IDEAS home Printed from https://ideas.repec.org/p/ags/aaea09/49512.html
   My bibliography  Save this paper

Obesity in Urban Food Markets: Evidence from Geo-referenced Micro Data

Author

Listed:
  • Chen, Susan E.
  • Florax, Raymond J.G.M.
  • Snyder, Samantha D.

Abstract

This paper provides quantitative estimates of the effect of proximity to fast food restaurants and grocery stores on obesity in urban food markets. Our empirical model combined georeferenced micro data on access to fast food restaurants and grocery stores with data about salient personal characteristics, individual behaviors, and neighborhood characteristics. We defined a "local food environment" for every individual utilizing 0.5-mile buffers around a person's home address. Local food landscapes are potentially endogenous due to spatial sorting of the population and food outlets, and the body mass index (BMI) values for individuals living close to each other are likely to be spatially correlated because of observed and unobserved individual and neighborhood effects. The potential biases associated with endogeneity and spatial correlation were handled using spatial econometric estimation techniques. Our policy simulations for Indianapolis, Indiana, focused on the importance of reducing the density of fast food restaurants or increasing access to grocery stores. We accounted for spatial heterogeneity in both the policy instruments and individual neighborhoods, and consistently found small but statistically significant effects for the hypothesized relationships between individual BMI values and the densities of fast food restaurants and grocery stores.

Suggested Citation

  • Chen, Susan E. & Florax, Raymond J.G.M. & Snyder, Samantha D., 2009. "Obesity in Urban Food Markets: Evidence from Geo-referenced Micro Data," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49512, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea09:49512
    as

    Download full text from publisher

    File URL: http://purl.umn.edu/49512
    Download Restriction: no

    References listed on IDEAS

    as
    1. repec:aph:ajpbhl:10.2105/ajph.2004.058040_8 is not listed on IDEAS
    2. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    3. Janet Currie & Stefano DellaVigna & Enrico Moretti & Vikram Pathania, 2010. "The Effect of Fast Food Restaurants on Obesity and Weight Gain," American Economic Journal: Economic Policy, American Economic Association, vol. 2(3), pages 32-63, August.
    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. Michael L. Anderson & David A. Matsa, 2011. "Are Restaurants Really Supersizing America?," American Economic Journal: Applied Economics, American Economic Association, vol. 3(1), pages 152-188, January.
    6. 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.
    7. David G. Blanchflower & Andrew J. Oswald & Bert Van Landeghem, 2009. "Imitative Obesity and Relative Utility," Journal of the European Economic Association, MIT Press, vol. 7(2-3), pages 528-538, 04-05.
    8. Eid, Jean & Overman, Henry G. & Puga, Diego & Turner, Matthew A., 2008. "Fat city: Questioning the relationship between urban sprawl and obesity," Journal of Urban Economics, Elsevier, vol. 63(2), pages 385-404, March.
    9. Cohen-Cole, Ethan & Fletcher, Jason M., 2008. "Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic," Journal of Health Economics, Elsevier, vol. 27(5), pages 1382-1387, September.
    10. Irani Arraiz & David M. Drukker & Harry H. Kelejian & Ingmar R. Prucha, 2010. "A Spatial Cliff-Ord-Type Model With Heteroskedastic Innovations: Small And Large Sample Results," Journal of Regional Science, Wiley Blackwell, vol. 50(2), pages 592-614.
    11. Currie, Janet & DellaVigna, Stefano & Moretti, Enrico & Pathania, Vikram, 2009. "The Effect of Fast Food Restaurants on Obesity," Working Papers 47830, American Association of Wine Economists.
    12. repec:aph:ajpbhl:2002:92:11:1761-1767_2 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Weatherspoon, Dave D. & Oehmke, James F. & Coleman, Marcus A. & Weatherspoon, Lorraine J., 2014. "Understanding Consumer Preferences for Nutritious Foods: Retailing Strategies in a Food Desert," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association (IFAMA), vol. 17(A).
    2. Cotti, Chad & Tefft, Nathan, 2013. "Fast food prices, obesity, and the minimum wage," Economics & Human Biology, Elsevier, vol. 11(2), pages 134-147.
    3. Bonanno, Alessandro & Goetz, Stephan J., 2012. "Food Store Density, Nutrition Education, Eating Habits and Obesity," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association (IFAMA), vol. 15(4).
    4. Li, Lan, 2012. "An Empirical Analysis of Fruit and Vegetable Consumption and Its Relationship to Adult Obesity in the U.S," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 125002, Agricultural and Applied Economics Association.
    5. Alviola, Pedro A. & Nayga, Rodolfo M. & Thomsen, Michael R. & Danforth, Diana & Smartt, James, 2014. "The effect of fast-food restaurants on childhood obesity: A school level analysis," Economics & Human Biology, Elsevier, vol. 12(C), pages 110-119.

    More about this item

    Keywords

    obesity; fast food; grocery store; spatial econometrics; micro data; Agricultural and Food Policy; Community/Rural/Urban Development; Consumer/Household Economics; Food Consumption/Nutrition/Food Safety; Health Economics and Policy; Public Economics; Research Methods/ Statistical Methods; C31; D12; I12; I18;

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

    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:ags:aaea09:49512. 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: (AgEcon Search). General contact details of provider: http://edirc.repec.org/data/aaeaaea.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.