IDEAS home Printed from https://ideas.repec.org/p/tor/tecipa/tecipa-421.html
   My bibliography  Save this paper

Partially Identified Poverty Status: A New Approach to Measuring Poverty and the Progress of the Poor

Author

Listed:
  • Gordon Anderson
  • Maria Grazia Pittau
  • Roberto Zelli

Abstract

Poverty measurement and the analysis of the progress (or otherwise) of the poor is beset with difficulties and controversies surrounding the definition of a poverty line or frontier. Here, using ideas from the partial identification literature and mixture models, a new approach to poverty measurement is proposed which avoids specifying a frontier, the price is that an agent's poverty status is only partially identified. Invoking variants of Gibrat's law to give structure to the distribution of outcomes for homogeneous subgroups of a population within the context of a finite mixture model of societal outcomes facilitates calculation of the probability of an agent's poverty status. From this it is straightforward to calculate all the usual poverty measures as well as other characteristics of the poor and non poor subgroups in a society. These ideas are exemplified in a study of 47 countries in Africa over the recent quarter century which reveals among other things a growing poverty rate and a growing disparity between poor and non poor groups not identified by conventional methods.

Suggested Citation

  • Gordon Anderson & Maria Grazia Pittau & Roberto Zelli, 2011. "Partially Identified Poverty Status: A New Approach to Measuring Poverty and the Progress of the Poor," Working Papers tecipa-421, University of Toronto, Department of Economics.
  • Handle: RePEc:tor:tecipa:tecipa-421
    as

    Download full text from publisher

    File URL: https://www.economics.utoronto.ca/public/workingPapers/tecipa-421.pdf
    File Function: Main Text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jean-Yves Duclos & David E. Sahn & Stephen D. Younger, 2006. "Robust Multidimensional Poverty Comparisons," Economic Journal, Royal Economic Society, vol. 116(514), pages 943-968, October.
    2. Slesnick, Daniel T, 1993. "Gaining Ground: Poverty in the Postwar United States," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 1-38, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Partially Identified Poverty Status: A New Approach to Measuring Poverty and the Progress of the Poor
      by maximorossi in NEP-LTV blog on 2011-02-01 21:31:06

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anderson, Gordon, 2011. "Polarization measurement and inference in many dimensions when subgroups can not be identified," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 5, pages 1-19.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gordon Anderson & Maria Pittau & Roberto Zelli, 2014. "Poverty status probability: a new approach to measuring poverty and the progress of the poor," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 12(4), pages 469-488, December.
    2. Espinoza-Delgado, José & Silber, Jacques, 2018. "Multi-dimensional poverty among adults in Central America and gender differences in the three I’s of poverty: Applying inequality sensitive poverty measures with ordinal variables," MPRA Paper 88750, University Library of Munich, Germany.
    3. Bruce D. Meyer & James X. Sullivan, 2011. "Consumption and Income Poverty Over the Business Cycle," Research in Labor Economics, in: Who Loses in the Downturn? Economic Crisis, Employment and Income Distribution, pages 51-82, Emerald Group Publishing Limited.
    4. Bruce D. Meyer & James X. Sullivan, 2012. "Identifying the Disadvantaged: Official Poverty, Consumption Poverty, and the New Supplemental Poverty Measure," Journal of Economic Perspectives, American Economic Association, vol. 26(3), pages 111-136, Summer.
    5. Jing Yang & Pundarik Mukhopadhaya, 2019. "Is the ADB’s Conjecture on Upward Trend in Poverty for China Right? An Analysis of Income and Multidimensional Poverty in China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(2), pages 451-477, June.
    6. Mussa, Richard, 2010. "Poverty and Inequality in Standards of Living in Malawi: Does Religious Affiliation Matter?," MPRA Paper 24438, University Library of Munich, Germany.
    7. Dirk Krueger & Fabrizio Perri, 2004. "On the Welfare Consequences of the Increase in Inequality in the United States," NBER Chapters, in: NBER Macroeconomics Annual 2003, Volume 18, pages 83-138, National Bureau of Economic Research, Inc.
    8. Eric A. Hanushek & Jacob D. Light & Paul E. Peterson & Laura M. Talpey & Ludger Woessmann, 2022. "Long-run Trends in the U.S. SES-Achievement Gap," Education Finance and Policy, MIT Press, vol. 17(4), pages 608-640, Fall.
    9. Christophe Muller & Asha Kannan & Roland Alcindor, 2016. "Multidimensional Poverty in Seychelles," Working Papers halshs-01264444, HAL.
    10. Slesnick, Daniel T., 2002. "Prices and Regional Variation in Welfare," Journal of Urban Economics, Elsevier, vol. 51(3), pages 446-468, May.
    11. Fukushige, Mototsugu, 1996. "Annual redistribution and lifetime redistribution," Economics Letters, Elsevier, vol. 52(3), pages 269-273, September.
    12. Christophe Muller, 2006. "Defining Poverty Lines As a Fraction of Central Tendency," Southern Economic Journal, John Wiley & Sons, vol. 72(3), pages 720-729, January.
    13. Pinaki Das & Bibek Paria & Shama Firdaush, 2021. "Juxtaposing Consumption Poverty and Multidimensional Poverty: A Study in Indian Context," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(2), pages 469-501, January.
    14. Alkire, Sabina & Santos, Maria Emma, 2014. "Measuring Acute Poverty in the Developing World: Robustness and Scope of the Multidimensional Poverty Index," World Development, Elsevier, vol. 59(C), pages 251-274.
    15. David Lander & David Gunawan & William Griffiths & Duangkamon Chotikapanich, 2020. "Bayesian assessment of Lorenz and stochastic dominance," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(2), pages 767-799, May.
    16. Fotis Papadopoulos & Panos Tsakloglou, 2015. "Chronic material deprivation and long-term poverty in Europe in the pre-crisis period," ImPRovE Working Papers 15/16, Herman Deleeck Centre for Social Policy, University of Antwerp.
    17. Wei Su & Gianni Betti & Baris Ucar, 2020. "Longitudinal measures of fuzzy poverty: a focus on Czechia, Hungary and Poland after the crisis," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 27-41, February.
    18. M. Azhar Hussain & Nikolaj Siersbæk & Lars Peter Østerdal, 2020. "Multidimensional welfare comparisons of EU member states before, during, and after the financial crisis: a dominance approach," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 55(4), pages 645-686, December.
    19. David Madden, 2011. "Health and income poverty in Ireland, 2003–2006," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 9(1), pages 23-33, March.
    20. Richard Mussa, 2013. "Spatial Comparisons of Poverty and Inequality in Living Standards in Malawi," South African Journal of Economics, Economic Society of South Africa, vol. 81(2), pages 192-210, June.

    More about this item

    Keywords

    Poverty Frontiers; Mixture Models; Gibrat's law; Partial Identification;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tor:tecipa:tecipa-421. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: RePEc Maintainer (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.