Several important studies show that, at the aggregate level, 30% to 40% of the total food supply in the United States goes uneaten, representing more than $160 billion in economic losses (Muth et al. 2011; Leib et al. 2013; Buzby, Wells, and Hyman 2014). Moreover, these waste figures mean that resources used to produce the uneaten food, including land, water, and labor, are wasted as well. Throughout its life cycle, discarded food is also a major source of greenhouse gas emissions (Chapagain and James 2011; Quested and Parry 2011; Venkat 2011; Beretta et al. 2013; FAO 2013). According to the U.N.'s Food and Agriculture Organization (FAO), food waste is responsible for about 3.3 gigatons of greenhouse gas annually, which would be, if regarded as a country, the third‐largest carbon‐emitting country after the U.S. and China (FAO 2013).

This article focuses on food waste at the consumer level and proposes a novel approach to empirically identify household food waste as input inefficiency in a production context. Along the food supply chain, the final consumption stage constitutes the largest share of food waste in the U.S. (Griffin, Sobal, and Lyson 2009; Buzby, Wells, and Hyman 2014; Bellemare et al. 2017). In addition, important food‐related policies and assistance programs may face inaccurate cost–benefit calculations because of the hidden costs of food waste. For instance, our results show that healthy dietary practices are associated with significantly more waste, suggesting programs aimed at promoting healthy eating should be evaluated for their implications on food waste. Methodologically, an accurate measurement of consumer food waste provides a means of calibrating actual consumption in both the traditional consumer demand models and newer models that treat food waste as a rational choice (Bellemare et al. 2017; Lusk and Ellison 2017; Hamilton and Richards 2019).

Specifically, we formulate household food consumption as a production process that converts food inputs, categorized by types of food and measured by gram weights of food acquired, into chemical energy required for the human body's metabolic process and physical activities. We then identify food waste as input inefficiency in this production process using a stochastic production frontier model (Aigner, Lovell, and Schmidt 1977; Jondrow et al. 1982). Importantly, this strategy for identifying food waste can be reasonably employed at other stages of the food supply chain as well. For instance, at the farm and retail levels, the output and input measures are generally well‐defined and observed; therefore, input inefficiencies can be directly estimated using various methods from the productivity analysis literature.

Current empirical studies that focus on measuring consumer food waste largely fall into two strands of literature. One strand considers the difference between reported purchases and actual food intakes, either across different data sources (Muth et al. 2011; Buzby, Wells, and Hyman 2014) or within a single dataset (Landry and Smith 2019). Although these studies provide a straightforward calculation of food waste that is easy for interpretation, they are often dependent on the choice of datasets and the availability of food intake data.1

Another strand utilizes innovative survey and experimental methods to obtain a set of “first‐hand” observations (for examples, see Stefan et al. 2013; Reynolds et al. 2014; Neff, Spiker, and Truant 2015; Secondi, Principato, and Laureti 2015; Qi and Roe 2016; Ellison and Lusk 2018; Roe et al. 2018). These methods are extremely useful in assessing attitudes toward food waste and the effects of household‐ or product‐specific characteristics, for example, date labels (Wilson et al. 2017; Roe et al. 2018). On the other hand, the accuracy of waste measures from survey data is influenced by participants' ability to effectively track and recall various wasting occasions, whereas experiments such as visual estimation, manual weighing, and digital photography are usually constrained to small‐scale settings with limited application for households more generally. Overall, in the emerging body of food waste studies, there is a need for comprehensive food‐waste estimates at the individual household level that can be generalized to a wide range of household groups. Consequently, the direct link between household‐specific characteristics and food waste has not been completely documented.

In this article, we overcome the data obstacle by conducting a productivity analysis of household production to obtain an input inefficiency measure that is interpreted as excess food inputs used to produce the current level of output in the form of energy expenditure. By construction, our model considers food diverted or recovered for nonfood purposes as food waste, which is consistent with the definitions used by the U.S. Department of Agriculture's Economic Research Service (ERS), the FAO, and the EU FUSIONS program (FAO 2013; Buzby, Wells, and Hyman 2014; FUSIONS 2016), but different from the latest categorization proposed by Bellemare et al. (2017). In addition, because we use the edible parts of food as inputs in the estimation, the waste estimates in this article point to avoidable food waste according to the definitions by the Waste and Resources Action Programme (WRAP) (Quested and Parry 2011).2

A useful advantage of our approach is that it only requires food acquisition data plus some biological measures (age, height, weight, and gender) of household members. Therefore, it can be replicated and tested by various consumer datasets that are commonly used. The particular data source used in this article is the 2012 National Household Food Acquisition and Purchase Survey (FoodAPS). Because the FoodAPS data lack sufficient information on physical activities, our baseline model treats only the sum of household members' basal metabolic rates as the output measure. We address this issue by examining two additional models. The second model treats employment status as an indicative proxy for physical activities and weekend shopping frequency as its instrument. The third model employs a two‐step procedure to impute individual physical activity levels by a person's biological measures and a set of demographic variables, using the 2011–2012 National Health and Nutrition Examination Survey (NHANES) and its reported metabolic equivalents for different types of activities (Institute of Medicine 2005). All three models yield similar results.

Our estimates show that the average amount of food wasted at the household level is 31.9% in the baseline model, which is in line with the existing findings at the aggregate level. By using the sample weights assigned to each household and their food expenditures, this estimated percentage translates to annual U.S. consumer‐level food waste valued at $240 billion. In addition, by allowing for heterogeneous wasting behavior across households, we examine how household‐specific attributes explain the variation of our food waste estimates. We consider three variables directly related to food management and eating behavior, and we find that better household food security, healthy dietary practices, and higher income lead to more household‐level food waste.

Because our stochastic frontier models are built upon a household production process that takes food inputs as given, we also conduct a post‐estimation analysis that focuses on factors related to shopping behavior and purchase decisions (Stefan et al. 2013; Porpino, Parente, and Wansink 2015). Our results show that shopping with a grocery list, participation in food‐assistance programs, longer distance to primary stores, and larger household sizes are all associated with lower waste estimates. These results, therefore, provide useful reference points for studies investigating the feasibility and effectiveness of possible food waste prevention policies that are aimed at particular food types, the retail environment, and, more importantly, particular household types.

Finally, we present a series of robustness and validity checks. For example, we include alternative input and output measures, and examine the presence of households currently receiving food‐assistance benefits or undergoing dieting practices. We also apply copula estimation, an instrument‐free approach, to address possible endogeneity of the food‐input variables. In addition, we discuss potential obstacles in incorporating more contextual variables into the stochastic frontier estimation. To test the general validity of the method, we show that when applied to a pure food‐intake dataset, the NHANES data, our model predicts waste estimates close to zero (as it should). Perhaps more importantly, though FoodAPS includes both food‐at‐home and food‐away‐from‐home consumption, we find that our method is also valid when applied to only food‐at‐home acquisition data with minor revisions. This last finding suggests that our approach is replicable to other widely used scanner datasets where food‐away‐from‐home information is absent.

The rest of the article is organized as follows: the next section presents the model specification and econometric approach, followed by discussions of the data and main results, including the variation in food waste across demographic groups and a post‐estimation of the effects of shopping behavior. Finally, a set of robustness checks and validations on replicating the model are provided.