IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v58y2024i4d10.1007_s11135-023-01811-8.html
   My bibliography  Save this article

Predictive power of composite socioeconomic indices for targeted programs: principal components and partial least squares

Author

Listed:
  • Stefanía D’Iorio

    (Universidad Nacional del Litoral)

  • Liliana Forzani

    (Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
    Universidad Nacional del Litoral)

  • Rodrigo García Arancibia

    (Universidad Nacional del Litoral
    Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET))

  • Ignacio Girela

    (Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
    Universidad Nacional de Córdoba, Bv. Enrique Barros, Ciudad Universitaria)

Abstract

Principal components analysis (PCA) and partial least squares (PLS) have been used for the construction of socioeconomic status (SES) indices to use as a predictor of the well-being status in targeted programs. Generally, these indicators are constructed as a linear combination of the first component. Due to the characteristics of the socioeconomic data, different extensions of PCA and PLS for non-metric variables have been proposed for these applications. In this paper, we compare the predictive performance of SES indices constructed using more than one component. Additionally, for the inclusion of non-metric variables, a variant of the normal mean coding is proposed that takes into account the multivariate nature of the variables, which we call multivariate normal mean coding (MNMC). Using simulations and real data, we found that PLS using MNMC as well as the classical dummy encoding method give the best predictive results with a more parsimonious SES index, both in regression and classification problems.

Suggested Citation

  • Stefanía D’Iorio & Liliana Forzani & Rodrigo García Arancibia & Ignacio Girela, 2024. "Predictive power of composite socioeconomic indices for targeted programs: principal components and partial least squares," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3497-3534, August.
    • Handle: RePEc:spr:qualqt:v:58:y:2024:i:4:d:10.1007_s11135-023-01811-8
    DOI: 10.1007/s11135-023-01811-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-023-01811-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11135-023-01811-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. David Coady & Margaret Grosh & John Hoddinott, 2004. "Targeting of Transfers in Developing Countries : Review of Lessons and Experience," World Bank Publications - Books, The World Bank Group, number 14902.
    2. Mathieu J. P. Poirier & Karen A. Grépin & Michel Grignon, 2020. "Approaches and Alternatives to the Wealth Index to Measure Socioeconomic Status Using Survey Data: A Critical Interpretive Synthesis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(1), pages 1-46, February.
    3. Matteo Mazziotta & Adriano Pareto, 2019. "Use and Misuse of PCA for Measuring Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(2), pages 451-476, April.
    4. Linden McBride & Christopher B. Barrett & Christopher Browne & Leiqiu Hu & Yanyan Liu & David S. Matteson & Ying Sun & Jiaming Wen, 2022. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 879-892, June.
    5. Jisu Yoon & Stephan Klasen, 2018. "An Application of Partial Least Squares to the Construction of the Social Institutions and Gender Index (SIGI) and the Corruption Perception Index (CPI)," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(1), pages 61-88, July.
    6. World Bank, 2015. "The State of Social Safety Nets 2015," World Bank Publications - Books, The World Bank Group, number 22101.
    7. Rema Hanna & Benjamin A. Olken, 2018. "Universal Basic Incomes versus Targeted Transfers: Anti-Poverty Programs in Developing Countries," Journal of Economic Perspectives, American Economic Association, vol. 32(4), pages 201-226, Fall.
    8. 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.
    9. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    10. Jisu Yoon & Tatyana Krivobokova, 2018. "Treatments of non-metric variables in partial least squares and principal component analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(6), pages 971-987, April.
    11. Forzani, Liliana & García Arancibia, Rodrigo & Llop, Pamela & Tomassi, Diego, 2018. "Supervised dimension reduction for ordinal predictors," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 136-155.
    12. Sabrina Duarte & Liliana Forzani & Pamela Llop & Rodrigo García Arancibia & Diego Tomassi, 2023. "Socioeconomic Index for Income and Poverty Prediction: A Sufficient Dimension Reduction Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 318-346, June.
    13. Wynne W. Chin & Barbara L. Marcolin & Peter R. Newsted, 2003. "A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study," Information Systems Research, INFORMS, vol. 14(2), pages 189-217, June.
    14. Stanislav Kolenikov & Gustavo Angeles, 2009. "Socioeconomic Status Measurement With Discrete Proxy Variables: Is Principal Component Analysis A Reliable Answer?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 55(1), pages 128-165, March.
    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. Stefanía D’Iorio & Liliana Forzani & Rodrigo García Arancibia & Ignacio Girela, 2023. "Predictive Power of Composite Socioeconomic Indices in Regression and Classification: Principal Components and Partial Least Squares," Working Papers 246, Red Nacional de Investigadores en Economía (RedNIE).
    2. Sabrina Duarte & Liliana Forzani & Pamela Llop & Rodrigo García Arancibia & Diego Tomassi, 2023. "Socioeconomic Index for Income and Poverty Prediction: A Sufficient Dimension Reduction Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 318-346, June.
    3. Paschalis Arvanitidis & Athina Economou & Christos Kollias, 2016. "Terrorism’s effects on social capital in European countries," Public Choice, Springer, vol. 169(3), pages 231-250, December.
    4. Juan M Villa, 2016. "A harmonised proxy means test for Kenya’s National Safety Net programme," Global Development Institute Working Paper Series 032016, GDI, The University of Manchester.
    5. Randell, Heather, 2016. "The short-term impacts of development-induced displacement on wealth and subjective well-being in the Brazilian Amazon," World Development, Elsevier, vol. 87(C), pages 385-400.
    6. Haseeb, Muhammad & Vyborny, Kate, 2022. "Data, discretion and institutional capacity: Evidence from cash transfers in Pakistan," Journal of Public Economics, Elsevier, vol. 206(C).
    7. Saini, Shweta & Sharma, Sameedh & Gulati, Ashok & Hussain, Siraj & von Braun, Joachim, 2017. "Indian food and welfare schemes: Scope for digitization towards cash transfers," Discussion Papers 261791, University of Bonn, Center for Development Research (ZEF).
    8. Brown, Joe & Hamoudi, Amar & Jeuland, Marc & Turrini, Gina, 2017. "Seeing, believing, and behaving: Heterogeneous effects of an information intervention on household water treatment," Journal of Environmental Economics and Management, Elsevier, vol. 86(C), pages 141-159.
    9. David Pérez-Mesa & à ngel S. Marrero, 2024. "Adult health and inequality of opportunity in Spain," Working Papers 671, ECINEQ, Society for the Study of Economic Inequality.
    10. Lannes, Laurence, 2015. "Improving health worker performance: The patient-perspective from a PBF program in Rwanda," Social Science & Medicine, Elsevier, vol. 138(C), pages 1-11.
    11. Lucio Esposito & Sunil Mitra Kumar & Adrián Villaseñor, 2020. "The importance of being earliest: birth order and educational outcomes along the socioeconomic ladder in Mexico," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(3), pages 1069-1099, July.
    12. Emily Aiken & Suzanne Bellue & Dean Karlan & Christopher R. Udry & Joshua Blumenstock, 2021. "Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance," NBER Working Papers 29070, National Bureau of Economic Research, Inc.
    13. Patrick Ward, 2014. "Measuring the Level and Inequality of Wealth: An Application to China," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(4), pages 613-635, December.
    14. 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.
    15. Yadira Diaz, Francisco Alejandro Espinoza, Yvonni Markaki, and Lina Maria Sanchez-Cespedes, 2015. "Targeting Grenada’s Most Deprived Population: A Multidimensional Living Conditions Assessment," OPHI Working Papers ophiwp092.pdf, Queen Elizabeth House, University of Oxford.
    16. Brown,Caitlin Susan & Kandpal,Eeshani & Lee,Jean Nahrae & Williams,Anaise Marie, 2022. "Unequal Households or Communities ? Decomposing the Inequality in Nutritional Status in South Asia," Policy Research Working Paper Series 10009, The World Bank.
    17. Schleicher, Michael & Souares, Aurélia & Pacere, Athanase Narangoro & Sauerborn, Rainer & Klonner, Stefan, 2016. "Decentralized versus Statistical Targeting of Anti-Poverty Programs: Evidence from Burkina Faso," Working Papers 0623, University of Heidelberg, Department of Economics.
    18. Yuki Higuchi & Nobuhiko Fuwa & Kei Kajisa & Takahiro Sato & Yasuyuki Sawada, 2019. "Disaster Aid Targeting and Self-Reporting Bias: Natural Experimental Evidence from the Philippines," Sustainability, MDPI, vol. 11(3), pages 1-13, February.
    19. Hermann Pythagore Pierre Donfouet & Shukri F. Mohamed & Eric Malin, 2021. "Socioeconomic inequality in tobacco use in Kenya: a concentration analysis," International Journal of Health Economics and Management, Springer, vol. 21(2), pages 247-269, June.
    20. Armand, Alex & Carneiro, Pedro & Locatelli, Andrea & Mihreteab, Selam & Keating, Joseph, 2017. "Do public health interventions crowd out private health investments? Malaria control policies in Eritrea," Labour Economics, Elsevier, vol. 45(C), pages 107-115.

    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:spr:qualqt:v:58:y:2024:i:4:d:10.1007_s11135-023-01811-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.