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County child poverty rates in the US: a spatial regression approach

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Abstract

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 re-examine 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 place-influences and family-influences on child poverty.

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Notes

  1. One of the paper’s reviewers requested information relating to our selection of a first-order queen specification for our weights matrix. Spatial weights matrices can specify a variety of configurations by which to capture neighborhood influences. We chose the common convention (first-order queen) for several reasons. First, after testing many alternatives, this specification was found to yield the highest Moran statistic (0.597) with strong statistical significance (z-value = 55.6) on the dependent variable. One weights matrix using a strong (order 6) inverse distance decay gave us a mildly higher Moran statistic (0.609) but with much weaker statistical significance (z-value = 41.8). We also believe the simple first-order queen is easy to explain to the reader and easy for the uninitiated reader to comprehend. Most important, however, we choose this convention because there is evidence when using county economic data that neighborhood influences extend out approximately 40–50 miles and then dampen appreciably (Wheeler, 2001)—quite unlike a smooth inverse distance decay. This distance (40–50 miles) will certainly include immediate neighbors for most counties in the US. In parts of the eastern US where counties are geographically small, this distance would occasionally pick up second-order neighbors as well (i.e., neighbors of neighbors), but in much of the western US a strict centroid selection rule of 50 miles would declare many counties to have no neighbors, whatever. Thus the first-order queen selection is deemed a useful compromise. The literature on this topic is growing. Two useful references include Griffith (1996) and Florax and Rey (1995).

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Acknowledgments

Portions of this research were earlier presented at the 2001 meeting of the Southern Demographic Association, the 2003 meeting of the Population Association of America, and the 2003 conference of the Regional Science Association International, British & Irish Section. The research was supported in part by the US Department of Agriculture, Hatch Grant WIS04536, by the National Institute for Child Health and Human Development, Center Grant HD05876, and by the Wisconsin Center for Demography and Ecology, through its Geographic Information and Analysis Core. The authors are grateful to Luc Anselin and Stewart Fotheringham for advice at an early stage of this analysis and to Glenn Deane, Katherine Curtis White, and Joe Francis for comments on an earlier draft of the paper. We also express our appreciation to Samantha Friedman and Daniel Lichter for providing the census data that served as the basis of the original 1998 analysis.

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Authors and Affiliations

  1. Department of Rural Sociology, University of Wisconsin-Madison, 340 Agriculture Hall, 1450 Linden Drive, Madison, WI, 53706, USA

    Paul R. Voss & David D. Long

  2. Department of Sociology, Oregon State University, Corvallis, OR, 97331, USA

    Roger B. Hammer

  3. Department of Sociology and Anthropology, Northeastern University, 500 Holmes Hall, Boston, MA, 02115, USA

    Samantha Friedman

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  1. Paul R. Voss
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Correspondence to Paul R. Voss.

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Voss, P.R., Long, D.D., Hammer, R.B. et al. County child poverty rates in the US: a spatial regression approach. Popul Res Policy Rev 25, 369–391 (2006). https://doi.org/10.1007/s11113-006-9007-4

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