IDEAS home Printed from https://ideas.repec.org/p/ind/igiwpp/2020-031.html
   My bibliography  Save this paper

A Simplified measure of nutritional empowerment using machine learning to abbreviate the Women's Empowerment in Nutrition Index (WENI)

Author

Listed:
  • Shree Saha

    (Cornell University)

  • Sudha Narayanan

    (Indira Gandhi Institute of Development Research)

Abstract

Measuring empowerment is both complicated and time consuming. A number of recent efforts have focused on how to better measure this complex multidimensional concept such that it is easy to implement. In this paper, we use machine learning techniques, specifically LASSO, us- ing survey data from five Indian states to abbreviate a recently developed measure of nutritional empowerment, the Women's Empowerment in Nutrition Index (WENI) that has 33 distinct indi- cators. Our preferred Abridged Women's Empowerment in Nutrition Index (A-WENI) consists of 20 indicators. We validate the A-WENI via a field survey from a new context, the west- ern Indian state of Maharashtra. We find that the 20-indicator A-WENI is both capable of reproducing well the empowerment status generated by the 33-indicator WENI and predicting nutritional outcomes such as BMI and dietary diversity. Using this index, we find that in our Maharashtra sample, on average, only 51.2 of mothers of children under the age of 5 years are nutritionally empowered, whereas 86.1 of their spouses are nutritionally empowered. We also find that only 22.3 of the elderly women are nutritionally empowered. These estimates are broadly consistent with those based on the 33-indicator WENI. The A-WENI will reduce the time burden on respondents and can be incorporated in any general purpose survey conducted in rural contexts. Many of the indicators in A-WENI are often collected routinely in contemporary household surveys. Hence, capturing nutritional empowerment does not entail significant additional burden. Developing A-WENI can thus aid in an expansion of efforts to measure nutritional empowerment; this is key to understanding better the barriers and challenges women face and help identify ways in which women can improve their nutritional well-being in meaningful ways.

Suggested Citation

  • Shree Saha & Sudha Narayanan, 2020. "A Simplified measure of nutritional empowerment using machine learning to abbreviate the Women's Empowerment in Nutrition Index (WENI)," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2020-031, Indira Gandhi Institute of Development Research, Mumbai, India.
  • Handle: RePEc:ind:igiwpp:2020-031
    as

    Download full text from publisher

    File URL: http://www.igidr.ac.in/pdf/publication/WP-2020-031.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    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. Esther Duflo, 2012. "Women Empowerment and Economic Development," Journal of Economic Literature, American Economic Association, vol. 50(4), pages 1051-1079, December.
    3. Sabina Alkire & James E. Foster & Suman Seth & Maria Emma Santos & Jose M. Roche & Paola Ballon, 2015. "Multidimensional Poverty Measurement and Analysis: Chapter 7 - Data and Analysis," OPHI Working Papers 88, Queen Elizabeth House, University of Oxford.
    4. Alkire, Sabina & Foster, James & Seth, Suman & Santos, Maria Emma & Roche, Jose Manuel & Ballon, Paola, 2015. "Multidimensional Poverty Measurement and Analysis," OUP Catalogue, Oxford University Press, number 9780199689491, December.
    5. Jayachandran, Seema & Biradavolu, Monica & Cooper, Jan, 2021. "Using Machine Learning and Qualitative Interviews to Design a Five-Question Women's Agency Index," IZA Discussion Papers 14221, Institute of Labor Economics (IZA).
    6. Solava Ibrahim & Sabina Alkire, 2007. "Agency and Empowerment: A Proposal for Internationally Comparable Indicators," Oxford Development Studies, Taylor & Francis Journals, vol. 35(4), pages 379-403.
    7. Sabina Alkire & James E. Foster & Suman Seth & Maria Emma Santos & Jose M. Roche & Paola Ballon, 2015. "Multidimensional Poverty Measurement and Analysis: Chapter 2 - The Framework," OPHI Working Papers 83, Queen Elizabeth House, University of Oxford.
    8. Linden McBride & Austin Nichols, 2018. "Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning," The World Bank Economic Review, World Bank, vol. 32(3), pages 531-550.
    9. Pratley, Pierre, 2016. "Associations between quantitative measures of women's empowerment and access to care and health status for mothers and their children: A systematic review of evidence from the developing world," Social Science & Medicine, Elsevier, vol. 169(C), pages 119-131.
    10. Robin A. Richardson, 2018. "Measuring Women’s Empowerment: A Critical Review of Current Practices and Recommendations for Researchers," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 137(2), pages 539-557, June.
    11. Yount, Kathryn M. & Cheong, Yuk Fai & Maxwell, Lauren & Heckert, Jessica & Martinez, Elena M. & Seymour, Gregory, 2019. "Measurement properties of the Project-Level Women’s Empowerment in Agriculture Index," IFPRI discussion papers 1798, International Food Policy Research Institute (IFPRI).
    12. Alsop, Ruth & Heinsohn, Nina, 2005. "Measuring empowerment in practice: structuring analysis and framing indicators," Policy Research Working Paper Series 3510, The World Bank.
    13. Narayanan, Sudha & Lentz, Erin & Fontana, Marzia & De, Anuradha & Kulkarni, Bharati, 2019. "Developing the Women's Empowerment in Nutrition Index in Two States of India," Food Policy, Elsevier, vol. 89(C).
    14. Sabina Alkire, James E. Foster, Suman Seth, Maria Emma Santos, Jose M. Roche and Paola Ballon, 2015. "Multidimensional Poverty Measurement and Analysis: Chapter 9 - Distribution and Dynamics," OPHI Working Papers ophiwp090_ch9.pdf, Queen Elizabeth House, University of Oxford.
    15. Sabina Alkire, James E. Foster, Suman Seth, Maria Emma Santos, José M. Roche and Paola Ballon, 2015. "Multidimensional Poverty Measurement and Analysis: Chapter 7 - Data and Analysis," OPHI Working Papers ophiwp088_ch7.pdf, Queen Elizabeth House, University of Oxford.
    16. Saha, Shree & Narayanan, Sudha, 2022. "A simplified measure of nutritional empowerment: Using machine learning to abbreviate the Women’s Empowerment in Nutrition Index (WENI)," World Development, Elsevier, vol. 154(C).
    17. Lentz, E.C. & Michelson, H. & Baylis, K. & Zhou, Y., 2019. "A data-driven approach improves food insecurity crisis prediction," World Development, Elsevier, vol. 122(C), pages 399-409.
    18. Naila Kabeer, 1999. "Resources, Agency, Achievements: Reflections on the Measurement of Women's Empowerment," Development and Change, International Institute of Social Studies, vol. 30(3), pages 435-464, July.
    19. Malapit, Hazel J. & Quisumbing, Agnes R. & Meinzen-Dick, Ruth Suseela & Seymour, Gregory & Martinez, Elena M. & Heckert, Jessica & Rubin, Deborah & Vaz, Ana & Yount, Kathryn M., 2019. "Development of the project-level Women’s Empowerment in Agriculture Index (pro-WEAI)," IFPRI discussion papers 1796, International Food Policy Research Institute (IFPRI).
    20. Sabina Alkire & James E. Foster & Suman Seth & Maria Emma Santos & Jose M. Roche & Paola Ballon, 2015. "Multidimensional Poverty Measurement and Analysis: Chapter 9 - Distribution and Dynamics," OPHI Working Papers 90, Queen Elizabeth House, University of Oxford.
    21. Sabina Alkire, James E. Foster, Suman Seth, Maria Emma Santos, José M. Roche and Paola Ballon, 2015. "Multidimensional Poverty Measurement and Analysis: Chapter 2 - The Framework," OPHI Working Papers ophiwp083_ch2.pdf, Queen Elizabeth House, University of Oxford.
    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. Saha, Shree & Narayanan, Sudha, 2022. "A simplified measure of nutritional empowerment: Using machine learning to abbreviate the Women’s Empowerment in Nutrition Index (WENI)," World Development, Elsevier, vol. 154(C).
    2. Galvin, Lauren & Verissimo, Cristiana K. & Ambikapathi, Ramya & Gunaratna, Nilupa S. & Rudnicka, Paula & Sunseri, Amy & Jeong, Joshua & O'Malley, Savannah Froese & Yousafzai, Aisha K. & Sando, Mary Mw, 2023. "Effects of engaging fathers and bundling nutrition and parenting interventions on household gender equality and women's empowerment in rural Tanzania: Results from EFFECTS, a five-arm cluster-randomiz," Social Science & Medicine, Elsevier, vol. 324(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. Jhonatan Clausen & Nicolas Barrantes, 2022. "Developing a Comprehensive Multidimensional Wellbeing Index Based on What People Value: An Application to a Middle-Income Country," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 17(6), pages 3253-3283, December.
    2. Abre-Rehmat Qurat-ul-Ann & Faisal Mehmood Mirza, 2021. "Multidimensional Energy Poverty in Pakistan: Empirical Evidence from Household Level Micro Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(1), pages 211-258, May.
    3. Londari Yamarak & Kevin A. Parton, 2023. "Impacts of mining projects in Papua New Guinea on livelihoods and poverty in indigenous mining communities," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(1), pages 13-27, January.
    4. 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.
    5. 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.
    6. Tavares, Fernando Flores & Betti, Gianni, 2021. "The pandemic of poverty, vulnerability, and COVID-19: Evidence from a fuzzy multidimensional analysis of deprivations in Brazil," World Development, Elsevier, vol. 139(C).
    7. Zaira Najam & John Gibson, 2022. "Does intra‐country poverty convergence depend on spatial spillovers and the type of poverty measure? Evidence from Pakistan," Asia and the Pacific Policy Studies, Wiley Blackwell, vol. 9(3), pages 516-535, September.
    8. Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2023. "Housing, imputed rent, and household welfare," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 131-168, March.
    9. Gaurav Datt, 2019. "Multidimensional poverty in the Philippines, 2004–2013: How much do choices for weighting, identification and aggregation matter?," Empirical Economics, Springer, vol. 57(4), pages 1103-1128, October.
    10. Nisreen Salti & Jad Chaaban & Alexandra Irani & Rima Al Mokdad, 2021. "A Multi-Dimensional Measure of Well-being among Youth: The Case of Palestinian Refugee Youth in Lebanon," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 154(1), pages 1-34, February.
    11. Dutta, Indranil & Nogales, Ricardo & Yalonetzky, Gaston, 2021. "Endogenous weights and multidimensional poverty: A cautionary tale," Journal of Development Economics, Elsevier, vol. 151(C).
    12. 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.
    13. Wajiha Haq & Mir Azam & Zaki Babar & Saad Amir & Fareyha Said, 2024. "Investigation of multidimensional poverty in Pakistan at the national, regional, and provincial level," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
    14. Juan-Francisco Sánchez-García & María-del-Carmen Sánchez-Antón & Rosa Badillo-Amador & María-del-Carmen Marco-Gil & Juan-Vicente LLinares-Ciscar & Susana Álvarez-Díez, 2019. "A New Extension of Bourguignon and Chakravarty Index to Measure Educational Poverty and Its Application to the OECD Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(2), pages 479-501, September.
    15. Julien Hanoteau, 2023. "Do foreign MNEs alleviate multidimensional poverty in developing countries?," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 13(4), pages 719-749, December.
    16. Andrea Brandolini & John Micklewright, 2020. "Tony Atkinson’s new book, Measuring Poverty Around the World. Some further reflections," Working Papers 518, ECINEQ, Society for the Study of Economic Inequality.
    17. Verónica Amarante & Maira Colacce, 2022. "Multidimensional Poverty Among Older People in Five Latin American Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 159(3), pages 945-965, February.
    18. Zaira Najam & Susan Olivia, 2021. "Does the impact of cash transfers differ across poverty measures? Evidence from Pakistan," Working Papers in Economics 21/09, University of Waikato.
    19. Kristi Mahrt & Andrea Rossi & Vincenzo Salvucci & Finn Tarp, 2020. "Multidimensional Poverty of Children in Mozambique," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 13(5), pages 1675-1700, October.
    20. Grant J. Cameron & Hai‐Anh H. Dang & Mustafa Dinc & James Foster & Michael M. Lokshin, 2021. "Measuring the Statistical Capacity of Nations," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(4), pages 870-896, August.

    More about this item

    Keywords

    Empowerment; nutrition; machine learning; LASSO; gender; India; South Asia;
    All these keywords.

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I00 - Health, Education, and Welfare - - General - - - General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

    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:ind:igiwpp:2020-031. 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: Shamprasad M. Pujar (email available below). General contact details of provider: https://edirc.repec.org/data/igidrin.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.