Bernard Ruffieux, Olivier Allais, Pierre Combris, Paulo Albuquerque, Natalie Rigal, Saadi Lahlou, Pierre Dubois, Pierre Chandon, Patrice Bertail, Céline Bonnet, Toulouse School of Economics (TSE), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut Européen d'administration des Affaires (INSEAD), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Paris Nanterre (UPN), Department of Geography & Environment - London School of Economics and Political Science (LSE), London School of Economics and Political Science (LSE), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Laboratoire d'Economie Appliquée de Grenoble (GAEL), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Toulouse School of Economics (TSE-R), Alimentation et sciences sociales (ALISS), Modélisation aléatoire de Paris X (MODAL'X), Springer, Université Toulouse 1 Capitole (UT1)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Department of Management - London School of Economics and Political Science (LSE), Université Paris Nanterre - Département de Psychologie, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Université Grenoble Alpes (UGA)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)
To examine whether four pre-selected front-of-pack nutrition labels improve food purchases in real-life grocery shopping settings, we put 1.9 million labels on 1,266 food products in four categories in 60 supermarkets and analyzed the nutritional quality of 1,668,301 purchases using the FSA nutrient profiling score. Effect sizes were 17 times smaller on average than those found in comparable laboratory studies. The most effective nutrition label, Nutri-Score, increased the purchases of foods in the top third of their category nutrition-wise by 14%, but had no impact on the purchases of foods with medium, low, or unlabeled nutrition quality. Therefore, Nutri-Score only improved the nutritional quality of the basket of labeled foods purchased by 2.5% (-0.142 FSA points). Nutri-Score’s performance improved with the variance (but not the mean) of the nutritional quality of the category. In-store surveys suggest that Nutri-Score’s ability to attract attention and help shoppers rank products by nutritional quality may explain its performance. Paper published in the Journal of the Academy of Marketing Science (2020) http://link.springer.com/article/10.1007%2Fs11747-020-00723-5