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An Application of Bayesian Methods to Small Area Poverty Rate Estimates

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Abstract

Efforts to estimate various sociodemographic variables in small geographic areas are proving difficult with the replacement of the Census long-form with the American Community Survey (ACS). Researchers interested in subnational demographic processes have previously relied on Census 2000 long-form data products in order to answer research questions. ACS data products promise to begin providing up-to-date profiles of the nation’s population and economy; however, unit- and item-level nonresponse in the ACS have left researchers with gaps in subnational coverage resulting in unstable and unreliable estimates for basic demographic measures. Borrowing information from neighboring areas and across time with a spatiotemporal smoothing process based on Bayesian statistical methods, it is possible to generate more stable and accurate estimates of rates for geographic areas not represented in the ACS. This research evaluates this spatiotemporal smoothing process in its ability to derive estimates of poverty rates at the county level for the contiguous United States. These estimates are then compared to more traditional estimates produced by the US Census Bureau, and comparisons between the two methods of estimation are carried out to evaluate the practical application of this smoothing method. Our findings suggest that by using available data from the ACS only, we are able to recreate temporal and spatial patterns of poverty in US counties even in years where data are sparse. Results show that the Bayesian methodology strongly agrees with the estimates produced by the SAIPE program, even in years with little data. This methodology can be expanded to other demographic and socioeconomic data with ease.

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Notes

  1. At the time this analysis was conducted, the 2011 5-year ACS estimates had not been released.

  2. The rule being applied here is σ 2 = 1/τ, and % Spatial Variance = \(\sigma_{v}^{2} /(\sigma_{v}^{2} + \sigma_{u}^{2} )\)

References

  • Alonso-Villar, O., & Del Rio, C. (2008). Geographical concentration of unemployment: A male-female comparison in Spain. Regional Studies, 42(3), 401–412. doi:10.1080/00343400701291559.

    Article  Google Scholar 

  • Assuncao, R. M., Potter, J. E., & Cavenaghi, S. M. (2002). A Bayesian space varying parameter model applied to estimating fertility schedules. Statistics in Medicine, 21(14), 2057–2075. doi:10.1002/sim.1153.

    Article  Google Scholar 

  • Assuncao, R. M., Schmertmann, C. P., Potter, J. E., & Cavenaghi, S. M. (2005). Empirical Bayes estimation of demographic schedules for small areas. Demography, 42(3), 537–558. doi:10.1353/dem.2005.0022.

    Article  Google Scholar 

  • Baker, J. L., & Grosh, M. E. (1994). Poverty reduction through geographic targeting—How well does it work? World Development, 22(7), 983–995. doi:10.1016/0305-750x(94)90143-0.

    Article  Google Scholar 

  • Bedi, T., Coudouel, A., & Simler, K. (2007). More than a pretty picture: Using poverty maps to design better policies and interventions Poverty Reduction & Equity. Washington, DC: The World Bank.

  • Bell, W., Basel, W., Cruse, C., Dalzell, L., Maples, J., O’Hara, B., et al. (2007). In U. S. C. Bureau (Ed.). Use of ACS data to produce SAIPE model-based estimates of poverty for counties. Washington, DC: U.S. Census Bureau.

  • Bell, W., Basel, W., Cruse, C., Dalzell, L., Maples, J., O’Hara, B., et al. (2007). Use of ACS data to produce SAIPE model-based estimates of poverty for counties. Washington, DC: U.S. Census Bureau. Retrieved from http://www.census.gov/did/www/saipe/publications/files/report.pdf.

  • Bernardinelli, L., Clayton, D., Pascutto, C., Montomoli, C., Ghislandi, M., & Songini, M. (1995). Bayesian analysis of space–time variation in disease risk. [Article]. Statistics in Medicine, 14(21–22), 2433–2443. doi:10.1002/sim.4780142112.

    Article  Google Scholar 

  • Besag, J., York, J. C., & Mollie, A. (1991). Bayesian Image Restoration, with two applications in spatial statistics (with discussion). Annals of the Institute of Statistical Mathematics, 43(1), 1–59. doi:10.1007/BF00116466.

    Article  Google Scholar 

  • Bigman, D., & Fofack, H. (2000). Geographical targeting for poverty alleviation: Methodology and applications Regional and Sectoral Studies (Vol. 1). Washington, DC: The World Bank.

  • Casella, G., & George, E. I. (1992). Explaining the Gibbs sampler. The American Statistician, 46(3), 167–174.

    Google Scholar 

  • Celeux, G., Forbes, F., Robert, C. P., & Titterington, D. M. (2006). Deviance information criteria for missing data models. Bayesian Analysis, 1(4), 651–673.

    Article  Google Scholar 

  • Census Bureau, U. S. (2008). A compass for understanding and using American Community Survey data: What general data users need to know. Washington, DC: U.S. Government Printing Office.

    Google Scholar 

  • Citro, C. E., & Kalton, G. (Eds.). (2007). Using the American community survey: Benefits and challenges. Washington, DC: The National Academy Press.

    Google Scholar 

  • Cuong, N. V. (2011). Poverty projection using a small area estimation method: Evidence from Vietnam. Journal of Comparative Economics, 39(3), 368–382. doi:10.1016/j.jce.2011.04.004.

    Article  Google Scholar 

  • Cushing, B. (1999). In K. Pandit & S. D. Withers (Eds.), Migration and persistent poverty in rural America. Lanham, MD: Rowmen and Littlefield press.

  • Edin, K., & Kissane, R. J. (2010). Poverty and the American family: A decade in review. Journal of Marriage and Family, 72(3), 460–479. doi:10.1111/j.1741-3737.2010.00713.x.

    Article  Google Scholar 

  • Elbers, C., Fujii, T., Lanjouw, P., Ozler, B., & Yin, W. (2007). Poverty alleviation through geographic targeting: How much does disaggregation help? Journal of Development Economics, 83(1), 198–213. doi:10.1016/j.jdeveco.2006.02.001.

    Article  Google Scholar 

  • Friedman, S., & Lichter, D. T. (1998). Spatial inequality and poverty among American children. Population Research and Policy Review, 17(2), 91–109. doi:10.1023/A:1005740205017.

    Article  Google Scholar 

  • Gelman, A. E., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003). Bayesian data analysis, 2nd Edn (2nd ed.). Boca Raton, FL: Chapman & Hall/CRC.

    Google Scholar 

  • Gonzalez-Quintela, A., Fernandez-Conde, S., Alves, M. T., Campos, J., Lopez-Raton, M., Puerta, R., et al. (2011). Temporal and spatial patterns in the rate of alcohol withdrawal syndrome in a defined community. Alcohol, 45(2), 105–111. doi:10.1016/j.alcohol.2010.08.001.

    Article  Google Scholar 

  • Hoff, P. D. (2009). A first course in Bayesian statistical methods. Boca Raton, FL: Chapman & Hall/CRC.

    Book  Google Scholar 

  • Johnson, K. M., & Lichter, D. T. (2010). Growing diversity among America’s children and youth: Spatial and temporal dimensions. Population and Development Review, 36(1), 151+.

    Google Scholar 

  • Kneebone, E., & Garr, E. (2010). The suburbanization of poverty: Trends in metropolitan America, 2000 to 2008 Metropolitan opportunity series. Washington, DC: The Brookings Institution.

    Google Scholar 

  • Knorr-Held, L. (2000). Bayesian modelling of inseparable space–time variation in disease risk. Statistics in Medicine, 19(17–18), 2555–2567. doi:10.1002/1097-0258(20000915/30)19:17/18<2555::AID-SIM587>3.0.CO;2-#.

  • Lawson, A. B. (2009). Bayesian disease mapping. Boca Raton, FL: Chapman & Hall/CRC.

    Google Scholar 

  • Lee, M. A., & Singelmann, J. (2005). Welfare reform amidst chronic poverty in the Mississipi delta. In W. A. Kandel & D. L. Brown (Eds.), Population change and rural society. Dordrecht: Springer.

    Google Scholar 

  • Lichter, D. T., & Johnson, K. M. (2007). The changing spatial concentration of America’s rural poor population. Rural Sociology, 72(3), 331–358. doi:10.1526/003601107781799290.

    Article  Google Scholar 

  • Lunn, D., Spiegelhalter, D., Thomas, A., & Best, N. (2009). The BUGS project: Evolution, critique and future directions (with discussion). Statistics in Medicine, 28(25), 3049–3067. doi:10.1002/sim.3680.

    Article  Google Scholar 

  • Mandal, R., St-Hilaire, S., Kie, J. G., & Derryberry, D. (2009). Spatial trends of breast and prostate cancers in the United States between 2000 and 2005. International Journal of Health Geographics, 8, 53. doi:10.1186/1476-072x-8-53.

    Article  Google Scholar 

  • Mckinnon, S., Potter, J. E., & Schmertmann, C. S. (2010). Municipality-level estimates of child mortality for Brazil: A new approach using Bayesian Statistics. Paper presented at the Population Association of America 2010 Annual Meeting, Dallas, TX. http://paa2010.princeton.edu/download.aspx?submissionId=101738.

  • O’Hare, W. P., & Johnson, K. M. (2004). Child poverty in rural America Reports on America (Vol. 4). Washington, DC: Population Reference Bureau.

  • Parisi, D., Grice, S., Taquino, M., & Gill, D. (2005). Community concentration of poverty and its consequences on nonmetro county persistence of poverty in Mississippi. Sociological Spectrum, 25(4), 469–483. doi:10.1080/027321790947234.

    Article  Google Scholar 

  • Partridge, M. D., & Rickman, D. S. (2006). The geography of American poverty: Is there a need for place-based policies. Kalamazoo, MI: W. E. Upjohn Institute for Employment Research.

    Google Scholar 

  • Pollard, K. M. (2004). A ‘New Diversity’: Race and ethnicity in the Appalachian Region. Demographic and socioeconomic change in Appalachia. Washington, DC: Population Reference Bureau.

  • Potter, J. E., Schmertmann, C. P., Assuncao, R. M., & Cavenaghi, S. M. (2010). Mapping the timing, pace, and scale of the fertility transition in Brazil. Population and Development Review, 36(2), 283–307. doi:10.1111/j.1728-4457.2010.00330.x.

    Article  Google Scholar 

  • Rao, J. K. (2003). Small Area Estimation. Hoboken, NJ): Wiley.

    Book  Google Scholar 

  • Saenz, R. (1997). Ethnic concentration and Chicano poverty: A comparative approach. Social Science Research, 26(2), 205–228. doi:10.1006/ssre.1997.0595.

    Article  Google Scholar 

  • Saenz, R., & Thomas, J. K. (1991). Minority poverty in Nonmetropolitan Texas. Rural Sociology, 56(2), 204–223.

    Article  Google Scholar 

  • Slack, T. L., Singelmann, J., Fontenot, K., Poston, D., Seanz, R., & Siodia, C. (2009). Poverty in the Texas borderland and lower Mississippi delta: A comparative analysis of differences by family type. Demographic Research, 20, 353–376.

    Article  Google Scholar 

  • Spiegelhalter, D. J., Best, N. G., Carlin, B. R., & van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society Series B-Statistical Methodology, 64, 583–616. doi:10.1111/1467-9868.00353.

    Article  Google Scholar 

  • Tayman, J., & Swanson, D. A. (1999). On the validity of MAPE as a measure of population forecast accuracy. Population Research and Policy Review, 18(4), 299–322. doi:10.1023/A:1006166418051.

    Article  Google Scholar 

  • Tayman, J., Swanson, D. A., & Barr, C. F. (1999). In search of the ideal measure of accuracy for subnational demographic forecasts. Population Research and Policy Review, 18(5), 387–409.

    Article  Google Scholar 

  • Tobler, W. (1970). A computer movie simulating population growth in the Detroit region. Economic Geography, 42, 234–240.

    Article  Google Scholar 

  • U.S. Department of Commerce, Bureau of the Census, U.S. Department of Labor, & Bureau of Labor Statistics. (1976). Concepts and methods used in labor force statistics derived from the Current Population Survey Current Population Reports. Washington, DC.

  • Voss, P., Long, D. D., Hammer, R. B., & Friedman, S. (2006). County child poverty rates in the U.S.: A spatial regression approach. Population Research and Policy Review, 25(4), 369–391. doi:10.1007/s11113-006-9007-4.

    Article  Google Scholar 

  • Zolnik, E. J. (2011). The geographic distribution of US unemployment by gender. Economic Development Quarterly, 25(1), 91–103. doi:10.1177/0891242410386592.

    Article  Google Scholar 

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Acknowledgments

We gratefully acknowledge the advice of the four anonymous reviewers, whose comments greatly improved the quality of this manuscript. This paper was originally presented at the Population Association of America annual meeting in San Francisco, CA.

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  1. Department of Demography, The University of Texas at San Antonio, 501 West Cesar E. Chavez Blvd, San Antonio, TX, 78207, USA

    Corey Sparks

  2. United States Automobile Association, 9800 Fredericksburg Road, San Antonio, TX, 78288, USA

    Joey Campbell

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Correspondence to Corey Sparks.

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Sparks, C., Campbell, J. An Application of Bayesian Methods to Small Area Poverty Rate Estimates. Popul Res Policy Rev 33, 455–477 (2014). https://doi.org/10.1007/s11113-013-9303-8

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