IDEAS home Printed from https://ideas.repec.org/p/koc/wpaper/1703.html
   My bibliography  Save this paper

Determinants of Obesity in Turkey: A Quantile Regression Analysis from a Developing Country

Author

Listed:
  • Deniz Karaoglan

    () (Bahcesehir University, Department of Economics)

  • Aysit Tansel

    (Middle East Technical University, IZA Bonn, ERF, Cairo)

Abstract

This study investigates the factors that may influence the obesity in Turkey which is a developing country by implementing Quantile Regression (QR) methodology. The control factors that we consider are education, labor market outcomes, household income, age, gender, region and marital status. The analysis is conducted by using the 2008, 2010 and 2012 waves of the Turkish Health Survey (THS) prepared by the Turkish Statistical Institute (TURKSTAT). The obesity indicator in our study is the individual’s Body Mass Index (BMI). QR regression results provide robust evidence that additional years of schooling has negative effect on individual’s BMI and this effect significantly raises across different quantiles of BMI. QR results also indicate that males tend to have higher BMI at lower quantiles of BMI, whereas females have higher BMI at the top quantiles. This implies that females have higher tendency to be obese in Turkey. Our findings also imply that the positive effect of age on individual’s BMI levels raises across the quantiles at a decreasing rate. In addition, the effect of living in urban or rural areas do not significantly differ at the highest quantile distributions of BMI. Our results also reveal that the negative effect of being single on BMI increases gradually in absolute value across the quantiles of BMI implying that single individuals have less tendency to be obese or overweight compared to the married or widowed/divorced individuals. Moreover, the negative effect of being in labor force on individual’s BMI increases across the quantiles of BMI implying that an individual is more likely to be obese if he/she is out of labor force. Finally, the impact of household income on BMI is positive and significant all quantiles.

Suggested Citation

  • Deniz Karaoglan & Aysit Tansel, 2017. "Determinants of Obesity in Turkey: A Quantile Regression Analysis from a Developing Country," Koç University-TUSIAD Economic Research Forum Working Papers 1703, Koc University-TUSIAD Economic Research Forum.
    • Handle: RePEc:koc:wpaper:1703
    as

    Download full text from publisher

    File URL: http://eaf.ku.edu.tr/sites/eaf.ku.edu.tr/files/erf_wp_1703.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Atella, Vincenzo & Pace, Noemi & Vuri, Daniela, 2008. "Are employers discriminating with respect to weight?: European Evidence using Quantile Regression," Economics & Human Biology, Elsevier, vol. 6(3), pages 305-329, December.
    2. García Villar, Jaume & Quintana-Domeque, Climent, 2009. "Income and body mass index in Europe," Economics & Human Biology, Elsevier, vol. 7(1), pages 73-83, March.
    3. Jürges, Hendrik & Reinhold, Steffen & Salm, Martin, 2011. "Does schooling affect health behavior? Evidence from the educational expansion in Western Germany," Economics of Education Review, Elsevier, vol. 30(5), pages 862-872, October.
    4. David M. Cutler & Edward L. Glaeser & Jesse M. Shapiro, 2003. "Why Have Americans Become More Obese?," Journal of Economic Perspectives, American Economic Association, vol. 17(3), pages 93-118, Summer.
    5. Chou, Shin-Yi & Grossman, Michael & Saffer, Henry, 2004. "An economic analysis of adult obesity: results from the Behavioral Risk Factor Surveillance System," Journal of Health Economics, Elsevier, vol. 23(3), pages 565-587, May.
    6. Costa-Font, Joan & Fabbri, Daniele & Gil, Joan, 2009. "Decomposing body mass index gaps between Mediterranean countries: A counterfactual quantile regression analysis," Economics & Human Biology, Elsevier, vol. 7(3), pages 351-365, December.
    7. Giorgio Brunello & Daniele Fabbri & Margherita Fort, 2013. "The Causal Effect of Education on Body Mass: Evidence from Europe," Journal of Labor Economics, University of Chicago Press, vol. 31(1), pages 195-223.
    8. Arendt, Jacob Nielsen, 2005. "Does education cause better health? A panel data analysis using school reforms for identification," Economics of Education Review, Elsevier, vol. 24(2), pages 149-160, April.
    9. Tansel, Aysit & Karaoglan, Deniz, 2014. "Health behaviors and education in Turkey," MPRA Paper 57322, University Library of Munich, Germany, revised 15 Jul 2014.
    10. Resul Cesur & Bahadir Dursun & Naci Mocan, 2014. "The Impact of Education on Health and Health Behavior in a Middle-Income, Low-Education Country," NBER Working Papers 20764, National Bureau of Economic Research, Inc.
    11. Kemptner, Daniel & Jürges, Hendrik & Reinhold, Steffen, 2011. "Changes in compulsory schooling and the causal effect of education on health: Evidence from Germany," Journal of Health Economics, Elsevier, vol. 30(2), pages 340-354, March.
    12. Rosen, Allison B. & Stewart, Susan T. & Cutler, David M., 2009. "Forecasting the Effects of Obesity and Smoking on U.S. Life Expectancy," Scholarly Articles 5344184, Harvard University Department of Economics.
    13. Webbink, Dinand & Martin, Nicholas G. & Visscher, Peter M., 2010. "Does education reduce the probability of being overweight?," Journal of Health Economics, Elsevier, vol. 29(1), pages 29-38, January.
    14. Tomas J. Philipson & Richard A. Posner, 2008. "Is the Obesity Epidemic a Public Health Problem? A Review of Zoltan J. Acs and Alan Lyles's Obesity, Business and Public Policy," Journal of Economic Literature, American Economic Association, vol. 46(4), pages 974-982, December.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Obesity; adults; BMI; quantile regression; Turkey;

    JEL classification:

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:koc:wpaper:1703. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sumru Oz). General contact details of provider: http://edirc.repec.org/data/dekoctr.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.