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Birthweight and Academic Achievement in Childhood


  • Pinka Chatterji
  • Dohyung Kim
  • Kajal Lahiri


Research has shown that birthweight has a lasting impact on later-life outcomes such as educational attainment and earnings. This paper examines the role of health at birth in determining academic achievement in childhood, which may provide the link between birthweight and adult outcomes. Using three waves of the PSID-CDS data over 1997-2009, we build on the literature by employing fetal growth rate as a proxy for nutritional intake in utero and propose a nested error-component two-stage least squares (NEC2SLS) estimator that draws on internal instruments from alternative dimensions of the multi-level panel data set. In particular, this alternative estimator allows us to exploit the information on children with no siblings in the sample, which comprises over 40 percent of the observations in our sample, as well as to obtain coefficient estimates for the time-invariant variables such as race and maternal education. This would not be feasible with the usual mother fixed effects estimation. We obtain modest but significant effects of fetal growth rate on math and reading scores, with the effects concentrated in the low birthweight range. Infant health measures appear to explain little of the well-documented racial disparity in test scores.

Suggested Citation

  • Pinka Chatterji & Dohyung Kim & Kajal Lahiri, 2014. "Birthweight and Academic Achievement in Childhood," CESifo Working Paper Series 4786, CESifo Group Munich.
  • Handle: RePEc:ces:ceswps:_4786

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    Cited by:

    1. Deborah A. Cobb-Clark & Julie Moschion, 2017. "Gender gaps in early educational achievement," Journal of Population Economics, Springer;European Society for Population Economics, vol. 30(4), pages 1093-1134, October.
    2. repec:spr:izalbr:v:6:y:2017:i:1:d:10.1186_s40172-017-0056-1 is not listed on IDEAS
    3. Chatterji, Pinka & Lahiri, Kajal & Kim, Dohyung, 2014. "Fetal growth and neurobehavioral outcomes in childhood," Economics & Human Biology, Elsevier, vol. 15(C), pages 187-200.
    4. Barry J. Milne & Roy Lay-Yee & Jessica M. Mc Lay & Janet Pearson & Martin von Randow & Peter Davis, 2015. "Modelling the Early life-course (MELC): A Microsimulation Model of Child Development in New Zealand," International Journal of Microsimulation, International Microsimulation Association, vol. 8(2), pages 28-60.
    5. McDonough, Ian K. & Millimet, Daniel L., 2017. "Missing data, imputation, and endogeneity," Journal of Econometrics, Elsevier, vol. 199(2), pages 141-155.

    More about this item


    birthweight; academic achievement; hierarchical panel data; nested error component 2SLS; mother fixed effects; racial disparity; mother’s education; PSID-CDS;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination


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