IDEAS home Printed from https://ideas.repec.org/p/bol/bodewp/wp1156.html
   My bibliography  Save this paper

Fuzzy poverty measurement: multidimensional and unidimensional indicators

Author

Listed:
  • Michele Costa

Abstract

This paper proposes a comparison between multidimensional and unidimensional poverty indicators. Sets of poor units identified by traditional head count ratio, fuzzy unidimensional and fuzzy multidimensional indices are compared by means of a rank correlation analysis. The robustness of the comparison is ensured by a simulation study, which allows to address several issues related not only to fuzzy sets based methods, such as the subjective choice of membership to the poor set, but also to the multidimensional measurement, such as the effect of the weighting system. Our results stress that the unidimensional indicators provide partial information on poverty condition.

Suggested Citation

  • Michele Costa, 2020. "Fuzzy poverty measurement: multidimensional and unidimensional indicators," Working Papers wp1156, Dipartimento Scienze Economiche, Universita' di Bologna.
    • Handle: RePEc:bol:bodewp:wp1156
    as

    Download full text from publisher

    File URL: http://amsacta.unibo.it/6562/1/WP1156.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gianni Betti & Bruno Cheli & Riccardo Cambini, 2004. "A statistical model for the dynamics between two fuzzy states: theory and an application to poverty analysis," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 391-411.
    2. Rolf Aaberge & Andrea Brandolini, 2014. "Multidimensional poverty and inequality," Discussion Papers 792, Statistics Norway, Research Department.
    3. Walter Bossert & Satya R. Chakravarty & Conchita D’Ambrosio, 2019. "Multidimensional Poverty and Material Deprivation with Discrete Data," Themes in Economics, in: Satya R. Chakravarty (ed.), Poverty, Social Exclusion and Stochastic Dominance, pages 191-209, Springer.
    4. 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.
    5. Francesco Andreoli & Claudio Zoli, 2020. "From unidimensional to multidimensional inequality: a review," METRON, Springer;Sapienza Università di Roma, vol. 78(1), pages 5-42, April.
    Full references (including those not matched with items on IDEAS)

    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. Martina Menon & Federico Perali & Eva Sierminska, 2016. "An asset-based indicator of wellbeing for a unified means testing tool: Money metric or counting approach?," Working Papers 421, ECINEQ, Society for the Study of Economic Inequality.
    2. Martina Menon & Federico Perali & Eva Sierminska, 2017. "An Efficiency Comparison of Means Testing Tools: Money Metric or Counting Approach?," CHILD Working Papers Series 57 JEL Classification: D1, Centre for Household, Income, Labour and Demographic Economics (CHILD) - CCA.
    3. Francisco Azpitarte & Jose Gallegos & Gaston Yalonetzky, 2020. "On the robustness of multidimensional counting poverty orderings," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(3), pages 339-364, September.
    4. Valérie Bérenger, 2017. "Using ordinal variables to measure multidimensional poverty in Egypt and Jordan," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(2), pages 143-173, June.
    5. Dalila de Rosa, 2020. "Are Italians getting multidimensionally poorer? Evidence on the lack of Equitable and Sustainable Well-being," CERBE Working Papers wpC34, CERBE Center for Relationship Banking and Economics.
    6. Paolo Liberati & Giuliano Resce & Francesca Tosi, 2023. "The probability of multidimensional poverty: A new approach and an empirical application to EU‐SILC data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(3), pages 668-700, September.
    7. Guo, Junping & Qu, Song, 2021. "Multidimensional and Relative Poverty in Rural China: Evidence from Micro-Level Data of 6145 Households," 2021 Conference, August 17-31, 2021, Virtual 315040, International Association of Agricultural Economists.
    8. Sabina Alkire, Mauricio Apablaza, 2016. "Multidimensional Poverty in Europe 2006-2012: Illustrating a Methodology," OPHI Working Papers 74, Queen Elizabeth House, University of Oxford.
    9. Rolf Aaberge & Eugenio Peluso & Henrik Sigstad, 2015. "The dual approach for measuring. Multidimesional deprivation and poverty," Discussion Papers 820, Statistics Norway, Research Department.
    10. Pinar, Mehmet & Stengos, Thanasis & Topaloglou, Nikolas, 2020. "On the construction of a feasible range of multidimensional poverty under benchmark weight uncertainty," European Journal of Operational Research, Elsevier, vol. 281(2), pages 415-427.
    11. Mekonnen Bersisa & Almas Heshmati, 2021. "A Distributional Analysis of Uni-and Multidimensional Poverty and Inequalities in Ethiopia," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(3), pages 805-835, June.
    12. Mauricio Gallardo, 2020. "Measuring Vulnerability to Multidimensional Poverty," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(1), pages 67-103, February.
    13. Ke-Mei Chen & Chao-Hsien Leu & Te-Mu Wang, 2019. "Measurement and Determinants of Multidimensional Poverty: Evidence from Taiwan," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(2), pages 459-478, September.
    14. Gallardo, Mauricio, 2022. "Measuring vulnerability to multidimensional poverty with Bayesian network classifiers," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 492-512.
    15. Sabina Alkire & Usha Kanagaratnam & Ricardo Nogales & Nicolai Suppa, 2022. "Revising the Global Multidimensional Poverty Index: Empirical Insights and Robustness," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S2), pages 347-384, December.
    16. Francesco Burchi & Daniele Malerba & Claudio E. Montenegro & Nicole Rippin, 2022. "Assessing Trends in Multidimensional Poverty During the MDGs," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(S2), pages 317-346, December.
    17. Rolf Aaberge & Andrea Brandolini, 2014. "Multidimensional poverty and inequality," Temi di discussione (Economic working papers) 976, Bank of Italy, Economic Research and International Relations Area.
    18. Suman Seth & Melba Verra Tutor, 2021. "Evaluation of Anti‐Poverty Programs’ Impact on Joint Disadvantages: Insights From the Philippine Experience," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(4), pages 977-1004, December.
    19. Mingyue Liu & Xiaolong Feng & Sangui Wang & Huanguang Qiu, 2020. "China’s poverty alleviation over the last 40 years: successes and challenges," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(1), pages 209-228, January.
    20. Daniel Nowak & Christoph Scheicher, 2017. "Considering the Extremely Poor: Multidimensional Poverty Measurement for Germany," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(1), pages 139-162, August.

    More about this item

    JEL classification:

    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    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:bol:bodewp:wp1156. 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: Dipartimento Scienze Economiche, Universita' di Bologna (email available below). General contact details of provider: https://edirc.repec.org/data/sebolit.html .

    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.