Food systems contribute 19%–29% of global anthropogenic greenhouse gas (GHG) emissions, releasing 9,800–16,900 megatonnes of carbon dioxide equivalent (MtCO2e) in 2008. Agricultural production, including indirect emissions associated with land-cover change, contributes 80%–86% of total food system emissions, with significant regional variation. The impacts of global climate change on food systems are expected to be widespread, complex, geographically and temporally variable, and profoundly influenced by socioeconomic conditions. Historical statistical studies and integrated assessment models provide evidence that climate change will affect agricultural yields and earnings, food prices, reliability of delivery, food quality, and, notably, food safety. Low-income producers and consumers of food will be more vulnerable to climate change owing to their comparatively limited ability to invest in adaptive institutions and technologies under increasing climatic risks. Some synergies among food security, adaptati...

https://sustainabledevelopment.un.org/content/documents/881annurev.pdf

Climate Change and Food Systems

A new data set on national poverty lines is combined with new price data and almost 700 household surveys to estimate absolute poverty measures for the developing world We find that 25% of the population lived in poverty in 2005, as judged by what “poverty” typically means in the world's poorest countries This is higher than past estimates Substantial overall progress is still indicated—the corresponding poverty rate was 52% in 1981—but progress was very uneven across regions The trends over time and regional profile are robust to various changes in methodology, though precise counts are more sensitive

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The Developing World is Poorer than We Thought, But No Less Successful in the Fight Against Poverty

This paper assesses the current state of evidence on the impact of trade policy reform on poverty in developing countries. There is little empirical evidence addressing this question directly, but a lot of related evidence on specific aspects. We summarize this evidence using an analytic framework addressing four key areas: economic growth and stability; households and markets; wages and employment and government revenue. Twelve key questions are identified and empirical studies and results are discussed. We argue that there is no simple generalizable conclusion about the relationship between trade liberalization and poverty, and the picture is much less negative than is often suggested. In the long run and on average, trade liberalization is likely to be strongly poverty alleviating, and there is no convincing evidence that it will generally increase overall poverty or vulnerability. But there is evidence that the poor may be less well placed in the short run to protect themselves against adverse effects and take advantage of favorable opportunities.

/pdf/trade-liberalization-and-poverty-the-evidence-so-far-59kawhxtwc.pdf

Trade Liberalization and Poverty: The Evidence So Far

Many definitions of food security exist, and these have been the subject of much debate. As early as 1992, Maxwell and Smith (1992) reviewed more than 180 items discussing concepts and definitions, and more definitions have been formulated since (DEFRA, 2006). Whereas many earlier definitions centered on food production, more recent definitions highlight access to food, in keeping with the 1996 World Food Summit definition (FAO, 1996) that food security is met when “all people, at all times, have physical and economic access to sufficient, safe, and nutritious food to meet their dietary needs and food preferences for an active and healthy life.” Worldwide attention on food access was given impetus by the food “price spike” in 2007–2008, triggered by a complex set of long- and short-term factors (FAO, 2009b; von Braun and Torero, 2009). FAO concluded, “provisional estimates show that, in 2007, 75 million more people were added to the total number of undernourished relative to 2003–05” (FAO, 2008); this is arguably a low-end estimate (Headey and Fan, 2010). More than enough food is currently produced per capita to feed the global population, yet about 870 million people remained hungry in the period from 2010 to 2012 (FAO et al., 2012). The questions for this chapter are how far climate and its change affect current food production systems and food security and the extent to which they will do so in the future (Figure 7-1).

Food security and food production systems

Global sustainability challenges, from maintaining biodiversity to providing clean air and water, are closely interconnected yet often separately studied and managed. Systems integration—holistic approaches to integrating various components of coupled human and natural systems—is critical to understand socioeconomic and environmental interconnections and to create sustainability solutions. Recent advances include the development and quantification of integrated frameworks that incorporate ecosystem services, environmental footprints, planetary boundaries, human-nature nexuses, and telecoupling. Although systems integration has led to fundamental discoveries and practical applications, further efforts are needed to incorporate more human and natural components simultaneously, quantify spillover systems and feedbacks, integrate multiple spatial and temporal scales, develop new tools, and translate findings into policy and practice. Such efforts can help address important knowledge gaps, link seemingly unconnected challenges, and inform policy and management decisions.

/pdf/systems-integration-for-global-sustainability-18596gn05j.pdf

Systems integration for global sustainability

In many poor countries, the recent increases in prices of staple foods raise the real incomes of those selling food, many of whom are relatively poor, while hurting net food consumers, many of whom are also relatively poor. The impacts on poverty will certainly be very diverse, but the average impact on poverty depends upon the balance between these two effects, and can only be determined by looking at real-world data. Results using household data for ten observations on nine low-income countries show that the short-run impacts of higher staple food prices on poverty differ considerably by commodity and by country, but, that poverty increases are much more frequent, and larger, than poverty reductions. The recent large increases in food prices appear likely to raise overall poverty in low income countries substantially.

Implications of Higher Global Food Prices for Poverty in Low-Income Countries

http://www-wds.worldbank.org/external/default/WDSContentServer/IW3P/IB/2008/04/16/000158349_20080416103709/Rendered/PDF/wps4594.pdf

Implications of higher global food prices for poverty in low-income countries

With the proliferation of Free Trade Agreements (FTAs) over the past decade, demand for quantitative analysis of their likely impacts has surged. The main quantitative tool for performing such analysis is Computable General Equilibrium (CGE) modeling. Yet these models have been widely criticized for performing poorly (Kehoe, 2002) and having weak econometric foundations (McKitrick, 1998; Jorgenson, 1984). FTA results have been shown to be particularly sensitive to the trade elasticities, with small trade elasticities generating large terms of trade effects and relatively modest efficiency gains, whereas large trade elasticities lead to the opposite result. Critics are understandably wary of results being determined largely by the authors’ choice of trade elasticities. Where do these trade elasticities come from? CGE modelers typically draw these elasticities from econometric work that uses time series price variation to identify an elasticity of substitution between domestic goods and composite imports (Alaouze, 1977; Alaouze, et al., 1977; Stern et al., 1976; Gallaway, McDaniel and Rivera, 2003). This approach has three problems: the use of point estimates as “truth”, the magnitude of the point estimates, and estimating the relevant elasticity. First, modelers take point estimates drawn from the econometric literature, while ignoring the precision of these estimates. As we will make clear below, the confidence one has in various CGE conclusions depends critically on the size of the confidence interval around parameter estimates. Standard “robustness checks” such as systematically raising or lowering the substitution parameters does not properly address this problem because it ignores information about which parameters we know with some precision and which we do not. A second problem with most existing studies derives from the use of import price series to identify home vs. foreign substitution, for example, tends to systematically understate the true elasticity. This is because these estimates take price variation as exogenous when estimating the import demand functions, and ignore quality variation. When quality is high, import demand and prices will be jointly high. This biases estimated elasticities toward zero. A related point is that the fixed-weight import price series used by most authors are theoretically inappropriate for estimating the elasticities of interest. CGE modelers generally examine a nested utility structure, with domestic production substitution for a CES composite import bundle. The appropriate price series is then the corresponding CES price index among foreign varieties. Constructing such an index requires knowledge of the elasticity of substitution among foreign varieties (see below). By using a fixed-weight import price series, previous estimates place too much weight on high foreign prices, and too small a weight on low foreign prices. In other words, they overstate the degree of price variation that exists, relative to a CES price index. Reconciling small trade volume movements with large import price series movements requires a small elasticity of substitution. This problem, and that of unmeasured quality variation, helps explain why typical estimated elasticities are very small. The third problem with the existing literature is that estimates taken from other researchers’ studies typically employ different levels of aggregation, and exploit different sources of price variation, from what policy modelers have in mind. Employment of elasticities in experiments ill-matched to their original estimation can be problematic. For example, estimates may be calculated at a higher or lower level of aggregation than the level of analysis than the modeler wants to examine. Estimating substitutability across sources for paddy rice gives one a quite different answer than estimates that look at agriculture as a whole. When analyzing Free Trade Agreements, the principle policy experiment is a change in relative prices among foreign suppliers caused by lowering tariffs within the FTA. Understanding the substitution this will induce across those suppliers is critical to gauging the FTA’s real effects. Using home v. foreign elasticities rather than elasticities of substitution among imports supplied from different countries may be quite misleading. Moreover, these “sourcing” elasticities are critical for constructing composite import price series to appropriate estimate home v. foreign substitutability. In summary, the history of estimating the substitution elasticities governing trade flows in CGE models has been checkered at best. Clearly there is a need for improved econometric estimation of these trade elasticities that is well-integrated into the CGE modeling framework. This paper provides such estimation and integration, and has several significant merits. First, we choose our experiment carefully. Our CGE analysis focuses on the prospective Free Trade Agreement of the Americas (FTAA) currently under negotiation. This is one of the most important FTAs currently “in play” in international negotiations. It also fits nicely with the source data used to estimate the trade elasticities, which is largely based on imports into North and South America. Our assessment is done in a perfectly competitive, comparative static setting in order to emphasize the role of the trade elasticities in determining the conventional gains/losses from such an FTA. This type of model is still widely used by government agencies for the evaluation of such agreements. Extensions to incorporate imperfect competition are straightforward, but involve the introduction of additional parameters (markups, extent of unexploited scale economies) as well as structural assumptions (entry/no-entry, nature of inter-firm rivalry) that introduce further uncertainty. Since our focus is on the effects of a PTA we estimate elasticities of substitution across multiple foreign supply sources. We do not use cross-exporter variation in prices or tariffs alone. Exporter price series exhibit a high degree of multicolinearity, and in any case, would be subject to unmeasured quality variation as described previously. Similarly, tariff variation by itself is typically unhelpful because by their very nature, Most Favored Nation (MFN) tariffs are non-discriminatory in nature, affecting all suppliers in the same way. Tariff preferences, where they exist, are often difficult to measure – sometimes being confounded by quantitative barriers, restrictive rules of origin, and other restrictions. Instead we employ a unique methodology and data set drawing on not only tariffs, but also bilateral transportation costs for goods traded internationally (Hummels, 1999). Transportation costs vary much more widely than do tariffs, allowing much more precise estimation of the trade elasticities that are central to CGE analysis of FTAs. We have highly disaggregated commodity trade flow data, and are therefore able to provide estimates that precisely match the commodity aggregation scheme employed in the subsequent CGE model. We follow the GTAP Version 5.0 aggregation scheme which includes 42 merchandise trade commodities covering food products, natural resources and manufactured goods. With the exception of two primary commodities that are not traded, we are able to estimate trade elasticities for all merchandise commodities that are significantly different form zero at the 95% confidence level. Rather than producing point estimates of the resulting welfare, export and employment effects, we report confidence intervals instead. These are based on repeated solution of the model, drawing from a distribution of trade elasticity estimates constructed based on the econometrically estimated standard errors. There is now a long history of CGE studies based on SSA: Systematic Sensitivity Analysis (Harrison and Vinod, 1992; Wigle, 1991; Pagon and Shannon, 1987) However, to date, all of these studies have taken their parameter distributions “from the literature”. None of these studies has been accompanied by an econometric study aimed at estimating the key parameters and their distributions at the relevant level of aggregation used in the CGE analysis. For this paper, we use the Gaussian Quadrature (GQ) approach to SSA, which has proven to be the most efficient and unbiased approach to systematically assessing the sensitivity of model results to parametric uncertainty (DeVuyst and Preckel, 1997; Arndt, 1996). We find that many of the results are qualitatively robust to uncertainty in the trade elasticities. In those cases where our findings are not robust, we explore the source of underlying uncertainty. In this way, the paper addresses the fundamental question: How Robust are CGE Analyses of Free Trade Agreements?

How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements?

https://www.nber.org/system/files/working_papers/w10477/w10477.pdf

Global food prices have increased substantially since mid-2010 as have prices in many developing countries. In this study we assess the poverty impact of the price changes between June and December 2010 in twenty-eight low and middle income countries. This is done by gathering detailed information on individual households' food production and consumption levels for thirty-eight agricultural and food commodities to assess the impacts on household welfare. This study estimates that this sudden food price surge increased the number of poor people globally, but with considerably different impacts in different countries. The heterogeneity of these impacts is partly related to the wide variation in the transmission of global prices to local prices and partly to differences in households' patterns of production and consumption. On balance, the adverse welfare impact on net buyers outweighs the benefits to net sellers resulting in an increase in the number of poor and in the depth of poverty. We estimate that the average poverty change was 1.1 percentage points in low income countries and 0.7 percentage points in middle income countries with a net increase of 44 million people falling below the $1.25 per day extreme poverty line.

Maros Ivanic

Papers

Implications of Higher Global Food Prices for Poverty in Low-Income Countries

Implications of higher global food prices for poverty in low-income countries

How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements?

How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements?

Estimating the Short-Run Poverty Impacts of the 2010-2011 Surge in Food Prices