IDEAS home Printed from https://ideas.repec.org/a/eco/journ1/2023-03-13.html
   My bibliography  Save this article

Measuring Distribution of Wealth in Zambia Using Census Micro Data: An Application of Principal Component Analysis

Author

Listed:
  • Floyd Mwansa

    (University of Lusaka, Zambia.)

Abstract

Census data for Zambia was used to estimate the distribution of wealth in Zambia by constructing the Wealth Index as a measure of socioeconomic status using Principal Component Analysis. The reliability of the index is observed from three fronts; coherence, robustness and validity in representing household socioeconomic status. Classifying the households across all quartiles is highly consistent and robust. The index s performance in predicting the welfare distribution is analogous to established and most widely used methods from Demographic Health Surveys, as evidenced by similarities in the statistical distributions. Unlike other survey estimates, the index has been produced at the subnational level, such as district, enabling the classification of Zambia s districts according to their socioeconomic status. The index can be used to predict other socioeconomic outcomes, such as education and health, via Small Area Estimation techniques and determine district-level resource allocation by the central government.

Suggested Citation

  • Floyd Mwansa, 2023. "Measuring Distribution of Wealth in Zambia Using Census Micro Data: An Application of Principal Component Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 13(3), pages 126-140, May.
  • Handle: RePEc:eco:journ1:2023-03-13
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijefi/article/download/14301/7272
    Download Restriction: no

    File URL: https://www.econjournals.com/index.php/ijefi/article/view/14301
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lefteris Tsoulfidis & Ioannis Athanasiadis, 2022. "A new method of identifying key industries: a principal component analysis," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 11(1), pages 1-23, December.
    2. Leonardo Gasparini & Walter Sosa Escudero & Mariana Marchionni & Sergio Olivieri, 2008. "Income, Deprivation, and Perceptions in Latin America and the Caribbean: New Evidence from the Gallup World Poll," IDB Publications (Working Papers) 45618, Inter-American Development Bank.
    3. Deon Filmer & Lant Pritchett, 2001. "Estimating Wealth Effects Without Expenditure Data—Or Tears: An Application To Educational Enrollments In States Of India," Demography, Springer;Population Association of America (PAA), vol. 38(1), pages 115-132, February.
    4. Rodrigo LOVATON DAVILA & Aine Seitz MCCARTHY & Dorothy GONDWE & Phatta KIRDRUAND & Uttan SHARMA, 2022. "Water, Walls, and Bicycles: Wealth Index Composition Using Census Microdata," JODE - Journal of Demographic Economics, Cambridge University Press, vol. 88(1), pages 79-120, March.
    5. Luc Christiaensen & Peter Lanjouw & Jill Luoto & David Stifel, 2012. "Small area estimation-based prediction methods to track poverty: validation and applications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(2), pages 267-297, June.
    6. Theodore Mariolis & Lefteris Tsoulfidis, 2018. "Less Is More: Capital Theory And Almost Irregular-Uncontrollable Actual Economies," Contributions to Political Economy, Cambridge Political Economy Society, vol. 37(1), pages 65-88.
    7. Diana K. L. Ngo & Luc Christiaensen, 2019. "The Performance Of A Consumption Augmented Asset Index In Ranking Households And Identifying The Poor," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(4), pages 804-833, December.
    8. Alessandro Tarozzi & Angus Deaton, 2009. "Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 773-792, November.
    9. Isabelle CASSIERS & Géraldine THIRY, 2014. "A High-Stakes Shift: Turning the Tide From GDP to New Prosperity Indicators," LIDAM Discussion Papers IRES 2014002, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    10. Alejandro de la Fuente & Andreas Murr & Ericka Rascón, 2015. "Mapping Subnational Poverty in Zambia," World Bank Publications - Reports 21783, The World Bank Group.
    11. Mark Montgomery & Michele Gragnolati & Kathleen Burke & Edmundo Paredes, 2000. "Measuring living standards with proxy variables," Demography, Springer;Population Association of America (PAA), vol. 37(2), pages 155-174, May.
    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. Lovaton Davila, Rodrigo & McCarthy, Aine Seitz & Gondwe, Dorothy & Kirdruang, Phatta & Sharma, Uttam, 2022. "Water, walls, and bicycles: wealth index composition using census microdata," Journal of Demographic Economics, Cambridge University Press, vol. 88(1), pages 79-120, March.
    2. Hai‐Anh Dang & Dean Jolliffe & Calogero Carletto, 2019. "Data Gaps, Data Incomparability, And Data Imputation: A Review Of Poverty Measurement Methods For Data‐Scarce Environments," Journal of Economic Surveys, Wiley Blackwell, vol. 33(3), pages 757-797, July.
    3. Hai‐Anh H. Dang, 2021. "To impute or not to impute, and how? A review of poverty‐estimation methods in the absence of consumption data," Development Policy Review, Overseas Development Institute, vol. 39(6), pages 1008-1030, November.
    4. Permanyer, Iñaki, 2013. "Using Census Data to Explore the Spatial Distribution of Human Development," World Development, Elsevier, vol. 46(C), pages 1-13.
    5. Dang, Hai-Anh & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," IZA Discussion Papers 14631, Institute of Labor Economics (IZA).
    6. Hai-Anh H. Dang & Peter F. Lanjouw & Umar Serajuddin, 2017. "Updating poverty estimates in the absence of regular and comparable consumption data: methods and illustration with reference to a middle-income country," Oxford Economic Papers, Oxford University Press, vol. 69(4), pages 939-962.
    7. Ravi Prakash & Abhishek Singh, 2014. "Who Marries Whom? Changing Mate Selection Preferences in Urban India and Emerging Implications on Social Institutions," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 33(2), pages 205-227, April.
    8. Barik, Debasis & Desai, Sonalde & Vanneman, Reeve, 2018. "Economic Status and Adult Mortality in India: Is the Relationship Sensitive to Choice of Indicators?," World Development, Elsevier, vol. 103(C), pages 176-187.
    9. Janina Isabel Steinert & Lucie Dale Cluver & G. J. Melendez-Torres & Sebastian Vollmer, 2018. "One Size Fits All? The Validity of a Composite Poverty Index Across Urban and Rural Households in South Africa," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(1), pages 51-72, February.
    10. Janz, Teresa & Augsburg, Britta & Gassmann, Franziska & Nimeh, Zina, 2023. "Leaving no one behind: Urban poverty traps in Sub-Saharan Africa," World Development, Elsevier, vol. 172(C).
    11. Assaad, Ragui & Hendy, Rana & Salehi-Isfahani, Djavad, 2019. "Inequality of opportunity in educational attainment in the Middle East and North Africa: Evidence from household surveys," International Journal of Educational Development, Elsevier, vol. 66(C), pages 24-43.
    12. Samia Badji, 2016. "The Wealth Paradox for Whom? Child Labor and the Identification of Households Excluded from the Land and the Labor Markets in Madagascar," Working Papers 1638, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    13. Abderrahman Yassine & Fatima Bakass, 2022. "Do Education and Employment Play a Role in Youth’s Poverty Alleviation? Evidence from Morocco," Sustainability, MDPI, vol. 14(18), pages 1-25, September.
    14. Giulia Greco, 2018. "Setting the Weights: The Women’s Capabilities Index for Malawi," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 457-478, January.
    15. Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021. "Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements," Policy Research Working Paper Series 9838, The World Bank.
    16. Carlo Azzarri & Gero Carletto & Benjamin Davis & Alberto Zezza, 2006. "Monitoring Poverty Without Consumption Data : An Application Using the Albania Panel Survey," Eastern European Economics, Taylor & Francis Journals, vol. 44(1), pages 59-82, February.
    17. Bernard Chasekwa & John A Maluccio & Robert Ntozini & Lawrence H Moulton & Fan Wu & Laura E Smith & Cynthia R Matare & Rebecca J Stoltzfus & Mduduzi N N Mbuya & James M Tielsch & Stephanie L Martin & , 2018. "Measuring wealth in rural communities: Lessons from the Sanitation, Hygiene, Infant Nutrition Efficacy (SHINE) trial," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-19, June.
    18. Choi, Jin Young & Lee, Sang-Hyop, 2006. "Does prenatal care increase access to child immunization? Gender bias among children in India," Social Science & Medicine, Elsevier, vol. 63(1), pages 107-117, July.
    19. Sulaimon T Adedokun & Victor T Adekanmbi & Olalekan A Uthman & Richard J Lilford, 2017. "Contextual factors associated with health care service utilization for children with acute childhood illnesses in Nigeria," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-14, March.
    20. Maitra, Sudeshna, 2016. "The poor get poorer: Tracking relative poverty in India using a durables-based mixture model," Journal of Development Economics, Elsevier, vol. 119(C), pages 110-120.

    More about this item

    Keywords

    principal component analysis; Wealth Index; Socioeconomic Status;
    All these keywords.

    JEL classification:

    • 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:eco:journ1:2023-03-13. 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: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    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.