IDEAS home Printed from https://ideas.repec.org/p/ags/uerstb/338949.html
   My bibliography  Save this paper

Assessment and Adjustment of Body Weight Measures in Scanner Data

Author

Listed:
  • Young, Sabrina K.
  • Page, Elina T.
  • Okrent, Abigail
  • Sweitzer, Megan

Abstract

Household scanner data are a rich resource for understanding food purchasing habits in the United States. The IRI Consumer Network provides a detailed account of the retail food purchases for a large, nationally representative sample. These data further include self-reported height and weight for a subset of households that complete the MedProfiler survey. Together, the Consumer Network and MedProfiler surveys provide a unique opportunity to study the relationship between diet and obesity. This report includes an assessment of the MedProfiler height and weight data in determining body mass index (BMI) for children and adults, using MedProfiler data from 2012 to 2018 and National Health and Nutrition Examination Survey data from 2011–2012 to 2017–2018. In addition, because self-reported height and weight may often be misreported in survey data, the report explores adjustment methods to account for any self-reporting measurement bias. Finally, since food-purchase data are collected at the household level, the report includes a comparison of methods for defining the obesity status of a household.

Suggested Citation

  • Young, Sabrina K. & Page, Elina T. & Okrent, Abigail & Sweitzer, Megan, 2023. "Assessment and Adjustment of Body Weight Measures in Scanner Data," Technical Bulletins 338949, United States Department of Agriculture, Economic Research Service.
    • Handle: RePEc:ags:uerstb:338949
    DOI: 10.22004/ag.econ.338949
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/338949/files/tb-1960.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.338949?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Matthias Staudigel, 2012. "How do obese people afford to be obese? Consumption strategies of Russian households," Agricultural Economics, International Association of Agricultural Economists, vol. 43(6), pages 701-714, November.
    2. Sweitzer, Megan & Brown, Derick & Karns, Shawn & Muth, Mary K. & Siegel, Peter & Zhen, Chen, 2017. "Food-at-Home Expenditures: Comparing Commercial Household Scanner Data From IRI and Government Survey Data," Technical Bulletins 291969, United States Department of Agriculture, Economic Research Service.
    3. Chen Zhen & Eric A. Finkelstein & James M. Nonnemaker & Shawn A. Karns & Jessica E. Todd, 2014. "Predicting the Effects of Sugar-Sweetened Beverage Taxes on Food and Beverage Demand in a Large Demand System," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(1), pages 1-25.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joey Blumberg & Gary Thompson, 2022. "Nonparametric segmentation methods: Applications of unsupervised machine learning and revealed preference," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(3), pages 976-998, May.
    2. Chen Zhen & Mary Muth & Abigail Okrent & Shawn Karns & Derick Brown & Peter Siegel, 2019. "Do differences in reported expenditures between household scanner data and expenditure surveys matter in health policy research?," Health Economics, John Wiley & Sons, Ltd., vol. 28(6), pages 782-800, June.
    3. Allais, Olivier & Etilé, Fabrice & Lecocq, Sébastien, 2015. "Mandatory labels, taxes and market forces: An empirical evaluation of fat policies," Journal of Health Economics, Elsevier, vol. 43(C), pages 27-44.
    4. Yu Chen & Chen Zhen, 2022. "The potential impact of reducing sodium in packaged food: The case of the Chinese instant noodles market," Agribusiness, John Wiley & Sons, Ltd., vol. 38(1), pages 3-20, January.
    5. Jakina Debnam, 2017. "Selection Effects and Heterogeneous Demand Responses to the Berkeley Soda Tax Vote," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(5), pages 1172-1187.
    6. Bonnet, Céline & Réquillart, Vincent, 2023. "The effects of taxation on the individual consumption of sugar-sweetened beverages," Economics & Human Biology, Elsevier, vol. 51(C).
    7. Zarko Kalamov, 2020. "A sales tax is better at promoting healthy diets than the fat tax and the thin subsidy," Health Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 353-366, March.
    8. Boonsaeng, Tullaya & Carpio, Carlos E., 2017. "Budget Allocation Patterns of American Household across Income Level in the 21 Century," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258245, Agricultural and Applied Economics Association.
    9. Violeta Chacon & Guillermo Paraje & Joaquin Barnoya & Frank J Chaloupka, 2018. "Own-price, cross-price, and expenditure elasticities on sugar-sweetened beverages in Guatemala," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-12, October.
    10. Hovhannisyan, Vardges, 2016. "New Evidence On The Structure Of Food Demand In China: An Easi Demand Model Estimated Via Panel Data Techniques," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236889, Agricultural and Applied Economics Association.
    11. Zarko Kalamov, 2021. "Evaluating Marginal Internalities: A New Approach," CESifo Working Paper Series 9476, CESifo.
    12. Andr'es Ram'irez-Hassan & Alejandro L'opez-Vera, 2021. "Semi-parametric estimation of the EASI model: Welfare implications of taxes identifying clusters due to unobserved preference heterogeneity," Papers 2109.07646, arXiv.org.
    13. Biondi, Beatrice & Cornelsen, Laura & Mazzocchi, Mario & Smith, Richard, 2020. "Between preferences and references: Asymmetric price elasticities and the simulation of fiscal policies," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 108-128.
    14. Juan Carlos Caro & Shu Wen Ng & Ricardo Bonilla & Jorge Tovar & Barry M Popkin, 2017. "Sugary drinks taxation, projected consumption and fiscal revenues in Colombia: Evidence from a QUAIDS model," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-16, December.
    15. Colchero, M.A. & Salgado, J.C. & Unar-Munguía, M. & Hernández-Ávila, M. & Rivera-Dommarco, J.A., 2015. "Price elasticity of the demand for sugar sweetened beverages and soft drinks in Mexico," Economics & Human Biology, Elsevier, vol. 19(C), pages 129-137.
    16. Fabrice Etilé & Anurag Sharma, 2015. "Do High Consumers of Sugar‐Sweetened Beverages Respond Differently to Price Changes? A Finite Mixture IV‐Tobit Approach," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1147-1163, September.
    17. Ferrier, Peyton & Zhen, Chen, 2014. "Explaining the Shift from Preserved to Fresh Vegetable Consumption," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170555, Agricultural and Applied Economics Association.
    18. Hussein, Mohamud & Law, Cherry & Fraser, Iain, 2021. "An analysis of food demand in a fragile and insecure country: Somalia as a case study," Food Policy, Elsevier, vol. 101(C).
    19. Richard Tiffin & Ariane Kehlbacher & Matthew Salois, 2015. "The Effects of A Soft Drink Tax in the UK," Health Economics, John Wiley & Sons, Ltd., vol. 24(5), pages 583-600, May.
    20. Nouve, Yawotse & McCullough, Ellen, 2021. "Consumption-Side Separability Test of Agricultural Households," 2021 Annual Meeting, August 1-3, Austin, Texas 314034, Agricultural and Applied Economics Association.

    More about this item

    Keywords

    Food Consumption/Nutrition/Food Safety; Health Economics and Policy; Research Methods/ Statistical Methods;
    All these keywords.

    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:uerstb:338949. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/ersgvus.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.