Advertisement

Journal of Consumer Policy

, Volume 42, Issue 4, pp 545–562 | Cite as

Evaluating USA’s New Nutrition and Supplement Facts Label: Evidence from a Non-hypothetical Choice Experiment

  • D. FangEmail author
  • R. M. NaygaJr.
  • H. A. Snell
  • G. H. West
  • C. Bazzani
Original Paper
  • 117 Downloads

Abstract

In May 2016, the FDA published new rules for Nutrition and Supplement Facts label formats and contents, which take effect in 2019. The revised labelling is designed to help consumers make better-informed product choices in support of a healthier diet. Changes in nutrition label requirements include the prominent display of “calories per serving” and “serving size” as well as updated nutritional requirements and information reflecting contemporary scientific knowledge about diet–disease relationships. No other known study however has directly examined whether these new upcoming changes will help consumers make healthier choices. To fill this void, we conducted a non-hypothetical choice experiment on “light” and “original” strawberry yogurt products at a major public University in the USA. Using a generalized mixed logit model with a scale parameter to account for taste heterogeneity among participants, we find that the new label changes food choice behavior. Specifically, we find that the new label reduces consumer’s preferences for both original and low-fat yogurts. This finding is evident among the more health-conscious subsample. It is possible that the new label generates an “alarm” effect given the revised features on calories, added sugar and serving size, especially among those who are more nutrition and health conscious and those who use labels for nutrient information.

Keywords

Nutrition facts label Non-hypothetical experiment WTP 

Notes

References

  1. Balcombe, K., Chalak, A., & Fraser, I. (2009). Model selection for the mixed logit with bayesian estimation. Journal of Environmental Economics and Management, 57(2), 226–237.Google Scholar
  2. Balcombe, K., Fraser, I., & Di Falco, S. (2010). Traffic lights and food choice: A choice experiment examining the relationship between nutritional food labels and price. Food Policy, 35(3), 211–220.Google Scholar
  3. Bazzani, C., Caputo, V., Nayga, R.M. Jr, & Canavari, M. (2017). Testing commitment cost theory in choice experiments. Economic Inquiry, 55(1), 383–396.Google Scholar
  4. Bello, M., & Abdulai, A. (2016). Impact of ex-ante hypothetical bias mitigation methods on attribute non-attendance in choice experiments. American Journal of Agricultural Economics, 1486–1506.Google Scholar
  5. Bialkova, S., & van Trijp, H. (2010). What determines consumer attention to nutrition labels?. Food quality and preference, 21(8), 1042–1051.CrossRefGoogle Scholar
  6. Bliemer, M. C., Rose, J. M., & Hess, S. (2008). Approximation of bayesian efficiency in experimental choice designs. Journal of Choice Modelling, 1(1), 98–126.Google Scholar
  7. Bonsall, P., & Lythgoe, B. (2009). Factors affecting the amount of effort expended in responding to questions in behavioural choice experiments. Journal of Choice Modelling, 2(2), 216–236.CrossRefGoogle Scholar
  8. Breck, A., Mijanovich, T., Weitzman, B. C., & Elbel, B. (2017). The current limits of calorie labeling and the potential for population health impact. Journal of Public Policy & Marketing, 36(2), 227–235.Google Scholar
  9. Burton, S., Garretson, J. A., & Velliquette, A. M. (1999). Implications of accurate usage of nutrition facts panel information for food product evaluations and purchase intentions. Journal of the Academy of Marketing Science, 27(4), 470–480.Google Scholar
  10. Campbell, D., Hensher, D. A., & Scarpa, R. (2014). Bounding wtp distributions to reflect the “actual” consideration set. Journal of Choice Modelling, 11, 4–15.Google Scholar
  11. Campbell, D., Mørkbak, M. R., & Olsen, S. B. (2017). Response time in online stated choice experiments: The non-triviality of identifying fast and slow respondents. Journal of Environmental Economics and Policy, 6(1), 17–35.Google Scholar
  12. Campbell, D., Mørkbak, M. R., & Olsen, S. B. (2018). The link between response time and preference, variance and processing heterogeneity in stated choice experiments. Journal of Environmental Economics and Management, 88, 18–34.Google Scholar
  13. Cassady, D. L., Liaw, K., & Miller, L. M. S. (2015). Disparities in obesity-related outdoor advertising by neighborhood income and race. Journal of Urban Health, 92(5), 835–842.Google Scholar
  14. Chowdhury, S., Meenakshi, J., Tomlins, K. I., & Owori, C. (2011). Are consumers in developing countries willing to pay more for micronutrient-dense biofortified foods? Evidence from a field experiment in uganda. American Journal of Agricultural Economics, 93(1), 83–97.Google Scholar
  15. de Bekker-Grob, E. W., Donkers, B., Jonker, M. F., & Stolk, E. A. (2015). Sample size requirements for discrete- choice experiments in healthcare: A practical guide. The Patient-Patient-Centered Outcomes Research, 8(5), 373384.Google Scholar
  16. De-Magistris, T., Gracia, A., & Nayga, R.M. Jr. (2013). On the use of honesty priming tasks to mitigate hypothetical bias in choice experiments. American Journal of Agricultural Economics, 95(5), 1136–1154.Google Scholar
  17. Drichoutis, A. C., Lazaridis, P., & Nayga, R. M. (2005). Nutrition knowledge and consumer use of nutritional food labels. European Review of Agricultural Economics, 32(1), 93118.Google Scholar
  18. Drichoutis, A. C., Lazaridis, P., Nayga, R. M., Kapsokefalou, M., & Chryssochoidis, G. (2008). A theoretical and empirical investigation of nutritional label use. The European Journal of Health Economics, 9(3), 293–304.Google Scholar
  19. Drichoutis, A. C., Nayga, R.M. Jr, & Lazaridis, P. (2009). Can nutritional label use influence body weight outcomes? Kyklos, 62(4), 500–525.Google Scholar
  20. Fiebig, D. G., Keane, M. P., Louviere, J., & Wasi, N. (2010). The generalized multinomial logit model: Accounting for scale and coefficient heterogeneity. Marketing Science, 29(3), 393–421.Google Scholar
  21. Food and Drug Administration, HHS. (2018). Food labeling: Revision of the nutrition and supplement facts labels and serving sizes of foods that can reasonably be consumed at one eating occasion; dual-column labeling; updating, modifying, and establishing certain reference amounts customarily consumed; serving size for breath mints; and technical amendments; extension of compliance dates. Retrieved from: https://www.federalregister.gov/documents/2018/05/04/2018-09476/food-labeling-revision-of-the-nutrition-and-supplement-facts-labels-and-serving-sizes-of-foods-that. Accessed 20 May 2018.
  22. Gracia, A., Loureiro, M. L., & Nayga, R.M. Jr. (2009). Consumers’ valuation of nutritional information: A choice experiment study. Food Quality and Preference, 20(7), 463–471.Google Scholar
  23. Graham, D. J., & Jeffery, R. W. (2011). Location, location, location: Eye-tracking evidence that consumers preferentially view prominently positioned nutrition information. Journal of the American Dietetic Association, 111(11), 1704–1711.CrossRefGoogle Scholar
  24. Graham, D. J., & Roberto, C. A. (2016). Evaluating the impact of us food and drug administration–proposed nutrition facts label changes on young adults’ visual attention and purchase intentions. Health Education & Behavior, 43(4), 389–398.CrossRefGoogle Scholar
  25. Grebitus, C., & Davis, G. C. (2017). Change is good!? Analyzing the relationship between attention and nutrition facts panel modifications. Food Policy, 73, 119–130.CrossRefGoogle Scholar
  26. Grunert, K. G., & Wills, J. M. (2007). A review of european research on consumer response to nutrition information on food labels. Journal of Public Health, 15(5), 385–399.CrossRefGoogle Scholar
  27. Harrison, G. W. (2006). Hypothetical bias over uncertain outcomes. Using experimental methods in environmental and resource economics, 41–69.Google Scholar
  28. Hess, R., Visschers, V. H., & Siegrist, M. (2012). The role of health-related, motivational and sociodemographic aspects in predicting food label use: A comprehensive study. Public Health Nutrition, 15(3), 407–414.Google Scholar
  29. Hieke, S., & Taylor, C. R. (2012). A critical review of the literature on nutritional labeling. Journal of Consumer Affairs, 46(1), 120–156.CrossRefGoogle Scholar
  30. Levings, J. L., Maalouf, J., Tong, X., & Cogswell, M. E. (2015). Peer reviewed: Reported use and perceived understanding of sodium information on us nutrition labels. Preventing Chronic Disease, 12.Google Scholar
  31. Levy, A. S., & Fein, S. B. (1998). Consumers’ ability to perform tasks using nutrition labels. Journal of Nutrition Education, 30(4), 210–217.CrossRefGoogle Scholar
  32. Lin, C. -T. J., & Yen, S. T. (2010). Knowledge of dietary fats among us consumers. Journal of the American Dietetic Association, 110(4), 613–618.CrossRefGoogle Scholar
  33. List, J. A., & Gallet, C. A. (2001). What experimental protocol influence disparities between actual and hypothetical stated values?. Environmental and Resource Economics, 20(3), 241–254.CrossRefGoogle Scholar
  34. Loureiro, M. L., Gracia, A., Nayga, R.M. Jr. (2006). Do consumers value nutritional labels? European Review of Agricultural Economics, 33(2), 249–268.CrossRefGoogle Scholar
  35. Lusk, J. L., & Schroeder, T. C. (2004). Are choice experiments incentive compatible? A test with quality differentiated beef steaks. American Journal of Agricultural Economics, 86(2), 467–482.CrossRefGoogle Scholar
  36. Malhotra, N. (2008). Completion time and response order effects in web surveys. Public Opinion Quarterly, 72(5), 914–934.CrossRefGoogle Scholar
  37. Malik, V. S., Willett, W. C., & Hu, F. B. (2016). The revised nutrition facts label: A step forward and more room for improvement. Jama, 316(6), 583–584.Google Scholar
  38. Matthews, Y., Scarpa, R., & Marsh, D. (2017a). Stability of willingness-to-pay for coastal management: A choice experiment across three time periods. Ecological Economics, 138, 64–73.Google Scholar
  39. Matthews, Y., Scarpa, R., & Marsh, D. (2017b). Using virtual environments to improve the realism of choice experiments: A case study about coastal erosion management. Journal of Environmental Economics and Management, 81, 193–208.Google Scholar
  40. Mayne, S. T., & Spungen, J. H. (2017). The US food and drug administration’s role in improving nutrition: Labeling and other authorities. Journal of Food Composition and Analysis, 64, 5–9.CrossRefGoogle Scholar
  41. Miller, C. K., Probart, C. K., & Achterberg, C. L. (1997). Knowledge and misconceptions about the food label among women with non-insulin-dependent diabetes mellitus. The Diabetes Educator, 23(4), 425–432.Google Scholar
  42. Murphy, J. J., Allen, P. G., Stevens, T. H., & Weatherhead, D. (2005). A meta-analysis of hypothetical bias in stated preference valuation. Environmental and Resource Economics, 30(3), 313–325.Google Scholar
  43. Nayga, R. M. (1996). Determinants of consumers’ use of nutritional information on food packages. Journal of Agricultural and Applied Economics, 28(2), 303–312.CrossRefGoogle Scholar
  44. Nayga, R.M. Jr, Lipinski, D., & Savur, N. (1998). Consumers use of nutritional labels while food shopping and at home. Journal of Consumer Affairs, 32(1), 106–120.Google Scholar
  45. Nogueira, L. M., Thai, C. L., Nelson, W., & Oh, A. (2016). Nutrition label numeracy: Disparities and association with health behaviors. American Journal of Health Behavior, 40(4), 427–436.Google Scholar
  46. Ogden, C. L., Carroll, M. D., Lawman, H. G., Fryar, C. D., Kruszon-Moran, D., Kit, B. K., & Flegal, K. M. (2016). Trends in obesity prevalence among children and adolescents in the United States, 1988-1994 through 2013-2014. Jama, 315(21), 2292–2299.Google Scholar
  47. Ollberding, N. J., Wolf, R. L., & Contento, I. (2011). Food label use and its relation to dietary intake among us adults. Journal of the American Dietetic Association, 111(5), S47–S51.Google Scholar
  48. Penn, J. M., & Hu, W. (2018). Understanding hypothetical bias: An enhanced meta-analysis. American Journal of Agricultural Economics.Google Scholar
  49. Petrovici, D., Fearne, A., Nayga, R.M. Jr, & Drolias, D. (2012). Nutritional knowledge, nutritional labels, and health claims on food: A study of supermarket shoppers in the south east of england. British Food Journal, 114(6), 768–783.Google Scholar
  50. Pieniak, Z., Verbeke, W., & Scholderer, J. (2010). Health-related beliefs and consumer knowledge as determi- nants of fish consumption. Journal of Human Nutrition and Dietetics, 23(5), 480–488.Google Scholar
  51. Sarrias, M., Daziano, R. A., & et al. (2017). Multinomial logit models with continuous and discrete individual heterogeneity in r: The gmnl package. Journal of Statistical Software, 79(2), 1–46.Google Scholar
  52. Scarpa, R., Campbell, D., & Hutchinson, W. G. (2007). Benefit estimates for landscape improvements: Sequential bayesian design and respondents rationality in a choice experiment.Land Economics, 83(4), 617–634.Google Scholar
  53. Scarpa, R., Thiene, M., & Train, K. (2008). Utility in willingness to pay space: A tool to address confounding random scale effects in destination choice to the Alps. American Journal of Agricultural Economics, 90(4), 994–1010.Google Scholar
  54. Schulze, W. D., d’Arge, R. C., & Brookshire, D. S. (1981). Valuing environmental commodities: Some recent experiments. Land Economics, 57(2), 151–172.Google Scholar
  55. Train, K., & Weeks, M. (2005). Discrete choice models in preference space and willingness-to-pay space. In R. Scarpa, & A. Alberini (Eds.), Applications of simulation methods in environmental and resource economics (pp. 116). Dordrecht: Springer.Google Scholar
  56. US Food and Drug Administration. (2018a). Changes to the nutrition facts label. Retrieved from: https://www.fda.gov/Food/GuidanceRegulation/GuidanceDocumentsRegulatoryInformation/LabelingNutrition/ucm385663.htm. Accessed 30 May 2018.
  57. US Food and Drug Administration. (2018b). Food serving sizes get a reality check. Retrieved from: https://www.fda.gov/ForConsumers/ConsumerUpdates/ucm386203.htm. Accessed 30 May 2018.
  58. Van Wezemael, L., Caputo, V., Nayga, R.M. Jr, Chryssochoidis, G., & Verbeke, W. (2014). European consumer preferences for beef with nutrition and health claims: A multi-country investigation using discrete choice experiments. Food Policy, 44, 167–176.Google Scholar
  59. Vanderlee, L., White, C. M., Bordes, I., Hobin, E. P., & Hammond, D. (2015). The efficacy of sugar labeling formats: Implications for labeling policy. Obesity, 23(12), 2406–2413.Google Scholar
  60. Zhang, D., Li, Y., Wang, G., Moran, A. E., & PagÃČÂa̧n, J. A. (2017). Nutrition label use and sodium intake in the U.S. American Journal of Preventive Medicine, 53(6), S220–S227.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Agricultural Economics and AgribusinessUniversity of ArkansasFayettevilleUSA
  2. 2.Department of Business AdministrationUniversity of VeronaVeronaItaly

Personalised recommendations