Performing Department
(N/A)
Non Technical Summary
Using household and retail scanner data on food purchases and sales, this project seeks to determine the causal effect of front of package and shelf summary nutrition labels on consumers and retailers in the United States and Europe. There is a strong interest in the U.S. and internationally to improve diet quality through the use of FOP nutrition labels. In 2021, the US House Appropriation Committee in its Fiscal Year 2022 agriculture spending bill encouraged the exploration of mandatory FOP labels that allow consumers to quickly assess product healthfulness (Congress 2021, p. 96). On September 28, 2022, the Biden-Harris Administration announced it plans to develop "a standardized FOP labeling system for food packages to help consumers, particularly those with lower nutrition literacy, quickly and easily identify foods that are part of a healthy eating pattern" (The White House 2022, p. 22). Although several European governments wish to make Nutri-Score mandatory, it is not possible under European law unless the European Commission makes Nutri-Score mandatory for all EU countries, which is something currently under consideration. Yet, like NuVal, the discretionary adoption of Nutri-Score poses challenges to efforts evaluating its causal effect on consumers. It is plausible that a manufacturer's decisions on whether to use Nutri-Score and on which brands to apply it are endogenous. These challenges have not been adequately addressed in prior studies of Nutri-Score, thus estimates of the effectiveness of Nutri-Score may be overstated. Our analysis will address this concern and provide unbiased estimates of the net effects of NuVal and Nutri-Score for both more and less advantaged households in efforts to inform policymakers of the likely effects of a mandatory nutrition label. Results from the European Union (EU) Nutri-Score portion of this study will not only help generalize the findings beyond that from a single label, but also be useful to US food manufacturers with a European presence. As more EU countries and food companies voluntarily adopt the Nutri-Score label, an understanding of the effect of Nutri-Score on consumer demand and retail prices will benefit US exporters who eventually need to decide whether to affix the Nutri-Score symbol on their products for the European market (USDA Foreign Agricultural Service 2022).
Animal Health Component
0%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
There is a strong interest in the U.S. and internationally to adopt Front-of-package (FOP) nutrition labels that provide a summary multiple-level rating of a product's healthfulness. The objective of this project is to use two natural experiments, where the scoring algorithms of two leading labels in the U.S. and Europe were revised, to estimate the causal effects of FOP labels on nutrition disparity and retailer pricing. We have four Aims:1: To quantify the net effect of the NuVal (US) and Nutri-Score (Europe) labels on diet quality by socioeconomic strata.2: To quantify suppliers price changes in response to changes in consumer demand.3: To disentangle the net effect of the labels on consumer purchases into the 1) labeling effect and 2) pricing effects.4: To quantify the effect of the NuVal label on revenue and profits.
Project Methods
We take an econometric-simulation approach supported by US and European scanner data to achieve Aims 1-4. In Aim 1, we use an event-study design to estimate changes in consumer diet quality following the revisions of the NuVal and Nutri-Score algorithms. In Aim 2, we use the difference-in-differences dose-response regression to estimate changes in retail prices in response to the changes in NuVal score and Nutri-Score ratings. In Aim 3, we employ the fixed-effects Poisson model to mimic consumers' store choice and product choices within a store, where the NuVal and Nutri-Score revisions act as demand shifters. For each Aim we consider responses separately by household characteristics to explore whether results differ for more and less advantaged households. In Aim 4 we first use the difference-in-differences regression to estimate the effect of the 2014 NuVal revision on participating retailers' revenue. We then assume a Bertrand-Nash price equilibrium and use the Aim 3 demand estimates to simulate the effect of the NuVal revision on retailers' profits. Also in Aim 4, we will examine the association of the estimated changes in revenue and profits with the observed timing of a retailer's exit from the NuVal program.