Reducing food waste is globally considered as a key challenge in developing sustainable food systems. Although most food waste is generated at the household level, consumers hardly recognize their responsibility, and the factors underpinning their perception of the quantity of food wasted at home are still unclear. This paper aims to fill this gap by analyzing the results of a large-scale survey conducted in Italy. The perceived quantity of household food waste was measured through a Likert scale and analyzed by means of a logistic regression against a set of predictors, including food waste motivations, perception of the effects of food waste, and sociodemographic variables. As expected, the perceived quantity of food waste declared by respondents was very low. Among the main determinants, food shopping habits and the level of awareness about the reasons why food is wasted played a key role. In contrast, the perception of the environmental effects of food waste seemed to be less important. Differences among subsamples recruited in different areas of Italy were detected, suggesting that further studies, as well as awareness-raising policies, should also consider context-related variables.

Purpose – The purpose of this paper is to provide insights on the relationships between consumers’ income and household food waste behaviors. Design/methodology/approach – Attitude toward food waste is a paradigmatic (economic) non-standard decision making. Based on behavioral economics concepts and empirical evidences, the study analyzes the frequency of household food waste and its main drivers with a focus on individual income. Through a panel of 1,403 Italian consumers, food waste behavior and its determinants are modeled for five food typologies using proportional odds models that adopt stepwise procedures and genetic algorithms. Findings – Results suggest the existence of complex relationships between per capita income and household food waste behavior. When considering food typologies that include high value added products, this relation can be explained by an inverse U-shaped curve: mid-to-low income consumers purchase higher amounts of lower quality products and waste more food. Research limitations/implications – The research highlights the importance of understanding the main socio-economic and behavioral determinants of household food waste, and the need for further researches. Practical implications – The research motivates specific pricing, commercial and policy strategies as well as organizational technological, and educational solutions to prevent/reduce household food waste. Social implications – Lower income class consumers show a greater attitude to waste certain food typologies. In turn, this implies that food waste can further worse economic inequality and relative poverty. Originality/value – The study identifies different patterns of relationship among individual income and consumers’ food waste behavior, and describes the conditions that limit a household “Food Waste Kuznets Curve.”

Highlights• Household food waste warrants the analysis of the whole consumer's food cycle.• The more upstream is the phase the stronger is the influence on household waste.• The gap between purchasing and outcome leads to additional uncertainty.• Individuals resort to heuristics and deviate from the standard economic model. • Situational factors (food retail) influence food waste generation in homes.

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In developed countries, the largest share of food waste is produced at household level. Most studies on consumers' food waste use models that identify covariates as significant when in fact they may not be, particularly where these models use many variables. Here, using EUlevel Eurobarometer data from 2013, we use alternative analytical methods that avoid these problems (Bayesian Networks) to identify the impact of household characteristics and other variables on self-assessed food waste. Our analysis confirmed that the country, the age of the respondent, the status (student/non-student), and a belief that the family wastes too much are related to the level of self-assessed food waste. But we found no evidence that waste behaviours differ between people living in urban and rural areas, and little support of a difference between genders. Households from lower-income EU countries (e.g. Portugal, Greece, Bulgaria, Cyprus and Latvia), as well as students and young adults tend to report higher levels of food waste. Hence, the adoption of an EU strategy based on the concept of subsidiarity, and of country-level policy measures targeting different age groups is suggested. Furthermore, our analysis shows that policy makers need to be wary of relying on analysis based on large datasets that do not control for false-positives, particularly when sample sizes are small.

Food waste from households contributes the greatest proportion to total food waste in developed countries. Therefore, food waste reduction requires an understanding of the socio-economic (contextual and behavioural) factors that lead to its generation within the household. Addressing such a complex subject calls for sound methodological approaches that until now have been conditioned by the large number of factors involved in waste generation, by the lack of a recognised definition, and by limited available data. This work contributes to food waste generation literature by using one of the largest available datasets that includes data on the objective amount of avoidable household food waste, along with information on a series of socio-economic factors. In order to address one aspect of the complexity of the problem, machine learning algorithms (random forests and boruta) for variable selection integrated with linear modelling, model selection and averaging are implemented. Model selection addresses model structural uncertainty, which is not routinely considered in assessments of food waste in literature. The main drivers of food waste in the home selected in the most parsimonious models include household size, the presence of fussy eaters, employment status, home ownership status, and the local authority. Results, regardless of which variable set the models are run on, point toward large households as being a key target element for food waste reduction interventions.

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