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India’S Mid-Day Meal Program And Schooling: An Evaluation Based On Machine Learning


  • BRAHMA, D.


Using state-level data on India’s Mid-Day Meal program (school lunch program for children) we scrutinize if funds disbursed and food-grains supplied for the purpose can actually serve as good determinants of number of children covered (fed) by the scheme (our dependent variable). Our standard regression studies find that the effect of food-grain supplied has statistically significant effect on the dependent variable while the marginal effects of funds/money disbursed is not statistically significant. Using LASSO based Machine Learning techniques and after controlling for several correlated variables and state level factors we find that both policy variables - funds and food-grains however act as good out-of-sample predictors of number of children being covered. Evaluation of out-of-sample prediction performance of regression models and covariates is a very important but mostly an uncharted territory in development economics. Note that funds are used partly to provide fixed costs of the food service provided, and therefore it is not surprising that the coefficient estimate of this covariate (i.e., its marginal effect on dependent variable) does not appear to be statistically significant; but it can still act as a good overall predictor for our dependent variable.

Suggested Citation

  • Brahma, D. & Mukherjee, D., 2018. "India’S Mid-Day Meal Program And Schooling: An Evaluation Based On Machine Learning," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 18(1), pages 141-152.
  • Handle: RePEc:eaa:aeinde:v:18:y:2018:i:1_10

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    References listed on IDEAS

    1. Dreze, Jean & Goyal, Aparajita, 2003. "Future of Mid-Day Meals," MPRA Paper 17386, University Library of Munich, Germany.
    2. 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.
    3. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    4. Abhijeet Singh & Albert Park & Stefan Dercon, 2014. "School Meals as a Safety Net: An Evaluation of the Midday Meal Scheme in India," Economic Development and Cultural Change, University of Chicago Press, vol. 62(2), pages 275-306.
    5. Rajshri Jayaraman & Dora Simroth, 2015. "The Impact of School Lunches on Primary School Enrollment: Evidence from India's Midday Meal Scheme," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(4), pages 1176-1203, October.
    6. Farzana Afridi, 2011. "The Impact of School Meals on School Participation: Evidence from Rural India," Journal of Development Studies, Taylor & Francis Journals, vol. 47(11), pages 1636-1656.
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    More about this item


    LASSO; Machine Learning; India’s Mid-Day Meal scheme; Schooling.;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy


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