Cost-benefit framework for policy action to
navigate food price spikes
Matthias Kalkuhl
Mekbib Haile
Lukas Kornher
Marta Kozicka
INTERDISCIPLINARY RESEARCH PROJECT
TO EXPLORE THE FUTURE OF GLOBAL
FOOD AND NUTRITION SECURITY
FOODSECURE Working paper no. 33
July 2015
Cost-benefit framework for policy action to navigate food price spikes
Matthias Kalkuhl 1, 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.
1
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 nonpredictable 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.
2
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 foodprice-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.
3
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.
4
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
� and variance, 𝜎𝛱2 ):
of profit (mean, 𝛱
𝐸𝐸(𝛱) = 𝐸𝐸(𝑝, 𝑤, 𝑙, 𝑧) −
1
2
𝛼𝜎𝛱2
(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.
5
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)
6
(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.
Impact of volatility
increase
Impact of fertilizer
increase
Impact of price
increase
Net effect
12%
8%
4%
0%
-4%
-8%
-12%
Wheat
Soybeans
Maize
Rice
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.
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).
7
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:
1
1
𝐸(𝐿) = 4𝑐(𝑤) 𝐸(𝑃 − 𝜇𝑃 )2 = 4𝑐(𝑤) 𝜎𝑃 2
(2)
where 𝜎𝑃 2 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
8
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 reallocate 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)𝑡 𝑦(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
9
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)
𝜕𝜕
(4)
𝑥𝑖 (𝐩, 𝑦)
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):
𝑝
𝐶𝐶(𝑝0 , 𝑝, 𝑦0 ) = ∫𝑝 𝑥(𝜙, 𝑦0 )𝑑𝑑
0
(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 𝐸𝐸(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)
10
𝑣(𝑝, 𝑦0 ) = 𝑣[𝑝0 , 𝑦0 − 𝐸𝐸(𝑝0 , 𝑝, 𝑦0 )]
Equivalent variation (EV):
(7)
Compensating variation and equivalent can also expressed as integrals over demand functions, using the
Hicksian demand:
𝑝
(8)
𝑝
(9)
𝐶𝐶(𝑝0 , 𝑝, 𝑦0 ) = ∫𝑝 0 ℎ(𝜙, 𝑣(𝑝0 , 𝑦0 ) )𝑑𝑑
𝐸𝐸(𝑝0 , 𝑝, 𝑦0 ) = ∫𝑝 ℎ(𝜙, 𝑣(𝑝, 𝑦0 ) )𝑑𝑑
0
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:
𝐸𝐸[𝑝, 𝑦0 + 𝐶𝐶 ∗ (𝑝0 , 𝑝, 𝑦0 )] = 𝑣(𝑝0 , 𝑦0 )
Ex-ante compensating variation:
𝐸𝐸(𝑝, 𝑦0 ) = 𝑣[𝑝0 , 𝑦0 − 𝐸𝐸 ∗ (𝑝0 , 𝑝, 𝑦0 )]
Ex-ante equivalent variation:
(10)
(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
2
∆𝜎𝑃
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.
11
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
12
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
13
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. GyimahBrempong 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
14
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.
15
News Count
6
5
Index: January
2000 = 100
Total
Wheat
Maize
Rice
450
400
350
300
4
250
3
200
150
2
100
1
50
0
2000m1
2000m7
2001m1
2001m7
2002m1
2002m7
2003m1
2003m7
2004m1
2004m7
2005m1
2005m7
2006m1
2006m7
2007m1
2007m7
2008m1
2008m7
2009m1
2009m7
2010m1
2010m7
2011m1
2011m7
2012m1
2012m7
0
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
16
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
reserves 3.
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 excessive 4 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).
Traditionally defined as speculation in excess of what would be required to satisfy hedging demand (Cheng &
Xiong 2013).
4
17
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 sideeffects 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
6
See further examinations on the efficient market hypothesis in Fama (1970).
See: www.foodsecurityportal.org/policy-analysis-tools/excessive-food-price-variability-early-warning-system
18
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 Directive 7 (MiFID II) and Dodd–Frank
Wall Street Reform and Consumer Protection Act respectively 8. 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
8
http://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=OJ:JOL_2014_173_R_0009&from=EN
http://www.gpo.gov/fdsys/pkg/PLAW-111publ203/html/PLAW-111publ203.htm
19
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 stocks 9.
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.
20
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 prices 10. 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.
21
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 exante 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
22
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 System 11 and the WFP Price
Monitor for domestic prices, IFPRI Excessive Food Price Variability Early Warning System 12 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
12
http://www.fao.org/giews/english/index.htm
http://www.ifpri.org/pressroom/briefing/excessive-food-price-variability-early-warning-system
23
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
Rosengrant 2008
Hoyos and Medvedev 2009
Hochman et al. 2011
Collins 2008
Lampe 2006
Glauber 2008
Rajagopal et al. 2009
Roberts and Schlenker 2009
Cui et al. 2011
Estimate
39%
21-22%
6%
10%
25-60%
19-26%
35%
3%
23-31%
10%
4-5%
15-28%
10-20%
35%
53%
Commodity
Maize, Wheat, Rice
Time period
2000-2007
Global Food Price Index
Maize
Maize
US Retail Food
Maize
Global Food Price Index
Commodities
Global Food Price Index
US Retail Food
Maize
Soy
Cereals
Maize
2005-2007
2007
2006-2008
2007-2008
2007-2008
2007-2008
2007-2009
2009
24
Taherpour et al. 2010
OECD-FAO 2008
Rosengrant 2006
Chen & Khanna 2012
13-20%
42%
34%
24%
41%
30%
23%
22%
Cereals
Coarse Grains
Vegetable Oils
Wheat
Maize
Wheat
Maize
Soy
2015
2008-2017
2020
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.
25
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.
26
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
27
(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 indexbased 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.
28
Table 2 Subjective quantification of the discussed policy measures
Policy
Regulation in
commodity
futures
markets
Policy measure
Increasing
transparency
on actors and
transactions
Introducing
limits on
futures trading
Imposing
transaction
taxes
Virtual reserves
Domestic
buffer stocks
Strategic
reserve
Benefits and
effectiveness
(volatility
reduction/coping)
Costs
Indirect
(efficiency
losses)
Fiscal
costs
Feasibility
(domestic/
internat’l)
low
low
low
medium
unclear
medium
low
medium
unclear
high
low
medium
medium
low
medi
um
low
high
high
high
high
high
low
medi
um
high
Regional
reserve
Information
and
transparency
Biofuel
policies
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.
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.
Public stocks
Trade
policies *
Remark
Trade
bans/quotas
Flexible trade
tariffs
Early warning
system
Information
about market
fundamentals
Lower/flexible
biofuel
mandates
Lower/flexible
biofuel
high
low
low
low
high
high
high
high
high
high
medi
um
medium
medium
low
low
high
low
low
low
medium
medium
medium
low
low
medium
medium
low
low
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.
Such policies could affect
world food prices depending
on the size of the economy.
Early warning facilitates
timely intervention and is
therefore only of relevance
when public intervention is
possible or international
donors provide food aid.
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.
29
Policies to
enhance
resilience
subsidies
Increasing
access to
financial
services, like
credit, savings
and insurance
Cash transfers
and food
vouchers
Food
distribution in
kind and food
subsidy
high
low
high
high
high
low
high
high
high
medium
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.
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
30
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 costbenefit 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.
References
Abbink, K. et al. (2011). “The Relevance of a Rules-based Maize Marketing Policy: An Experimental Case Study of
Zambia.” Journal of Development Studies, 47 (2), 207-230.
Abbott, P. (2013). “Biofuels, Binding Constraints and Agricultural Commodity Price Volatility”, Working Paper Series
No. 18873, National Bureau of Economic Research, Cambridge, MA.
Acemoglu, D. & Robinson, J. (2001). “A Theory of Political Transitions,” American Economic Review, 91, 938–963.
Acemoglu, D. & Zilibotti, F. (1997). “Was Prometheus Unbound by Chance? Risk, Diversification, and Growth.”
Journal of Political Economy, Vol. 105, No. 4, 709-751.
Aidt, T. S. & Leon, G. (2014). The Democratic Window of Opportunity: Evidence from Riots in Sub-Saharan Africa.
CESinfo Working Paper Series No. 4884, SSRN.
31
Alesina, A. et al. (1996). “Political Instability and Economic Growth.” Journal of Economic Growth, 1(2), 189-211.
Algieri, B. (2014). “The Influence of Biofuels, Economic and Financial Factors on Daily Returns of Commodity
Futures Prices." Energy Policy, Vol. 69, 227-247.
Almánzar, M. et al. (2013). Futures Commodities Prices and Media Coverage. ZEF Discussion Papers on
Development Policy, No. 178, Center for Development Research (ZEF), Bonn.
Al-Riffai, P. et al. (2010). European Union and United States Biofuel Mandates: Impacts on World Markets. InterAmerican Development Bank, Technical Notes No. IDB-TN-191, Washington DC.
AMIS (n.d.). About AMIS. Retrieved June 20, 2014, from http://www.amis-outlook.org/amis-about/en/.
Anderson, K. (2012). “Government Trade Restrictions and International Price Volatility.” Global Food Security, 1(2),
157–166.
Anderson, K. & Nelgen, S. (2012). “Trade Barrier Volatility and Agricultural Price Stabilization.” World
Development, Elsevier, Vol. 40 (1), 36-48.
Anderson, K. et al. (2013). “Estimating Effects of Price-Distorting Policies Using Alternative Distrotions Databases.”
Handbook of Computable General Equilibrium Modeling, Vol. 1, 877-931.
Arezki, R. & Brückner, M. (2014). “Effects of International Food Price Shocks on Political Institutions in Low-Income
Countries: Evidence from an International Food Net-Export Price Index.” World Development, 61, 142-153.
Arndt, C. et al. (2012). Effects of Food Price Shocks on Child Malnutrition: The Mozambican Experience 2008/09.
Working Paper No. 2012/89, World Institute for Development Economic Research (UNU-WIDER), Helsinki.
Arnold, I. J. M. & Vrugt, E. B. (2008). “Fundamental Uncertainty and Stock Market Volatility.” Applied Financial
Economics, 18(17), 1425–1440.
Atkin, D. (2012). “Habit Formation and the Gains from Price Stability.” CESifo Economic Studies, 58(2), 373-384.
Ayel, G. et al. (2014). “Food Reserve and Regulating Market Volatility in Africa.” A Savior, No. 23, Agence Francaise
de Développement (AFD), Paris.
Baltagi, B. et al. (2006). “Transaction Tax and Stock Market Behavior: Evidence from an Emerging Market.”
Empirical Economics, 31(2), 393-408.
Beckman, J. et al. (2012). “Structural Change in the Biofuels Era.” European Review of Agricultural Economics,
39(1), 137–156.
Behrman, J. R. & Rosenzweig, M. R. (2004). “Returns to Birthweight.” Review of Economics and Statistics, 86(2),
586-601.
Bellemare, M. F. (2014). “Rising Food Prices, Food Price Volatility, and Social Unrest.” American Journal of
Agricultural Economics, forthcoming.
Bellemare, M. F. et al. (2013). “The Welfare Impacts of Commodity Price Volatility: Evidence from Rural Ethiopia.”
American Journal of Agricultural Economics, 95(4), 877-899.
Bhutta, Z. A. et al. (2008). „What Works? Interventions for Maternal and Child Undernutrition and Survival.”
Lancet, 371(9610), 417–40.
Black, R. E. et al. (2013). “Maternal and Child Undernutrition and Overweight in Low-Income and Middle-Income
Countries.” The Lancet, 382(9890), 427-451.
Blackorby, C. & Donaldson, D. (1988). “Cash versus Kind, Self-Selection and Efficient Transfers.” The American
Economic Review, 78(4), 691–700.
Bouet, A. and D. Laborde. (2010). Economics of Export Taxation in a Context of Food Crisis: A Theoretical and CGE
Approach Contribution. IFPRI Discussion Paper 00994. Washington, DC: International Food Policy Research
Institute.
32
Briones, M. R. (2011). Regional Cooperation for Food Security: The Case of Emergency Rice Reserves in the ASEAN
Plus Three. ADB Sustainable Development Working Paper Series No. 18, Asian Development Bank (ADB),
Manila.
Brockhaus, J. H. & Kalkuhl, M. (2014). Can the Agricultural Market Information System (AMIS) Help to Reduce Food
Price Volatility ? In: Poster prepared for presentation at the Agricultural & Applied Economics
Association’s 2014 AAEA Annual Meeting, Minneapolis, MN. Retrieved from http://purl.umn.edu/170391.
Brückner, M. & Ciccone, A. (2011). “Rain and the Democratic Window of Opportunity.” Econometrica, 79(3), 923947.
Brunetti, C. et al. (2011). “Speculators, Prices and Market Volatility.” SSRN Electronic Journal.
doi:10.2139/ssrn.1736737.
Byerlee, D. et al. (2006). “Managing Food Price Risks and Instability in a Liberalizing Market Environment: Overview
and Policy Options.” Food Policy, 31 (4), 275–287.
Campbell, A.A. et al. (2010). “Household Rice Expenditure and Maternal and Child Nutritional Status in
Bangladesh.” The Journal of Nutrition, Vol. 140, No. 1, 189S-194S.
Chavas, J.P. (2000). “The Microeconomics of Food Security.” Australian Journal of Agricultural and Resource
Economics, Vol. 44(1), 1-29.
Cheng, I. & Xiong, W. (2013). “The Financialization of Commodity Markets.” SSRN Electronic Journal. Retrieved
from http://dx.doi.org/10.2139/ssrn.2350243.
Collier, P. & Hoeffler, A. (2004). “Greed and Grievance in Civil War.” Oxford Economic Papers, 56, 563-595.
Coyle, B. T. (1999). “Risk Aversion and Yield Uncertainty in Duality Models of Production: A Mean-Variance
Approach.” American Journal of Agricultural Economics, 81(3), 553-567.
Currie, J. & Gahvari, F. (2008). “Transfers in Cash and in Kind: Theory Meets the Data.”Journal of Economic
Literature, American Economic Association, Vol. 46(2), 333-83.
Dawe, D. (2001). “How Far Down the Path to Free Trade? The Importance of Rice Price Stabilization in Developing
Asia.” Food Policy, 26, 163-175.
De Schutter, O. (2010). Food Commodities Speculation and Food Price Crises: Regulation to Reduce the Risks of
Price Volatility, Briefing Note No.2, United Nations Special Rapporteur on the Right to Food.
Deaton, A. (1991). “Saving and Liquidity Constraints.” Econometrica, Vol. 59, No. 5, 1221-1248.
Demeke, M. et al. (2009). Country responses to the food security crisis: Nature and preliminary implications of the
policies pursued. Technical report, FAO, Rome.
Devereux, S. (2006). Cash Transfers and Social Protection. In: Paper prepared for the regional workshop on “Cash
transfer activities in southern Africa”, co-hosted by the Southern African Regional Poverty Network
(SARPN), Regional Hunger and Vulnerability Programme (RHVP) and Oxfam GB (Vol. 2002), Johannesburg.
D'Souza, A. & Jolliffe, D. (2014). “Food Insecurity in Vulnerable Populations: Coping with Food Price Shocks in
Afghanistan.” American Journal of Agricultural Economics, 96(3), 790-812.
Fama, E. F. (1970). “Efficient Capital Markets: A Review of Theory and Empirical Work.” The Journal of Finance,
25.2 (1970), 383-417.
FAO (2008). The State of Food Insecurity in the World. High Food Prices and Food Security-Threats and
Opportunities. Rome.
FAO et al. (2011). Price Volatility in Food and Agricultural Markets: Policy Responses. OECD, Paris.
Finkelshtain, I. & Chalfant, J. A. (1997). “Commodity Price Stabilization in a Peasant Economy.” American Journal of
Agricultural Economics, 79(4), 1208-1217.
33
Fogel, R. W. (1994). “Economic Growth, Population Theory, and Physiology: The Bearing of Long-Term Processes
on the Making of Economic Policy.” American Economic Review, 84(3), 369-95.
Freund, C. & Özden, C. (2008). “Trade Policy and Loss Aversion.” American Economic Review, 98(4), 1675-91.
G20 Study Group on Commodities (2011). Report of the G20 Study Group on Commodities under the Chairmanship
of Mr. Hiroshi Nakaso.
Galtier, F. & Vindel, B. (2013). “Managing Food Price Instability in Developing Countries: A Critical Analysis of
Strategies and Instrument.” A Savoir, 17, Agence Francaise de Développement (ADF), Paris.
Gentilini, U. (2007). Cash and Food Transfers: A Primer. Occasional Papers No. 18, World Food Programme, Rome.
Gilbert, C. L. (2010). Speculative Influences On Commodity Futures Prices 2006-2008. UNCTAD Discussion Papers.
Retrieved from http://ideas.repec.org/p/unc/dispap/197.html.
Gilbert, C. L. & Morgan, C. W. (2010). “Food Price Volatility.” Philosophical Transactions of the Royal Society B:
Biological Sciences, 365(1554), 3023–3034.
Gilbert, C. L. & Pfuderer, S. (2014). The Financialization of Commodity Futures Markets. In: R. Jha, R., Gaiha, T. &
Deolalikar, A., eds., Handbook on Food: Demand, Supply, Sustainability and Security, Edwar Elgar
Publishing, Inc., Northhampton, MA.
Gouel, C. (2013a). “Optimal Food Price Stabilisation Policy.” European Economic Review, 57, 118–134.
Gouel, C. (2013b). Food Price Volatility and Domestic Stabilization Policies in Developing Countries. Working Paper
18934, National Bureau of Economic Research (NBER), Cambridge, MA.
Grosche, S. & Heckelei, T. (2014). “Price Dynamics and Financialization Effects in Corn Futures Markets with
Heterogeneous Traders.” Discussion Paper, No 2014:5, Institute for Food and Resource Economics,
University of Bonn.
Grundfest, J. A. & Shoven, J. B. (1991). “Adverse Implications of a Security Transaction Excise Tax.” Journal of
Accounting, Auditing and Finance, 6, 409- 442.
Gustafson, R. L. (1958). Carryover Levels for Grains: A Method for Determining Amounts that are Optimal Under
Specified Conditions. Technical Bulletin, United States Department of Agriculture.
Gyimah-Brempong, K. & Wilson, M. (2004). “Health Human Capital and Economic Growth in Sub-Saharan African
and OECD countries.” The Quarterly Review of Economics and Finance, 44(2), 296-320.
Haile, M. G. et al. (2014a). “Inter‐and Intra‐Seasonal Crop Acreage Response to International Food Prices and
Implications of Volatility.” Agricultural Economics, 45, 1-18.
Haile, M. G. et al. (2014b). Acreage Supply Response to International Food Prices and Volatility: A Dynamic Panel
Data Analysis. Manuscript. Center for Development Research (ZEF), Bonn.
Headey, D. (2011). “Rethinking the Global Food Crisis: The Role of Trade Shocks.” Food Policy, 36(2), 136–146.
Helms, L. J. (1985). “Errors in the Numerical Assessment of the Benefits of Price Stabilization.” American Journal of
Agricultural Economics, Vol. 97, No. 1, 93-100.
Hernandez, M. A. & Torero, M. (2010). Examining the Dynamic Relationship Between Spot and Future Prices of
Agricultural Commodities. Discussion Paper No. 00988, International Food Policy Research Institute
(IFPRI), Washington, DC.
Hernandez, M. A. et al. (2014). “How Far Do Shocks Move Across Borders? Examining Volatility Transmission in
Major Agricultural Futures Markets.” European Review of Agricultural Economics, 41(2), 301-325.
Hidrobo, M. et al. (2014). “Cash, Food, or Vouchers? Evidence from a Randomized Experiment in Northern
Ecuador.” Journal of Development Economics, 107, 144–156.
34
Hirshleifer, J. (2001). The Dark Side of the Force: Economic Foundations of Conflict Theory. Cambridge University
Press, Cambridge.
Hoddinott, J. et al. (2013). Enhancing WFP’s Capacity and Experience to Design, Implement, Monitor, and Evaluate
Vouchers and Cash Transfer Programmes: Study Summary. Retrieved from
http://www.ifpri.org/sites/default/files/project_summary_final_july2013.pdf.
Iannotti, L. & Robles, M. (2011). “Negative Impact on Calorie Intake Associated with the 2006–08 Food Price Crisis
in Latin America.” Food & Nutrition Bulletin, 32(2), 112-123.
Iannotti, L. L. et al. (2012). “Food Prices and Poverty Negatively Affect Micronutrient Intakes in Guatemala.” The
Journal of Nutrition, 142(8), 1568-1576.
IPCC (2011). IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation. Prepared by
Working Group III of the Intergovernmental Panel on Climate Change.
Ivanic, M. & Martin, W. (2008). “Implications of Higher Global Food Prices for Poverty in Low-Income Countries.”
Agricultural Economics, 39, 405-416.
Jacks, D.S. et al. (2011). “Commodity Price Volatility and World Market Integration Since 1700.” The Review of
Economics and Statistics, 93(3), 800-813.
Jacoby, H. (1997). “Self-Selection and the Redistributive Impact of In-Kind Transfers: An Econometric Analysis.”
Journal of Human Resources, 32(2), 223–249.
Jayne, T. S. et al. (2008). “The Effects of NCPB Marketing Policies on Maize Market Prices in Kenya.” Agricultural
Economics, 38(3), 313–325.
Jensen, R. T. & Miller, N. H. (2008). “Giffen Behavior and Subsistence Consumption.” The American Economic
Review, 98, 1553-1553.
Kalkuhl, M. (2014). “How Strong Do Global Commodity Prices Influence Domestic Food Prices in Developing
Countries? A Global Price Transmission and Vulnerability Mapping Analysis.” Discussion Papers on
Development Policy No. 191, Center for Development Economics (ZEF), Bonn.
Kalkuhl, M. et al. (2013a). “Renewable Energy Subsidies: Second-Best Policy or Fatal Aberration for Mitigation?”
Resource and Energy Economics, 35(3), 217-234.
Kalkuhl, M. et al. (2013b). Conceptual Framework on Price Volatility and its Impact on Food and Nutrition Security
in the Short Term. FoodSecure Working Paper No.15.
Koester, U. (1986). Regional Cooperation to Improve Food Security in Southern and Eastern African Countries.
Research Report 53, International Food Policy Research Institute (IFPRI), Washington, D.C.
Kornher, L. & Kalkuhl, M. (2013). Food Price Volatility in Developing Countries and its Determinants. In: 53rd
Annual Conference GEWISOLA, Berlin.
Kornher, L. & Kalkuhl, M. (2014a). Assessing the Costs and Benefits of Storage Coordination. Presentation at the
2nd International ZEF-IFPRI Workshop on Food Price Volatility and Food Security. Retrieved from
http://www.zef.de/2525.html.
Kornher, L. & Kalkuhl, M. (2014b). Private Storage Behavior – Evidence from Ghana. Presentation at the 2nd
International ZEF-IFPRI Workshop on Food Price Volatility and Food Security. Retrieved from
http://www.zef.de/2525.html.
Kozicka, M. et al. (2014). Modeling Indian Wheat and Rice Sector Policies. In: 2014 AAEA Annual Meeting,
Minneapolis, MN. Retrieved from http://ageconsearch.umn.edu/bitstream/169808/2/AAEA Indian
Policies paper.pdf.
Kupiec, P. H. (1996). “Noise Traders, Excess Volatility, and a Securities Transaction Tax.” Journal of Financial
Services Research, 3, 117-120.
35
Lagi, M. et al. (2011). “The Food Crises and Political Instability in North Africa and the Middle East”, New England
Complex Systems Institute, Cambridge, MA.
Lansink, A. O. (1999). “Area Allocation Under Price Uncertainty on Dutch Arable Farms.” Journal of Agricultural
Economics, 50(1), 93-105.
Loayza, N. V. et al. (2007). “Macroeconomic Volatility and Welfare in Developing Countries: An Introduction.” The
World Bank Economic Review, Vol. 21, No. 3, 343-357.
Lucas, R.E. (1973). “Some International Evidence on Output–Inflation Tradeoffs.” American Economic Review 63,
326–334.
Mahul, O. et al. (2009). Innovations in Insuring the Poor: Index-Based Livestock Insurance in Mongolia. 2020 Vision
Focus Brief, 17(9), International Food Polica Research Institute (IFPRI), Washington, D.C.
Martin, W. and K. Anderson (2012) “Export Restrictions and Price Insulation During Commodity Price Booms,"
American Journal of Agricultural Economics, 94 (2), 422-427.
Martins-Filho, C. (2011). Basic Concepts and How to Measure Price Volatility. Paper presented at the AGRODEP
Workshop on Analytical Tools for Food Prices and Price Volatility. Dakar, Senegal.
Martins-Filho, C. et al. (2012). “High Order Conditional Quantile Estimation Based on Nonparametric Models of
Regression.” Econometric Reviews, forthcoming.
Mason, N. M. & Myers, R. J. (2013). “The Effects of the Food Reserve Agency on Maize Market Prices in Zambia.”
Agricultural Economics, 44(2), 203–216.
Masters, M. W. (2008, May 20). Testimony Before the Committee on Homeland Security and Governmental Affairs
United States Senate, Washington DC.
Matz, J. et al. (2014). The Short-term Impact of Price Shocks on Food Security – Evidence from Urban and Rural
Ethiopia. Conference paper for the International Economics Association World Congress, Jordan, June 610, 2014.
Maunder, N. (2013). What is known about the impact of emergency and stabilization reserves on resilient food
systems? Overseas Development Institute (ODI), London.
McKay, A. & Tarp, F.(2014). Distributional Impacts of the 2008 Global Food Price Spike in Vietnam. Working Paper
2014/030, World Institute for Development Economic Research(UNU WIDER), Helsinki.
Miranda, M. J. & Helmberger, P. G. (1988). “The Effects of Commodity Price Stabilization Programs.” The American
Economic Review, 78(1), 46–58.
Moschini, G. & Hennessy, D.A. (2001). “Uncertainty, Risk Aversion, and Risk Management for Agricultural
Producers.” In: Gardner, B.L. & Rausser, G.C., eds., Handbook of Agricultural Economics. Amsterdam:
Elsevier, 88–153.
Mu, X. (2007). “Weather, Storage, and Natural Gas Price Dynamics: Fundamentals and Volatility.” Energy
Economics, 29(1), 46–63.
Murdoch, J. C. & Sandler, T. (2002): “Economic Growth, Civil Wars, and Spatial Spillovers.” The Journal of Conflict
Resolution, Vol. 46, No. 1, 91-110.
Myers, R. J. (2006). “On the Costs of Food Price Fluctuations in Low-Income Countries.” Food Policy 31, 288-301.
Newbery, D. M. G. & Stiglitz, J. E. (1981). The Theory of Commodity Price Stabilization: A Study in the Economics of
Risk (p. xv, 462 p.). Oxford: Clarendon Press.
OECD (2008). “Rising Food Prices: Causes and Consequences”. OECD Publishing, Paris.
Persson, K. G. (1999). Grain Markets in Europe, 1500–1900: Integration and Deregulation. Cambridge University
Press. Cambridge, UK.
36
Pindyck, R.S. (1988). “Irreversible Investment, Capacity Choice, and the Value of the Firm.” The American Economic
Review, Vol. 78, No. 5, 969-985.
Porteous, O.C. (2012). Empirical Effects of Short-Term Export Bans: The Case of African Maize. Working Paper,
Dept of Agricultural Economics, University of California, Berkeley.
Ramey, G. & Ramey V. A. (1995). “Cross-Country Evidence on the Link Between Volatility and Growth.” The
American Economic Review, Vol. 85, No. 5, 1138-1151.
Rashid, S. & Lemma, S. (2011). Strategic Grain Reserves in Ethiopia. Institutional Design and Operational
Performance. Discussion Paper No. 01054 , International Food Policy Research Institute (IFPRI),
Washington, D.C.
Robles, M. et al. (2010). “Understanding the Impact of High Food Prices in Latin America.” Economia, 117-164.
Sabates-Wheeler, R. & Devereux, S. (2010). “Cash Transfers and High Food Prices: Explaining Outcomes on
Ethiopia’s Productive Safety Net Programme.” Food Policy, 35(4), 274–285.
Sahoo P. & Kumar R. (2008). Impact of Proposed Commodity Transaction Tax on Futures Trading in India, Working
Paper 216, Indian Council for Research on International Economic Relations.
Salant, S. W. (1983). “The Vulnerability of Price Stabilization Schemes to Speculative Attack.” The Journal of
Political Economy, Vol. 91, No. 1, 1-38.
Serra, T. (2011). “Volatility Spillovers Between Food and Energy Markets: A Semiparametric Approach.” Energy
Economics, 33(6), 1155-1164.
Serra, T. & Gil, J. M. (2013). “Price Volatility in Food Markets: Can Stock Building Mitigate Price Fluctuations?”
European Review of Agricultural Economics, 40, 507–528.
Soros, G. (2008). Testimony before the U.S. House of Representatives Committee on Oversight and Government
th
Reform. November, 13 , Washington DC.
Staritz, C. & Küblböck, K. (2013). Re-Regulation of Commodity Derivative Markets – Critical Assessment of Current
Reform Proposals in the EU and the US. Working Paper (No. 45), Austrian Research Foundation for
International Development (ÖFSE), Vienna.
Stiglitz, J. E. (1989). “Using Tax Policy to Curb Speculative Short-Term Trading.” Journal of Financial Services
Research, 3, 101-115.
Strauss, J. & Thomas, D. (1998). “Health, Nutrition, and Economic Development.” Journal of Economic Literature,
766-817.
Summers, L.H. & Summers, V. P. (1989). “When Financial Markets Work Too Well: A Cautious Case For a Securities
Tax.” Journal of Financial Services Research, Vol. 3, 275.
SUN (n.d.). Scaling Up Nutrition. Retrieved August 12, 2014, from
http://www.thousanddays.org/partnerships/scaling-up-nutrition-info/.
Tadesse, G. & Guttormsen, A. G. (2011). “The Behavior of Commodity Prices in Ethiopia.” Agricultural Economics,
42(1), 87–97.
Tadesse, G. et al. (2014). “Drivers and Triggers of International Food Price Spikes and Volatility.” Food Policy, 47,
117–128.
Timmer, C. P. (2000). “The Macro Dimensions of Food Security: Economic Growth, Equitable Distribution, and Food
Price Stability.” Food Policy, 25, 283–295.
Timmer, C. P. (2011). Managing Food Price Volatility: Approaches at the Global, National, and Household Levels.
st
Standford Symposium Series on Global Food Policy and Food Security in the 21 Century, Standford
University.
37
Torlesse, H. et al. (2003). “Association of Household Rice Expenditure with Child Nutritional Status Indicates a Role
for Macroeconomic Food Policy in Combating Malnutrition.” The Journal of Nutrition, 133, 1320-1325.
Turnovsky, S. J. et al. (1980). “Consumer's Surplus, Price Instability, and Consumer Welfare.” Econometrica: Journal
of the Econometric Society, 135-152.
UNCTAD (2012). Don’t Blame the Physical Markets: Financialization is the Root Cause of Oil and Commodity Price
Volatility. Policy Brief No. 25, United Nations Conference on Trade and Development (UNCTAD), Geneva.
van der Ploeg, F. & Poelhekke, S. (2007). ‘‘Volatility, Financial Development and the Natural Resource Curse’’. EUI
Working Paper ECO 2007/36, European University Institute, Firenze.
von Braun, J. & Torero, M. (2009). Implementing Physical and Virtual Food Reserves to Protect the Poor and
Prevent Market Failure. IFPRI Poliy Brief, No. 10., International Food Policy Research Institute, Washington
DC.
von Braun, J. & Tadesse, G. (2012). “Food Security, Commodity Price Volatility and the Poor”. In: Aoki, M., Timur
Kuran, T. & Roland, G., eds., Institutions and Comparative Economic Development: Palgrave Macmillan
Publ. IAE Conference Volume 2012.
von Braun, J., Algieri, B. and M. Kalkuhl (2014): “World Food System Disruptions in the Early 2000s: Causes,
Impacts, Cures.” World Food Policy, 1(1), 1-22.
Vu, L. & Glewwe, P. (2011). “Impacts of Rising Food Prices on Poverty and Welfare in Vietnam.” Journal of
Agricultural and Resource Economics, 36(1), 14-27.
Wang, X. & Taniguchi, K. (2003). Does Better Nutrition Enhance Economic Growth? The Economic Cost of Hunger.
FAO, Agricultural and Development Economics Division.
WB (2012). Safety Nets Work: During Crisis and Prosperity. Attachement for the April 21, 2012, Development
Committee Meeting, Washington, DC.
Weil, D. N. (2007). “Accounting for the Effect of Health on Economic Growth.” The Quarterly Journal of Economics,
122(3), 1265-1306.
Wolf, H. (2005). “Volatility: Definitions and Consequences”. In: Aizenman, J. & Pinto, B., eds., Managing Economic
Volatility and Crises: Cambridge University Press. Cambridge, UK.
Working, H. (1949). “The Theory of Price of Storage.” The American Economic Review, 39(6), 1254–1262.
Wright, B. D. (2012). “International Grain Reserves and Other Instruments to Address Volatility in Grain Markets.”
The World Bank Research Observer, 27(2), 222-260.
38
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
This project is funded by the European Union
under the 7th Research Framework Programme
(theme SSH) Grant agreement no. 290693
T
F
E
I
+31 (0) 70 3358370
+31 (0) 70 3358196
foodsecure@wur.nl
www.foodscecure.eu