Kriging poverty measures for school neighborhoods
Track: Administration and Policy
Authors: Laura Nixon
Neighborhood characteristics influence students' educational outcomes, but neighborhoods are dynamic and socioeconomic indicators are limited. We developed a new option to identify neighborhood poverty. Using American Community Survey sample and empirical Bayesian kriging, we create estimates of the income-to-poverty ratio for neighborhoods surrounding every public school. Results suggest these estimates have advantages over data for traditional neighborhood proxies, like census tracts.