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Predicting Consumption Poverty Using Non-consumption Indicators : Experiments Using Indonesian Data

Author

Listed:
  • Sudarno Sumarto

    (SMERU)

  • 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.

Suggested Citation

  • Sudarno Sumarto & Daniel Suryadarma & Asep Suryahadi, 2006. "Predicting Consumption Poverty Using Non-consumption Indicators : Experiments Using Indonesian Data," Development Economics Working Papers 22542, East Asian Bureau of Economic Research.
  • Handle: RePEc:eab:develo:22542
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    References listed on IDEAS

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    1. 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 473-487, December.
    2. Geda, A. & de Jong, N. & Mwabu, G. & Kimenyi, M.S., 2001. "Determinants of poverty in Kenya : a household level analysis," ISS Working Papers - General Series 19095, International Institute of Social Studies of Erasmus University Rotterdam (ISS), The Hague.
    3. 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.
    4. Kai-yuen Tsui, 2002. "Multidimensional poverty indices," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 19(1), pages 69-93.
    5. Costa, Michele, 2002. "A multidimensional approach to the measurement of poverty," IRISS Working Paper Series 2002-05, IRISS at CEPS/INSTEAD.
    6. 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, March.
    7. 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.
    8. Sami Bibi, 2004. "Comparing Multidimensional Poverty between Egypt and Tunisia," Cahiers de recherche 0416, CIRPEE.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Gemini Mtei & Josephine Borghi & Kara Hanson, 2015. "Predicting Consumption Expenditure for the Analysis of Health Care Financing Equity in Low Income Countries: a Comparison of Approaches," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 124(2), pages 339-355, November.
    2. Bah, Adama & Bazzi, Samuel & Sumarto, Sudarno & Tobias, Julia, 2014. "Finding the Poor vs. Measuring Their Poverty: Exploring the Drivers of Targeting Effectiveness in Indonesia," MPRA Paper 59759, University Library of Munich, Germany.
    3. 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.
    4. 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).
    5. Baris Ucar, 2015. "The Usability of Asset Index as an Indicator of Household Economic Status in Turkey: Comparison with Expenditure and Income Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 121(3), pages 745-760, April.
    6. repec:eee:ijrema:v:30:y:2013:i:1:p:4-18 is not listed on IDEAS
    7. Adama BAH, 2013. "Finding the Best Indicators to Identify the Poor," Working Papers 201324, CERDI.
    8. Pave Sohnesen,Thomas & Stender,Niels, 2016. "Is random forest a superior methodology for predicting poverty ? an empirical assessment," Policy Research Working Paper Series 7612, The World Bank.

    More about this item

    Keywords

    consumption; poverty; predictor; data; Indonesia;

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth

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