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In the emerging literature on resilience in relation to food security, a growing number of studies stress the need to expand our analysis beyond conventional socio-economic factors such as assets or social capital, and to consider less tangible elements such as risk perception, self-efficacy or aspiration. Drawing on the recent literature and the authors’ own experience, a conceptual framework of subjective resilience is proposed. The framework helps locating the subjective element of resilience within the wider resilience conceptualization as currently developed in the literature on food security and to clarify how it links to the more tangible elements of that conceptualization. Empirical data are then used to test the framework. The analysis demonstrates the relevance of the concept of subjective resilience and the central role that psychosocial factors and individual perceptions play in people’s construct of resilience in the context of humanitarian and food security crises. The article concludes with a discussion of the implications of those findings.

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Monte Carlo simulations of profit functions indicate that flexible functional forms may be ranked nearly the same with respect to Allen-Uzawa partial substitution elasticities or price and fixed factor elasticities. Flexible functional form approximations of output price elasticities are generally worse than approximations of input price or fixed factor elasticities. Minflex Laurent functional forms did not appear to approximate underlying elasticities any better than did their translog and generalized Leontief counterparts.Monte Carlo studies have been employed to assess the accuracy of flexible functional forms (FFF) in approximating known underlying technologies and preferences. Monte Carlo production studies have compared Allen-V zawa partial elasticities of substitution generated from cost functions (Guilkey and Lovell; Guilkey, Lovell, and Sickles; Chalfant and Gallant) or profit functions (Dixon, Garcia, and Anderson) with Allen-Uzawa partial substitution elasticities (AVES) derived from a known underlying production function.While the Monte Carlo production studies have measured dual approximations solely in technical terms, many recent agricultural economics studies have utilized FFF specifications of the profit function to derive price elasticities (Sidhu and Baanante, Weaver, Shumway, Lopez, Antle). These applications of FFF raise the methodological issue of the extent to which conclusions about the performance of FFF in relation to AVES can be extrapolated to price elasticities. The purpose of this study is to perform quantitative comparisons of different functional forms with respect to the two types of measures-AVES and price elasticities-that are of interest in empirical studies.A Monte Carlo simulation is used to measure the performance of five FFF in recovering AVES and price elasticities from samples Gary D. Thompson is an assistant professor, and Mark Langworthy is an assistant research scientist, Department of Agricultural Economics, University of Arizona.where these parameters are known. Three commonly used FFF-translog, generalized Leontief, and quadratic-and two newer FFF-minflex Laurent translog and minflex Laurent generalized Leontief-are compared for the single output normalized profit function. V se of Allen-V zawa partial substitution elasticities and price elasticities for evaluating dual profit function approximations provides information about the FFF's ability to track both technical and economic relationships. Price and Allen-Uzawa ElasticitiesThe theoretical link between price elasticities and Allen-V zawa partial elasticities of substitution can be derived from Lau's conjugate duality development of the single output normalized profit function. With certain regularity conditions satisfied (see Lau,, the primal production function and the dual profit function are related directly through Hessian matrices. In particular, F u ] and [G u ] are the (n x n) Hessian matrices of the production function y = f(x; z) and the normalized profit function G = G(w; z), respectively, y is outp...

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