notable amounts. A one-standard-deviation increase in transit use in 2000 reduces the predicted future poverty rate by almost a percentage point. The regression results in Table 2 are based on a data from all 100 MSAs, allowing for MSA-specific fixed effects. But there is much variation across MSAs in the socioeconomic characteristics of their populations, their housing characteristics, and public transit systems. 4 illustrates this variation for five major urban areas. In each case, the average value reported is the unweighted average across an census tracts. As shown in the Table, there are MSA’s differences in the averages for black and Hispanic residents, percentage of older housing units, and percentage of residents who take transit to work. Table 4 Average of census tract data Los New York Chicago Houston Phoenix from 2000 Angeles City 11.6 13.9 16.3 14.0 11.7 Poverty Rate Percentage non-Hispanic 21.6 18.0 8.5 20.9 4.0 Black Percentage Hispanic 16.0 27.8 40.6 19.0 24.5 Ratio rent to MSA median 12.9 13.9 19.6 18.1 17.5 family income Percentage non-multi-family 16.0 13.4 20.9 18.8 11.2 rental units Percentage housing units 22.9 23.5 28.7 32.4 17.4 Percentage housing units built 40.8 29.2 50.3 47.7 18.8 from 1940 through 1969 Percentage housing units built 22.3 3.9 10.7 28.0 1.6 pre-1940 Percentage housing units 5.6 7.5 4.0 5.3 9.7 Percentage workers who 13.4 3.6 6.8 31.0 1.9 take transit to work Such structural and demographic differences across cities make one wonder whether the pattern for the coefficients estimated using the data from the 100 urban areas as a group holds for individual urban areas. To test the stability of our estimates across a few large MSAs, we equation (2) separately for the five urban areas in Table 4: Chicago, Houston, Los New York City, and Phoenix. Of course, in these cases, there are no MSA fixed effects. As before, the dependent variable is the logit transformation of the 2010-14 census tract poverty rates and the data are weighted by the inverse of the standard error of the 2010-14 estimated poverty rate. Table 5 presents the results. Table 5
notable amounts. A one-standard-deviation increase in transit use in 2000 reduces the predicted future poverty rate by almost a percentage point. The regression results in Table 2 are based on a data from all 100 MSAs, allowing for MSA-specific fixed effects. But there is much variation across MSAs in the socioeconomic characteristics of their populations, their housing characteristics, and public transit systems. 4 illustrates this variation for five major urban areas. In each case, the average value reported is the unweighted average across an MSA’s census tracts. As shown in the Table, there are differences in the averages for black and Hispanic residents, percentage of older housing units, and percentage of residents who take transit to work. Table 4 Average of census tract data from 2000 Chicago Houston Los Angeles New York City Phoenix Poverty Rate 11.6 13.9 16.3 14.0 11.7 Percentage non-Hispanic Black 21.6 18.0 8.5 20.9 4.0 Percentage Hispanic 16.0 27.8 40.6 19.0 24.5 Ratio rent to MSA median family income 12.9 13.9 19.6 18.1 17.5 Percentage non-multi-family rental units 16.0 13.4 20.9 18.8 11.2 Percentage housing units 22.9 23.5 28.7 32.4 17.4 Percentage housing units built from 1940 through 1969 40.8 29.2 50.3 47.7 18.8 Percentage housing units built pre-1940 22.3 3.9 10.7 28.0 1.6 Percentage housing units 5.6 7.5 4.0 5.3 9.7 Percentage workers who take transit to work 13.4 3.6 6.8 31.0 1.9 Such structural and demographic differences across cities make one wonder whether the pattern for the coefficients estimated using the data from the 100 urban areas as a group holds for individual urban areas. To test the stability of our estimates across a few large MSAs, we equation (2) separately for the five urban areas in Table 4: Chicago, Houston, Los New York City, and Phoenix. Of course, in these cases, there are no MSA fixed effects. As before, the dependent variable is the logit transformation of the 2010-14 census tract poverty rates and the data are weighted by the inverse of the standard error of the 2010-14 estimated poverty rate. Table 5 presents the results. Table 5
Which%20Urban%20Neighborhoods%20-%20Revised.docx