County Child Poverty Rates in the US: A Spatial Regression Approach

• Published in: Population research and policy review Vol. 25; no. 4; pp. 369 - 391
• Main Authors: Voss, Paul R., Long, David D., Hammer, Roger B., Friedman, Samantha
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer 01-08-2006; Springer Nature B.V
• Series: Population Research and Policy Review
• Subjects: Agricultural economics; Autocorrelation; Child Poverty; Child welfare - United States; Children; Children & youth; Counties; Data analysis; Demography; Econometrics; Economic hardship; Economic models; Economics; Exploratory spatial data analysis; Externality; Families & family life; Family; Geography; Living conditions; Neighborhoods; Policy studies; Poverty; Poverty - United States; Poverty rates; Regression analysis; Science; Social sciences; Sociology; Spatial Analysis; Spatial data; Spatial error models; Spatial lag models; Spatial models; Spatial regression; U.S.A; United States of America; United States > US
• ISSN: 0167-5923; 1573-7829
• DOI: 10.1007/s11113-006-9007-4

We apply methods of exploratory spatial data analysis (ESDA) and spatial regression analysis to examine intercounty variation in child poverty rates in the US. Such spatial analyses are important because regression models that exclude explicit specification of spatial effects, when they exist, can lead to inaccurate inferences about predictor variables. Using county-level data for 1990, we reexamine earlier published results [Friedman and Lichter (Popul Res Policy Rev 17: 91-109,1998)]. We find that formal tests for spatial autocorrelation among county child poverty rates confirm and quantify what is obvious from simple maps of such rates: the risk of a child living in poverty is not (spatially) a randomly distributed risk at the county level. Explicit acknowledgment of spatial effects in an explanatory regression model improves considerably the earlier published regression results, which did not take account of spatial autocorrelation. These improvements include: (1) the shifting of "wrong sign" parameters in the direction originally hypothesized by the authors, (2) a reduction of residual squared error, and (3) the elimination of any substantive residual spatial autocorrelation. While not without its own problems and some remaining ambiguities, this reanalysis is a convincing demonstration of the need for demographers and other social scientists to examine spatial autocorrelation in their data and to explicitly correct for spatial externalities, if indicated, when performing multiple regression analyses on variables that are spatially referenced. Substantively, the analysis improves the estimates of the joint effects of placeinfluences and family-influences on child poverty.