Course Description
Nutrition globally is increasingly characterized by significant consumption of energy-dense, nutrient-poor food products that can contribute to poor health outcomes. This course will teach technically advanced but practical microeconomic and econometric tools to conduct robust and timely nutrition and food policy analysis using large data on food sales and purchases.
Athena Title
Adv Nutrition Policy Analytics
Prerequisite
(AAEC 6580-6580L or AAEC 6590 or ECON 8010 or ECON 8020) AND (AAEC 6610 or AAEC 6620 or AAEC 8610 or ECON 8110 or ECON 8120)
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
In this Ph.D.-level course, the student will learn: 1) how to use a variety of consumer demand models (continuous, censored, and discrete-choice) to analyze food purchase and consumption data and to inform contemporary food and nutrition policy issues, 2) practical considerations in choosing the preferred approach among the alternatives, and 3) the benefits and costs of each approach. Through the class projects, students will apply these advanced but tractable econometric techniques to nutrition and health policy analyses. The instructor will provide extremely large and detailed retail and household scanner datasets (a.k.a. “big data”) for these projects upon the student signing appropriate data confidentiality agreements. The ultimate goal for the students is to publish the projects in peer-reviewed journals independently or as a team.
Topical Outline
1. Overview of the relationship between food and nutrition policy and obesity 1.1. Access policy 1.2. Pricing policy 1.3. Information provision policy 2. U.S. food assistance programs: the economics and how participants’ food choices are affected 2.1. The Supplemental Nutrition Assistance Program 2.2. The Women, Infant, and Children Program 3. Tools for analyzing food sales and purchase data 3.1. Price indexes and scanner data 3.2. The two-part model 3.3. Censored utility-theoretic flexible demand systems: the Almost Ideal Demand and Exact Affine Stone Index models 3.4. Discrete choice models: nested logit and random coefficient logit 3.5. Dynamic models of habits and stockpiling 3.6. Endogeneity biases and corrective actions
Syllabus
Public CV