Using Food Availability Data to Answer Research Questions


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Presentation to a National Academies Workshop on Improving the U.S. Food Availability Data System

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Using Food Availability Data to Answer Research Questions

  1. 1. The Food Availability Data System: Importance for Research Laurian Unnevehr Professor Emerita, University of Illinois Presentation to the National Academies Workshop: Data and Research to to Improve the U.S. Food Availability Data System April 8, 2014
  2. 2. Introduction • FADS grew out of ag statistical system focused on production estimates • Provides estimates of aggregate demand (Disappearance) that reflect basic components of supply: Production, Trade, Processing, Stocks • Data are uniquely suited for understanding: – Demand at the aggregate or population level – How supply and demand are linked
  3. 3. FADS Shows Food System Trends • What share of the food supply is imported? • Which meat (or fruit, vegetable, dairy product) is the most consumed? • What is the trend in total calories per capita? • What share of the food supply is lost or wasted? • How does the U.S. food system compare with other countries?
  4. 4. FADS Use in Research • Aggregate demand response to changes in supply, market shocks, advertising • Forecasts of demand, supply, and prices • How well aggregate demand reflects dietary guidance • Environmental impact of food demand
  5. 5. Research Stakeholders Have Expanded • Agricultural economists – Less important over time as research focus shifted with changes in consumer behavior • Public health and nutrition – Widespread use since development of nutrient and serving data within FADS • Industrial ecology, life cycle, environmental systems – Most recent and growing use in these fields Tracking use of the data in research would provide guidance about its value to research stakeholders.
  6. 6. FADS Is One Source of Food Consumption Data Source: Morrison, Smith, and Lin. Amber Waves. March 2009
  7. 7. Questions FADS Cannot Answer • What is the demand for specific food products (within a commodity aggregate)? • How does food demand vary with household demographic characteristics? With SNAP benefits? • What is the demand for differentiated food products and/or for quality attributes of food? • What is the demand for FAFH and how does it differ from demand for FAH?
  8. 8. Research Example: Market Forecasts • How are commodity prices expected to change in the future? (ERS Baseline Model) – Uses per capita consumption of foods from major commodities as part of model – Population growth, income lead to growth in total demand – Changes in supply intersect with demand to give new equilibrium prices – Used for a wide variety of policy questions
  9. 9. Research Example: Changes in Food Preferences • Was there structural change in U.S. meat demand in the 1980s? – Rapid increase in chicken consumption relative to beef led to question of whether driven by relative prices or preferences – Many studies in the 1980s used FADS data to estimate U.S. meat demand and to test for a change in preferences – Most recent was McGuirk et al., JARE, 1995
  10. 10. Research Example: Generic Advertising Impact • What is the impact of generic advertising on commodity demand? (eg. Zheng, Kinnucan, Kaiser, Applied Economics, 2010) – Non-alcoholic beverage data 1970-2004 from FADS – Estimated how advertising expenditures rotate demand curves with special interest in how generic dairy advertising influences milk demand
  11. 11. Research Example: Media Impact on Markets • How did meat demand respond to food safety media coverage? (Piggott and Marsh, AJAE, 2004) – Use quarterly data combined with index of media coverage – Found short-lived effects of significant media events • Most studies of food safety incidents have used scanner data (eg. Arnade et al., AEPP, 2009) – Reasons include time and product specificity needed to tease out market impacts
  12. 12. Research Example: Nutritional Epidemiology • How do different patterns of food consumption correlate with the incidence of disease? (Willett, Nutritional Epidemiology, 2012) – Shows example of meat consumption in different countries correlated with incidence of colon cancer – Recognizes limitations of such studies but also their use in setting up hypotheses for controlled investigation
  13. 13. Research Example: Food Safety Epidemiology • How is the incidence of pathogens related to food commodity sources? (Guo et al., Foodborne Pathogens and Disease, 2011) – FADS data used to represent consumption within larger risk assessment model for Salmonella – Find that chicken, ground beef, and turkey are three most important sources of foodborne Salmonella
  14. 14. Research Example: Production Needed for Improved Diets • How would U.S. food production change if diets matched guidelines? (Young and Kantor, 1999; Buzby, Wells, and Vocke, 2006 ) – Show how crop acreage would need to shift for production to meet demand that follows dietary guidance – Renewed interest in this question as shown by recent citations in articles regarding local foods and sustainable food supply
  15. 15. Research Example: Climate Change and Food Losses • How much do food losses contribute to greenhouse gas emissions? (Venkat, Intl J. Food System Dynamics, 2011) – Uses FADS estimates of losses for 134 commodities – Estimates that “avoidable” food loss (at consumer level) accounts for 2% of U.S. greenhouse emissions
  16. 16. Research Example: Food Miles • How does food transportation contribute to greenhouse gas emissions? (Weber and Matthews, Env. Sci. Tech., 2008) – Use FADS and Commodity Flow Survey to examine relative impacts of transportation versus dietary patterns to greenhouse gas emissions – Find that transportation is much smaller contributor than production and changes in diets would reduce emissions more than “eating local”
  17. 17. Concluding Comments • How well our food system meets a variety of demands– economic, social, environmental is of continuing interest • FADS will remain key to answering many questions about food system performance • Loss estimates critical to both better characterization of demand and understanding environmental impacts • How can improvements to the data support more definitive answers to important questions?