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Measuring maternal autonomy and its effect on child nutrition in rural India

Author

Listed:
  • Arulampalam, Wiji

    (Department of Economics, University of Warwick)

  • Bhaskar, Anjor

    (Azim Premji University, Bangalore)

  • Srivastava, Nisha

    (nstitute for Human Development, Delhi)

Abstract

We examine the link between a mother’s autonomy - the freedom and ability to think, express, make decisions, and act, independently - and the nutritional status of her children. We design a novel statistical framework that accounts for the cultural and traditional environment to create a measure of maternal autonomy treating this as a latent characteristic that is fixed in the short term. Using data from India, we deal with two econometric challenges : (i) creation and measurement of the ‘autonomy’ index, and (ii) endogeneity caused by selection due to son preference. We find : (i) one standard deviation (SD) higher autonomy score is associated with a 0.16 SD higher Height-for-Age Z-scores (HAZ) ; and an (ii)10% lower prevalence of stunting (HAZ

Suggested Citation

  • Arulampalam, Wiji & Bhaskar, Anjor & Srivastava, Nisha, 2023. "Measuring maternal autonomy and its effect on child nutrition in rural India," The Warwick Economics Research Paper Series (TWERPS) 1455, University of Warwick, Department of Economics.
    • Handle: RePEc:wrk:warwec:1455
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    File URL: https://warwick.ac.uk/fac/soc/economics/research/workingpapers/2023/twerp_1455_-_arulampalam.pdf
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    More about this item

    Keywords

    Child Nutrition ; Latent Factor Models ; Maternal Autonomy ; Endogenous selection ; Son preference JEL codes: I15 ; I14 ; C38;
    All these keywords.

    JEL classification:

    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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