic of these industries. Specifically, construction and wholesale employment may not be as clustered within a given county-level pattern of dispersion, while manufacturing and government employment may be. There is evidence that certain kinds of manufacturing firms (particularly, small manufacturers in the more technologically advanced industries) tend to cluster to realize Marshallian agglomeration economies. Meanwhile, retail and service firms do cluster to some extent, but because they are population-serving they tend to also be pulled out to follow the more dispersed pattern of the residential development that they serve.
This clustering explanation for the regression results shown in this initial research makes sense only to the extent that industry sector clustering patterns drive overall commute patterns, because we have not controlled for occupational characteristics of residents in this analysis. Exploring this relationship is a subject for future research.
TABLE 6. OLS on Log Commute Distance using Pooled Panel, With Industry By Sector
We also estimated models controlling for the endogeneity of housing costs, using an instrumental variables procedure in which predicted housing costs (LR_HCOST) are used in place of actual housing costs. Predicted housing costs are obtained by using coefficients on the independent variables from a simple hedonic price model. Table 7 shows the results of this model. While the model is clearly an improvement on the previous one, the coefficient on SUB_EMP is more or less the same as in the simple OLS model.
TABLE 7. Two-Stage Least Squares on Log Commute Distance using Pooled Panel
Finally, controlling for the clustering of individuals within housing units does not change the results importantly in Table 8.
TABLE 8. Two-Stage Least Squares on Log Commute Distance Controlling for Panel Effects