Note: Here, we briefly summarize results of the first-stage regressions focusing on the effects of our instrumental variables. The results show that the coefficient of distance to major roadways is positively associated with changes in overall job accessibility, but the coefficient of distance to subcenter is negatively associated with the variable. The latter makes sense because as mentioned above, jobs are generally clustered into subcenters for agglomeration economies. However, the former is more complicated to interpret because transportation facilities affect spatial distribution of jobs and households in a various way. One plausible explanation is that suburban areas have different development characteristics as compared to central areas. Particularly, major roadways are slightly more widely distributed in suburban areas due to the low-density development than those in central areas, even though they are spatially equally distributed. Thus, the average distance between the major roadways and overall households is bigger in suburbs than in central areas. As shown in Figure 2, the value of changes in job accessibility is higher in suburbs. For this reason, it is reasonable that the coefficient of distance to major roadways is positively associated with changes in job accessibility. In spite of the intuitive explanation on the relationship between two instruments and changes in overall job accessibility, the relationship between our instrumental variables and other accessibility indices by race and income are more complicated to interpret.
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In general, firms are likely to locate near major roadways, which leads to the positive relationship between major roadways and changes in job accessibility. However, changes in job accessibility are also affected by other factors such as housing supply or household location choice as described in Section 3.1. For example, close to major roadways provides great benefits to firms, thus leads to an increase in job opportunities. And growing jobs attract more households. However, major roadways also can cause negative externalities (i.e. traffic congestion, noise, and pollution) and raise housing prices. In this case, households regard them as a disamenity or struggle to find affordable housings. If the major roadways attract jobs more than households, job accessibility would increase, whereas if the major roadways attract households more than jobs, accessibility would decrease. However, residential preferences are different by income and race as well as dependent on available affordable housings. Accordingly, the effect of major roadways on changes in job accessibility by income and race can be positive, negative, or neutral, and thus it is more related to an empirical question. Similarly, distance to subcenters also affects job accessibility by income and race in a various way. For example, empirical studies demonstrate that high-income and white households may not take into consideration access to subcenters when they choose their residential locations (Cho et al, 2008). This is probably because most of them commute by car, thus their communities seem to be slightly far from the subcenters. In contrast, low-income and African-American households are likely to consider access to subcenters or good public transit services because relatively numerous workers in the groups commute by public transportation. Unfortunately, lack of enough public transportation service and affordable housings in suburban areas makes them have various spatial distribution patterns. Recent studies found that low-income and African-American households are struggling to find affordable housings in suburban areas (Raphael and Stoll, 2010). However, a question still remains because our instrumental variables show opposite directions between
In general, firms are likely to locate near major roadways, which leads to the positive relationship between major roadways and changes in job accessibility. However, changes in job accessibility are also affected by other factors such as housing supply or household location choice as described in Section 3.1. For example, close to major roadways provides great benefits to firms, thus leads to an increase in job opportunities. And growing jobs attract more households. However, major roadways also can cause negative externalities (i.e. traffic congestion, noise, and pollution) and raise housing prices. In this case, households regard them as a disamenity or struggle to find affordable housings. If the major roadways attract jobs more than households, job accessibility would increase, whereas if the major roadways attract households more than jobs, accessibility would decrease. However, residential preferences are different by income and race as well as dependent on available affordable housings. Accordingly, the effect of major roadways on changes in job accessibility by income and race can be positive, negative, or neutral, and thus it is more related to an empirical question. Similarly, distance to subcenters also affects job accessibility by income and race in a various way. For example, empirical studies demonstrate that high-income and white households may not take into consideration access to subcenters when they choose their residential locations (Cho et al, 2008). This is probably because most of them commute by car, thus their communities seem to be slightly far from the subcenters. In contrast, low-income and African-American households are likely to consider access to subcenters or good public transit services because relatively numerous workers in the groups commute by public transportation. Unfortunately, lack of enough public transportation service and affordable housings in suburban areas makes them have various spatial distribution patterns. Recent studies found that low-income and African-American households are struggling to find affordable housings in suburban areas (Raphael and Stoll, 2010). However, a question still remains because our instrumental variables show opposite directions between