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Spillovers of health education at school on parents' physical activity

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  • Lucila Berniell;
  • Dolores de la Mata;
  • M. Nieves Valdes

Abstract

To prevent modern health conditions like obesity, cancer, cardiovascular illness, and diabetes, which have reached epidemic-like proportions in recent decades, many health experts argue that students should receive Health Education (HED) at school. Although this type of education aims mainly to improve children's health pro les, it might affect other family members as well. This paper exploits state HED reforms as quasi-natural experiments to estimate the causal impact of HED received by children on their parents' physical activity. We use data from the Panel Study of Income Dynamics (PSID) for the period 1999-2005 merged with data on state HED reforms from the National Association of State Boards of Education (NASBE) Health Policy Database, and the 2000 and 2006 School Health Policies and Programs Study (SHPPS). To identify the spillover effects of HED requirements on parents' behavior we use a "differences-in-differences in- differences" (DDD) methodology in which we allow for different types of treatments. We find a positive effect of HED reforms at the elementary school on the probability of parents doing light physical activity. Introducing major changes in HED increases the probability of fathers engaging in physical activity by 12.4 percentage points, while this probability for mothers does not seem to be affected. We find evidence of two channels that may drive these spillovers. We conclude that the gender specialization of parents in childcare activities, as well as information sharing between children and parents, may play a role in generating these indirect effects and in turn, in shaping healthy lifestyles within the household.

Suggested Citation

  • Lucila Berniell; & Dolores de la Mata; & M. Nieves Valdes, 2012. "Spillovers of health education at school on parents' physical activity," Health, Econometrics and Data Group (HEDG) Working Papers 12/13, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:12/13
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    References listed on IDEAS

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    1. Clark, Andrew E. & Etile, Fabrice, 2006. "Don't give up on me baby: Spousal correlation in smoking behaviour," Journal of Health Economics, Elsevier, pages 958-978.
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    3. Burkhauser, Richard V. & Cawley, John & Schmeiser, Maximilian D., 2009. "The timing of the rise in U.S. obesity varies with measure of fatness," Economics & Human Biology, Elsevier, vol. 7(3), pages 307-318, December.
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    5. Jonathan Guryan & Erik Hurst & Melissa Kearney, 2008. "Parental Education and Parental Time with Children," Journal of Economic Perspectives, American Economic Association, vol. 22(3), pages 23-46, Summer.
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    Cited by:

    1. repec:eee:socmed:v:183:y:2017:i:c:p:56-61 is not listed on IDEAS
    2. Andre Chagas & Carlos Azzoni & Alexandre Almeida, 2015. "A Spatial Difference-in-Difference Analysis to Measure the Sugarcane Producing Impact in Respiratory Health," ERSA conference papers ersa15p511, European Regional Science Association.
    3. Tengtrakul, Pitikorn & Peha, Jon M., 2013. "Does ICT in schools affect residential adoption and adult utilization outside schools?," Telecommunications Policy, Elsevier, vol. 37(6), pages 540-562.
    4. Chagas, André L.S. & Azzoni, Carlos R. & Almeida, Alexandre N., 2016. "A spatial difference-in-differences analysis of the impact of sugarcane production on respiratory diseases," Regional Science and Urban Economics, Elsevier, vol. 59(C), pages 24-36.

    More about this item

    Keywords

    physical activity; healthy lifestyles; indirect treatment effects; health education; triple differences.;

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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