IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v155y2021i2d10.1007_s11205-021-02617-7.html
   My bibliography  Save this article

Composite Measures for Assessing Multidimensional Social Exclusion in Later Life: Conceptual and Methodological Challenges

Author

Listed:
  • Sinéad Keogh

    (National University of Ireland Galway)

  • Stephen O’Neill

    (National University of Ireland Galway
    London School of Hygiene and Tropical Medicine)

  • Kieran Walsh

    (National University of Ireland Galway)

Abstract

Although there are a number of approaches to constructing a measure of multidimensional social exclusion in later life, theoretical and methodological challenges exist around the aggregation and weighting of constituent indicators. This is in addition to a reliance on secondary data sources that were not designed to collect information on social exclusion. In this paper, we address these challenges by comparing a range of existing and novel approaches to constructing a composite measure and assess their performance in explaining social exclusion in later life. We focus on three widely used approaches (sum-of-scores with an applied threshold; principal component analysis; normalisation with linear aggregation), and three novel supervised machine-learning based approaches (least absolute shrinkage and selection operator; classification and regression tree; random forest). Using an older age social exclusion conceptual framework, these approaches are applied empirically with data from Wave 1 of The Irish Longitudinal Study on Ageing (TILDA). The performances of the approaches are assessed using variables that are causally related to social exclusion.

Suggested Citation

  • Sinéad Keogh & Stephen O’Neill & Kieran Walsh, 2021. "Composite Measures for Assessing Multidimensional Social Exclusion in Later Life: Conceptual and Methodological Challenges," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(2), pages 389-410, June.
  • Handle: RePEc:spr:soinre:v:155:y:2021:i:2:d:10.1007_s11205-021-02617-7
    DOI: 10.1007/s11205-021-02617-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11205-021-02617-7
    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/s11205-021-02617-7?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. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020. "lassopack: Model selection and prediction with regularized regression in Stata," Stata Journal, StataCorp LP, vol. 20(1), pages 176-235, March.
    2. Jiahua Chen & Zehua Chen, 2008. "Extended Bayesian information criteria for model selection with large model spaces," Biometrika, Biometrika Trust, vol. 95(3), pages 759-771.
    3. Todd E. Clark, 2004. "Can out-of-sample forecast comparisons help prevent overfitting?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 115-139.
    4. Cuenca-García, Eduardo & Sánchez, Angeles & Navarro-Pabsdorf, Margarita, 2019. "Assessing the performance of the least developed countries in terms of the Millennium Development Goals," Evaluation and Program Planning, Elsevier, vol. 72(C), pages 54-66.
    5. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    6. A.B. Atkinson & John Hills, 1998. "Exclusion, Employment and Opportunity," CASE Papers 004, Centre for Analysis of Social Exclusion, LSE.
    7. Koen Decancq & María Ana Lugo, 2013. "Weights in Multidimensional Indices of Wellbeing: An Overview," Econometric Reviews, Taylor & Francis Journals, vol. 32(1), pages 7-34, January.
    8. Luo, Ye & Hawkley, Louise C. & Waite, Linda J. & Cacioppo, John T., 2012. "Loneliness, health, and mortality in old age: A national longitudinal study," Social Science & Medicine, Elsevier, vol. 74(6), pages 907-914.
    9. Prattley, Jennifer & Buffel, Tine & Marshall, Alan & Nazroo, James, 2020. "Area effects on the level and development of social exclusion in later life," Social Science & Medicine, Elsevier, vol. 246(C).
    10. Rosanna Scutella & Roger Wilkins & Weiping Kostenko, 2009. "Estimates of Poverty and Social Exclusion in Australia: A Multidimensional Approach," Melbourne Institute Working Paper Series wp2009n26, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    11. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
    12. Kieran Walsh & Thomas Scharf & Norah Keating, 2017. "Social exclusion of older persons: a scoping review and conceptual framework," European Journal of Ageing, Springer, vol. 14(1), pages 81-98, March.
    13. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    14. J. Vrooman & Stella Hoff, 2013. "The Disadvantaged Among the Dutch: A Survey Approach to the Multidimensional Measurement of Social Exclusion," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 113(3), pages 1261-1287, September.
    15. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    16. A.B. Atkinson & John Hills, 1998. "Exclusion, Employment and Opportunity," CASE Papers case04, Centre for Analysis of Social Exclusion, LSE.
    17. 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.
    18. Salvatore Greco & Alessio Ishizaka & Menelaos Tasiou & Gianpiero Torrisi, 2019. "On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 61-94, January.
    19. Matteo Mazziotta & Adriano Pareto, 2016. "Methods for Constructing Non-Compensatory Composite Indices: A Comparative Study," Forum for Social Economics, Taylor & Francis Journals, vol. 45(2-3), pages 213-229, August.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Matheus Pereira Libório & Alexandre Magno Alves Diniz & Hamidreza Rabiei-Dastjerd & Oseias da Silva Martinuci & Carlos Augusto Paiva da Silva Martins & Petr Iakovlevitch Ekel, 2023. "A Decision Framework for Identifying Methods to Construct Stable Composite Indicators That Capture the Concept of Multidimensional Social Phenomena: The Case of Social Exclusion," Sustainability, MDPI, vol. 15(7), pages 1-17, April.
    2. Matheus Pereira Libório & Oseias da Silva Martinuci & Alexei Manso Correa Machado & Renata de Mello Lyrio & Patrícia Bernardes, 2022. "Time–Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 159(2), pages 569-591, January.
    3. Thomas Hansen & Marcela Petrová Kafková & Ruth Katz & Ariela Lowenstein & Sigal Naim & George Pavlidis & Feliciano Villar & Kieran Walsh & Marja Aartsen, 2021. "Exclusion from Social Relations in Later Life: Micro- and Macro-Level Patterns and Correlations in a European Perspective," IJERPH, MDPI, vol. 18(23), pages 1-16, November.
    4. Jiménez-Fernández, Eduardo & Sánchez, Angeles & Ortega-Pérez, Mario, 2022. "Dealing with weighting scheme in composite indicators: An unsupervised distance-machine learning proposal for quantitative data," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).

    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. Luna Bellani & Conchita D’Ambrosio, 2011. "Deprivation, Social Exclusion and Subjective Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 104(1), pages 67-86, October.
    2. Qingliang Fan & Yaqian Wu, 2020. "Endogenous Treatment Effect Estimation with some Invalid and Irrelevant Instruments," Papers 2006.14998, arXiv.org.
    3. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 21(3), pages 915-958.
    4. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
    5. Abdul Hameed & Zara Qaiser, 2019. "Estimating Social Exclusion in Rural Pakistan: A Contribution to Social Development Policies," Business & Economic Review, Institute of Management Sciences, Peshawar, Pakistan, vol. 11(1), pages 103-122, March.
    6. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020. "lassopack: Model selection and prediction with regularized regression in Stata," Stata Journal, StataCorp LP, vol. 20(1), pages 176-235, March.
    7. Shi, Zhentao & Huang, Jingyi, 2023. "Forward-selected panel data approach for program evaluation," Journal of Econometrics, Elsevier, vol. 234(2), pages 512-535.
    8. Tovar Reaños, Miguel A., 2021. "Fuel for poverty: A model for the relationship between income and fuel poverty. Evidence from Irish microdata," Energy Policy, Elsevier, vol. 156(C).
    9. Falco J. Bargagli-Dtoffi & Massimo Riccaboni & Armando Rungi, 2020. "Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints," Working Papers 01/2020, IMT School for Advanced Studies Lucca, revised Jun 2020.
    10. 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.
    11. Agnese Peruzzi, 2015. "From Childhood Deprivation to Adult Social Exclusion: Evidence from the 1970 British Cohort Study," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 120(1), pages 117-135, January.
    12. Francesca Giambona & Erasmo Vassallo, 2014. "Composite Indicator of Social Inclusion for European Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 116(1), pages 269-293, March.
    13. Mariateresa Ciommi & Chiara Gigliarano & Francesco M. Chelli & Mauro Gallegati, 2022. "It is the Total that Does [Not] Make the Sum: Nature, Economy and Society in the Equitable and Sustainable Well-Being of the Italian Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 491-522, June.
    14. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    15. Tania Burchardt & Julian Le Grand, 2002. "Constraint and Opportunity: Identifying Voluntary Non-Employment," CASE Papers case55, Centre for Analysis of Social Exclusion, LSE.
    16. Greco, Salvatore & Ishizaka, Alessio & Tasiou, Menelaos & Torrisi, Gianpiero, 2018. "σ-µ efficiency analysis: A new methodology for evaluating units through composite indices," MPRA Paper 83569, University Library of Munich, Germany.
    17. Maite Blázquez Cuesta & Santiago Budría, 2014. "Deprivation and Subjective Well-Being: Evidence from Panel Data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(4), pages 655-682, December.
    18. Tomasz Panek & Jan Zwierzchowski, 2022. "Examining the Degree of Social Exclusion Risk of the Population Aged 50 + in the EU Countries Under the Capability Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 163(3), pages 973-1002, October.
    19. Xiaotong Shen & Wei Pan & Yunzhang Zhu & Hui Zhou, 2013. "On constrained and regularized high-dimensional regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(5), pages 807-832, October.
    20. Fusco, Elisa, 2023. "Potential improvements approach in composite indicators construction: The Multi-directional Benefit of the Doubt model," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).

    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:soinre:v:155:y:2021:i:2:d:10.1007_s11205-021-02617-7. 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.