ASSESSING HOUSEHOLD-LEVEL FOOD LOSS: AN EFFICIENCY ANALYSIS

The USDA's Economic Research Service, its collaborators, and other researchers have published rigorous research on the massive economic consequences from food loss in the farm-to-table supply chain, yet these efforts have been limited to indirect approximations for specific food categories. Virtually none of this important research quantitatively addresses the role that individual households play in determining the amount of food loss. The goal of the proposed food-loss exploratory research project will depart from existing methodologies for estimating food loss by food categories, and instead estimate household-specific food loss, framed in percentage terms, for a nationally representative sample of U.S. households.For each household in our dataset (USDA-ERS's new FoodAPS dataset), we propose to (i) classify all at-home and away-from-home food purchases into a range of food groups and/or food types; (ii) calculate an aggregate household measure of body mass for all members in the household; (iii) represent the production of household body mass as a function of all the measured food groups and food types; (iv) use well-known efficiency analysis techniques (including linear programming and econometric techniques) to estimate household-level food-loss as the percentage of inefficiency found in the household production of body mass; and (v) use econometric methods to analyze the relationship between these food-loss estimates and a number of household characteristics. Additionally, we will conduct sensitivity analyses for methodological choices made in estimating household-level body-mass production and calibration checks using other data.While the general method of estimating firms' production inefficiencies is well established, it has rarely been applied at the household production level, and never - to our knowledge - for the production of body mass as a function of food purchases. Our food-loss estimates will tell us which households are most efficient at converting food purchases into body mass, and which households are less efficient. Ultimately, our analysis will allow us to comment on whether possible food-loss prevention policies should be aimed at food types (such as fresh or processed foods), food sources (such as certain types of food retailers or certain types of restaurants), or at particular household types (such as households in certain income or education ranges). We will also be able to comment on whether SNAP requirements might be altered to improve a household's food-loss estimate.