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The effects of meternal education versus cognitive test scores on child nutrition in Kenya

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  • Melanie Fox-Kean
  • Alok Bhargava

Abstract

This paper estimates dynamic random effects models for intakes by 100 Kenyan school children (6-9 years) of dietary energy, protein, calcium, iron, zinc, niacin, riboflavin, thiamin, and vitamins A, C, D, and E within a multivariate longitudinal framework. The explanatory variables were socioeconomic and background variables, children’s height and weight, and maternal education, cognitive test scores and morbidity spells. The model parameters were estimated using the maximum likelihood method controlling for the unobserved between-children differences. The main findings were, first, that while maternal education was usually not a significant predictor of the intakes, maternal scores on cognitive tests strongly predicted children’s dietary intakes. Second, the paternal cognitive scores and maternal morbidity levels were not significantly associated with the intakes. Third, an index of socioeconomic status and cash income was a significant predictor. The results indicated the need to consider broader measures of human development in developing countries and of implementing educational programs for women without school education.

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 North American Winter Meetings with number 39.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:nawm04:39

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Keywords: Anthropometry; cognitive tests; econometric models; economic development; maternal education; maximum likelihood estimation; nutrition;

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References

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  1. Bicego, George T. & Ties Boerma, J., 1993. "Maternal education and child survival: A comparative study of survey data from 17 countries," Social Science & Medicine, Elsevier, vol. 36(9), pages 1207-1227, May.
  2. Bhargava, Alok & Sargan, J D, 1983. "Estimating Dynamic Random Effects Models from Panel Data Covering Short Time Periods," Econometrica, Econometric Society, vol. 51(6), pages 1635-59, November.
  3. Bouis, Howarth E. & Haddad, Lawrence J., 1992. "Are estimates of calorie-income fxelasticities too high? : A recalibration of the plausible range," Journal of Development Economics, Elsevier, vol. 39(2), pages 333-364, October.
  4. Bhargava, Alok, 1991. "Identification and Panel Data Models with Endogenous Regressors," Review of Economic Studies, Wiley Blackwell, vol. 58(1), pages 129-40, January.
  5. Steven Block, 2002. "Nutrition Knowledge Versus Schooling in the Demand for Child Micronutrient Status," Working Papers in Food Policy and Nutrition 10, Friedman School of Nutrition Science and Policy.
  6. Alok Bhargava & Jiang Yu, 1997. "A Longitudinal Analysis of Infant and Child Mortality Rates in Developing Countries," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 32(2), pages 141-153, July.
  7. Bhargava, Alok, 1997. "Nutritional status and the allocation of time in Rwandese households," Journal of Econometrics, Elsevier, vol. 77(1), pages 277-295, March.
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Cited by:
  1. Rubalcava, Luis N. & Teruel, Graciela M., 2004. "The role of maternal cognitive ability on child health," Economics & Human Biology, Elsevier, vol. 2(3), pages 439-455, December.
  2. Bhargava, Alok, 2013. "Iron status, malaria parasite loads and food policies: Evidence from sub-Saharan Africa," Economics & Human Biology, Elsevier, vol. 11(1), pages 108-112.
  3. Alderman, Harold, 2007. "Improving Nutrition through Community Growth Promotion: Longitudinal Study of the Nutrition and Early Child Development Program in Uganda," World Development, Elsevier, vol. 35(8), pages 1376-1389, August.

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