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

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  • Paul Voss

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  • David Long
  • Roger Hammer
  • Samantha Friedman

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. Copyright Springer Science+Business Media B.V. 2006

Suggested Citation

  • Paul Voss & David Long & Roger Hammer & Samantha Friedman, 2006. "County child poverty rates in the US: a spatial regression approach," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 25(4), pages 369-391, August.
  • Handle: RePEc:kap:poprpr:v:25:y:2006:i:4:p:369-391
    DOI: 10.1007/s11113-006-9007-4
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    References listed on IDEAS

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    Cited by:

    1. Fatih Celebioglu & Sandy Dall’erba, 2010. "Spatial disparities across the regions of Turkey: an exploratory spatial data analysis," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 45(2), pages 379-400, October.
    2. Corey Sparks & Joey Campbell, 2014. "An Application of Bayesian Methods to Small Area Poverty Rate Estimates," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 33(3), pages 455-477, June.
    3. Jeremy Porter, 2012. "A Simplified Indicator of Social Well-Being in the United States: Examining the Ecological Impact of Family Formation within a County Level Framework," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 108(3), pages 421-440, September.
    4. Archibald, Matthew E. & Putnam Rankin, Caddie, 2013. "A spatial analysis of community disadvantage and access to healthcare services in the U.S," Social Science & Medicine, Elsevier, vol. 90(C), pages 11-23.
    5. Deka, Devajyoti & Gonzales, Eric J., 2014. "The generators of paratransit trips by persons with disabilities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 181-193.
    6. Brian Whitacre & Roberto Gallardo & Sharon Strover, 2014. "Does rural broadband impact jobs and income? Evidence from spatial and first-differenced regressions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(3), pages 649-670, November.
    7. Paul Voss, 2007. "Demography as a Spatial Social Science," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(5), pages 457-476, December.
    8. Craig Wesley Carpenter & F. Carson Mencken & Charles M. Tolbert & Michael Lotspeich, 2018. "Locally Owned Bank Commuting Zone Concentration and Employer Start-Ups in Metropolitan, Micropolitan and Non-Core Rural Commuting Zones from 1970-2010," Working Papers 18-34, Center for Economic Studies, U.S. Census Bureau.
    9. Tse-Chuan Yang & Leif Jensen, 2015. "Exploring the Inequality-Mortality Relationship in the US with Bayesian Spatial Modeling," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 34(3), pages 437-460, June.
    10. Porter, Jeremy R. & Purser, Christopher W., 2010. "Social disorganization, marriage, and reported crime: A spatial econometrics examination of family formation and criminal offending," Journal of Criminal Justice, Elsevier, vol. 38(5), pages 942-950, September.
    11. Stephen Matthews & Daniel M. Parker, 2013. "Progress in Spatial Demography," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(10), pages 271-312, February.
    12. Sri Ranjith & Anil Rupasingha, 2012. "Social and Cultural Determinants of Child Poverty in the United States," Journal of Economic Issues, Taylor & Francis Journals, vol. 46(1), pages 119-142.
    13. J. G. Sri Ranjith, 2015. "The Effect of Microenterprises on Poverty: A Cross-county Analysis on US Urban Poverty," International Journal of Economics and Empirical Research (IJEER), The Economics and Social Development Organization (TESDO), vol. 3(3), pages 128-150, March.
    14. Khatiwada, Lila K., 2014. "Modeling and Explaining County-level Prosperity in the U.S," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 0(Issue 2).
    15. repec:gam:jsusta:v:9:y:2017:i:12:p:2212-:d:120951 is not listed on IDEAS

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