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Dietary patterns and body mass indices among adults in Korea: evidence from pseudo panel data

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  • Chang Keun Kwock
  • Junhyung Park

Abstract

The debate over the association between dietary patterns and obesity has not been settled in the literature. Some studies suggest that there are significant differences in mean body mass index across dietary patterns whereas other studies refute that result. The objective of this study is to test whether dietary patterns have a significant effect on body mass index in Korean adults when controlling for calorie intake and several sociodemographic factors. We present new evidence derived from pseudo panel data created from a series of cross-sections. Our results from the pseudo panel analysis show that some of the dietary patterns that were identified from the Korean adults’ food intake survey have a significant effect on body mass index. Specifically, males with the “beef and processed food” (P > 0.05), or “pork and alcohol” (P > 0.05) dietary pattern had significantly higher BMIs, whereas females with the “fast food” (P > 0.1) or “ramen and bakery” (P > 0.01) dietary pattern had higher BMI.

Suggested Citation

  • Chang Keun Kwock & Junhyung Park, 2015. "Dietary patterns and body mass indices among adults in Korea: evidence from pseudo panel data," Agricultural Economics, International Association of Agricultural Economists, vol. 46(2), pages 163-172, March.
  • Handle: RePEc:bla:agecon:v:46:y:2015:i:2:p:163-172
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    File URL: http://hdl.handle.net/10.1111/agec.12148
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    1. Verbeek, Marno & Nijman, Theo, 1992. "Can Cohort Data Be Treated as Genuine Panel Data?," Empirical Economics, Springer, vol. 17(1), pages 9-23.
    2. Moffitt, Robert, 1993. "Identification and estimation of dynamic models with a time series of repeated cross-sections," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 99-123, September.
    3. Keele, Luke & Kelly, Nathan J., 2006. "Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables," Political Analysis, Cambridge University Press, vol. 14(2), pages 186-205, April.
    4. Franco Sassi & Marion Devaux & Michele Cecchini & Elena Rusticelli, 2009. "The Obesity Epidemic: Analysis of Past and Projected Future Trends in Selected OECD Countries," OECD Health Working Papers 45, OECD Publishing.
    5. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
    6. Propper, Carol & Rees, Hedley & Green, Katherine, 2001. "The Demand for Private Medical Insurance in the UK: A Cohort Analysis," Economic Journal, Royal Economic Society, vol. 111(471), pages 180-200, May.
    7. Dolores Collado, M., 1997. "Estimating dynamic models from time series of independent cross-sections," Journal of Econometrics, Elsevier, vol. 82(1), pages 37-62.
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