#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

The effect of ‘Traffic-Light’ nutritional labelling in carbonated soft drink purchases in Ecuador


Autoři: Luis A. Sandoval aff001;  Carlos E. Carpio aff002;  Marcos Sanchez-Plata aff003
Působiště autorů: Department of Agribusiness, Zamorano University, Tegucigalpa, Honduras aff001;  Department of Agricultural and Applied Economics, Texas Tech University, Lubbock, TX, United States of America aff002;  Department of Animal and Food Science, Texas Tech University, Lubbock, TX, United States of America aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222866

Souhrn

Overweight and obesity have become global concerns in developed and developing countries due to their rise in recent years and their association with the prevalence of non-communicable diseases including diabetes, hypertension and cardiovascular diseases. In fact, it is estimated that roughly 39% of adults worldwide are overweight and 13% are obese. Ecuador is an example of a developing country concerned with the overweight and obesity problem, where it is estimated that 30% of children, 26% of teenagers and 63% of adults are either overweight or obese and where 1 in 4 deaths are attributed to chronic diseases. To address the overweight and obesity problem via the promotion of healthy eating habits, in 2013 the country approved technical regulation for the labelling of packed processed food products. The regulation included a mandatory traffic-light (TL) supplemental nutritional information labelling system to be displayed on the package of all processed foods for sale in the country. This new labelling system displays a traffic light panel for the product content of sugar, fat and salt in addition to the traditional nutrient declaration label. The objective of this paper was to evaluate the effect of the TL supplemental nutritional information on consumers’ buying behavior in Ecuador. More specifically, we concentrated on the purchasing behavior of carbonated soft drinks. For our analysis, we used monthly aggregated purchase data (total expenditures, quantities and average prices) of carbonated soft drinks from January 2013 to December 2015 obtained from Kantar World Panel—Ecuador. We estimated a non-linear Almost Ideal Demand System where we model the demand for high sugar and low sugar carbonated soft drinks. We found that the introduction of the traffic light supplemental nutrition labelling did not have the expected effect of reducing purchases of carbonated soft drinks during its first year of implementation, especially those high in sugar. Additionally, we found that lower income-status households tend to spend more on and consume more calories from CSD than households with higher socio-economic status. Finally, we identified that over time purchases of high sugar soft drinks decreased while purchases of low and no sugar soft drinks increased. Beyond our contribution of evaluating the effect of the traffic light on the purchases of carbonated soft drinks, we also estimated price and income elasticities of carbonated soft drinks which can be useful in the evaluation of fiscal policies.

Klíčová slova:

Food – Obesity – Habits – Food consumption – Nutrients – Ecuador – Demand curves


Zdroje

1. World Health Organization. Obesity and overweight: fact sheet. Available from: http://www.who.int/mediacentre/factsheets/fs311/en/. Cited 17 October 2016.

2. Freire WB, Ramirez-Luzuriaga MJ, Belmont P, Mendieta MJ, Silva-Jaramillo MK, Romero N, et al. Tomo I: Encuesta Nacional de Salud y Nutricion de la poblacion ecuatoriana de cero a 59 años. ENSANUT-ECU 2012. Ministerio de Salud Publica/Instituto Nacional de Estadísticas y Censo; 2014.

3. Ministerio de Industria y Productividad. Resolución No. 14 511. Reglamento Técnico Ecuatoriano RTE INEN 022 (2R) “Rotulado de productos Alimenticos procesados, envasados y empaquetados”. Ecuador; 2013.

4. Popkin BM, Hawkes C. The sweetening of the global diet, particularly beverages: patterns, trends and policy responses for diabetes prevention. Lancet Diabetes Endocrinol. 2016 February: 4(2): 174–186. doi: 10.1016/S2213-8587(15)00419-2 26654575

5. Crockett RA, King SE, Marteau TM, Prevost AT, Bignardi G, Roberts NW, et al. Nutritional labelling for healthier food or non‐alcoholic drink purchasing and consumption. The Cochrane Library; 2018

6. Sacks G, Rayner M, Swinburn B. Impact of front-of-pack ‘traffic-light’ nutrition labelling on consumer food purchases in the UK. Health Promotion International. 2009; 24(4): 344–352. doi: 10.1093/heapro/dap032 19815614

7. Biblioteca del Congreso Nacional de Chile. Sobre Composición Nutricional de los Alimentos y su Publicidad. [Cited 1 May 2019]. Available from: https://www.leychile.cl/Navegar?idNorma=1041570&idVersion=2012-07-06.

8. FAO and WHO. Codex Alimentarius guidelines on nutrition labelling. 2001 [Cited 17 October 2016.]. Available from: http://www.fao.org/docrep/005/y2770e/y2770e06.htm#bm06.

9. Hersey JC, Wohlgenant KC, Arsenault JE, Mosa KM, Muth M. Effects of front-of-package and shelf nutrition labeling systems on consumers. Nutrition Reviews. 2013;71(1): 1–14. doi: 10.1111/nure.12000 23282247

10. Kelly B, Hughes C, Chapman K, Chun Yu JL, Dixon H, Crawford J, et al. Front-of-pack food labelling: Traffic light labelling gets the the green light. Dietitians Association of Australia 27th National Conference 2009. pp. A17-A17.

11. Arrúa A, Machín L, Curutchet MR, Martínez J, Antúnez L, Alcaire F, et al. Warnings as a directive front-of-pack nutrition labelling scheme: comparison with the Guideline Daily Amount and traffic-light systems. Public Health Nutrition. 2017; 20(13): 2308–2317. doi: 10.1017/S1368980017000866 28625228

12. Roberto CA, Bragg MA, Schwartz MB, Seamans MJ, Musicus A, Novak N, et al. Facts Up front versus traffic light food labels: a randomized controlled trial. American Journal of Prevenive Medicine. 2012; 43(2): 134–141.

13. Hieke S, Wilczynski P. Colour Me In–an empirical study on consumer responses to the traffic light signposting system in nutrition labelling. Public Health Nutrition. 2012; 15(05): 773–782.

14. Dodds P, Wolfenden L, Chapman K, Wellard L, Hughes C. Wiggers J. The effect of energy and traffic light labelling on parent and child fast food selection: a randomised controlled trial. Appetite. 2014; 73: 23–30. 24511614

15. Arrúa A, Curutchet MR, Rey N, Barreto P, Golovchenko N, Sellanes A. et al. Impact of front-of-pack nutrition information and label design on children's choice of two snack foods: Comparison of warnings and the traffic-light system. Appetite. 2017; 116: 139–146. doi: 10.1016/j.appet.2017.04.012 28428151

16. Sacks G, Tikellis K, Millar L, Swinburn B. Impact of ‘traffic-light’ nutrition information on online food purchases in Australia. Australian and New Zealand Journal of Public Health. 2011;35(2): 122–126. doi: 10.1111/j.1753-6405.2011.00684.x 21463406

17. Franckle RL, Levy DE, Macias-Navarro L, Rimm EB Thorndike AN. Traffic-light labels and financial incentives to reduce sugar-sweetened beverage purchases by low-income Latino families: a randomized controlled trial. Public Health Nutr. 2018: 1–9.

18. Freire WB, Waters WF, Rivas-Mariño G, Nguyen T, Rivas P. A qualitative study of consumer perceptions and use of traffic light food labelling in Ecuador. Public Health Nutrition. 2016;20(5): 805–813. doi: 10.1017/S1368980016002457 27618994

19. Maya ME. Etiquetado de semáforo; estudio del hábito de compras en jugos procesados, en el barrio San Carlos de la ciudad de Quito. Master in Business Administration thesis. Universidad Andina Simón Bolivar, Ecuador Campus. 2015.

20. De Souza JA. Analisis del impacto de etiquetas de alimentos procesados. Undergraduate Thesis. Universidad San Francisco de Quito. 2015.

21. Sexton RJ. Theory on Information and its Application to the Effect of Labeling on Food Products. Staff Paper No. P79-35. St. Paul: Department of Agricultural and Applied Economics, University of Minnesot, Oct. 1979.

22. Teisl MF, Bockstael NE, Levy A. Measuring the welfare effects of nutrition Information. American Journal of Agricultural Economics. 2001; 83(1): 133–149.

23. Johnshon JP, Myatt DP. On the simple economics of advertising, marketing and product design. American Economic Review. 2006; 96: 756–784.

24. Zheng Y, Kinnucan H, Kaiser H. Measuring and testing advertising-induced rotation in the demand curve. Applied Economics. 2010; 42(13): 1601–1614.

25. Acosta S. Officer at Kantar World Panel. Personal communication. August 4th, 2016.

26. World Bank. World Bank Data–Ecuador, 2017. Avaiable from: http://data.worldbank.org/country/ecuador

27. Deaton AS, Muellbauer J. An Almost Ideal Demand System. American Economic Review. 1980;70: 418–30.

28. Greene WH. Econometric Analysis. 7th ed. 2012.

29. Klein LR. Single equation vs. equation system methods of estimation in econometrics. Econometrica. pre-1986; 28(4): p.866.

30. Lewbel A, Pendakur K. Tricks with hicks: the EASI demand system. The America Economic Review. 2009;99:827–63. doi: 10.1257/aer.99.3.827

31. Boonsaeng T, Fletcher S, Carpio CE. European Union Import Demand for In-Shell Peanuts. Journal of agricultural and Applied Economics. 2008;40(3): 941–951.

32. Goncalves S, White H. Bootstrap Standard Error Estimates for Linear Regression. Journal of the American Statistical Association. 2005;100: 970:79.

33. Paraje G. The Effect of Price and Socio-Economic Level on the Consumption of Sugar-Sweetened Beverages (SSB): The Case of Ecuador. PLoS ONE. 2016;11(3): e0152260. Available from: doi: 10.1371/journal.pone.0152260 27028608

34. Colchero MA, Salgado JC, Unar-Munguia M, Hernandez-Avila M, Rivera-Dommarco JA. Price elasticity of the demand for sugar sweetened beverages and soft drinks in Mexico. Economics & Human Biology. 2015;19: 129–137.

35. Zhen C, Finkelstein EA, Nonnemaker JM, Karns SA, Todd JE. Predicting the Effects of Sugar-Sweetened Beverage Taxes on Food and Beverage Demand in a Large Demand System. American Journal of Agricultural Economics 2014; 96:1–25. doi: 10.1093/ajae/aat049 24839299

36. Cornelsen L, Mazzocchi M, Green R, Dangour AD, Smith RD. Estimating the Relationship between Food Prices and Food Consumption—Methods Matter. Applied Economic Perspectives and Policy. 2016; 38: 546–561.

37. Casella G, Berger RL. Statistical inference (Vol. 2). Pacific Grove, CA: Duxbury. 2002.

38. Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied linear statistical models (Vol. 5). Boston: McGraw-Hill Irwin. 2005.

39. Ares G, Arrúa A, Antúnez L, Vidal L, Machín L, Martínez J, et al. Influence of label design on children’s perception of two snack foods: Comparison of rating and choice-based conjoint analysis. Food quality and preference. 2016; 53: pp.1–8.

40. Theil H. The Information Approach to Demand Analysis. Econometrica. 1965;33: 67–87.

41. Barten AP. Maximum Likelihood Estima tion of a Complete System of Demand Equations. Euro. Econ. Rev. 1969;1: 7–73.

42. Díaz AA, Veliz PM, Rivas-Mariño G, Mafla CV, Altamirano LM, Jones CV. Etiquetado de alimentos en Ecuador: implementación, resultados y acciones pendientes. Revista Panamericana de Salud Pública. 2017; 41: p.e54. doi: 10.26633/RPSP.2017.54


Článok vyšiel v časopise

PLOS One


2019 Číslo 10
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Aktuální možnosti diagnostiky a léčby litiáz
nový kurz
Autori: MUDr. Tomáš Ürge, PhD.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#