Measuring postharvest loss inequality: Method and applications

Sustainably meeting future food demand requires increases in food production and reductions in the amount of food that is lost and wasted. This paper examines inequality in postharvest losses to reveal patterns and opportunities for intervention. We present a measure that provides information on the distribution of postharvest losses in a single graph, the postharvest loss inequality curve, or an index number, the postharvest loss inequality index. Inequality measurement can help direct policy measures to units generating the greatest postharvest losses and thereby support more favorable policy outcomes and cost/benefit relationships. Concepts and methods introduced here are empirically analyzed based on the African Postharvest Losses Information System data for maize losses in Sub-Saharan Africa. Empirical results indicate the presence of a great deal of variability and inequality in postharvest losses as measured by the postharvest loss inequality index. In the data analyzed, the postharvest loss inequality index better captures anomalies in data distribution such as outliers, skewness and kurtosis than the more direct measure of postharvest losses as a share of total maize production.