IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v12y2015i11p14640-14668d58953.html
   My bibliography  Save this article

The Effects of Weight Perception on Adolescents’ Weight-Loss Intentions and Behaviors: Evidence from the Youth Risk Behavior Surveillance Survey

Author

Listed:
  • Maoyong Fan

    (Economics Department, Ball State University, Muncie, Indiana, USA)

  • Yanhong Jin

    (Department of Agricultural, Food and Resource Economics, Rutgers University, New Brunswick, 08901, New Jersey, USA)

Abstract

The objective of this study was to examine the correlation between self-perception of being overweight and weight loss intentions, eating and exercise behaviors, as well as extreme weight-loss strategies for U.S. adolescents. This study uses 50,241 observations from the Youth Risk Behavior Surveillance Survey (YRBSS) 2001–2009, which were nationally representative sample of 9th- through 12th-grade students in both public and private schools in the US. This study finds that, irrespective of the weight status base on self-reported weight and height, adolescents who perceive themselves as overweight have a stronger intention to lose weight, but do not develop better eating and exercise habits, compared with their counterparts of same gender and reported weight status. Normal-weight adolescents, if they perceive themselves as overweight, are more likely to engage in health-compromising weight-loss methods. This study shows that it is critical to transform weight-loss intentions into actual behaviors among overweight/obese adolescents and improve the efficacy of behavioral interventions against childhood obesity. It also highlights the need of establishing a correct perception of body weight among normal weight adolescents to curb extreme weight-loss methods.

Suggested Citation

  • Maoyong Fan & Yanhong Jin, 2015. "The Effects of Weight Perception on Adolescents’ Weight-Loss Intentions and Behaviors: Evidence from the Youth Risk Behavior Surveillance Survey," IJERPH, MDPI, vol. 12(11), pages 1-29, November.
  • Handle: RePEc:gam:jijerp:v:12:y:2015:i:11:p:14640-14668:d:58953
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/12/11/14640/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/12/11/14640/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    2. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    3. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    4. Matthew Rabin & Ted O'Donoghue, 1999. "Doing It Now or Later," American Economic Review, American Economic Association, vol. 89(1), pages 103-124, March.
    5. Black, Dan A. & Smith, J.A.Jeffrey A., 2004. "How robust is the evidence on the effects of college quality? Evidence from matching," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 99-124.
    6. Just, David R. & Wansink, Brian, 2009. "Smarter Lunchrooms: Using Behavioral Economics to Improve Meal Selection," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 24(3), pages 1-7.
    7. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    8. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc.
    9. Alberto Abadie & David Drukker & Jane Leber Herr & Guido W. Imbens, 2004. "Implementing matching estimators for average treatment effects in Stata," Stata Journal, StataCorp LP, vol. 4(3), pages 290-311, September.
    10. 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.
    11. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    12. Thaler, Richard, 1981. "Some empirical evidence on dynamic inconsistency," Economics Letters, Elsevier, vol. 8(3), pages 201-207.
    13. Case, Anne & Fertig, Angela & Paxson, Christina, 2005. "The lasting impact of childhood health and circumstance," Journal of Health Economics, Elsevier, vol. 24(2), pages 365-389, March.
    14. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
    15. Jeffrey Smith, 2000. "A Critical Survey of Empirical Methods for Evaluating Active Labor Market Policies," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 136(III), pages 247-268, September.
    16. Maoyong Fan, 2010. "Do Food Stamps Contribute to Obesity in Low-Income Women? Evidence from the National Longitudinal Survey of Youth 1979," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(4), pages 1165-1180.
    17. Pate, R.R. & Heath, G.W. & Dowda, M. & Trost, S.G., 1996. "Associations between physical activity and other health behaviors in a representative sample of US adolescents," American Journal of Public Health, American Public Health Association, vol. 86(11), pages 1577-1581.
    18. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    19. Saarni, S.E. & Pietiläinen, K. & Kantonen, S. & Rissanen, A. & Kaprio, J., 2009. "Association of smoking in adolescence with abdominal obesity in adulthood: A follow-up study of 5 birth cohorts of Finnish twins," American Journal of Public Health, American Public Health Association, vol. 99(2), pages 348-354.
    20. Stefano DellaVigna & Ulrike Malmendier, 2006. "Paying Not to Go to the Gym," American Economic Review, American Economic Association, vol. 96(3), pages 694-719, June.
    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. Andrea L. Deierlein & Alomi Malkan & Jaqueline Litvak & Niyati Parekh, 2019. "Weight Perception, Weight Control Intentions, and Dietary Intakes among Adolescents Ages 10–15 Years in the United States," IJERPH, MDPI, vol. 16(6), pages 1-10, March.
    2. Anda-Valentina Trandafir & Maria Fraseniuc & Lucia Maria Lotrean, 2022. "Assessment of Actual Weight, Perceived Weight and Desired Weight of Romanian School Children-Opinions and Practices of Children and Their Parents," IJERPH, MDPI, vol. 19(6), pages 1-16, March.
    3. Furong Xu & Steven A. Cohen & Mary L. Greaney & Geoffrey W. Greene, 2018. "The Association between US Adolescents’ Weight Status, Weight Perception, Weight Satisfaction, and Their Physical Activity and Dietary Behaviors," IJERPH, MDPI, vol. 15(9), pages 1-13, September.
    4. Rasa Jankauskiene & Migle Baceviciene & Simona Pajaujiene & Dana Badau, 2019. "Are Adolescent Body Image Concerns Associated with Health-Compromising Physical Activity Behaviours?," IJERPH, MDPI, vol. 16(7), pages 1-13, April.
    5. Francesco Napolitano & Francesco Bencivenga & Erika Pompili & Italo Francesco Angelillo, 2019. "Assessment of Knowledge, Attitudes, and Behaviors toward Eating Disorders among Adolescents in Italy," IJERPH, MDPI, vol. 16(8), pages 1-11, April.
    6. Rasa Jankauskiene & Migle Baceviciene, 2019. "Body Image Concerns and Body Weight Overestimation Do Not Promote Healthy Behaviour: Evidence from Adolescents in Lithuania," IJERPH, MDPI, vol. 16(5), pages 1-14, March.

    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. Maoyong Fan & Yanhong Jin, 2015. "The Supplemental Nutrition Assistance Program and Childhood Obesity in the United States: Evidence from the National Longitudinal Survey of Youth 1997," American Journal of Health Economics, MIT Press, vol. 1(4), pages 432-460, Fall.
    2. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    3. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    4. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    5. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand," NBER Technical Working Papers 0330, National Bureau of Economic Research, Inc.
    6. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    7. Carlos A. Flores & Oscar A. Mitnik, 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," Working Papers 2010-10, University of Miami, Department of Economics.
    8. Eliasson, Kent, 2006. "How Robust is the Evidence on the Returns to College Choice? Results Using Swedish Administrative Data," Umeå Economic Studies 692, Umeå University, Department of Economics.
    9. Peter R. Mueser & Kenneth R. Troske & Alexey Gorislavsky, 2007. "Using State Administrative Data to Measure Program Performance," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 761-783, November.
    10. Kyungchul Song, 2007. "Testing Conditional Independence via Rosenblatt Transforms," PIER Working Paper Archive 07-026, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    11. Zhao, Zhong, 2008. "Sensitivity of propensity score methods to the specifications," Economics Letters, Elsevier, vol. 98(3), pages 309-319, March.
    12. Jose C. Galdo & Jeffrey Smith & Dan Black, 2008. "Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data," Annals of Economics and Statistics, GENES, issue 91-92, pages 189-216.
    13. Eliasson, Kent, 2006. "College Choice And Earnings Among University Graduates In Sweden," Umeå Economic Studies 693, Umeå University, Department of Economics.
    14. Luis Aranda, 2013. "Doubling Up: A Gift or a Shame? Multigenerational Households and Parental Depression of Older Europeans," Working Papers 2013:29, Department of Economics, University of Venice "Ca' Foscari", revised 2013.
    15. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
    16. Tommaso Nannicini, 2007. "Simulation-based sensitivity analysis for matching estimators," Stata Journal, StataCorp LP, vol. 7(3), pages 334-350, September.
    17. Dettmann, Eva & Becker, Claudia & Schmeißer, Christian, 2010. "Is there a Superior Distance Function for Matching in Small Samples?," IWH Discussion Papers 3/2010, Halle Institute for Economic Research (IWH).
    18. Eliasson, Kent, 2006. "The Role of Ability in Estimating the Returns to College Choice: New Swedish Evidence," Umeå Economic Studies 691, Umeå University, Department of Economics.
    19. Jochen Kluve & Boris Augurzky, 2007. "Assessing the performance of matching algorithms when selection into treatment is strong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 533-557.
    20. Ferraro, Paul J. & Miranda, Juan José, 2014. "The performance of non-experimental designs in the evaluation of environmental programs: A design-replication study using a large-scale randomized experiment as a benchmark," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 344-365.

    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:gam:jijerp:v:12:y:2015:i:11:p:14640-14668:d:58953. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.