INTERDISCIPLINARY RESEARCH PROJECT TO EXPLORE THE FUTURE OF GLOBAL FOOD AND NUTRITION SECURITY

Cost-benefit framework for policy action to navigate food price spikes

Matthias Kalkuhl Mekbib Haile Lukas Kornher Marta Kozicka

FOODSECURE Working paper no. 33 July 2015

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Cost-benefit framework for policy action to navigate food price spikes

Matthias Kalkuhl1, Mekbib Haile, Lukas Kornher and Marta Kozicka

Center for Development Research (ZEF), University of Bonn

FOODSECURE Working Paper no. 33

July 2015

Abstract: This paper develops a cost-benefit framework for evaluating the impacts of price spikes and price volatility in food commodities ranging from allocative, political-economy and health to human welfare effects. After reviewing the research on impacts of price spikes and volatility, several policies to reduce volatility or its adverse impacts are classified and evaluated. The evaluation of policies is visualized according to different criteria that allow assessment of strengths and weaknesses of several policies.

JEL codes: D61, E30, E64, F13, Q18

1 Corresponding author; e-mail: mkalkuhl@uni-bonn.de Acknowledgement: We would like to thank Regine Weber for assistance in creating Figure 3 and helpful editing of the document. Financial support the Federal Ministry for Economic Cooperation and Development of Germany (Price Volatility Project) from the European Union's Seventh Framework Programme FP7/2007-2011 under Grant Agreement n° 290693 FOODSECURE is acknowledged. This paper is work in progress; comments are welcome. The authors only are responsible for any omissions or deficiencies. Neither the FOODSECURE project and any of its partner organizations, nor any organization of the European Union or European Commission are accountable for the content of papers in this series.

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1. Introduction

Price spikes and price volatility are closely related (von Braun & Tadesse 2012): While price spikes are usually measured as a relative change of prices over two consecutive periods (i.e. by using differences in logarithmic values as an approximation for the percentage change), volatility measures the variance of price changes over a certain period. Volatility can be calculated using unconditional volatility measures where the variance of historic price changes (or price levels) is used as a measure of realized variability; volatility can, however, also be estimated conditional on past volatilities (Kalkuhl et al. 2013b). In a statistical sense, conditional measures provide often a better forecast for the variance of the price change in the next period than unconditional ones and are therefore preferable for modeling risk and uncertainties.

Volatility results from the fact that prices change in a non-predictable way which is rooted in non-predictable shocks of the underlying demand and supply fundamentals. Prices convey important information on scarcities which is publicly and readily available – in contrast to the underlying market fundamentals. Changes in prices are therefore important to induce market participants to respond to periods of scarcities and abundance, e.g. by expanding production or adjusting stock levels. The large price swings and excessive levels of volatility in 2007-2011 have raised doubts whether commodity prices actually reflect agricultural market fundamentals or whether they are ‘polluted’ by spillover effects from energy and financial markets and exaggerations by traders (including large countries’ trade policies).

Sudden price shocks and high price risks have, however, also negative impacts on societies. As long as there exist complete insurance and risk markets, households and firms can hedge against price spikes and price volatility. Producers, for instance, may hedge against downward price risks by engaging in futures markets. However, these markets are incomplete regarding their temporal and spatial coverage, and they might involve large relative transaction costs for smaller traders and as price spikes and volatility might affect societies and economies as a whole. Thus, collective action and policy responses could potentially achieve a Pareto-improving allocation or a substantial welfare enhancement.

This article elaborates on the broader costs of price spikes and price volatility for societies, in particular in developing countries, and develops a conceptual framework for assessing the costs and benefits of policies to reduce volatility or the costs of volatility. As a comprehensive quantitative cost-benefit analysis is daunting and confronted with methodological challenges and data availability problems, we provide an overview on methods to quantify negative impacts and costs and benefits of policies. The article is organized as follows: Section 2 presents the conceptual framework of this paper, sections 3-5 review quantitative and qualitative impact assessments of price volatility on different domains. In particular, these sections discuss the theoretical mechanisms and empirical evidences on market-related costs, impacts on human welfare, and on political economy of price spikes and volatility. While section 6 presents several policy options in order to manage price volatility or reduce its costs, sections provides a normative cost-benefit evaluation of these policy options. The last section concludes.

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2. Conceptual framework

In this paper, we discuss three major impact domains of food price spikes and volatility: market-related costs reducing allocative efficiency, impacts on health and human welfare, and political economy aspects including conflicts and unrests. While these impacts manifest on a rather local or national level, they are connected to other markets (including the global market) through trade. Figure 1 depicts these relationships which are discussed in more details in sections 3-5.

Policies have in general two different channels to reduce the negative impacts of volatility: (i) the can reduce volatility and the occurrence of price spikes, acting on the fundamental causes of price variability (affecting blue boxes and the relationships between them exclusively) and (ii) they can reduce the impact of price changes on the actors which is related to the arrows between blue and orange boxes. The different policies are presented and evaluated in detail in sections 6-7. Evaluation of the policies will be done according to the following criteria: The policies are grouped into major classes and then evaluated qualitatively according to three criteria:

1. Benefits: this is defined by how effective a policy measure is to (i) reduce volatility and the occurrence of price spikes, and (ii) to increase coping capacity of households and firms against volatility. Although these are distinct benefits, we cannot evaluate whether a policy that reduces price volatility or that improves coping capacity of the poor against it is better. Thus, we give equal weights and combined them into a single criterion. The policy measure is evaluated to have a high or low benefit in terms of the magnitude as well as the size of markets and people covered.

2. Costs: this assesses how costly a particular policy measure is both at the implementation stage or after it is executed. The costs could include both direct fiscal costs as well as indirect costs which include transaction costs, deadweight losses, efficiency losses and possible side-effects. It also considers if there are also ‘negative’ costs, i.e. additional benefits apart from the food-price-related benefits.

3. Feasibility: this criterion assesses how easy a policy measure is to implement on a local, national or international level. Under this criterion we also consider major obstacles and challenges, and if there have already been any attempts to implement the policy. The challenges we consider include political costs, conflict of interests, potential time it will take to get implemented, among others.

The application of this framework will help to categorize policies according to the benefit-cost potential and the political feasibility which provides important categories for policy makers in evaluating policies and finding an optimal portfolio of policies.

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Figure 1. Conceptual framework of the impacts of price spikes and volatility and policy measures.

3. Market related allocative efficiency costs of volatility

Not all price variations are troublesome. If prices change along a smooth trend or exhibit a regular cyclical or seasonal pattern, economic agents can anticipate them and make necessary ex-ante adjustments in their economic decisions. However, variations in prices become costly for economic agents when they are abrupt and unanticipated (FAO et al. 2011). Such price dynamics are problematic since they create uncertainty that introduces risk for economic agents, which could be producers, consumers, traders or the government. The food price dynamics that we have experienced since the middle of the past decade is characterized by long and extended spikes, sometimes called low frequency volatility. Such price variations do not necessarily reflect market fundamentals and they can lead to suboptimal economic decisions. In other words, price volatility is problematic since it induces risk averse economic agents to make inefficient investment decisions. Von Braun and Tadesse (2012) provide a detailed description of the different varieties of price dynamics and their potentially differential impacts.

Following the 2007-2008 food price spike, several studies have shown the adverse effects of price dynamics on economic growth, food production, trade, poverty, and food and nutrition security, among others (von Braun & Tadesse 2012; Ivanic & Martin 2008; FAO 2008; Haile et al. 2014a). In this section, we discuss three general mechanisms and/or channels to analyze the impacts of food price volatility and spikes on production and on economic growth.

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3.1. Production and investment disincentives

High agricultural commodity prices are typically expected to bring about a supply response in which producers allocate more land and other inputs to the agricultural sector and increase investment to improve yield growth (OECD 2008). The recent increase in price levels was, however, accompanied by higher fluctuations (Gilbert & Morgan 2010). Such price variation introduces output price risk, which has disincentive effects on producers’ resource allocation and investment decisions, which in turn result in a distressed state of agriculture (Moschini & Hennessy 2001; Persson 1999). In the absence of instruments for managing price risks, fluctuations in food commodity prices imply that agricultural producers make their farming choices based on “expected outcome”. Since farmers tend to be averse to risk (OECD 2008), the implication is that they tend to prefer low-risk and low-return outcomes at the expense of higher payoffs that are more uncertain. Moreover, large fluctuations in output prices make it difficult for producers to understand the underlying trends in price levels. This has detrimental effects on long term investments in agriculture, such as on adoption of modern agricultural technologies by producers, and investments on research and development (R&D) by governments, private research institutions, and input supply enterprises.

Agricultural producers in many developing countries are often exposed to the effects of international agricultural market price instability, depending on the extent of price transmission to local markets. To this end, a recent study by ZEF researchers, (Haile et al. 2014b), analyses the supply responsiveness of key world staple food commodities—namely, wheat, corn, soybeans, and rice—to changes in output prices and volatility. Box 1 gives a summary of the major findings. The study shows that the global supplies of these commodities respond negatively to price volatility in general and that the price volatility, which occurred during the 2006-2010 time period, significantly weaken the positive response towards higher price levels.

The Mean-Variance Approach (MVA) can be used as an underlying theoretical model for our analysis of the impacts of food price volatility on production and general investments that in turn affect economic growth. Following Coyle (1999), Lansink (1999), and our own recent study (Haile et al. 2014a), the risk preferences of a typical agricultural producer can be specified in terms of a utility function where the certainty equivalent of the expected utility maximization is expressed in term of the first two moments of profit (mean, 𝛱� and variance, 𝜎𝛱2):

𝐸𝐸(𝛱) = 𝐸𝛱(𝑝,𝑤, 𝑙, 𝑧) − 12𝛼𝜎𝛱2 (1)

where 𝛱(𝑝,𝑤, 𝑙, 𝑧) = 𝑝′𝑦(𝑥, 𝑙, 𝑧) −𝑤′𝑥 is the producer’s profit, 𝑝 and 𝑦 are vectors of output price and quantity, respectively; 𝑤 and 𝑥 are vectors of input price and quantity, respectively; 𝑙 denotes the vector of land which in its sum is fixed but allocatable; 𝑧 is a vector of other fixed inputs (machinery and equipment); and α is a measure of risk aversion representing risk averse (𝛼 > 0), risk neutral (𝛼 = 0), and risk loving (𝛼 < 0) producers respectively. Assuming a risk averse producer and treating input prices as predetermined, the theoretical model shows that larger mean prices translate into larger expected profit, whereas higher price volatility translates into large profit variability, which in turn have positive and negative impacts on expected utility, respectively.

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Box 1: Supply response impacts of international price changes

Using data from 1961-2010 for all major global producer countries of the four staple crops (31 countries) and grouping all the others in to the rest-of-world, Haile et al. (2014b) apply the system generalized method of moments (GMM) to estimate a dynamic supply response model of these key staple crops. In general, the results show that global production, acreage, and yield responses to own prices are positive and statistically significant, consistent with economic theory. In other words, higher output prices induce producers to increase acreage and to invest in improving crop yields, implying that global food supply response to prices appears to occur through both acreage and yield changes. Output price volatility, however, has negative correlations with crop supply, indicating that farmers shift land, other inputs, and yield-improving investments to crops with less volatile prices.

The results show that soybeans and corn have the largest production responses to own-crop prices, followed by wheat and rice. Conditional on other covariates, a 10% rise in the expected own crop price leads to a production increase of about 4% for soybeans, 2% for corn, 1% for wheat, and 0.5% for rice in the short run. These production responses typically reflect the acreage and yield adjustments. An equivalent increase in the respective international crop prices induces farmers to increase their land allocated to soybean and corn cultivation by about 1.5% and 0.8%. Moreover, the yields of both soybeans and corn respond by an increase of about 1% following similar increases in international own crop prices. Global wheat acreage and yield also respond to output prices, with short-run elasticities of 0.08 and 0.17, respectively. In line with the production response results, rice has relatively weaker acreage and yield responses to own prices.

(Continued on the next page)

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In addition to the above discussed impact on production of staple crops, which has adverse implications for economic growth of developing countries with a large agricultural sector, general uncertainties impede the development processes of several poor economies. In an early stage of development, economies are capable of investing only in a limited number of and usually imperfectly correlated projects (Acemoglu & Zilibotti 1997). Hence, the desire of economies to avoid risk is much larger at an early stage of development than at later stages. Their typical mechanism, therefore, becomes to invest in safe but low productive activities and projects. This makes low productivity, and thereby slow capital accumulation, endogenous to poor economies, contributing to their slow development (Ibid. p. 710).

(Box 1, continued)

International output price volatility, on the other hand , has negative correlations with crop supply, implying that farmers shift land, other inputs, and yield-improving investments to crops with less volatile prices. In order to evaluate the actual impact of the 2006-2010 price level versus price volatility on global supply, the study conducts a simulation analysis. To this end, the differences in the predicted outcome variables are calculated under the realized prices and under a counterfactual scenario where all output prices and volatility, as well as fertilizer prices after 2006, are set equal to their 1980–2005 mean values. Figure 2 below shows the production impact from the acreage and yield simulations by using the identity that production equals the product of acreage and yield responses to own prices.

Figure 2. Impacts of the 2006–2010 price dynamics on production

In general, the simulation results show that more volatile output prices and higher input prices have weakened the extent to which rising international agricultural commodity prices would have increased output production. Figure 1 shows that the net impact of the 2006–2010 price dynamics on production is about a 3% increase for soybeans, a 2% increase for corn, 1% increase for rice, and about a 1% decrease for wheat.

-12%

-8%

-4%

0%

4%

8%

12%

Wheat Soybeans Maize Rice

Impact of volatilityincreaseImpact of fertilizerincreaseImpact of priceincreaseNet effect

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Richer economies, on the other hand, are capable of undertaking a larger number of projects that create opportunities for agents to diversify risk and are willing to allocate their savings to risky investments. The authors argue that a typical development pattern begins with a lengthy period of “primitive accumulation”, which is characterized by considerable output variability and randomness in the economy. Jacks et al. (2011) support this idea that poor countries are more volatile than rich countries, which in turn negatively affects economic growth. Several other studies also indicate that commodity prices are a key source of such volatility, which has a negative impact on growth (Ramey & Ramey 1995; Jacks et al. 2011). Wolf (2005), among others, reviews the mechanisms through which macroeconomic volatility negatively affects growth, including the link between tightening investment constraints and economic and political uncertainties.

3.2. Allocative costs

While the previous impact of price volatility on investment and production occurs under the assumption of risk averse economic agents, market volatility does also affect investments when economic agents do not have full information and markets are imperfect regardless of risk behavior. Since future market conditions are uncertain, firms only receive a market signal at the time of investment. If the price signal has a lot of noise, it is more likely that the firms’ expectations deviate from the actual market conditions at a later stage. This means that the firm or producer loses and the expected loss increases with larger volatility in the market. A parsimonious producer model developed by Martins-Filho (2011) shows that the expected loss of a typical producer has a monotonically increasing relationship with price volatility. If 𝑐(𝑦;𝑤)) is the producer’s cost function, where 𝑦 is output and 𝑤 is input price, profit maximizing requires that marginal cost, 𝑐’(𝑦 ∗;𝑤), equals the expected price (𝜇𝑃). Because producers cannot instantly adjust output with price changes; however, they achieve suboptimal profit whenever the expected price differs from the actual price. After some algebraic manipulation, Martins-Filho (2011) show that this loss is given by:

𝐸(𝐿) = 14𝑐(𝑤)𝐸(𝑃 − 𝜇𝑃)2 = 1

4𝑐(𝑤)𝜎𝑃2 (2)

where 𝜎𝑃2 is the variance of price, and 𝑐(𝑤) is a constant. This implies that larger price volatility increases the likelihood of increased resource misallocation, resulting in suboptimal outcome.

Similarly, market volatility affects long-term investments that require irreversible initial expenditures. “Most major investment expenditures are at least partly irreversible: the firm cannot disinvest, so the expenditures are sunk costs” (Pindyck 1988, p. 969). In such a situation, the firm loses its option to invest at any time in the future and it cannot recover all the costs, in case that adverse market conditions require a disinvestment. Furthermore, firms have larger incentives to delay their investments when markets are more volatile. In other words, increased uncertainty discourages current investments by raising the opportunity cost of investing. Such impacts can be described as allocative costs of market volatility because it constrains firms’ freedom to productively invest at any given time. Lucas (1973) also showed that high transitory instability in market prices under conditions of imperfect information reduces the quality of their signal in all sectors of an economy. This was referred to as signal extraction problem where economic agents are confused between price changes that are “permanent” (as a result

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of changes in the fundamentals) and “temporary” (spillovers from some other sector). As a consequence of such weak price signal, investment funds may be allocated to sectors of the economy with suboptimal future outcomes (Dawe 2001). Such investment inefficiencies have ultimate adverse economic growth effects.

3.3. Spillover effects

A third channel through which food price volatility affects growth of an economy is through spillover effects. According to Timmer (2011) the spillover effect of price volatility slows down economic growth and structural transformations that are crucial for poverty reduction in any poor economy. He considers a simple model to show such spillover effects in a typical developing economy. If the global economy and food prices are reasonably stable, the government of this typical country can focus on financing long-run investments. However, if the food economy is highly volatile, the government needs to re-allocate its budget resources from long-run inclusive growth to the management of food price volatility and to the support of poor people in coping with volatile food prices. Similar negative spillover effects of unstable food markets can be observed with donor agencies and firms, which end up spending their human and financial capital on overcoming short-run fluctuations rather than on long-term development strategies and investments. In the presence of market failure and price inelastic demand, Dawe (2001) also shows how fluctuations in world prices can lead to fluctuations in nominal income available for policy makers to spend elsewhere in the economy. In a country with large budget share of a commodity, large international price increases (decreases) of this commodity imply less money available for an importing (exporting) country to spend on other goods and services in the economy. Such spillovers have significant impacts on efficiency, investment and growth for several developing countries, where markets do not function properly. In general, these two studies show how food price volatility can lead to macroeconomic and political instabilities that, in turn, adversely affect investment and economic growth.

Myers (2006) extends the standard welfare analysis to account for the spillover growth effects of price fluctuations, which were indicated by Timmer (2000) and Dawe (2001). While stabilizing prices around the trend price, 𝑝(𝑡), in the standard welfare analysis reduces household income fluctuations with no effect on the trend income, removing price fluctuations increases the growth rate of the trend income. Meyer (2006) shows that the trend income under the stabilized price 𝑝𝑡∗ = 𝑝(𝑡) can be given by:

𝑦∗(𝑡) = (1 + 𝑔)𝑡𝑦(𝑡) (3)

where 𝑦(𝑡) is trend income under the original price process and 𝑔 is a measure of the extra income growth being generated by the shift to the stabilized price process. It is possible that growth effects of stabilizing price fluctuations can be greater than its welfare benefits for the poor.

Needless to say that managing price fluctuation is difficult in many developing countries, especially in Africa where well-functioning policies are not in place. Large fluctuations in the food economy result in

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macroeconomic instability, including economic, political and policy-related instabilities, which have detrimental impacts on output growth and thus on future consumption pattern (Loayza et al. 2007).

Last but not least, van der Ploeg and Poelhekke (2007) argue that volatility of unanticipated output growth, which could be explained by the volatility of commodity prices, negatively affects long-run growth of a an economy. They have shown that volatility in commodity prices is the foremost explanation for the often discussed “curse” of a natural resource. This is typically true for countries that heavily depend on exporting natural resources. Large windfall revenues as a result of an upward swing in international commodity prices will lead to government spending bonanzas that will increase volatility that, in turn, slows down growth due to upcoming inevitable revenue dips.

4. Human welfare impacts with particular impact on nutrition

4.1. Standard economic welfare measures

The impact of prices changes of a consumption good on overall consumption can be decomposed into a substitution and an income effect: while the former depicts consumers’ choice of alternative consumption goods with similar characteristics (i.e. substitutes) when the price of a particular good increases, the latter describes the change in the consumption bundle due to the induced change of overall real income. This fundamental relationship is expressed in the Slutsky equation, where the Marshallian (uncompensated) demand of the good 𝑥𝑗 changes for a marginal change in price of good j according to:

𝜕𝑥𝑗(𝐩,𝑦)

𝜕𝑝𝑖= 𝜕ℎ𝑗�𝐩,𝑣(𝐩,𝑦)�

𝜕𝑝𝑖− 𝜕𝑥𝑗(𝐩,𝑦)

𝜕𝑦𝑥𝑖(𝐩,𝑦) (4)

where 𝐩 is the vector of all commodity prices, y the households budget (income), ℎ𝑗�𝐩, 𝑣(𝑝,𝑦)� is the Hicksian (compensated) demand at income y and 𝑣(𝐩,𝑦) is the indirect utility function.

A widely used measure for the welfare impacts of a price change from 𝑝0 to 𝑝 is consumer surplus, defined as the integral of the Marshallian (uncompensated) demand of the good 𝑥(𝑝, 𝑦0) over the price 𝑝:

Consumer surplus (CS): 𝐶𝐶(𝑝0,𝑝,𝑦0) = ∫ 𝑥(𝜙,𝑦0)𝑑𝜙𝑝𝑝0

(5)

However, consumer surplus lacks a theoretical foundation from utility maximization. Therefore, an exact way to assess welfare changes is to calculate the income change required to make the household indifferent. Under compensating variation, the original income of the household 𝑦0 under the new price p is changed by 𝐶𝐶(𝑝0,𝑝,𝑦0) such that the utility is equal to the baseline with income 𝑦0 and price 𝑝0. Under equivalent variation, the income under the old price 𝑝0 is reduced by the amount 𝐸𝐶(𝑝0,𝑝,𝑦0) to obtain the same utility as under the new (baseline) price. Thus:

Compensating variation (CV): 𝑣[𝑝,𝑦0 + 𝐶𝐶(𝑝0,𝑝,𝑦0)] = 𝑣(𝑝0,𝑦0) (6)

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Equivalent variation (EV): 𝑣(𝑝, 𝑦0) = 𝑣[𝑝0,𝑦0 − 𝐸𝐶(𝑝0,𝑝, 𝑦0)] (7)

Compensating variation and equivalent can also expressed as integrals over demand functions, using the Hicksian demand:

𝐶𝐶(𝑝0,𝑝,𝑦0) = ∫ ℎ(𝜙, 𝑣(𝑝0,𝑦0) )𝑑𝜙𝑝0𝑝 (8)

𝐸𝐶(𝑝0,𝑝,𝑦0) = ∫ ℎ(𝜙, 𝑣(𝑝,𝑦0) )𝑑𝜙𝑝𝑝0

(9)

With the Slutsky equation, it follows that EV > CS > CV and consumer surplus can be considered as an approximation to the ‘exact’ welfare measures of compensating or equivalent variation.2 In case households are also producers of food, their income 𝑦 changes with prices which can easily be incorporated in the equations for compensating and equivalent variation (see, for example, Robles et al. 2010). One implication is that households who are net-sellers of food will benefit from price increases while net-buyers will suffer from reduced welfare.

Adding uncertainty of food price changes, it is possible to calculate the welfare impacts of fluctuations of food prices 𝑝 around the mean 𝑝0. As Helms (1985) showed, taking the expectation of the (deterministic) welfare measures CV, EV, or CS does not represent consumer preferences appropriately. Rather, the costs of price fluctuations (compared to full stabilization) are related to the risk aversion of households and need to be calculated as the amount 𝐶𝐶∗ needed to compensate a consumer’s expected utility ex ante:

Ex-ante compensating variation: 𝐸𝑣[𝑝, 𝑦0 + 𝐶𝐶∗(𝑝0,𝑝,𝑦0)] = 𝑣(𝑝0,𝑦0) (10)

Ex-ante equivalent variation: 𝐸𝑣(𝑝,𝑦0) = 𝑣[𝑝0,𝑦0 − 𝐸𝐶∗(𝑝0,𝑝,𝑦0)] (11)

Using these approaches, Turnovsky et al. (1980) identify conditions where consumer households benefit from pure price stabilization. A second-order approximation of the equivalent variation measures gives the relative welfare change ∆𝑊 (as percentage of income) due to reduced price volatility (change in squared coefficient of price variation ∆𝜎𝑃2):

∆𝑊 = [𝛾(𝜂 − 𝜌) − 𝛼]𝛾 ∆𝜎𝑃2

2 (12)

where 𝛼 and 𝜂 are price and income elasticities of demand, 𝛾 is the budget share of the commodity and 𝜌 the relative risk aversion (Gouel 2013b). Hence, consumers are likely to benefit from pure stabilization if the own-price elasticity is high, the risk aversion is high and the income elasticity is low. One implication is that risk-neutral (or sufficiently risk-averse) consumers will prefer price instability as it allows them to exploit substitution possibilities.

2 In case of quasi-linear utility functions, the three welfare measures coincide, this EV = CS = CV.

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Finkelshtain and Chalfant (1997) generalize the findings of Turnovsky et al. (1980) for peasant households that sell food which is subject to price fluctuations. They conclude that households in developing countries are likely to benefit from price stabilization because food consumption has a high share on total expenditures and farmers’ income depends to a large share on food sales.

The positive effects of price volatility on sufficiently risk-averse consumers can be reversed if preferences adapt to historic consumption (habit formation). Atkin (2012) shows that under certain conditions on the stochastic price process, volatility can reduce average calorie consumption in case of habit formation.

A different channel of welfare impacts of price (and real income) risk can be through increased precautionary savings (Deaton 1991): In the absence of complete insurance markets, risk-averse consumers will save not only for investment purposes but also for self-insurance in case of shocks. As these savings need to be liquid to have rapid access in case of need (e.g. cash), they might not be directed to high-return investments (Dawe 2001). In particular cash savings generate negative real returns due to inflation which reduces the overall real

Several empirical works applied the concept of equivalent variation or compensating variation to quantify the welfare impacts of price changes and price volatility. Robles et al. (2010) calculated the welfare impacts of food price increases in four Latin American countries using compensating variation: For households that do not benefit from price increases due to their net-buying position, a 10 percent price increase requires compensation between 4.4 percent (poorest quintile) and 2.0 percent (richest quintile) of their expenditure. For the case of Vietnam, Vu and Glewwe (2011) find wide-spread positive impacts on aggregate welfare due to the large share of poor net sellers of rice and food items. Although a uniform food price increase has positive impacts on aggregate welfare, poorest urban households are the most severely affected while rural middle-income households gain most. They also report that most of the welfare losses for the poor are due to changes in real incomes as substitution effect between food items is small due to low cross-price elasticities.

On the impact of price volatility, Gouel (2013b) gives a table for welfare impacts of volatile prices for a wide range of parameter constellations using equation (12). Even for highly risk averse consumers with large budget share on food commodities (30 percent) and low income elasticity, food price fluctuations with a coefficient of variation of 30% are always below 1.5% of consumers’ income. Extending the models of Turnovsky et al. (1980) and Finkelshtain and Chalfant (1997) for the multiple-commodity case of smallholder farmers (who are consumers and sellers of food commodities), Bellemare et al. (2013) calculate the welfare costs of the realized price volatility for smallholder farmers in Ethiopia. They find that an average household is willing to spend 18 percent of its income to stabilize prices of important food commodities at their means, although high-income households would have an even higher relative benefit.

The empirical findings so far show a very heterogeneous pattern on the impact of price changes on the welfare of households, depending on the selling-buying position, risk aversion, substitution elasticities and incomes. While price volatility can also have in principle positive welfare effects (in particular for

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wealthy consumers), empirical evidence suggests that most poor households in developing countries are negatively affected.

4.2. Nutrition and human capital impacts

The economic measures above are based on willingness-to-pay to stabilize prices and reduce price risk. The derived monetary value reflects the individual’s preference for price stability. While this approach is widely used by economists, it faces some limitations: First, as it focuses on individual costs, the social dimension of the costs of price shocks and volatility (including its distribution) is neglected. Second, individuals might underestimate the benefits of good nutrition due to lack of education or imperfect monitoring of their health status and its implication on (future) productivity and income (Chavas 2000). Third, good nutrition and health status might have aggregate spillover effects on the development of societies which is not captured by the individually appropriable benefits (positive externalities). Fourth, as reflected in several political declarations like the Millennium Development Goals, food security and good health can be considered as intrinsic social values independent of their instrumental role on growth and development and their individual valuations. For these reasons, it is useful to directly focus on health and nutrition impacts and their implications for development as alternative approaches to assess the costs and benefits of price changes and price risk.

Closely related to the microeconomic welfare approaches discussed above are impact analyses using food-consumption related indicators like calorie intakes, micronutrient intakes, diet diversity or frequency of meals (see Kalkuhl et al. 2013b for a comprehensive discussion).

Increasing staple prices affect food consumption through income and substitution effects with heterogeneous results. Jensen and Miller (2008) report a Giffen behavior for Chinese households: while extreme households reduce calorie consumption if staple prices increase, moderately poor household increase calorie consumption as they substitute from more expensive (but often also micro-nutrient rich) food items to staples. D’Souza and Jolliffe (2014) find for Afghan households during the 2007-08 price increase that households with very low calorie intake hardly reduce their calorie intake (as they substitute to less expensive energy-intensive foods) while households with higher calories reduce calories substantially. In Latin American countries, wealthier households (fourth and fifth quintile) tend to increase calorie intake while poor households always reduce their calorie intake (Iannotti & Robles 2011). The calculated median calorie reduction for the food price increase during 2006-2008 amounts to eight percent. Iannotti et al. (2012) estimate micronutrients and calorie intake elasticities in Guatemala and report heterogeneous findings according to the food group but overall negative elasticities. Rising food prices and the recent food crisis have increased micronutrient inadequacy. Finally, Matz et al. (2014) calculate the impact of food price increases in Ethiopia on rural and urban households’ calorie intake, diet diversity, meal frequency and consumption of less preferred foods. While calorie intake and diet diversity hardly change for short-term price increases, consumption of less preferred food and reduction of the number of meals are pervasive coping strategies.

In contrast to the impact on consumption-based nutrition indicators, the implications of price changes for anthropometric indicators have hardly been studied. From a comprehensive survey on research on

14

the impacts of income, price and weather shocks on child nutrition in Kalkuhl et al. (2013b), only few papers study the direct impact of price shocks on child nutrition: Arndt et al. (2012) in Mozambique and Campbell et al. (2010) as well as Torlesse et al. (2003) in Bangladesh. In all these three papers, higher staple prices increased the prevalence of children that were stunted or underweight. Because periods of excess food consumption cannot compensate periods of inadequate consumption, volatility is also expected to increase malnutrition. Empirical evidence on this effect is provided by Kalkuhl et al. (2013b) that uses a global country-panel on underweight and stunting: Despite conventional explanatory variables such as GDP and access to improved sanitation, high volatility of food prices is found to significantly increase underweight and stunting of children below 5 years.

The consequences of an inadequate nutritional status for children are far-reaching as they do not only directly influence disease burden and child mortality in the short to medium-run but also affect stature, health, physical and cognitive abilities in the later adult phase. Hence, inadequate nutrition during childhood can have irreversible implications for human capital and life expectancy –determining both individual income and well-being as well as a nation’s wealth.

Using micro-data, Weil (2007) finds that each additional centimeter of adult height – which is strongly correlated to nutrition and health during childhood – implies a 3.3 to 9.4% increase of wages. Behrman and Rosenzweig (2004) provide further evidence on the returns to birthweight on schooling and wages. As birthweight is also affected by the mother’s nutritional status (Black et al. 2013), price shocks can influence human’s income even before they have been born. The role of improved nutrition for economic growth and development has been extensively discussed in Fogel (1994) and Strauss and Thomas (1998). Wang and Taniguchi (2003) estimate the long-run growth effects of a 500-kcal/day increase in the dietary energy supply for developing countries to be 0.5 percentage points. Gyimah-Brempong and Wilson (2004) find evidence that child mortality – as proxy for health conditions in a country – reduces growth in both OECD and Sub-Saharan African (SSA) countries although the impact in the latter countries is stronger because of poor health conditions and high child mortality. As child mortality is to a large extent caused by undernutrition (Black et al. 2013 estimate that 45% of all child deaths in 2011 are associated to undernutrition), the growth implications of nutrition become obvious.

5. Political risks of unrests

The impact of food price spikes and instability on economic welfare has been conceptualized and discussed lengthily in the preceding sections. Insofar, it is not surprising that food price fluctuations may be linked to social unrests understood as public protests against governmental authorities.

Mistrust and dissatisfaction against politics are widespread phenomena in developing countries where the poor feel to lack a political voice. In an instance of a negative transitory shock the costs of protesting against the system are relatively low (Acemuglu & Robinson 2001). In other words, there is “nothing to lose”, even in the prospect of a violent suppression of the uprising (Lagi et al. 2011). It is elaborated above that high food prices and uncertainty involve an ambiguous welfare effect on different types of households, hitting the urban net consumers severely. On this account, a food price shock could

15

translate into social unrests and protesting most likely in urban agglomerations. Hirshleifer (2001) introduces perceptions to the concept of conflict evolution. This allows for wrong interpretations of grievances or incorrect allegations against the political leadership for unfortunate price developments (Collier & Hoeffler 2004).

Figure 3 gives an overview of world-wide food related unrests during the period from 2004 to 2011. The most prominent examples are associated with the Arab Spring in Tunisia, Libya, and Egypt. Clearly, there is a strong correlation of food riots with the international food price development. Subsequent of the global food crisis in 2007/2008, there have been empirical studies on the causal link between food price dynamics and unrests. Bellemare (2014) focuses on the endogenous relationship between food riots and prices). This means food riots can affect food prices and vice-versa (i.e. food prices can affect the occurrence of social unrests). Using natural disasters in a given month as instrument for international food price changes, Bellemare (2014) identified a strong significant impact of price levels on number of food riots. Likewise, Arezki and Brückner (2014) find that consumption-weighted international price movements induce political instability by the incidence of political riots and civil conflicts. Lagi et al. (2011) go beyond and argue that food riots are more likely to occur if a certain price threshold of the FAO international food price index has been crossed. Yet, as a major shortcoming, all studies ignore the spread of political protests across borders, for example from neighboring countries.

The negative aspects of social unrests are apparent. On the one hand, riots and counter-measures by political authorities cause casualties. On the other hand, social unrests can translate into political turmoil and endanger a country’s political stability. In turn, political instability hinders economic growth (Alesina et al. 1996) and can increase unemployment and poverty. In contrast to this, some empirical evidence suggest that social revolts can facilitate regime changes. This is also referred to as a `window of opportunity´ to change the political system into a more democratic (Brückner & Ciccone 2011; Aidt & Leon 2014). From the standpoint of a researcher it is difficult to evaluate both arguments. Albeit, one may argue that negative effects are rather short-run vis-a-vis positive long-run effects of democratic upheavals.

In conclusion, there is theoretical and empirical evidence that food price dynamics contain a political dimension that needs to be considered when interpreting rationales of public policies.

16

Figure 3. Price development of export prices for major grains

Notes: While the green lines are the export prices, the red bars indicate the number of reported food riots in Africa in the respective month.

Source: Social conflict in Africa Database (SCAD) (https://www.strausscenter.org/scad.html), International Grains Council (IGC).

6. Integration of policies into cost-benefit framework

This section presents and discusses several policies to navigate food prices and the adverse impacts of price shocks and price volatility. The policies are grouped into major classes and then evaluated qualitatively according to three criteria (1) benefits, (2) costs and (3) feasibility as outlined in Section 2. The evaluation will also consider if there are high uncertainties in assessing costs and benefits due to lacking or mixed evidence or difficulties in empirical research.

We underline here that an evaluation of most of the policies with respect to each of the above criteria is context specific. For instance, the benefits and costs of most of the resilience enhancing policies discussed below largely depend on whether countries have well-functioning institutions. Whether there are sound political institutions or not also matters for the feasibility of such policies. Although we primarily evaluate national policies – based on the aforementioned criteria – from the perspective of the implementing country in light of a food price crisis, certain national policies may have impacts on other countries or on international prices. This is particularly true for trade policies where an export ban or lowering import duty raises international food prices if implemented by large economies. Evaluating domestic policies, trade policies in particular, from the country’s perspective also means that we do not consider the effects of possible retaliations which may not be trivial for agricultural commodities with

0

50

100

150

200

250

300

350

400

450

0

1

2

3

4

5

6

2000

m1

2000

m7

2001

m1

2001

m7

2002

m1

2002

m7

2003

m1

2003

m7

2004

m1

2004

m7

2005

m1

2005

m7

2006

m1

2006

m7

2007

m1

2007

m7

2008

m1

2008

m7

2009

m1

2009

m7

2010

m1

2010

m7

2011

m1

2011

m7

2012

m1

2012

m7

Index: January 2000 = 100 News Count

Total Wheat

Maize Rice

17

concentrated markets (e.g. soybeans). Moreover, governments can implement multiple policies or likewise a combination of policies which makes it difficult to assign specific welfare impacts and costs to a single policy instrument. Despite these limitations, the framework provides a basis to assess various policies related to price volatility and spike. Another caveat in our framework is that it evaluates each policy separately while it is possible the costs and benefits of some policies (for instance, buffer stocks and trade policies) may change if they are implemented together.

a. Regulation in commodity futures markets

Impact of the speculation activities on the prices of the agricultural commodities has been widely debated after the world food crisis in 2007-08. In their testimonies before the United States Congress Soros (2008) and Masters (2008) directly blamed speculators for the commodity price spikes. The possible ways to curb the excessive speculation are (i) increasing transparency on actors and transactions, (ii) introducing limits on futures trading, (iii) imposing transaction taxes, or (iv) influencing prices and price expectations directly by intervening in commodity markets through physical and virtual reserves3.

The empirical evidence on the role of speculation is ambiguous with some evidence that activities of particular trader groups (e.g. speculators) and financial market linkages influence commodity prices (see von Braun et al. 2014 for an overview on the literature). In a recent empirical analysis, Tadesse et al. (2014) find that excessive4 speculation could have increased maize and soybean prices between July 2007 and June 2008 by 38 percent and 22 percent, respectively. Although the authors do not find a significant impact of speculation on volatility, Cheng and Xiong (2013) and Grosche and Heckelei (2014) show how non-commercial traders’ activities can lead to increased price volatility when they build their expectations based on the current price developments, which results in the trend chasing. As US spot prices are closely linked to the futures prices (Hernandez & Torero 2010) and both influence domestic food prices for hundreds of millions of poor people through price transmission (Kalkuhl 2014), less volatile futures markets could have a strong impact on many people.

The mixed empirical evidence points out possible correlations but there have been difficulties to establish clear causalities. Econometric analysis, in particular, has problems modeling and simulating the impact of specific policies (like position limits, transaction taxes etc.) because of different data frequency and lacking structural models. Although some researchers claim that transaction taxes could reduce noise trading and hence return volatility (Stiglitz 1989; Summers & Summers 1989), others caution also increasing volatility effects (Grundfest & Shoven 1991 Kupiec 1996; Baltagi et al. 2006; Sahoo & Kumar 2008). Hence, the impact of position limits and transaction taxes on volatility and the occurrence of price spikes is therefore uncertain. The impact of increased transparency is likely to be positive but difficult to quantify. Intervening in grain markets by releasing (public) stocks or taking short-positions on futures exchanges requires a solid assessment of current market conditions and large financial

3 More in (von Braun & Torero 2009). 4 Traditionally defined as speculation in excess of what would be required to satisfy hedging demand (Cheng & Xiong 2013).

18

commitment to change prices (von Braun & Torero 2009). Interventions bear also the risk of unintended price effects or of being ineffective if market participants speculate against the intervention (Wright 2012).

While market intervention (or a credible announcement of intervening) with virtual and physical grain reserves requires substantial (public) resources, position limits, transaction taxes and transparency reforms require almost no fiscal costs or even raise fiscal budget (in case of transaction taxes). Aside from transparency reforms, the other three policy approaches might, however, involve (indirect) economic costs in terms of transaction costs and efficiency losses which are difficult to quantify.

Virtual and physical grain reserves can seriously distort price formation on markets if interventions are large and not justified by the market fundamentals (Wright 2012). Apart from possible price distorting impacts, speculation and trading on commodity markets have also important benefits on price formation: Economic theory suggests that speculation of ‘informed’ traders is necessary to bring prices close to their fundamental value. Traders, who exploit informational advantages and changes of underlying fundamentals quickly, are able to make an excess profit.5 Their transactions imply that prices adjust quickly to reflect all available information on market fundamentals. As prices are public information, changes in underlying fundamentals become therefore visible for all. The capability of agricultural futures markets to incorporate information based on news (which might also be sometimes exaggerated) is notably illustrated by Almánzar et al. (2013). Additionally, high trading volumes increase liquidity which might reduce volatility (Brunetti et al. (2011) provide some evidence for this). As futures markets are in general an important instrument for price risk hedging of commercial traders and for price discovery, any type of regulation needs to be carefully designed not to distort these functions but at the same time to prevent the excessive volatility of the food commodity prices.

An ex-ante analysis of the efficiency costs of these policies is in general very difficult to conduct. The high complexity of financial and commodity markets as well as their fast and sensitive responsiveness advise cautions against exaggerated interventions. An alternative approach to reduce negative side-effects of suboptimal regulation would therefore be to intervene only in times of crises with a predetermined set of policies and to encourage also banks, fund managers and institutional investors to temporarily reduce or suspend potential price-distorting trading activities. This requires a solid and comprehensive information base to detect severity and extent of upcoming crises. Existing tools can be used for this: the excessive volatility early warning system developed by IFPRI (Martins-Filho et al. 2012) provides a daily assessment of excessive volatility levels for major agricultural commodities which is publicly accessible.6 Using transmission analyses from international markets to domestic local food markets, Kalkuhl (2014) identifies countries vulnerable to international price shocks including an estimation of the number of poor people potentially affected by price increases. Nevertheless, the effectiveness of voluntary approaches to really reduce volatility is questionable as incentives for investors to apply such principles are low and the ex-ante quantitative impact assessment of context-

5 See further examinations on the efficient market hypothesis in Fama (1970). 6 See: www.foodsecurityportal.org/policy-analysis-tools/excessive-food-price-variability-early-warning-system

19

dependent regulatory interventions faces the same problems as the (permanent) policies discussed above.

The international community, including the G20 (G20 Study Group on Commodities 2011) and the UN (De Schutter 2010; UNCTAD 2012), has recognized the need of increasing transparency and regulation of the agricultural futures markets. Both the EU and the US have implemented several measures to do so, among others in their revised Markets in Financial Instruments Directive7 (MiFID II) and Dodd–Frank Wall Street Reform and Consumer Protection Act respectively8. As assessed in Staritz and Küblböck, (2013), important steps have been taken through these regulations, however they include several exemptions, in particular exemptions from position limits and a number of exemptions for commercial traders. Thus, regulation of commodity markets has to deal with high lobbying power of interest groups. As commodity exchanges are linked globally (Hernandez et al. 2014), a high coordination effort is necessary to harmonize regulation.

b. Public stocks

The link between stocks and prices is well established (Gustafson 1958; Working 1949). The demand for stocks raises price levels whereas additional supply from stocks induces prices to come down. Likewise, international trade can transfer excess supply at location i to location j where excess demand prevails. Thus, both storage and trade policies have the potential to stabilize prices (Miranda & Helmberger 1988). However, due to the non-negativity constraint, stocks are more effective in mitigating low prices than price hikes (Tadesse & Guttormsen 2011).

The theory of competitive storage assumes an equilibrium price to be reached when today’s price (𝑝𝑡) equals the expected price tomorrow (𝑝𝑡+1) plus the costs of storage. In developing countries, high costs of capital and high transaction costs cause inefficiencies and costs of storage are usually high. In consequence, the level of stocks may not be optimal from the perspective of a policy maker having a desired level of price stabilization. On this note, it is rational to hold public stocks to achieve price stability and assure food security and/or higher farm gate prices (Newbery & Stiglitz 1981). Secondly, the experience from the global food crisis has shown the unpredictability of international food prices and trade policies of exporting countries.

The empirical evidence shows that public stockholding is successful in stabilizing prices (see Kornher & Kalkuhl 2013; Serra & Gil 2013) for a cross-country-cross-commodity panel and Mason & Myers (2013) and Jayne et al. (2008) for specific countries, Malawi and Kenya respectively). Indeed, the extent that public stabilization programs impact on price dynamics varies and largely depends on the quantities of government purchases and releases (Kornher & Kalkuhl 2013; Serra & Gil 2013). In general, two modes of interventions are distinguished in theory: first, buffer stocks, and second, strategic reserves. The former is involved in buying and selling at all times and attempts to stabilize farm gate and consumer prices. In doing so, additional supply is provided to the market when prices exceed a predetermined

7 http://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=OJ:JOL_2014_173_R_0009&from=EN 8 http://www.gpo.gov/fdsys/pkg/PLAW-111publ203/html/PLAW-111publ203.htm

20

ceiling. On the other hand, whenever prices are low, governments serve as buyer of last resort for producers. In contrast, strategic reserves hold stocks for emergency situation only in order to supply the most vulnerable during periods of food shortage or price hikes. In doing so, reserves are very efficient to overcome temporary supply shortages.

Yet, government intervention causes fiscal and economic costs. Fiscal costs arise by the nature of the intervention: buying above and selling below market prices. In addition, storage involves costs for the operation of warehouses, deterioration of stocks, employment of staffs and possible leakage effects due to inefficient management and monitoring. As opposed to this, economic costs do not stress on public finances, but arise from inefficiencies that are associated with public involvement in the private sector (Kozicka et al. 2014, p. 40). The empirical evidence from India suggests massive crowding-out effect of private wheat and rice stocks by expanding public stocks. Accordingly, an increment in public stocks by one metric ton (mt) reduces the level of private stocks by 0.46 mt (Kozicka et al. 2014). On the contrary, trade policies do not seem to have an effect on the level of private stocks. These results are supported by findings by Gouel (2013a) who solves the competitive storage model with rational expectations and simulates the effects on prices and private stock levels. The way stabilization policies influence the private sector is obvious: government interventions impact on market prices and create uncertainty (Abbink et al. 2011; Kornher & Kalkuhl 2014b). In other words, private actors do not believe market prices are predictable and reduce their business activities. The extent of crowding-out and impacts on private business activities also largely depends on the level of public intervention. The narrower the band of allowed prices that specifies the need of intervention, the stronger the price stabilization, and the larger the amount of stocks needed (Miranda & Helmberger 1988). In fact, public stockholding is usually associated with large fiscal costs that naturally increase with the level of stocks9.

Apart from economic and social costs, several other negative aspects associated with public storage are noted by the literature. First, the vulnerability of government programs to speculative attacks by the private sector. Accordingly, speculators doubt the ability of the government program to guarantee supply at subsidized prices and withhold stock until the government is forced to abandon the specified price ceiling (Salant 1983). Secondly, the opportunity of rent seeking by bureaucrats when funds are provided to the stock agency (Byerlee et al. 2006). And lastly, mismanagement of public stocks and excessive stocking with the risk of deterioration (Rashid & Lemma 2011).

Against this, strategic reserves only intervene in markets during severe food crisis when the private sector fails to provide sufficient supply, and thus do not cause significant market distortions (Maunder 2013). In this instance, fiscal costs are lower depending on how large the reserves are. An even more cost-effective way, is to hold joint regional reserves that make use of risk pooling to reduce required reserves since production shocks are not perfectly correlated among member countries (Koester 1986; Kornher & Kalkuhl 2014a; Briones 2011).

In summary, public storage can be a very effective instrument to stabilize prices and ensure food security. Public buffer stock programs not only supply the vulnerable with subsidized food item but also

9 Ayel et al. (2014) provide an overview about costs of interventions in India, Indonesia, Philippines, and Zambia.

21

raise farm level income and enhance domestic production. On the other hand, they are associated with high fiscal and economic costs. As alternative, national and regional strategic reserves represent an efficient instrument to mitigate severe food crises and come at moderate fiscal costs without significant impact on private business activities. Notably, Gouel and Jean (2012) argue for an optimal mix of storage and trade policies depending on whether international prices are high or low.

Public storage and stabilization programs can be considered as politically feasible. Buffer stocks aim at benefiting producers and consumers, by far the most populous lobby groups in developing countries, and thus are backed by the population. In equal manner, it applies for strategic reserves that benefit consumers only. Regional reserves seem to be less politically feasible than national policies as they require coordination among countries.

c. Trade policies

The functioning as well as costs and benefits of trade policies can be very miscellaneous depending on the circumstances. Here, we consider only trade policy reactions that are the response to international price changes with the purpose of stabilizing domestic food prices. In the aftermath of 2007/2008, trade measures have been the most commonly used policies (Demeke et al. 2009). They remain to be the major source of agricultural market distortions (Anderson & Nelgen 2012). Export restrictions can be in form of export taxes and quotas or total bans of exports of selected food crops or certain crop varieties. The underlying assumption is that integrated markets are more likely to be exposed to price shock transmission from international to domestic food markets. It can be distinguished between two forms of polices: first, price based policies that include tariffs and subsidies that aim at determining the export price directly, and secondly, quantity based measures that restrict the quantity or values exported, and thus indirectly impact on export prices10. In contrast, import duties are designated to protect domestic producers by increasing prices of imported goods or to generate public funds. In addition to that, import depending countries can make use of flexible import duties. By lowering tariffs the wedge between international and domestic prices declines and domestic prices reduce.

In consequence of export restrictions, diminishing supply at the international level impacts on export prices and thus has negative implications for food importers. The empirical evidence on the effect of national trade policies on international prices is very strong across all internationally traded commodities (Headey 2011). According to (Martin and Anderson, 2012), trade restrictions have had a substantial effect on international price increases since the 1970s, and they explained 30 and 45 percent of price increases of wheat and rice, respectively, during 2007/2008. They highlight the collective action problem associated with high international food prices. This implies the larger international price spikes due to trade restrictions, the more countries are interested to follow suit and impose counter-measures to prevent international price shocks to enter domestic markets. In general, the negative consequences for importers make it necessary to not only consider domestic effects of policy implementation but also international externalities in a cost-benefit assessment.

10 International prices remain the same for small exporters.

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The global food crisis has shown that most of the large exporters (e.g. India, Vietnam) were successful in stabilizing domestic prices (Martin and Anderson, 2012), whereas export restrictions of regional exporters in Africa were less effective due to the possibility of cross-border smuggling (Porteous 2012). In contrast, Kornher and Kalkuhl (2013) find no positive long-term effect of trade restrictiveness on price volatility as countries’ limited access to global markets reduces also the possibility to trade in times of scarce or abundant domestic supply. In theory, export taxes and quantity based quotas can have the same effect on domestic and international prices being different in their effect on public finances, while export quotas and bans do not entail fiscal revenues. Depending on the prices responses, higher tariffs can yield to significantly reduced exports and lower export revenues. However, exporters usually incorporate tax revenue in their calculus, and thus restrictions are costly and cause budgetary deficits. On the other hand, export taxes do not provide complete certainty about domestic price responses whereas quantity based restrictions define a maximum export quantity. Export restrictions benefit consumers and harm producers of food crops, and therefore, are accompanied by distributional effects (McKay & Tarp 2014). Both price and quantity based restrictions are also highly distortive in driving prices away from there equilibrium levels under free trade. As a medium to long-term consequence, production can drop when prices cannot create sufficient incentives for producers.

A number of countries apply flexible import duties to offset the increment of international prices for domestic consumers (Demeke et al. 2009). Cereal importers in international food trade are small countries and changes in import duties have no effect on international prices. A reduction of import duties de-facto corresponds to subsidized food distribution and is effective in stabilizing domestic market prices. This in turn stresses public budgets. At the same time, ad-hoc changes in trade regulations create market uncertainty for domestic producers and traders.

Starting with the assessment of domestic effects, two grounds can be identified according to which governments find it rational to impose trade restrictions. On the one hand, risk aversion of powerful lobby groups in the country (net consumers) who prefer domestic prices to be detached from international price movements (Freund & Özden 2008). Secondly, the comparative advantage of an ex-ante intervention vis-à-vis ex-post policy programs that cope with the impacts of higher price variability. Therefore, domestically it can be considered politically feasible to use trade measures to protect consumer at costs of hurting exporters. On the other hand, international free trade arrangements that prevent all sorts of tariffs and restrictions were not successful at the latest WTO meeting.

Trade restrictions have appeared to be a very effective instrument for main food exporters to insulate domestic from international prices and mitigate the impacts of the global food crisis. However, they come at costs of redistributing from producers to consumers and may not be beneficial for overall social welfare. More importantly, in a globalized world, policy decisions cannot be evaluated country by country in isolation. They clearly harm importing countries through an increase of international prices. Furthermore, changes in trade policies often create strategic reactions of trading partners, and thus cannot be evaluated separately (Bouet and Laborde, 2010). Therefore, the overall assessment needs to take into account negative externalities of national policy responses as well as long term effects of strategic interaction (von Braun & Tadesse 2012). Anderson (2012) emphasizes the need of a multilateral agreement that limits the use of export restrictions in exchange for domestic price

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stabilization and ex-post social protection policies. It could be also conceivable to establish some sort of transfer mechanism to financially support policies in export countries.

d. Information and transparency

Price changes should reflect the developments in market fundamentals to give food producers adequate signals and incentives. Better information on supply, demand, trade and stocks means more market efficiency. Also more transparency regarding the market fundamentals could curb price volatility (Arnold & Vrugt 2008; Brockhaus & Kalkuhl 2014; Mu 2007). Brockhaus and Kalkuhl (2014) discuss the theoretical model which provides a link between the uncertainty of the market fundamentals and price volatility through the agents’ supply expectations. Better information and analysis on the local and global levels have been identified as one of the key measure to prevent the situations similar to the 2007/08 food crisis. There are two directions of action – improving information on availability and demand and analyzing price developments.

The Agricultural Market Information System (AMIS), established in 2011 as a G20 initiative is an example of the internationally coordinated initiative ‘to enhance food market transparency and encourage coordination of policy action in response to market uncertainty’ (AMIS n.d.). However, Brockhaus and Kalkuhl (2014) found that the information provided by the AMIS leaves a lot of uncertainty, as there are major differences between the estimates from different sources, especially related to stock levels. Authors suggest that this is a result of countries’ reluctance to share their data and in addition often missing data on private sector stockholdings. Empirical analysis of the uncertainty regarding stocks and production based on the AMIS data suggests its significant and positive impact on the food price volatility (Brockhaus & Kalkuhl 2014). Improving the information on the stock levels, for example by incentivizing the governments to provide better data on the government held stocks and collect more information about the private stockholding could reduce price volatility of the food commodities. International coordination and collection of the data has some costs and requires smart incentives for revealing information and increasing transparency, especially in times of shortages. Nevertheless, it is a non-distortive way of reducing volatility which has also additional benefits related to improved and timely food relief measures.

Price monitoring and early warning systems are crucial for effective and timely policy responses to price surges and excessive price volatility for governments and international organizations. Important examples of such systems are FAO Global Information and Early Warning System11 and the WFP Price Monitor for domestic prices, IFPRI Excessive Food Price Variability Early Warning System12 for international prices and FEWS.NET for local harvest conditions. As these systems work with much publicly available data, the operating costs are rather low; nevertheless, high-frequency and high quality price data is still not available for many developing countries. Investment in additional data collection could improve these tools further but data collection provides a public good benefitting many

11 http://www.fao.org/giews/english/index.htm 12 http://www.ifpri.org/pressroom/briefing/excessive-food-price-variability-early-warning-system

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institutions, firms and individuals and requires therefore public resources. Developing and testing these systems on a solid scientific and empirical base needs also upfront costs in research and time.

e. Biofuel policies

The expansion of biofuel production has increased the demand for agricultural land and crops that can be converted to ethanol or biodiesel which tends to push prices of agricultural commodities in general and food crops in particular. To the extent that food or feed crops can be used for energy production (e.g. corn), food prices may also be linked more closely to energy prices as these food crops become a substitute for fossil energy. As the conversion of agricultural products to energy is mainly driven by policies due to its high costs, there is a large potential to influence food price dynamics by an appropriate policy design. The major policies considered are: subsidies on biofuel production, blending mandates or quotas (minimum requirements for biofuel production) or taxes, or restrictions on carbon emissions (which are effectively taxes on fossil fuels).

Evaluating the role of biofuel policies requires a clear distinction between their effects on price levels and price volatility. The impact on price levels is mainly determined by the quantity of biofuel production which shifts the demand curve for agricultural products. Table 1 presents an overview on different estimations on the impact of the conversion of agricultural crops to energy. While the estimates differ according to the model assumptions, commodities or aggregates and policy scenarios (counterfactuals) considered, there is a large consensus that biofuel policies can have a substantial impact on price levels, in particular with increasing conversion quantities and increasing land scarcities. However, some studies (Al-Riffai et al. 2010; Gilbert 2010) also provide support for the contrary hypothesis, that there are only limited effects on food prices due to strong supply response (devoting land from non-food production to energy crop production or converting non-agricultural land, e.g. forest land, for agricultural use).

Table 1. Estimates of the food commodity price increases due to the past and future biofuel mandates

Source Estimate Commodity Time period Rosengrant 2008 39%

21-22% Maize, Wheat, Rice 2000-2007

Hoyos and Medvedev 2009 6% Global Food Price Index 2005-2007 Hochman et al. 2011 10% Maize 2007 Collins 2008 25-60%

19-26% Maize US Retail Food

2006-2008

Lampe 2006 35% 3%

Maize Global Food Price Index

2007-2008

Glauber 2008 23-31% 10% 4-5%

Commodities Global Food Price Index US Retail Food

2007-2008

Rajagopal et al. 2009 15-28% 10-20%

Maize Soy

2007-2008

Roberts and Schlenker 2009 35% Cereals 2007-2009 Cui et al. 2011 53% Maize 2009

25

Taherpour et al. 2010 13-20% Cereals 2015 OECD-FAO 2008 42%

34% 24%

Coarse Grains Vegetable Oils Wheat

2008-2017

Rosengrant 2006 41% 30%

Maize Wheat

2020

Chen & Khanna 2012 23% 22%

Maize Soy

2010-2022

Source: Own design

Recently, the impact of the biofuel mandates on commodity price volatility received more attention in the literature (Beckman et al. 2012, Abbott 2013): Mandates, for example, can create a demand for grains which is more inelastic than the demand for food and feed which, in turn, can increase food price volatility due to supply variability (see Beckman et al. (2012) for a quantitative analysis of US and EU mandates). In case of biofuel subsidies or taxes on fossil fuels, oil price volatility can spill over to food price volatility but also reduce food price volatility as both markets – energy and food – are connected and supply and demand shocks dampened. Whether food price volatility increases or decreases in this case depends on the correlation and the relative magnitude of demand and supply shocks in both markets.

Econometric analyses often find close linkages between energy and biofuel market returns and commodity market returns on different time frequencies (e.g. Serra 2011, Algieri 2014). Due to lacking explicit representation of policy instruments in structural equations, it is more difficult to derive robust policy conclusions.

While there are clear and well-researched impacts of biofuel policies on price levels, the role of biofuel policies on volatility is less clear and depends a lot on the specific policy design which has the potential for increasing as well as decreasing price volatility independently from the impact on the (mean) price level.

Biofuel subsidies (also in terms of tax deductions and low-interest loans) involve direct fiscal costs while quotas and mandates transfer these costs to energy consumers. Taxes on fossil fuel consumption (as well as emission trading schemes) promote biofuels indirectly while simultaneously raising revenues. The major motivation of biofuel policies is, however, related to environmental (reducing carbon emissions) and energy security (reduce oil imports) issues. Additionally, biofuel subsidies may reduce overall energy prices (with adverse effects on energy demand and thus, environmental goals, see Kalkuhl et al. 2013a). Hence, there are clear trade-offs between food price levels and environmental and energy policy goals which can be addressed through technological progress and innovation in biofuel technologies (become more land and energy efficient, see IPCC 2011) and additional social protection policies to ensure food security. Policies that reduce volatility in the food sector (like flexible subsidies or mandates) might also lead to higher volatility in the energy sector which could have adverse effects on macroeconomic stability and growth.

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Apart from the environmental and energy security concerns, biofuel policies are susceptible of being hidden subsidy and support programs for farmers. As farmers’ income is affected, resistance to reduced mandates and subsidies has been strong in recent times. Environmental issues enjoy often lower lobbying pressure and biofuel policies are therefore to a lower extent driven by environmental groups. Contrary, biofuel policies have been criticized for their low emission saving potential due to high energy use (fertilizer), indirect land-use changes (IPCC 2011) and food security risks. The latter led, for example, the German government to revise its biofuel targets. While biofuel policies enjoy high support from the agricultural sector and are also seen – if well designed in form of emission or fossil fuel taxes – as crucial technology to achieve ambitious climate goals by economists (IPCC 2011), they receive only limited support from the public and environmental groups.

f. Policies to enhance resilience

All the above discussed policy measures relate to reduce volatility or to stabilize commodity prices. However, moderation of the price fluctuation may not be sufficient, as even the small and temporal price swings can result in irreversible damages to the poor households and some fluctuations are necessary to reflect supply and demand conditions. Hence, policies which help vulnerable households cope with price shocks in the short run and build their resilience in the medium/long run play a crucial role in achieving food security in developing countries. The policies to enhance resilience can be based on market or public interventions (similar classification was used by Galtier and Vindel (2013). The first group includes improving functioning of the financial markets and increasing access to financial services: futures market services, insurance (crop insurance and weather-indexed insurance); and banking services: consumer and producer credit and savings. The second group consists of nutrition programs and safety nets, which can have a form of cash transfers (conditional or unconditional) or food vouchers, food distribution in kind, universal food subsidy and employment-based safety nets.

In practice, the two main categories of safety nets are cash transfers (unconditional or conditional) and food access-based approaches. There is a broad discussion in the literature concerning advantages of one over another (for example, an overview can be found in Hidrobo et al. 2014. The recent evidence from three pilot projects to assess the comparative performance of cash transfers, food payments, and vouchers in Ecuador, Uganda, Niger, and Yemen (Hoddinott et al. 2013) is that the performance of the policy measure significantly depends on different factors, such as severity of food insecurity or functioning of the markets. There were detected differences in effects on quantity of food and quality of diets, as well as differences in recipient’s preferences to particular type of the transfer depending on the context.

In practice, it is often advised to deliver cash if markets are functioning well (Gentilini 2007) or a combination of cash and food (Sabates-Wheeler & Devereux 2010). Food transfers may be more effective when food markets do not function properly or in case of extreme price spikes. It is also essential to link cash transfers and employment wages to food prices to ensure stable purchasing power (Sabates-Wheeler & Devereux 2010). However, setting the cash transfer level and adjusting it for inflation might be challenging, as food price levels and inflation often vary substantially between the different regions.

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Nutrition programs can efficiently target specific problems by supplying fortified foods, which are not available locally. In populations with insufficient food, provision of food supplements proved to be successful in increasing the height-for-age Z scores by 0.41 (Bhutta et al. 2008). Other effective programs in improving nutrition are supplementation for pregnant women with iron, folate and other micronutrients and for children with vitamin A, preventive zinc supplements, iron supplements for children in areas where malaria is not endemic, and universal promotion of iodized salt (Bhutta et al. 2008). These programs are generally recommended measures to improve nutritional outcomes at all times, however they are even more important in times of insufficient food intake (e.g. due to price spikes), as the amount of micronutrients in supplements is usually sufficient to fully meet the recipients’ micronutrient needs.

In addition to nutrition-specific approaches, governments can improve functioning of the financial sector with the focus on improving access of the poor to the financial services. These measures are aimed to prevent income instability due to the price volatility. Access to futures markets, credit, savings and insurance can be an important buffer to protect the poor farmers and consumers from the effects of food price volatility. These tools are important for both - net food producers, in times of price drops and net food consumers, during the price hikes. These instruments can be easily adjusted to the individual needs and preferences, like attitude to risk. In addition, they can support the poor in other critical situations, not (directly) related to the price volatility. However, the poor often have problems accessing the financial instruments as they do not have enough credibility, assets for collateral or the means to pay for the insurance.

In general, the measures belonging to the ‘resilience’ group are relatively market friendly (non-distortive for markets); however, they can be associated with relatively high fiscal costs. In efficient economies, cash transfers are non-distortive in a sense that they bring the economy from one Pareto optimum to another, which does not have to be true in case of in-kind transfers. However, when the information about individual preferences is not available to the government, the cash transfers are no longer superior to the in-kind transfers (Blackorby & Donaldson 1988). Cash transfers are also usually associated with lower cost of delivery than in kind transfers (Hoddinott et al. 2013; Jacoby 1997). On the other hand, the in-kind transfers may have a lower inclusion targeting error, as the fact of being a beneficiary is more visible (Blackorby & Donaldson 1988; Currie & Gahvari 2008).

The height of the costs depends on the scope of coverage and efficiency of the program. In general, safety nets rarely account for more than 1%-2% of GDP, even in countries with generous social protection systems. Safety net programs in Mexico or Brazil cost around 0.5% of GDP (WB 2012). India is an example of the country with a big scale food subsidy program, however with a high fiscal cost (food subsidy close to 0.8% of GDP) and several market distortions because the program serves different goals (supporting both farmers and consumers) and intervenes in many stages of production, marketing and storage (Kozicka et al. 2014).

It is essential to develop and maintain food safety net systems in advance and only scale them up during high price phases because it requires capacity and takes time to build the infrastructure and so it may take too long to reach the poor people within the short time, when they need the support the most

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(Demeke et al. 2009). This strategy is also promoted by development organizations (FAO et al. 2011). So facilitating the system in ‘normal’ times requires constant financing but also necessary financial reserves should be kept in order to activate the safety net in times of crisis. These funds can also come from the international community through bilateral and multilateral international development assistance and programs by international development organizations. For example, the framework Scaling-Up Nutrition (SUN), launched in 2010, has now 30 participating poorer countries. By implementing the framework countries receive assistance in mitigating the negative nutritional impact of high food prices and food price volatility. The SUN embraces “evidence-based nutrition interventions” as well as “integrating nutrition goals across sectors” (SUN n.d.).

In developing countries, where financial markets often do not function well and the poor lack information and access to the financial institutions, improving access to risk management markets mostly involves improving market information, enabling the environment (including legal framework) for risk markets to emerge and improving the education about risk, risk management as well as functioning of the financial markets and their services. Public – private partnerships (for example index-based livestock insurance in Mongolia (Mahul et al. 2009)) can be one instrument. So the fiscal costs of improving access to the financial services are rather moderate and the benefits go beyond improved food security promoting overall development.

Designing the safety nets, like determining type and amount of transferred goods (cash, food, food vouchers, inputs,…), defining and identifying the recipients is a challenging task. There is no universally best policy measure (Devereux 2006). The responses to price volatility should be country-specific and depend on the social and development characteristics as well as the fiscal capacity to finance them. It can be also difficult to ensure that the safety nets interplay with the agricultural and rural development programs in order to achieve the maximum efficiency.

Both building the safety nets and improving access to the financial services are long term processes. However, all the measures in the ‘resilience’ group of policies are politically easy to implement as they are popular and acceptable in different groups of actors and do not require international cooperation.

7. Synthesis for quantitative assessment

Table 2 below summarizes all policies discussed in the previous section and attempts to quantify benefits/ effectiveness, economic and fiscal costs, as well as their feasibility as described above. In doing so, we provide a subjective assessment of policies based on state of the art literature provided. Since an evaluation of a policy may be conditioned by several factors and contexts, some level of arbitrariness is inevitable. Nevertheless, we attempt to point out major factors that could alter the designated evaluations in Table 2. For instance, policy i may be effective in country n but not in country m depending on their institutional capacity and existing administrative structures. Therefore, the policy follows conventional wisdom of the literature and considers on average effects. This list of policies may not be exhaustive but includes the most common policy proposals that are discussed nationally and internationally.

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Table 2 Subjective quantification of the discussed policy measures

Policy Policy measure

Benefits and effectiveness

(volatility reduction/coping)

Costs Feasibility

(domestic/ internat’l)

Remark

Indirect (efficiency

losses)

Fiscal costs

Regulation in commodity

futures markets

Increasing transparency on actors and transactions

low low low medium

Mixed empirical evidence about the effectiveness of these policies, finding a reliable trigger is not trivial for virtual reserves. The empirical evidence with regards to the effect of the financialization of commodity futures markets is ambiguous.

Introducing limits on

futures trading unclear medium low medium

Imposing transaction

taxes unclear high low medium

Virtual reserves medium low medium low

Public stocks

Domestic buffer stocks high high high high Size of the respective stocks

and releasing mechanisms affects the benefits and costs.

The number and type of countries involved could affect the feasibility of a regional reserve.

The success of public storage depends highly on the management of the reserve and possible leakage in the system. Further, strategic reserves require a functioning distribution channel.

Strategic reserve high low medi

um high

Regional reserve high low low low

Trade policies *

Trade bans/quotas high high high high Such policies could affect

world food prices depending on the size of the economy. Flexible trade

tariffs high high medium medium

Information and

transparency

Early warning system medium low low high Early warning facilitates

timely intervention and is therefore only of relevance when public intervention is possible or international donors provide food aid.

Information about market fundamentals

low low low medium

Biofuel policies

Lower/flexible biofuel

mandates medium medium low low

A smart biofuel policy can help to reduce volatility if it is counter-cyclical, i.e. increased demand if prices are low and vice versa.

Lower/flexible biofuel medium medium low low

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subsidies

Policies to enhance resilience

Increasing access to financial

services, like credit, savings and insurance

high low high high

Whether well-functioning market and financial institutions exist in the particular country affects all the criteria.

Cash transfers and food voucher systems require a functioning targeting mechanism and distribution channels.

Cash transfers and food vouchers

high low high high

Food distribution in kind and food

subsidy

high medium high high

Source: Authors’ representation

* only domestic costs and benefits were taken into account

8. Conclusions

Prices of agricultural commodities are inherently variable. Price variations that reflect underlying market fundamentals are important because they contain useful public information on which economic agents base their economic decisions. However, not all price variations necessarily reflect underlying demand and supply fundamentals. This can be the case, for example, when price changes are abrupt and at excessive levels. Some studies indicate that the stronger connection of agricultural commodity markets to energy and financial markets might have contributed to the large price spikes and excessive levels of volatility during the 2007-08 food crisis (Gilbert & Pfuderer 2014; Tadesse et al. 2014). In such situations where there is a lot of noise about the true market price signal, in other words, when market prices are ‘polluted’ by some sort of spillovers, there is room for collective action and policy responses to potentially achieve a Pareto-improving allocation or a substantial welfare enhancement.

This paper discusses the theoretical mechanisms and empirical evidence on market-related costs, impacts on human welfare, and on political economy of price spikes and volatility. An unanticipated price variation introduces output price risk, which has disincentive effects on resource allocation and investment decisions of a risk averse economic agent. Empirical evidence shows that the 2006-2010 volatility in output prices have weakened the extent to which rising international agricultural commodity prices would have increased global output production of key staple commodities. Besides its effect through risk aversion, market volatility also affects investment when price signals contain a lot of noise, which is the case in an imperfect market situation. Moreover, food price volatility affects economic growth through spillover effects; this is particularly true in countries with large budget shares of food commodities (Timmer 2011). The welfare impact of changes in price levels on consumers is theoretically more obvious as compared to the impact of price volatility. For a typical urban consumer or net-buying rural household a price increase reduces the overall real income, implying a lower consumption of the same item or shifting consumption to cheaper (inferior) food commodities. The welfare impact of price volatility, however, depends on price and income elasticity of demand, risk aversion behavior of the

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individual, and budget share of the commodity. Nevertheless, most empirical evidence suggests that high prices and volatility have negative effects on most poor households in developing countries. Furthermore, some studies have found evidence of direct health and nutrition impacts of price changes and price risk especially on children and women. From the political-economy point of view, there is some theoretical and empirical evidence that hints at a positive association of food price dynamics and social unrest.

Given this backdrop regarding the impacts of price volatility and price spikes, several policy options were proposed and implemented to manage price volatility and/or reduce its costs. As it is true for the effectiveness of these policies in reducing price volatility or the impacts thereof, their fiscal and economic costs of implementation vary substantially. In addition, implementations of some policies are politically less feasible than others. To this end, we provide a normative cost-benefit evaluation of several policy options in terms of their benefits, costs, and feasibility. However, the cost-benefit framework should be interpreted with caution since there are large uncertainties, both theoretically and empirically, with regard to particularly the effectiveness of some of the policies. Moreover, the economic and fiscal costs depend largely on the size or coverage of the policy measure and could affect different parts of the economy, making an exact quantification quite challenging. Nevertheless, the cost-benefit framework helps governments and international organizations to assess their policy measures in terms of these criteria. For instance, establishing early warning systems and investing on information and transparency are relatively more effective and feasible policy measures, which are also relatively cheaper and non-distortive. In contrast, the framework indicates that policies which are the most effective are generally associated with larger fiscal and economic costs.

Acknowledgments

We would like to thank Regine Weber for assistance in creating Figure 3 and helpful editing of the document. Financial support from the European Commission (FoodSecure Project) and the Federal Ministry for Economic Cooperation and Development of Germany (Price Volatility Project) is acknowledged.

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This project is funded by the European Union under the 7th Research Framework Programme (theme SSH) Grant agreement no. 290693

The FOODSECURE project in a nutshell Title FOODSECURE – Exploring the future of global food and nutrition security

Funding scheme 7th framework program, theme Socioeconomic sciences and the humanities

Type of project Large-scale collaborative research project

Project Coordinator Hans van Meijl (LEI Wageningen UR)

Scientific Coordinator Joachim von Braun (ZEF, Center for Development Research, University of Bonn)

Duration 2012 - 2017 (60 months)

Short description

In the future, excessively high food prices may frequently reoccur, with severe

impact on the poor and vulnerable. Given the long lead time of the social

and technological solutions for a more stable food system, a long-term policy

framework on global food and nutrition security is urgently needed.

The general objective of the FOODSECURE project is to design effective and

sustainable strategies for assessing and addressing the challenges of food and

nutrition security.

FOODSECURE provides a set of analytical instruments to experiment, analyse,

and coordinate the effects of short and long term policies related to achieving

food security.

FOODSECURE impact lies in the knowledge base to support EU policy makers

and other stakeholders in the design of consistent, coherent, long-term policy

strategies for improving food and nutrition security.

EU Contribution

€ 8 million

Research team 19 partners from 13 countries

FOODSECURE project office LEI Wageningen UR (University & Research centre) Alexanderveld 5 The Hague, Netherlands

T +31 (0) 70 3358370 F +31 (0) 70 3358196 E foodsecure@wur.nl I www.foodscecure.eu