IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-18-00256.html
   My bibliography  Save this article

India's Universal Immunization Program: a lesson from Machine Learning

Author

Listed:
  • Dweepobotee Brahma

    (Western Michigan University)

  • Debasri Mukherjee

    (Western Michigan University)

Abstract

This paper examines the predictors of immunization coverages of children across Indian states and evaluates the role of Universal Immunization Program (UIP) - a comprehensive policy of the Indian government in that light. Employing Machine Learning methods such as LASSO and hierarchical LASSO, we find that not the UIP expenditure by itself, but health infrastructure turns out to be a robust predictor of immunization coverage. The policy prescription that follows from our study is that the immunization program should focus on promoting the required health infrastructure in addition to monitoring the usage of funds closely for facilitating effective usage of the money. We also scrutinize performances of ‘BIMARU', states that are considered traditionally underperforming states in terms of health and education.

Suggested Citation

  • Dweepobotee Brahma & Debasri Mukherjee, 2019. "India's Universal Immunization Program: a lesson from Machine Learning," Economics Bulletin, AccessEcon, vol. 39(1), pages 581-591.
    • Handle: RePEc:ebl:ecbull:eb-18-00256
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2019/Volume39/EB-19-V39-I1-P59.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    2. Sumon Kumar Bhaumik & Ralitza Dimova & Subal C. Kumbhakar & Kai Sun, 2018. "Is Tinkering with Institutional Quality a Panacea for Firm Performance? Insights from a Semiparametric Approach to Modeling Firm Performance," Review of Development Economics, Wiley Blackwell, vol. 22(1), pages 1-22, February.
    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. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    2. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
    3. 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.
    4. Thiemo Fetzer & Stephan Kyburz, 2018. "Cohesive Institutions and Political Violence," HiCN Working Papers 271, Households in Conflict Network.
    5. Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
    6. Sascha O. Becker & Thiemo Fetzer, 2018. "Has Eastern European Migration Impacted UK-born Workers?," CAGE Online Working Paper Series 376, Competitive Advantage in the Global Economy (CAGE).
    7. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    8. Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021. "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers 2109.12042, arXiv.org, revised Sep 2021.
    9. Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, revised Nov 2020.
    10. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    11. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," MPRA Paper 110703, University Library of Munich, Germany.
    12. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    13. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    14. Kristoffer B. Birkeland & Allan D. D'Silva & Roland Füss & Are Oust, 2021. "The Predictability of House Prices: "Human Against Machine"," International Real Estate Review, Global Social Science Institute, vol. 24(2), pages 139-183.
    15. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    16. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
    17. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    18. Ratzanyel Rincón, 2023. "Quarterly multidimensional poverty estimates in Mexico using machine learning algorithms/Estimaciones trimestrales de pobreza multidimensional en México mediante algoritmos de aprendizaje de máquina," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 38(1), pages 3-68.
    19. Kwabena Adu-Ababio & Aliisa Koivisto & Eliya Lungu & Evaristo Mwale & Jonathan Msoni & Kangwa Musole, 2023. "Estimating tax gaps in Zambia: A bottom-up approach based on audit assessments," WIDER Working Paper Series wp-2023-25, World Institute for Development Economic Research (UNU-WIDER).
    20. Daoud, Adel & Johansson, Fredrik, 2019. "Estimating Treatment Heterogeneity of International Monetary Fund Programs on Child Poverty with Generalized Random Forest," SocArXiv awfjt, Center for Open Science.

    More about this item

    Keywords

    Immunization in India; Machine Learning; variable selection and shrinkage; LASSO;
    All these keywords.

    JEL classification:

    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    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:ebl:ecbull:eb-18-00256. 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: John P. Conley (email available below). General contact details of provider: .

    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.