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Profiling Poverty with Multivariate Adaptive Regression Splines

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  • Barrios, Erniel B.
  • Mina, Christian D.

Abstract

Using data from the 2003 Family Income and Expenditure Survey and 2005 Community-based Monitoring System for a city, Multivariate Adaptive Regression Splines (MARS) is used in identifying household poverty correlates in the Philippines. Models produced by MARS are more parsimonious yet contain theoretically and empirically sound set of household poverty correlates and have high accuracy in identifying a poor household. MARS provides a better alternative to logistic regression for a more efficient and effective implementation of a proxy means test in the identification of potential beneficiaries of poverty alleviation programs.

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Bibliographic Info

Paper provided by Philippine Institute for Development Studies in its series Discussion Papers with number DP 2009-29.

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Length: 56
Date of creation: 2009
Date of revision:
Handle: RePEc:phd:dpaper:dp_2009-29

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Keywords: community-based monitoring system; multivariate adaptive regression splines; logistic regression; poverty correlates; proxy means test;

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References

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  1. Dean P. Foster & Robert A. Stine, 2001. "Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy," Center for Financial Institutions Working Papers 01-05, Wharton School Center for Financial Institutions, University of Pennsylvania.
  2. Reyes, Celia M., 2006. "Alternative Means Testing Options Using CBMS," Discussion Papers DP 2006-22, Philippine Institute for Development Studies.
  3. Houssou, Nazaire & Zeller, Manfred & Alcaraz V., Gabriela & Schwarze, Stefan & Johannsen, Julia, 2007. "Proxy Means Tests for Targeting the Poorest Households -- Applications to Uganda," 106th Seminar, October 25-27, 2007, Montpellier, France 7946, European Association of Agricultural Economists.
  4. Balisacan, Arsenio M, 1993. "Agricultural Growth, Landlessness, Off-farm Employment, and Rural Poverty in the Philippines," Economic Development and Cultural Change, University of Chicago Press, vol. 41(3), pages 533-62, April.
  5. Kuhnert, Petra M. & Do, Kim-Anh & McClure, Rod, 2000. "Combining non-parametric models with logistic regression: an application to motor vehicle injury data," Computational Statistics & Data Analysis, Elsevier, vol. 34(3), pages 371-386, September.
  6. Lipton, Michael & Ravallion, Martin, 1993. "Poverty and policy," Policy Research Working Paper Series 1130, The World Bank.
  7. Tabuga, Aubrey D., 2007. "International Remittances and Household Expenditures: the Philippine Case," Discussion Papers DP 2007-18, Philippine Institute for Development Studies.
  8. Ahmed, Akhter U. & Bouis, Howarth E., 2002. "Weighing what's practical: proxy means tests for targeting food subsidies in Egypt," Food Policy, Elsevier, vol. 27(5-6), pages 519-540.
  9. Foster D.P. & Stine R.A., 2004. "Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 303-313, January.
  10. Manasan, Rosario G. & Cuenca, Janet S., 2007. "Who Benefits from the Food-for-School Program and Tindahan Natin Program: Lessons in Targeting," Discussion Papers DP 2007-10, Philippine Institute for Development Studies.
  11. Aniceto C. Orbeta Jr., 2005. "Poverty, Vulnerability and Family Size : Evidence from the Philippines," Development Economics Working Papers 22671, East Asian Bureau of Economic Research.
  12. Rosario G. Manasan & Janet S. Cuenca, 2007. "Who Benefits from the Food-for-School Program : Lessons in Targeting," Development Economics Working Papers 21929, East Asian Bureau of Economic Research.
  13. Aubrey D. Tabuga, 2007. "International Remittances and Household Expenditures : The Philippine Case," Development Economics Working Papers 22698, East Asian Bureau of Economic Research.
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