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Predicting Consumption Poverty using Non-Consumption Indicators: Experiments using Indonesian Data

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  • Sudarno Sumarto
  • Daniel Suryadarma
  • Asep Suryahadi

    ()

Abstract

Although consumption expenditure data is crucial for assessing the level of peoples welfare and calculating important welfare measures such as the poverty headcount rate, collecting such data requires significant time and effort. In this study, we experiment with three approaches to predict consumption expenditure and poverty at household and aggregate level as simpler alternatives to using consumption expenditure. The idea is not to use these alternatives as a substitute for consumption expenditure data, rather to use it for the purposes of rapid monitoring and appraisal of welfare. The three approaches are i) consumption correlates model, ii) poverty probability model, and iii) the wealth index Principal Components Analysis (PCA). We test each approachs performance and found that the consumption correlates model is the best approach to predict poverty quickly and relatively accurately. We found that education level, asset ownership, and consumption pattern are the best predictors of expenditure and poverty.

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Bibliographic Info

Article provided by Springer in its journal Social Indicators Research.

Volume (Year): 81 (2007)
Issue (Month): 3 (May)
Pages: 543-578

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Handle: RePEc:spr:soinre:v:81:y:2007:i:3:p:543-578

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Web page: http://www.springer.com/economics/journal/11135

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Related research

Keywords: consumption; data; expenditure; Indonesia; predictor; poverty;

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References

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  1. Asep Suryahadi & Sudarno Sumarto, 2003. "Poverty and Vulnerability in Indonesia Before and After the Economic Crisis," Asian Economic Journal, East Asian Economic Association, vol. 17(1), pages 45-64, 03.
  2. Pritchett, Lant & Suryahadi, Asep & Sumarto, Sudarno, 2000. "Quantifying vulnerability to poverty - a proposed measure, applied to Indonesia," Policy Research Working Paper Series 2437, The World Bank.
  3. Costa, Michele, 2002. "A multidimensional approach to the measurement of poverty," IRISS Working Paper Series 2002-05, IRISS at CEPS/INSTEAD.
  4. Sami Bibi, 2004. "Comparing Multidimensional Poverty between Egypt and Tunisia," Cahiers de recherche 0416, CIRPEE.
  5. Pradhan, Menno, et al, 2001. "Eating Like Which "Joneses?" An Iterative Solution to the Choice of a Poverty Line "Reference Group."," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 47(4), pages 473-87, December.
  6. Costa, Michele, 2003. "A comparison between unidimensional and multidimensional approaches to the measurement of poverty," IRISS Working Paper Series 2003-02, IRISS at CEPS/INSTEAD.
  7. Kai-yuen Tsui, 2002. "Multidimensional poverty indices," Social Choice and Welfare, Springer, vol. 19(1), pages 69-93.
  8. Alemayehu Geda & Niek de Jong & Mwangi S. Kimenyi & Germano Mwabu, 2005. "Determinants of Poverty in Kenya: A Household Level Analysis," Working papers 2005-44, University of Connecticut, Department of Economics.
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Cited by:
  1. Deressa, Temesgen & Hassan, Rashid M. & Ringler, Claudia, 2008. "Measuring Ethiopian farmers' vulnerability to climate change across regional states:," IFPRI discussion papers 806, International Food Policy Research Institute (IFPRI).
  2. Andy Sumner & Peter Edward, 2013. "From Low Income, High Poverty to High-Income, No Poverty? An Optimistic View of the Long-Run Evolution of Poverty in Indonesia By International Poverty Lines, 1984–2030," Working Papers in Economics and Development Studies (WoPEDS) 201310, Department of Economics, Padjadjaran University, revised Jun 2013.
  3. Adama BAH, 2013. "Finding the Best Indicators to Identify the Poor," Working Papers 201324, CERDI.

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