Mason-D'Croz. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. 2 AbstractIn this paper, we assess the levels of infrastructure investment and rates for return on investments to reduce postharvest losses (PHL). Food security impacts and rates of return to reducing PHL are compared to rates of return to productivity-increasing research and development (R&D) investment. First we undertake of review of the literature on the magnitude of PHL. Next we undertake an econometric analysis of the impact of infrastructure investments on PHL using a panel data set. Third, we quantify the investments required for any given level of PHL reduction by combining marginal effect analysis based on the econometric estimation with data on unit costs for specific infrastructural variables. Fourth, we undertake a cost-benefit analysis of the required infrastructural investments to assess whether or not significant efforts in PHL reduction are economically feasible; and compare these to the rates of return to investments in R&D.These scenarios show that investment in infrastructure for PHL reduction contributes to lower food prices, higher food availability, and improved food security, and has positive economic rates of return. However, improvements in food security and marginal returns to investment in agricultural research are considerably higher for investment in agricultural research than for investment in PHL reduction. Reductions in PHL are not a low-cost alternative to productivity growth for achieving food security. Rather, reduction in PHL through improved infrastructure requires large public investments and is complementary to investments in long-term productivity growth to achieve food security. 3The 2008-2011 food price spikes brought the issue of postharvest losses (PHL) back to the forefront of policy debate, and observers are again calling for a reduction in PHL as a tool to feed the expanding global population. Food losses due to improper postharvest handling, lack of appropriate infrastructure, and poor management techniques, have once again become a matter of serious concern. Food losses, defined as "any decrease in food mass throughout the edible food supply chain," can occur in any point of the marketing stages-from production (e.g., crop damage, spillage), postharvest and processing stages (e.g., attacks from insect or microorganisms during storage), distribution, and retail sale until home consumption (e.g., spoilage, table waste) (Rosegrant et al. 2013). Kummu et al. (2012) suggest an additional 1 billion people could be fed if food crop losses were halved, which could potentially relieve some of the pressure on the significant increase in production that would be required. Achieving lower levels of food losses, however requires both investments in technologies that help prevent losses as well as in overall infrastructure. Understanding the magnitude of these investments and their impa...

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Reported rates of return to agricultural R&D are generally high, but they are likely to be biased, particularly because of attribution problems—mismatching research benefits with costs. The importance of attribution biases is illustrated here with new evidence for Brazil. During 1981–2003, varietal improvements in upland rice, edible beans, and soybeans yielded benefits of $14.8 billion in present value (1999 prices) terms. Attributing all of the benefits to Embrapa, a public research corporation accounting for more than half of Brazil's agricultural R&D spending, the benefit-cost ratio would be 78:1. Under alternative attribution rules, the ratio drops to 16:1. Copyright 2006, Oxford University Press.

Tanzania's recent growth boom has been accompanied by a threefold increase in the share of the rural labour force working in nonfarm employment. Although households with nonfarm enterprises are less likely to be poor, a substantial fraction of these households fall below the poverty line. Heterogeneity in the labour productivity of rural nonfarm businesses calls for a two-pronged strategy for rural transformation. Relatively unproductive enterprises may be part of a poverty reduction strategy but should not be expected to contribute to employment and labour productivity growth. Failure to account for this heterogeneity is likely to lead to disappointing outcomes.

work is licensed under a Creative Commons IGO 3.0 AttributionNonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/ legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose. No derivative work is allowed.Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IDB's name for any purpose other than for attribution, and the use of IDB's logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this CC-IGO license.Note that link provided above includes additional terms and conditions of the license. Since the initial publication of this Technical Paper, others have sought to extend the sample of countries covered in the GTAP-POV module. This has led to the development of improved techniques for dealing with challenges posed by the household survey data. This revised version of the technical paper includes four appendices which provide STATA code to accomplish key steps in the process of constructing a GTAP-POV module for an individual country. We have also included a new section at the end of part 3 of the paper in which we compare patterns of poverty across the full range of countries available to us at this point in time. Our hope is that this Technical Paper will continue to inspire members of the GTAP network, as well as others, to contribute additional poverty modules. Eventually we hope to cover most developing countries. This would permit more definitive analysis of the poverty impacts of global economic and environmental policies.JEL Classification: C54, D58, I32, Q12