Performing Department
Agricultural Economics & Rural
Non Technical Summary
As of 2012, 76 million American adults were obese, and the prevalence of this disease, which directly affects more than two-thirds of the adult population, is two-to-three times higher than it was in the 1970s. However, optimal policies to address long-standing obesity problems are not clear, with policy targets ranging from dietary fat, empty calories, and energy balance to consumer education, nutrition labeling, and even certain fat- or sugar-based taxes. These later policies emphasize the role of consumer behavior, and scientists from both social physical science fields have linked addictive behaviors with obesity.Like some previous efforts, including economic studies that capture some aspect of consumer dynamics, we intend to investigate addition both towards specific products and to ingredients or nutrients. Previous research on addictive behavior in food has captured some dynamic aspects of behavior. However, a fully dynamic model with endogenous demand that can identify both stockpiling and addictive behavior not been developed and applied towards food products. Thus, our proposed research will (a) develop a new structural model of rational addiction that is more thoroughly dynamic, (b) investigate addictive behavior at both the product level and nutrient level, (c) consider heterogeneous behavior based on consumers' health status, and (d) investigate the implications for hypothetical policies aimed at stemming the obesity epidemic in the U.S. Our project will make use of micro-level consumer purchase data linked to self-stated health outcomes.
Animal Health Component
0%
Research Effort Categories
Basic
80%
Applied
20%
Developmental
(N/A)
Goals / Objectives
The long-term goal of our proposed research project is to develop a deeper understanding of underlying consumer behavior that links dietary choices and health outcomes. In other words, we would like to know if some consumers are behaving as if they are addicted to certain unhealthy foods (or nutrients), and how this type of behavior interacts with other incentive-based behaviors, including general consumer response to average prices and temporary price reductions. Armed with this better understanding, we also plan to investigate how consumers of varying health status respond to a number of policy interventions, including taxes on some unhealthy projects. To achieve our long-term goal, this proposal has the following six specific objectives:Objective 1. Access and manage IRI Consumer Panel Data, IRI MedProfiler consumer health data, and Ad$pender advertising data to develop a household-specific dataset that links 2010-2017 food-purchase information to health and marketing data.Objective 2: Qualitatively evaluate the purchasing patterns across various food products, both health and unhealthy, and across nutrients for households in both the full dataset and data subsamples based on obesity status and other factors.Objective 3: Design and estimate a structural dynamic economic model of nutrient-specific food addiction with endogenous consumption and stockpiling in multiple food categories, and apply this model to consumer-level food purchase data.Objective 4: Assess the role of health information, consumer learning, advertising and promotion, and related factors in influencing the development of addiction.Objective 5: Investigate the impact of health status, including incidence of obesity, diabetes, and other chronic diseases, on addictive behavior.Objective 6: Under the framework of rational food addiction, evaluate the effectiveness of various policies aimed at reducing the consumption of unhealthy food and beverages by predicting how all consumers, as well as consumer subsamples differentiated by health status, respond to policy interventions.
Project Methods
3.1 Data Preparation, Stage 1The proposed project plans to utilize a combination of several large datasets, including (1) IRI consumer panel data, (2) IRI MedProfiler consumer health data, (3) Nexis Uni news and information data, and (4) Ad$pender advertising data. We expect to build a household-specific dataset that links 2010-2017 food-purchase information to health information/news, consumer health, and marketing data. A broad set of food categories will be chosen in this stage, including sugar-sweetened beverages, candy, salty snacks, frozen prepared food, caffeinated drinks, and similar products.The IRI consumer panel data include over 120,000 households, with 46 to 52 percent of the households providing sufficient purchase data to be included in the static panel used for analyses. We plan to collect the purchase information of all chosen food categories, including brand names, prices, package sizes, purchase quantity, and purchase date. The data also include key demographic variables including household size, age of head of household, income, ethnicity, race, children, and related characteristics. The IRI data also contain substantial calorie and macronutrient content from products' nutrition facts panel information.The IRI MedProfiler data reflect an opt-in survey on medical conditions offered to all households in the IRI consumer panel data to identify health opinions and concerns, medical conditions, and diet and lifestyle choices. About one-third of the consumer panel households had at least one member respond to the MedProfiler survey, with responses received from 95,000 to 123,000 individuals in those households. For each individual, the data contain information about the health demographics (such as age, BMI, diagnosed status of chronic diseases, etc.), health concerns and opinions, lifestyle (exercise) and diet (gluten free, low fat, vegetarian, etc.). The data can be linked with the IRI consumer panel data to provide a more complete picture of consumer health and wellness.Nexis Uni news and information data provide access to over 15,000 news, business, and legal sources, and we will consider newspapers, magazines, TV, radio, and online sources in our study.Ad$pender advertising data, obtained from Kantar Media, provide advertising expenditure data n product brands and parent companies for many major markets in the U.S. market. In addition, the advertising expenditure are classified by various types of media.3.2 Preliminary and Reduced-Form Analysis of Nutrient-Specific Addiction, Stage 2Using the prepared dataset, we plan to conduct extensive preliminary data analyses. We will first qualitatively evaluate the purchasing patterns across various food products, both healthy and unhealthy, and across nutrients for households in both the full dataset and data subsamples based on obesity status and other factors.Our reduced-form analysis is targeted for documenting descriptive evidence for (1) the nutrient-specific addictive behavior in each food category; (2) the impact of individual's health status (e.g. obesity and heart disease) on addictive behavior; (3) the impact of health news and consumer learning on addictive behavior; and (4) the impact of product advertising on addictive behavior.To empirically implement and estimate the effects of health information, for each nutrient-specific food category, we construct a two-equation model, one that specifies current consumption as a function of past and expected future consumption and prices, and a second that specifies the weight given to expected future consumption as a function of health, health news advertising, and household-specific characteristics.Even without a full set of dynamic behaviors accounted for, we expected result from these preliminary, reduced-form estimations to be interesting to peer researchers, policymakers, and industry professions. Thus, we plan to develop results from this stage into a series of outputs, e.g., presentations and manuscripts, that will be dysmyelinated to our peers and interested readers. The estimation is conducted by fixed-effects IV regression.3.3 Structural Econometric Analysis of Nutrient-Specific Addiction, Stage 3Our structural econometric analysis is aimed at addressing three related consumer-response issues. First, we propose to assess the role that information, both in terms of advertising and consumer learning, plays in influencing the development of addition. Second, we plan to investigate how consumers, and consumer subgroups differentiated by health status, respond to various policy interventions. And lastly, we propose to examine the efficacy of alternative policies targeted at reducing the consumption unhealthy foods and beverages.We build on the frameworks of Hendel and Nevo (2006), Richards, Patterson, and Tegene (2006), and Wang (2015) to estimate a dynamic demand model of rational addiction incorporating endogenous consumption and stockpiling. The advantages of this model are threefold: (a) it allows us to account for household purchases across a range of food categories, (b) it allows individual household's utility to be a function of specific nutrients, and (c) it accounts for the possibility of nutrient-specific addition and stockpiling behavior.Households in our demand model maximize the sum of discounted utilities derived from consuming their chosen products and an outside good. Similar to the model setup in Hendel and Nevo (2006) and Wang (2015), each household obtains a per period flow utility and specify a functional formed used in previous studies.A unique feature of the Hendel and Nevo (2006) model setup is the separation of the utility of consumption and the utility of purchase, which significantly decreases computational cost. We also follow Richards, Patterson, and Tegene (2006)'s model setup in implementing the Becker and Murphy (1988) rational addiction framework. More specifically, we include a parameter that measures the product-specific preferences of a household. The model allows for state-dependent preferences in the following way: It allows utility to reflect both habit persistence and rational addiction. Utility depends on the attribute-space distance of each nutrient purchased in the current period from that of the previous period and that of the following period. If either distance rises, then the household is variety-seeking. If utility falls with the distance between the current and last periods, then the household is habituated, but if the distance falls between the current and next period, then the household is thought to be rationally addicted.Once parameters of the above model are recovered, our counterfactual analysis will allow us to independently consider the three objectives listed at the beginning of this section.Using the estimated parameters above dynamic model, we will proceed to evaluate a series of policies aimed at decreasing the consumption of unhealthy food and beverages. Specifically, we plan to conduct and compare the effectiveness of the following sets of policies.(1) First, we will consider a set of tax policies on certain nutrients (sugar, salt, caffeine, etc.) that will raise retail prices in certain food categories.(2) Second, we will investigate potential policies which limit the amount of salt, sugar, or caffeine that can be added in food.(3) Third, we assess the impact of advertising restrictions in certain food categories under the framework of rational addiction.(4) Lastly, we will simulate the impact of a nation-wide public health campaign to discourage consumption of unhealthy food and beverage.