Source: UNIVERSITY OF ARKANSAS submitted to
FOOD PREFERENCES AND DEMAND, OBESITY, AND HEALTH
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
REVISED
Funding Source
HATCH
Reporting Frequency
Annual
Accession No.
1019410
Grant No.
(N/A)
Project No.
ARK02643
Proposal No.
(N/A)
Multistate No.
(N/A)
Program Code
(N/A)
Project Start Date
Jul 1, 2019
Project End Date
Jun 30, 2024
Grant Year
(N/A)
Project Director
Nayga, RO, M.
Recipient Organization
UNIVERSITY OF ARKANSAS
(N/A)
FAYETTEVILLE,AR 72703
Performing Department
Agricultural Economics & Agribusiness
Non Technical Summary
The project will focus on examining the effect of a myriad of factors (e.g., food environment, demographics, psychological) on food demand/consumption and health using statisticaland quasi-experimental methods. In addition, this research will evaluate the effectiveness of a number of food programs and policy interventions on food consumption behavior, obesity, and health. Results from this research can be used in the design of more effective policies aimed at improving the consumption behavior and health of people.
Animal Health Component
0%
Research Effort Categories
Basic
(N/A)
Applied
90%
Developmental
10%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
60760103010100%
Knowledge Area
607 - Consumer Economics;

Subject Of Investigation
6010 - Individuals;

Field Of Science
3010 - Economics;
Goals / Objectives
The objective of this research is to assess the effect of a myriad of factors (e.g., food environment, demographics, psychological) on food demand/consumption and health using econometric and quasi-experimental methods. In addition, this research will evaluate the effectiveness of a number of food programs and policy interventions on food consumption behavior, obesity, and health. Results from this research can be used in the design of more effective policies aimed at improving the consumption behavior and health of people.
Project Methods
Large existing secondary data will be tapped to assess the effects of various factors on food prefences, demand, and health outcomes such as obesity. These secondary data could include Nielsen Homescan, Arkansas BMI data, InfoGroup data, etc. Econometric modeling will be employed with careful attention given to identification issues to determine, whenever possible, the causal effects of policies, interventions, environmental and behavioral factors, among others, on food choices, demand, obesity, and health. For example, most statistical models estimate average effects within a population. However, it is also important to investigate to what extent associations between, for example, attitudes towards health and food consumption vary across different groups in the population or over time. We will use models such as latent class and cohort models (Øvrum, 2011), and will also employ quasi-experimental techniques including instrumental variables, difference-in-difference (DID), and regression discontinuity. We will also use propensity score matching, which is a technique used to address the possible occurrence of selection bias and to estimate treatment effects when treatment is endogenous to the outcome. The propensity score was introduced by Rosenbaum and Rubin (1983) to provide an alternative method for estimating treatment effects when treatment assignment is not random, but can be assumed to be unconfounded. Matching methods focus attention on a specific causal effect of interest, and treat all variables other than the treatment variable as potentially confounding variables. The influence of confounding variables is then reduced by non-parametrically balancing the vector of characteristics across treatment, solely to obtain the best possible estimate of the causal effect of the treatment on the outcome variable. We will also test the robustness of findings to inherent assumptions behind these estimation techniques.Additionally, with longitudinal data, we can apply panel data methods on the matched data set to take account of the correlation within each individual in the different time periods. Hence, we could employ matching techniques prior to running our quasi-experimental panel models. For instance, Heckman, Ichimura, and Todd (1997) concluded that DID matching helps control for heterogeneity in initial conditions and also controls for unobserved determinants. In the same spirit as in Angelucci and Attanasio (2013), panel DID estimation can deal with time-invariant unobserved factors while the matching rebalances the sample to deal with time-varying unobserved factors.

Progress 10/01/19 to 09/30/20

Outputs
Target Audience:Researchers in the fields of economics, food marketing, and public health as well as public policy decisions makers. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals?Continue with the research.

Impacts
What was accomplished under these goals? Published a number of papers in refereed journals articles.

Publications

  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Segovia, M., M. Palma, and R.M. Nayga, Jr., Can Episodic Future Thinking Affect Food Choices, Journal of Economic Behavior & Organization, 177(2020): 371-389.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Fang, D., R.M. Nayga, Jr., G. West, C. Bazzani, W. Yang, B. Lok, C. Levy, and H. Snell, On the Use of Virtual Reality in Mitigating Hypothetical Bias in Choice Experiments, American Journal of Agricultural Economics, 2020, doi.org/10.1111/ajae.12118
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Drichoutis, A. and R.M. Nayga, Jr., Economic Rationality under Cognitive Load, The Economic Journal, 2020, doi: 10.1093/ej/ueaa052
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Codjo, S., A. Durand-Morat, L. Nalley, R.M. Nayga, Jr., G. West, and E. Wailes, Estimating Demand Elasticities for Rice in Benin, Agricultural Economics, accepted and forthcoming.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Lee, J., R.M. Nayga, Jr., C. Deck, and A. Drichoutis, Cognitive Ability and Bidding Behavior in Second Price Auctions: An Experimental Study, American Journal of Agricultural Economics, doi:10.1002/ajae.12082.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Luckstead, J., R.M. Nayga, Jr., and H. Snell, Labor Issues in the Food Supply Chain Amid the Covid-19 Pandemic, Applied Economic Perspectives and Policy, 2020, doi.org/10.1002/aepp.13090.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Zare, S., M. Thomsen, R.M. Nayga, Jr., and A. Goudie, Use of Machine Learning to Determine the Information Value of a Body Mass Index Screening Program, American Journal of Preventive Medicine, accepted and forthcoming.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Dunn, R., R.M. Nayga, Jr., M. Thomsen, and H. Rouse, A Longitudinal Analysis of Fast-food Exposure on Child Weight Outcomes: Identifying Causality through School Transitions, Q Open, accepted and forthcoming.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Chavez, D., M. Palma, R.M. Nayga, Jr., and J. Mjelde, Product Availability in Discrete Choice Experiments with Private Goods, Journal of Choice Modelling, 2020, doi.org/10.1016/j.jocm.2020.100225
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Kim, B., M. Thomsen, R.M. Nayga, Jr., D. Fang, and A. Goudie, Move more, Gain less: Effect of a Recreational Trail System on Childhood BMI, Contemporary Economic Policy, 38,2(2020): 270-288.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Caputo, V., J. Lusk, and R.M. Nayga, Jr., Am I Getting a Good Deal? Reference Dependent Decision Making when the Reference Price is Uncertain, American Journal of Agricultural Economics, 102(2020): 132-153.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Atallah, S., C. Bazzani, K. Ha, and R.M. Nayga, Jr., Does the Origin of Inputs and Processing Matter? Evidence from Consumers Valuation for Craft Beer, Food Quality and Preference, accepted and forthcoming.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Pappalardo, G., S. Cerroni, R.M. Nayga, Jr., and W. Yang, Impact of Covid-19 on Household Food Waste: The Case of Italy, Frontiers in Nutrition, 2020, doi.org/10.3389/fnut.2020.585090
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Ruggeri, G., S. Corsi, and R.M. Nayga, Jr., Eliciting Willingness to Pay for Fairtrade Products with Information, Food Quality and Preference, 87(2021): 104066.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Lee, J., Y. Qian, G. Gustavsen, R.M. Nayga, Jr., and K. Rickertsen, Effects of Consumer Cohorts and Age on Meat Expenditures in the United States, Agricultural Economics, 51(2020): 505-517.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Huseynov, S., M. Palma, and R.M. Nayga, Jr., General Public Preferences for Allocating Scarce Medical Resources During COVID-19, Frontiers in Public Health, accepted and forthcoming.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Kemper, N. J. Popp, and R.M. Nayga, Jr., A Query Theory Account of a Discrete Choice Experiment under Oath, European Review of Agricultural Economics, 47(2020): 1133-1172.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Okpiaifo, G., A. Durand-Morat, G. West, L. Nalley, R.M. Nayga, Jr., and E. Wailes, Consumers Preferences for Sustainable Rice Practices in Nigeria, Global Food Security, 2020, doi.org/10.1016/j.gfs.2019.100345.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Schauder, S., M. Thomsen, and R.M. Nayga, Jr., Agent-Based Modeling Insights into the Optimal Distribution of the Fresh Fruit and Vegetable Program, Preventive Medicine Reports, 20(2020):101173.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Lim, K., W. Hu, and R.M. Nayga, Jr., Seeing Food Safety Assurance in Grass-fed Beef, Journal of Agricultural and Resource Economics, accepted and forthcoming.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Liu, R., Z. Gao, R.M. Nayga, Jr., L. Shi, L. Oxley, and H. Ma, Can Green Food Certification Achieve both Sustainable Practices and Economic Benefits in a Transitional Economy? The Case of Kiwifruit Growers in Henan Province, China, Agribusiness: An International Journal, 2020, doi.org/10.1002/agr.21641.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Lee, J., N., Pavasopon, O. Napasintuwong, and R.M. Nayga, Jr., Consumers Valuation for Geographical Indication Labelled Food: The Case of Hom Mali Rice in Bangkok, Asian Economic Journal, 34(2020): 79-96.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Liu, R., Z. Gao, R.M. Nayga, Jr., H. Snell, and H. Ma, Consumers Valuation for Food Traceability in China: Does Trust Matter?, Food Policy, 2020, doi.org/10.1016/j.foodpol.2019.101768
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: De Marchi, E., A. Cavaliere, R.M. Nayga, Jr., and A. Banterle, Incentivizing Vegetable Consumption in School-Aged Children: Evidence from a Field Experiment, Journal of Consumer Affairs, 54(2020): 261-285.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Asioli, D., J. Aschemann-Witzel, and R.M. Nayga, Jr., Sustainability-Related Food Labels, Annual Review of Resource Economics, 12(2020): 13.1-13.15.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: Gustavsen, G., D. Dong, R.M. Nayga, Jr., and K. Rickertsen, Ethnic Variation in Immigrants Diets and Food Acculturation  United States 1999-2012, Agricultural and Resource Economics Review, 2020, doi.org/10.1017/age.2020.17.
  • Type: Journal Articles Status: Accepted Year Published: 2020 Citation: He, C., R. Liu, Z. Gao, X. Zhao, C. Sims, and R.M. Nayga, Jr., Does Local Label Bias Consumer Taste Buds and Preference? Evidence from a Strawberry Sensory Experiment, Agribusiness: An International Journal, accepted and forthcoming.
  • Type: Other Status: Published Year Published: 2020 Citation: Nayga Jr., R.M., and D. Zilberman, Research Priorities to Fill Critical Knowledge Gaps Caused by the Coronavirus Pandemic, in Economic Impacts of COVID-19 on Food and Agricultural Markets, Council for Agricultural Science and Technology, QTA2020-3, June 2020.


Progress 07/01/19 to 09/30/19

Outputs
Target Audience:researchers, academics Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Project was initiated in July so I have nothing yet to report.

Publications