Gini's coefficient of mean difference as a measure of adoption speed: theoretical issues and empirical evidence from India

This paper represents a variant on the empirical analysis of technological adoption by Griliches. It follows up on previous research regarding the potential of the Gini coefficient of mean difference as a distribution-free measure of adoption speed. A formal proof is supplied for the derivation of an empirically workable definition of the Gini. Data regarding the uptake of high-yielding wheat varieties in India are used to compare the performance of the Gini relative to that of the logistic and Weibull adoption speed coefficients. The results suggest that, for the purpose of ex-post analyses of monotonically increasing adoption processes, use of the Gini in measuring adoption speed can result in a better explanation of aggregate adoption behavior.     

Why do global long‐term scenarios for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison

Recent studies assessing plausible futures for agricultural markets and global food security have had contradictory outcomes. To advance our understanding of the sources of the differences, 10 global economic models that produce long‐term scenarios were asked to compare a reference scenario with alternate socioeconomic, climate change, and bioenergy scenarios using a common set of key drivers. Several key conclusions emerge from this exercise: First, for a comparison of scenario results to be meaningful, a careful analysis of the interpretation of the relevant model variables is essential. For instance, the use of “real world commodity prices” differs widely across models, and comparing the prices without accounting for their different meanings can lead to misleading results. Second, results suggest that, once some key assumptions are harmonized, the variability in general trends across models declines but remains important. For example, given the common assumptions of the reference scenario, models show average annual rates of changes of real global producer prices for agricultural products on average ranging between ?0.4% and +0.7% between the 2005 base year and 2050. This compares to an average decline of real agricultural prices of 4% p.a. between the 1960s and the 2000s. Several other common trends are shown, for example, relating to key global growth areas for agricultural production and consumption. Third, differences in basic model parameters such as income and price elasticities, sometimes hidden in the way market behavior is modeled, result in significant differences in the details. Fourth, the analysis shows that agro‐economic modelers aiming to inform the agricultural and development policy debate require better data and analysis on both economic behavior and biophysical drivers. More interdisciplinary modeling efforts are required to cross‐fertilize analyses at different scales.     

Eine Typisierung dynamischer Input-Outputmodelle

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