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The financialization of food and the 2008-2011 food price spikes

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The treatment of recent global food price volatility in the neoclassical academic literature is problematic in its limited conceptual and empirical scope. This study presents new empirical data and analysis linking financial speculation by index swap dealers (‘index funds’) with US and global food price volatility. Marxian circuits of capital are used to illustrate the connection between index funds and food consumers. The findings show that financial speculation by index swap dealers and hedge funds significantly contributed to the price volatility of food commodities between June 2006 and December 2014. The key conceptual contribution is that it articulates geographical economic interpretation of food price volatility and financial speculation in a literature awash with neoclassical economic analyses.
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Article
The financialization of
food and the 2008–2011
food price spikes
Sean Field
Queen’s University Kingston, Canada
Abstract
The treatment of recent global food price volatility in the neoclassical academic literature is
problematic in its limited conceptual and empirical scope. This study presents new empirical
data and analysis linking financial speculation by index swap dealers (‘index funds’) with US and
global food price volatility. Marxian circuits of capital are used to illustrate the connection
between index funds and food consumers. The findings show that financial speculation by
index swap dealers and hedge funds significantly contributed to the price volatility of food
commodities between June 2006 and December 2014. The key conceptual contribution is that
it articulates geographical economic interpretation of food price volatility and financial speculation
in a literature awash with neoclassical economic analyses.
Keywords
Circuits of capital, food price spikes, financial speculation, index funds, hedge funds
There is a growing consensus amongst researchers that financial speculation and global food
price volatility are linked, but no consensus on whether this link is causal (Akram-Lodhi,
2013; Clapp, 2012). Some common causal explanations of global food price volatility include
demand factors such as population increases, increases in per capita food consumption, and
expanded biofuel production (Gilbert, 2010; Lagi et al., 2011a). Many explanations
emphasize supply side factors such as poor crop yields and volatile energy prices that
raise the cost of production (Irwin et al., 2009, 2011; Irwin and Sanders, 2010, 2011).
Financial speculation remains the most contentious causal explanation of food price
volatility, because it is empirically difficult to connect speculation with price volatility and
because it is politically contentious to connect financial speculation in the global north with
food price volatility in the global south. A causal connection means that financial capitalists
helped spark the food crises in countries across the Middle East and North Africa that led to
widespread rioting in 2011. The estimated, although under-reported, death-tolls associated
with the 2011 food riots were large. Over 10,000 people are reported to have died in Libya;
over 900 people were reported to have died in Syria; over 800 people were reported to have
Corresponding author:
Sean Field, Department of Geography, Queen’s University Kingston, Ontario K7L 3N6, Canada.
Email: sean.field@queensu.ca
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DOI: 10.1177/0308518X16658476
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died in Egypt; and over 300 people are estimated to have died in each of Tunisia and Yemen
as a result of the acute rise in food prices in 2011 (Lagi et al., 2011b). Food price volatility
since the mid-2000s has also been shown to have an adverse affect on food security in Mexico
and Afghanistan (D’Souza and Jolliffe, 2012; Valero-Gila and Valerob, 2008). Making
matters worse is the compounding factor that when food prices go up food aid donations
for the poorest and hunger-stricken regions of the world fall (Clapp, 2012: 35).
The mainstream economic literature’s neoclassical analyses of the link between financial
speculation and increased global food price volatility is problematic. Neoclassical analyses
have done little to demystify the complexity that shrouds commodity speculation as they rely
conceptually on the black box of ‘the market’ (Irwin et al., 2009, 2011; Irwin and Sanders,
2010, 2011). The empirical analyses in the literature also rely on relatively short time series
data that is highly aggregated (especially data older than June 2006). This study fills this gap
in the academic literature in three ways. First, this article presents new evidence on the
statistical link between financial speculation and commodity price volatility. This analysis
uses more specific, and longer, time series data from the CFTC than previous studies on this
topic. Second, it delves deeper into the relationship between financial speculation and
commodity price volatility using in-depth interview data from key informants. Third, this
study provides an alternative approach to understanding the link between financial
speculation and food price volatility using Marxian circuits of capital to illustrate the
connection between financial speculators and food consumers. The findings show that
financial speculation, significantly contributed to the price volatility of several CBOT
commodities between June 2006 and December 2014.
Financial speculation and global food price volatility
Commodity index swaps experienced a surge in popularity amongst large institutional
investors in the mid-2000s around the time the US Mortgage Crisis started to emerge
(CFTC, 2015a, 2015b, 2015c; Clapp, 2014; Lagi et al., 2011a, 2011b; USS, 2009). Index
swaps (‘‘funds’’) are like mutual funds marketed to investors like pension funds that want to
invest in a diversified portfolio of commodities. When a pension fund buys a commodity
index swap, they buy a contract from a index swap dealer (like Goldman Sachs) who, in
exchange, breaks up the investors money into smaller pots and invests that money in the
series of commodities specified in the swap contract (Gorton and Rouwenhorst, 2006; Greer,
2000). Although commodity speculation is an old practice, this speculation is historically
significant because of (a) the unprecedented quantity of money used to speculatively
purchase futures and (b) the magnifying effect of the practice of buying using margin
accounts. One of the main differences between physical commodity trading and futures
trading is that futures trading only requires a fraction of the money (a margin) to buy
and sell commodities. By using margins, buyers and sellers can make a deposit, usually
equal to about 10% of the total value of the futures contracts, to a margin account with
the commodity exchange house to secure ownership of their contracts. The requirement to
only have to pay margin leaves 90% of the investment money idle, so that it can be invested
in T-Bills for example (Gorton and Rouwenhorst, 2006; Greer, 2000). Till and Gunzberg
(2005: 6) make a similar argument for hedge funds, arguing that commodity futures
speculation can yield ‘‘investors earn an ‘insurance premium’ for being systematically long
commodity futures contracts.’’
Commodity buyers who use futures markets to secure the price of commodities
they intend to use (e.g. refining, baking, milling, etc.) are referred to as hedgers because
they are trying to hedge the risk of unexpected price increases. Commodity producers that
2Environment and Planning A 0(0)
use futures markets to secure the price of commodities they intend to sell are also referred to
as hedgers because they are trying to hedge the risk of unexpected price decreases.
Commodity speculators use commodity futures markets to buy and sell commodity
futures contracts for the purpose of making profit and neither produce nor use (i.e. have
an underlying interest) the commodity in question. The three main strategies for making
profit using commodity futures contracts are: (a) buying futures’ contracts at a low price and
selling them at a higher price in the future (speculating ‘‘long’’), (b) selling futures’ contracts
at a high price then buying them back at lower price in the future (speculating ‘‘short’’), and
(c) buying and selling contracts for the same commodity to capitalize on discrepancies in
prices across expiry periods (called arbitrage, Gorton and Rouwenhorst, 2006; Greer, 2000).
US agricultural commodity futures markets (located in Chicago, Kansas City, and
Minneapolis) are central to determining global agrifood prices (i.e. ‘benchmarks’), and
index funds are most actively traded in these markets (Murphy et al., 2012; USS, 2009).
The US agency that regulates futures speculation, the CFTC, sets limits on the number of
futures contracts speculators can own. According to the U.S. Senate Investigation, however,
between 2005 and 2009, the CFTC issued four speculative exemptions to index swap dealers.
These CFTC exemptions allowed these funds to hold up to ‘‘10,000, 17,500, 26,000, and
53,000 wheat futures contracts, respectively’’ (USS, 2009: 105). Since 2006, the CFTC has
also permitted ‘‘six index traders to hold a total of up to almost 130,000 wheat futures
contracts in any single month and in all months combined’’ (USS, 2009: 105).
Speculation in the futures contracts for food commodities traded in US commodity
futures markets offered an under-tapped source of speculative profit in the mid-2000s that
was exploited by index swap dealers (Lagi et al., 2011a, 2011b). U.S. and global commodity
prices for wheat corn and soybeans began to rise when index swap dealers bought
commodity futures’ contracts using investor capital and bid-up CBOT futures’ contract
prices. They bid up prices by buying up all the available futures contracts on price A,
then all the contracts being offered at (the slightly higher) price B, then all the contracts
being offered as price C, etc., until the investment money was exhausted.
The role of speculation in food prices has a global effect because US futures price
volatility affects global food price volatility. The two are linked because the US is a net
exporter of agricultural commodities and because international markets look to US
commodity futures markets when setting prices (i.e. US prices are ‘benchmark’ prices;
Murphy et al., 2012; USS, 2009). Table 1 lists the largest index swap dealer firms and the
breakdown of their main commodity investment indexes. While commodity index swaps
that used these indexes allocate less than one-third of investors’ money into agricultural
commodity futures, for the US commodity exchange houses in Chicago, Kansas City,
and Minneapolis (CBOT, KCBT, and MGEX), this influx of money from index
speculators is large (CFTC, 2015a; USS, 2009). CFTC Commissioner Gary Gensler
testified to the US Senate in 2009, for example, that index speculators flooded the
relatively small but important Chicago market for soft red wheat around the 2008
food price spike:
The Chicago contract is really a very small market, about $1.5 billion a year annual production,
real farmers producing wheat. It is about $1.5 billion. It is only 2% of the global production in
wheat. However, this is a global contract that many investors are looking at and are looking to
try to get exposure, to use a financial word, ‘‘exposure’’ to this asset class. But it is real wheat. It
is real farmers. It is only $1.5 billion of production. So the influx of index investors over this
period of time has effectively taken about half of the long position. About half of the contracts
are owned by effectively index investors. That is equivalent to about 3 years of annual
Field 3
production. So, on the shoulders of a very hearty Midwestern crop is placed the whole global
financial markets trying to get exposure to wheat. (USS, 2009: 17)
Investment reports released by Citigroup and Goldman Sachs in the spring of 2008 confirm
the relationship between flows of investment money and rising commodity prices. Citigroup
reported ‘‘many commodity prices hit new highs in recent weeks, driven largely by
investment inflow’’ (quoting Citigroup, Ainger, 2008; Masters, 2008), while Goldman
Sachs reported ‘‘without question, increased fund flow into commodities has boosted
prices’’ (quoting Goldman Sachs, Ainger, 2008; Masters, 2008).
The work of Gorton and Rouwenhorst (2006) and Irwin et al. (2009, 2011) has shielded
swap dealers and speculators from the criticism that financial speculation has material
impact on people’s lives. Gorton and Rouwenhorst (2006) popularized the view that
commodity index swaps are appropriate for institutional investors interested ‘‘hedging
inflation’’ and gaining ‘‘commodity exposure.’’ Irwin et al. (2009, 2011) and Irwin and
Sanders (2010) reassured investors and regulators that financial speculation using
commodity futures’ markets is unrelated to commodity price volatility.
In actuality, refiners, bakers, and wholesalers passed price increases onto consumers
(Murphy et al., 2012; USS, 2009). In the global north, where median household incomes
are relatively high compared with the global south, the impact was hardly noticed (Clapp,
2009, 2012). In some parts of the global south, where median household incomes are lower
and considerably more household income is spent on food, the impact sparked food riots
(Clapp and Cohen, 2009; Lagi et al., 2011b; World Bank, 2015).
Literature review
The economic literature on the causal relationship between financial speculation
and increased global food price volatility can be divided between neoclassical and
Table 1. Investment weights of large investible index funds by sector.
Index
S&P Goldman
Sachs Commodity
Index
JPMorgan
Commodity
Curve Index
Dow Jones
(S&P) AIG
Commodity
Index
Dow Jones
(S&P) UBS
Commodity
Index
Total commodities
included in index
24 33 19 22
Energy 72% 46% 33% 32%
Industrial metals 7% 25% 18% 17%
Agriculture, including: 12% 18% 30% 31%
Wheat
Corn
Oats
Soybeans and
soybean meal
Soybean oil
Rice
Livestock
Precious metals 2% 9% 8% 14%
Livestock 6% 3% 11% 5%
Source: Gordon (2006); Shemilt and Unsal (2004).
4Environment and Planning A 0(0)
non-neoclassical perspectives – see online appendix for tabular summary. Neoclassical
economic perspectives exemplified (and primarily authored) by Irwin et al. (2009) account
for approximately half of this literature. This cluster of literature argues that there is no
causal or empirical connection between financial speculation and global food price volatility
(Irwin et al., 2009, 2011; Irwin and Saunders, 2010).
Irwin et al.’s (2009, 2011) influential neoclassical methodology is theoretically and
empirically problematic. Theoretically, it depends on the neoclassical concept of the all-
efficient market as conceptual justification for their claim that market prices reflect supply
and demand conditions. They argue
[L]onger–term equilibrium prices are ultimately determined in cash markets where buying and
selling of physical commodities must reflect fundamental supply and demand forces. (Irwin et al.,
2009: 379)
The authors conclude that limiting the participation of large financial speculators such as
index swap dealers ‘‘would rob the markets of an important source of liquidity and risk–
bearing capacity’’ (Irwin et al., 2009: 389). Irwin et al. (2009) are saying not only that
commodity index speculation is unrelated to commodity price volatility but also that they
believe limiting commodity speculation by index swap dealers would be bad for the entire US
agrifood regime. They repeat this conclusion in their highly circulated OECD report:
[L]imiting the participation of index fund investors could unintentionally deprive commodity
futures markets of an important source of liquidity and risk-absorption capacity at times when
both are in high demand. (Irwin and Sanders, 2010: 1)
Using the neoclassical concept of ‘the market’ as the price determination mechanism
distracts attention away from the underlying buyers and sellers that actually determine
prices. The market pays no attention on who buyers and sellers are, or how much
bargaining power they have. Ideological constructions like the market also facilitate the
process of obscuration and fetishism by making it appear as though prices, production,
and exchange are determined by an external agent or force rather than being socially
determined (Cetina, 2006; McFall and Dodsworth, 2009). This abstraction obscures the
fact that capitalist prices, production, and exchange are socially determined by human
actors.
Empirically, the majority of this literature relies on relatively short and aggregated time
series data from the CFTC. It also relies on a few econometric techniques that are sensitive
to misspecification. Frenk (2010) argues that the one-week lag periods (the time separating
trades and price movements) adopted by Irwin and Saunders (2010) in their empirical model
were inappropriately short. Other authors, meanwhile, have challenged Irwin et al.’s (2009,
2011) findings using more robust empirical frameworks. Gilbert (2009, 2010), Mayer (2012),
Lagi et al. (2011a, 2011b, 2012), and Tadesse et al. (2014) all find that a statistically
significant link exists but do not challenge Irwin et al.’s (2009, 2011) conceptual
framework. This article fills a gap in the literature by adding to the growing pool of
empirical findings linking financial speculation to food price volatility and by formulating
an alternative conceptual framework for understanding the link.
Theoretical framework
One way to illustrate the link between financial speculators and food consumers is by using
Marxian circuits of capital. Circuits make it possible to visualize the connections
between actors engaged in exchange. Capital circuits similarly convey the idea of
Field 5
exchange between actors, but take a wider view by going beyond the commodity supply
chain to include inputs, production, and consumption (see Figure 1).
The circuits illustrated above are a partial representation of the complex web of
commodities flowing between circuits of industrial, commercial, and finance capital.
A diagram depicting all commodity flows would be an indiscernible mess of crisscrossing
lines connecting inputs, output, and capital circuits. Index swap dealers represented by the
financial circuit of capital fit into global agrifood supply chains represented by the
agricultural circuit of capital through commodity futures markets. Labor, finance, and
agricultural inputs are the primary inputs that feed U.S. wheat, corn, and soybean
production, which is represented by the industrial circuit of capital located in the middle
of Figure 1. Once the grains are produced, they enter the physical wholesale market where
they are sold to end-users or stored for future consumption (see online appendix for a
breakdown of these circuits).
Futures markets are central to index swap strategies because they allow swap dealers to
use the majority of investor’s money to buy Treasury bills (T-bills) while using the remainder
of the money to speculate on commodities using only the money required cover margin calls.
A margin call is a request from an exchange house to increase the amount of money in a
margin account (or else have your trades liquidated by the exchange house). A margin call
occurs when the amount of money in a margin account falls below the threshold percentage
set by the exchange house to secure the contracts. The actual cost of financial speculation is
passed on to bakers, millers, and refiners that are wholesale consumers of raw grain.
I distinguish between two groups of workers in Figure 1, workers with savings and
workers without savings. This distinction, while crude, is essential for including the main
source of index swap money, pension funds, and institutional investors in the global north.
Figure 1 situates Desai (1979) style micro circuits within Harvey (1989, [1982] 2006)-style
macro circuits of industrial agriculture and finance capital. Harvey ([1982] 2006, 1989) uses
circuits to describe ‘‘the overall structure of relations constituting the circulation of capital’’
in contemporary capitalist economies as organized being organized into primary, secondary,
and tertiary circuits. Aalbers (2008) incorporates financial markets into Harvey’s circuits by
adding a fourth ‘quaternary’ circuit representing financial (credit) markets. Where Harvey’s
(1989, [1982] 2006) and Aalbers’ (2008) circuits emphasis the macroeconomy, Desai’s (1979)
circuits emphasize individual and firm level processes starting with money (M).
For Desai (1979), the money is the most important because production is undertaken for
profit not for use, and starting with the money makes the role of class relations in Marxian
value theory clear. Money (M) appears seven times in the circuits framework. Money enters
the circuits at the beginning of (a) the agricultural finance circuit, (b) the agricultural input
production circuit, (c) the agricultural production circuit, (d) physical wholesale circuit,
(e) the baker-miller-refiner circuit, (f) the retailing circuit, and (g) the index swap dealer
circuit. In the index swap dealer circuit, money (M) from investors is split between
commodity futures exchange and Treasury bond exchange by index swap dealers. Where
money enters the circuits of capital in these seven places, it denotes the beginning of one
cycle in the capitalist process, which ends with more money (M
1
). If the capitalist process
generates a loss instead of a profit M
1
is negative. Beginning the commodity cycle/circuit
with money (M) makes the role of class relations in Marxian value theory clear, as Desai
(1979) notes, and indicates that the people who control money, not the market, determine
prices. Each segment in the circuit obscures the segments and circuits that preceded it (Desai,
1979). The transformation of money (M) into commodities (C) and credit (Cr) and then back
into money (M
1
¼MþM) estranges people from other people connected by exchange. The
transformation estranges people from the material process commodity production
6Environment and Planning A 0(0)
AGRICULTURAL
FINANCE
M – M + i
AGRICULTURAL INPUT
PRODUCTION
M - C<
LMP
…P… C
1inputs
– M
1
LEGEND:
C = Commodity inputs
C
1
= Commodity outputs
i = Interest
M = Money
M
1
= Money gained from sale of C
1
P = Production
MP = Means of Production (non-labour inputs)
= money flow
= futures contracts flow
= text box information
AGRICULTURAL PRODUCTION
M - C<
LMP
…P… C
1Grain
– M
1
LABOUR/WORKERS w/o
SAVINGS
C
labour
– M
Wages
- C
Consumption
(grain)
PHYSICAL (WHOLESALE )
MARKETS
M – C
Grain
– M
1
FUTURES MARKETS
M – C
(1 Year futures contract)
– M
1
M – C
(7 Month futures contract)
– M
1
M – C
(6 Month futures contract)
– M
1
M – C
(3 Month futures contract)
– M
1
BAKERS, MILLERS, REFINERS
M - C<
LMP
…P… C
1Bead
– M
1
LABOUR/WORKERS with
PENSION SAVINGS invested in
INDEX FUNDS/SWAPS
C
labour
– M
wages
–C
consumption
M
Savings
(food/bread/flour)
(loaned money)
(labour & wages)
(non-labour inputs)
INDEX SWAPS DEALERS
management fee + performance fee
(10%*M) + ( 90%*M)
(futures contract expiry &
current-future price convergence)
(futures contracts created with margin
deposit account requirement of 10%)
TREASURY/
GOVERNMENT
BONDS
M –M + I
((M
1
) +(90%*M + i))
RETAIL MARKETS
M – C
Bread
– M
1
(food
/
bre
a
d
/
flour)
INTEGRATED
COMMODITY CIRCUITS
Grain commodities
Physical commodity storage
Figure 1. Partial circuit of index swaps and U.S. wheat, corn, and soybean commodities.
Source: author.
Field 7
(C 5L
MP ...P...C
1
) and consumption (C
consumption
) and invites commodity fetishism (Desai,
1979). Harvey’s (1989, [1982] 2006) circuits and Desai’s (1979) circuits should be read as two
ways describing the same process from slightly different vantage points.
The intersection of quadiary (financial) and primary (agricultural) circuits is
representative of a spatial fix to a crisis in the quadiary circuit using the primary circuit
of capital. The advantage of using commodity swaps is that they provide a temporal and
credit fix to crisis in capital accumulation using finance. The credit fix comes from the fact
that the margin accounts allow speculators to multiply the purchasing power of their money.
The temporal fix is that futures speculation removes the temporal-spatial barriers to
speculation associated with the time it takes to produce and get agricultural commodities
to the point of exchange (and speculation). The profit extracted by financial speculators from
commodity hedgers through commodity futures’ exchange and the cost of speculative price
volatility incurred by merchants, millers, and bakers (hedgers) are passed on to food
consumers in the form of higher food prices.
Commodity future markets effect current commodity prices because current and future
commodity prices are linked. Storage, Working (1949) shows, links prices across time
periods by carrying physical commodities from one time period to another. Contract
arbitrage by financial speculators also link current and future commodity prices by
allowing speculators to exploit deviations in a commodity price across contract time
periods, discounted for the expected cost of storage (Working, 1949). Because physical
commodities and commodity futures contracts can be traded simultaneously and because
deviations in these prices can be exploited using contract arbitrage, commodity prices across
contract time-periods are linked. The socially constructed linkage between current and
future commodity prices (through storage and contract arbitrage) means that
speculatively hoarding futures’ contracts has a similar impact on current prices as
speculatively hoarding physical commodities. This means that ‘synthetically’ hoarding
food commodities using commodity futures contracts has a similar impact on food prices
as hoarding physical food commodities.
Empirical framework
This case study focuses on the incursion of US commodity index funds into US agricultural
commodity futures markets. Quantitative data were sourced from the U.S. Commodities
Futures Trading Commission (CFTC, 2015a, 2015b); the United Nations Food and
Agricultural Organization (FAO, 2015); the United States Department of Agriculture
(USDA, 2015a, 2015b, 2015c); and the Agriculture and Horticulture Development Board
(AHDB, 2015) Market Data Centre. Both the commitment of traders (COT) data and index
swap report data from the CFTC were used in the analysis – see empirical appendix for more
information. Ordinary least squares (OLS) and quantile regression were used for the
quantitative estimations. OLS regression measures the mean change in dependent variable
caused by a change in the independent variable. Quantile regression measures the mode
(rather than the mean) and its estimates are less susceptible to be skewed by outliers. The
combination of results provide a more empirically robust picture into the relationship
between speculation and prices than either type of regression model on their alone. The
models with two independent variables (index dealers and hedge funds) were found to be
unaffected by collinearity between the independent variables, measured by Variance
Inflation Factor scores. Pearson correlation tests were run between the regression
residuals and the independent variables to estimate the presence of endogeniety (missing
variable bias). Relevant coefficients are reported in Tables 3 and 4 alongside the regression
8Environment and Planning A 0(0)
models results, models that were found to suffer from endogeniety were dropped. Table 2
reports the descriptive statistics for the variables included in the analysis.
Interviews with 28 key informants were also conducted. Key informants were drawn from
across the United States, Canada, and Europe. Key informants were selected based on their
insider and expert knowledge about U.S. commodity futures trading and commodity index
speculation. Informants included individuals from a U.S. major commodity exchange house,
the Minneapolis Grain Exchange (MGEX); and from firms central to the production of
commodity index swaps, such as Standard and Poor’s Dow Jones (S&PDJ) and Goldman
Sachs. Informants from MGEX, S&PDJ, and Goldman Sachs were drawn from the senior
and executive management teams. Interviews with two large California pension fund
investors were also conducted. Interviews were conducted between January and October
2012.
Empirical results
Figure 2 plots the month-to-month change in the FAO’s food price index and illustrates
global food price volatility since 1990. It shows that between 1990 and 2007, the month-to-
month change in the FAO’s food price index varied by less than three points on average. It
also shows that, while the 1995–1996 US Drought had an observable impact on global food
prices (Figure 2), month-to-month changes in prices during the 1995–1996 US Drought
remained well within historical averages. The 1995–1996 US Midwestern drought is a
good example of how a US drought impacted US crop production and US commodity
prices (Light and Shevlin, 1996). The drought caused a 26% decline in US grain
production and a 75% reduction in US grain stocks, resulting in a grain price shock
(Light and Shevlin, 1996). Since 2008, by contrast, there have been five 10-point month-
to-month increases in the FAO’s food price index and no corresponding shifts in (material)
commodity supply and demand to account for these fluctuations (FAO, 2015). The FAO’s
(2015) global food price index is highly and significantly correlated (p <0.01), however, with
the nearby futures prices CBOT wheat, corn, soybeans, soybean meal, and soybean oil. This
correlation has to do with the US’s dominant position in setting global food prices.
The results of the regression analysis indicate that the speculative activity of index swap
dealers and hedge funds is statistically linked to the price and price volatility of US wheat,
corn, and soybean futures. Table 3 presents the results of the regression analysis. Table 4
presents the results on the impact of speculation on price volatility measured by month-to-
month changes in price. The t-observed statistics and R
2
estimators in Table 3 show a strong
causal association between the speculative activity of index swap dealers and the price of
CBOT Corn and CBOT Soybean Oil prices. The speculative activity of hedge funds was also
found to significantly affect the price CBOT Corn, KCBT Wheat, and CBOT Soybean Oil
prices. The negative and positive signs on the t-observed statistics indicate whether
speculators contributed to food price volatility by bidding prices up or down, on average.
Interestingly, the results show hedge funds bid prices up while index swap dealers bid prices
down, both contributing to price volatility.
Table 4 provides a clearer look at the relationship between financial speculation and food
price volatility by measuring how swap dealers and hedge funds affect month-to-month
changes in commodity prices. The results show index funds significantly contributed to
volatility in the prices of CBOT Wheat, CBOT Corn, CBOT Soybean, and CBOT
Soybean Oil futures. The results in Table 4 similarly show that the speculative activity of
hedge funds is also important, accounting for 9–18% of the price volatility in nearby CBOT
Corn futures and 15–18% of the price volatility in nearby CBOT Soybean Oil futures.
Field 9
Table 2. Descriptive statistics for quantitative variables.
Variable Obs Mean Std. dev. Min Max
CBOT wheat price 103 645 148 374 1096
KCBT wheat price 89 694 163 464 1148
MGEX wheat price 103 745 216 451 1773
CBOT corn price 103 494 150 230 804
CBOT soybean price 103 1160 277 542 1695
CBOT soybean oil price 103 43 10 24 64
CBOT soybean meal price 103 342 87 160 531
FAO food price index 103 191 31 125 240
FAO cereal price index 103 199 41 113 268
FAO oil price index 103 195 46 108 287
Index swap dealers CBOT wheat (swap report) 65 179,508 29,082 132,000 232,000
Index swap dealers KCBT wheat (swap report) 65 39,246 11,520 18,000 60,000
Index swap dealers CBOT corn (swap report) 65 392,354 48,773 227,000 466,000
Index swap dealers CBOT soybeans
(swap report)
65 167,154 23,436 97,000 197,000
Index swap dealers CBOT wheat (COT report) 103 139,069 30,567 79,118 194,073
Index swap dealers KCBT wheat (COT report) 103 27,395 8309 10,448 46,382
Index swap dealers MGEX wheat (COT report) 103 1700 1526 (125) 4684
Index swap dealers CBOT corn (COT report) 103 283,766 66,738 164,971 405,614
Index swap dealers CBOT soybeans
(COT report)
103 106,385 30,295 49,661 178,819
Index swap dealers CBOT soybean oil
(COT report)
103 70,849 15,362 32,935 101,316
Index swap dealers CBOT soybean meal
(COT Report)
103 17,581 16,258 (6082) 54,864
Hedge funds CBOT wheat (COT report) 103 1399 33,347 (67,536) 70,624
Hedge funds KCBT wheat (COT report) 103 21,846 15,501 (5766) 57,972
Hedge funds MGEX wheat (COT report) 103 7240 4611 (3397) 17,488
Hedge funds CBOT corn (COT report) 103 154,221 107,547 (90,999) 386,489
Hedge funds CBOT soybeans (COT report) 103 89,581 57,466 (39,576) 228,041
Hedge funds CBOT soybean oil (COT report) 103 14,937 32,526 (56,139) 78,795
Hedge funds CBOT soybean meal (COT report) 103 35,627 26,040 (46,411) 89,979
Producers & Merchants CBOT wheat
(COT report)
103 (110,388) 41,847 (195,536) (5704)
Producers & Merchants KCBT wheat
(COT report)
103 (47,572) 20,591 (102,864) (11,306)
Producers & Merchants MGEX wheat
(COT report)
103 (10,342) 5973 (23,266) 1610
Producers & Merchants CBOT corn
(COT report)
103 (390,491) 158,126 (731,192) (45,357)
Producers & Merchants CBOT soybeans
(COT report)
103 (176,720) 84,025 (324,870) 51,520
Producers & Merchants CBOT soybean oil
(COT report)
103 (96,419) 46,381 (183,362) (8164)
Producers & Merchants CBOT soybean meal
(COT report)
103 (71,280) 40,095 (135,460) 29,928
Source: CFTC (2015a, 2015b, 2015c); FAO (2015); AHDB (2015).
10 Environment and Planning A 0(0)
Swap dealer and hedge fund speculation combined account for 16–26% of the month-to-
month price volatility in nearby CBOT Corn, on average, between June 2006 and
December 2014.The significance of this finding is that the price of CBOT Corn affects corn
prices the world over because the US is a major producer and exporter of corn (FAO, 2015;
Murphy et al., 2012; Weis, 2007). The results also indicate that swap dealer and
hedge fund speculation combined accounts for 18–22% of the price volatility in
CBOT Soybean Oil, on average over the same time period. The results of the regression
-20
-17.5
-15
-12.5
-10
-7.5
-5
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0
2.5
5
7.5
10
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15
1990
1991
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1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Month-to-month change in the
FAO Food Price Index
10+ point
increase in the
FAO Food Price
Index
1996 U.S. Drought
10+
0+
0
0
0
0
0
+
0
0
0
0
0
0
0
0
0
0
0
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+
0+
0
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+
+
i
nc
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FAO
D
r
oug
ht
Figure 2. Month-to-month change in the FAO food price index 1990–2015.
Source: FAOSTAT (2015; available at: http://faostat.fao.org/site/609/default.aspx#ancor).
Table 3. OLS and quantile regression results.
Dependent variable June
2006–December 2014
Independent
variable
Average nominal monthly prices
OLS regression Quantile regression
tR
2
Endogeniety? t R
2
Endogeniety?
CBOT wheat price Index swap dealers
(swap report)
1.91
a
0.06 No 0.91 0.02 No
CBOT corn price Index swap dealers 5.50
b
0.23 No 3.77
b
0.14 No
MGEX wheat price Hedge funds 1.06 0.01 No 0.37 0.01 No
CBOT corn price 6.50
b
0.29 No 2.81
b
0.16 No
CBOT soybean
oil price
No 0.36 0.00 No
KCBT wheat price Index swap dealers 0.81 0.26 No 2.72
b
0.20 No
Hedge funds 5.49
b
No 7.09
b
No
CBOT corn price Index swap dealers 6.85
b
0.52 No 5.48
b
0.31 No
Hedge funds 7.77
b
No 4.44
b
No
CBOT soybean
oil price
Index swap dealers 3.79
b
0.12 No 5.27
b
0.15 No
Hedge funds 0.30 No 0.07 No
a
Indicates statistical significance at 10%.
b
Significance at 1%.
Sources: CFTC (2015a, 2015b, 2015c); FAO (2015); AHDB (2015).
Field 11
analyses further show that speculation by swap dealers and hedge funds also statistically
contributed to the nearby price volatility of CBOT Wheat, KCBT Wheat, and MGEX Wheat.
Index swap dealers were found to significantly influence CBOT Wheat prices while hedge
funds were found to significantly influence MGEX Wheat prices. While statistically
significant, the overall effect on wheat is relatively small, accounting for one to six percent
of price variation between June 2006 and December 2014.
The results in Tables 3 and 4 likely underreport the strength of statistical relationships
described in because data from CFTC (2015a, 2015b) and AHBD (2015) is aggregated and
data had to be averaged monthly to be combined.
Ultra-precise (e.g. hourly, daily) on the speculative positions of swap dealers, hedge funds,
and commodity prices would have alleviated this under-reporting effect, but were not
available for this project.
Interview data from the financial sector informants reveal insights into the substantive
meaning of speculating using commodity futures’ contracts. Informant sixteen, a hedge fund
manager, described net long commodity futures’ speculation as ‘‘synthetic hoarding’’ where
commodities are held in synthetic storage using commodity futures contracts. The idea of
‘synthetic storage’ plays on Working’s (1949) theory of storage and implies that synthetic
storage acts as a proxy for actual commodity futures storage (which requires production,
transport, storage, etc.). The informant explains:
A big concern to me is the index funds because they are just kind of this constant bid. Effectively,
to me, what they’re doing is they are just effectively hoarding, I mean they’re sitting there. Even
if they don’t reallocate they’re constantly just there. They own all these commodities and
effectively what they do is it ends up becoming synthetic storage in some form ...And
Table 4. Regression results continued.
Dependent variable June
2006–December 2014
Independent
variable
Month-to-month change in nominal prices
OLS regression Quantile regression
tR
2
Endogeniety? t R
2
Endogeniety?
CBOT wheat price Index swap dealers
(swap report)
1.87
a
0.05 no 0.82 0.01 no
CBOT wheat price Index swap dealers 1.81
a
0.03 no 0.91 0.01 no
CBOT corn price 2.91
b
0.08 no 0.17
a
CBOT soybean price 3.48
b
0.11 no 2.89
b
0.07 no
MGEX wheat price Hedge funds 2.21
c
0.05 no 4.14
b
0.06 no
CBOT corn price 4.63
b
0.18 no 3.76
b
0.09 no
CBOT soybean price 3.46
b
0.11 no 0.17
a
CBOT soybean oil price 4.74
b
0.18 no 4.41
b
0.15 no
CBOT corn price Index swap dealers 3.26
b
0.26 no 4.25
b
0.16 no
Hedge funds 4.88
b
no 5.40
b
no
CBOT soybean
oil price
Index swap dealers 2.21
c
0.22 no 2.54
b
0.18 no
Hedge funds 4.44
b
no 3.82
b
no
a
Indicates statistical significance at 10%.
b
Significance at 1%.
c
Significance at 5%.
Sources: CFTC (2015a, 2015b, 2015c); FAO (2015); AHDB (2015).
12 Environment and Planning A 0(0)
effectively what’s happened is the index funds because they don’t ever take delivery are taking
storage synthetically. (No. 16)
The conceptual extension made by the informant is that net long financial speculation not
only triggers changes in physical commodity production and storage but also acts as a
substitute for accumulating physical commodities with similar price effects because of the
equalization that occurs between futures and current prices.
Data from the agricultural sector informants yielded insights into the impact of financial
speculation on grain millers. The key informant from Weston Foods Ltd. (a Canadian miller
and baker) confirmed the impact that financial speculation was having on processors of grain
by increasing the cost of hedging, which was confirmed by informants at CP Rail and
Friedberg Mercantile Group. Informant 26, a former commodity analyst with Weston
said, for example, that it is consumers that lose out when the cost of speculation is passed
onto in the form of higher prices.
[A]t the end of the day, it just goes back to consumer, if you like it or not, it just goes back, so
even if prices increase or decrease, it is the customer who has to pay for that. (No. 26)
This is not just the case when industrial millers and bakers lose money hedging when the
futures and current market prices do not converge. Higher hedging costs, the data show, are
also incurred from higher ‘margin account calls’ due to higher prices, and when industrial
millers and bakers speculate using futures. Informant 26 explains,
I mean there is an interesting thing that happened to us at Weston. We made an analysis that Maple
Leafs’ were making horrible bets on wheat futures which resulted in them forcibly increasing the
price of their bread, from a $1.99 to something else. So in response, we did the same thing, we just
matched their price but because we needed a futures for resulting gains for us, we were just making a
lot more money. So the consumer was forced to accept the higher price simply because everyone was
charging the same price, and George Weston reaped the benefits. (No. 26)
Canadian millers and bakers and US commodity markets are linked because all of the
George Weston’s hedges were made in the US, ‘‘our futures contracts were all in the US’’
(No. 26).
As for the relationship between the financial and agricultural circuits of capital, Informant
18 (a Swiss hedge fund manager) explains that commodity prices have been increasing
determined by speculation rather than genuine supply and demand. Informant 18 explains
If you look at the interaction between commodity markets and the financial market ...the vector
that the financial markets took to basically enter or interact in commodities space was through
the futures markets, which initially were there to provide a source of price stability or protection
for various actors in the value chain at the production level, supply level or the consumption
level ...Futures markets rapidly became very speculative because the biggest chunk of the trade
volumes that were taking place on these future markets were not generated by an underlying
physical trade. As more and more money was pouring into the futures market, it created some
disparities between the value of the future and the value of the underlying commodity. (No. 18)
What this means, he explains, is
The futures market ended up being not anymore the confrontation of supply and demand of the
underlying commodity that was traded against the future, but became the price not the
commodity but the supply and demand of the financial instrument. (No. 18)
Field 13
The behavior of hedge funds made institutional investors change how they invested
beginning in the1980s. The informant 2 (from a California Pension fund) explains:
If you go back and look in the 1980’s and institutional investors started expanding the use and
allowing managers to go into the equity markets, while it isn’t a one to one dollar flow, you can
see a push, like a tailwind pushing them because there’s more capital flowing in. (No. 2)
Informant 2 blamed hedge funds for the large flows of capital and the resultant price
volatility. They explain:
[T]he creation of the hedge funds—which really in terms of being around in size, is only a
phenomena of the last decade ...the hedge funds by using as much as eight to one to ten to
one leverage suddenly can come in on such large positions. And almost every other investor in
this global financial market is trying to track an index, trying to invest and outperform relative to
some market. Hedge funds are the one area where generally their benchmark is zero, they are just
trying to make money and they have no limits or boundaries so that can cause somebody who is
not a natural investor in the oil markets to suddenly come into the oil markets in size. And that
type of movement in and out makes the markets more dynamic in our opinion. (No. 2)
Informant 8 is a retired senior executive with RBC Dominion Securities and former
Canadian grain company executive explained that index swap dealers and hedge funds
both contribute to increased food price volatility.
[C]ommodities became a separate asset class and it wasn’t originally recognized as such by
pension fund managers ...it is that particular realization and requirement for this asset class
that has fuelled a lot of the hedge funds, created a lot of volume you might say, in a lot of the
commodity futures, none more so than in probably oil. But nevertheless that basically is what
has transpired. And I’ve watched it develop or evolve since the early seventies actually. (No. 8)
Informant 27, from the CFTC’s Market Oversight division, said that what is driving
increased price volatility is increased financial speculation.
[Speculators] are showing an awful lot of money into these markets, so the hedge funds are
getting bigger. And they are trying to, you know, get better returns in the markets that they are
not getting from other places. You know you can’t just park your money in treasuries and make
any kind of real rate of return, and the stock market of course has been sort of a frightening
place as well, so it has kind of turned to the commodity markets in hopes that they could get a
really rate of return there. (No. 27)
Informant 27 argues that the speculative position limits set by the CFTC are ‘‘really, really
large.’’ These high position limits allow index swap dealers and hedge funds to speculatively
buy and sell hundreds of thousands of commodity futures contracts, each worth as much as
5000 bushels of grain. I illustrate these unprecedentedly large speculative trades for nearby
CBOT Corn contracts in Figure 3, which show the net buying and selling behavior of swap
dealers, hedge funds, and actual users of grain. The figure shows that the speculative long
(buying) positions of hedge funds and index funds alone is roughly equal to all corn being sold
by producers and wholesalers using CBOT corn futures. This makes them the largest
speculative buyers of CBOT Corn between 2006 and 2014.
Discussion
The data show that an empirical connection between financial speculation and increased
food price volatility exists. Quantitative data from the CFTC (2015a, 2015b) show that the
14 Environment and Planning A 0(0)
influx of speculative money from swap dealers and hedge funds significantly influenced US
and global food price volatility between June 2006 and December 2014. The results of the
empirical analysis coincide with results of Mayer (2012) who finds that index swap dealer
and hedge fund speculation has a statistically significant impact on the CBOT nearby futures
price of soybean oil and soybean meal (see online appendix for details). The results also
coincide this findings of Gilbert (2010) who argues that a link exists, but uses different
covariates than this study and Mayer (2012).
The interview data adds depth to this information and confirms this linkage between
financial speculation and price volatility. The concept of synthetic hoarding raised by
informant sixteen is useful for understanding how commodity futures’ contracts can be
used transcend spatial-temporal barriers to speculatively buying and hoarding
commodities. Speculating on commodity future’s contracts rather than physical
commodities gives speculators greater purchasing power (by only having to post
‘margin’), and avails them of having to transport, store, and maintain physical goods.
The synthetic-ness of commodity futures contracts gives them the additional quality of
being virtually unlimited, meaning that a virtually unlimited number of speculative
commodity futures contracts can be created and unwound prior to physical delivery
(when the contracts are set to expire). By never taking delivery and only speculating on
commodity futures contracts, index swap dealers (and hedge funds) circumvent the
biological limits of commodity speculation. Because commodity futures are credit, there is
no biological (production) limit to the number futures contracts that can be created or
destroyed. By speculating on commodity futures index swap dealers (and hedge funds)
were able to circumvent having to physically store and transport the commodities being
speculated on. In this regard, commodity futures speculation combines the unbridled
speculative capacity of credit markets with material commodities that are absolutely vital
to human survival.
This synthetic-ness stems from the fact that they are a form of credit. As credit, there are
no material barriers to the number of futures’ contracts than can be created, giving futures’
-800000
-600000
-400000
-200000
0
200000
400000
600000
800000
2006
2007
2008
2009
2010
2011
2012
2013
2014
Net Number of Contracts
(5,000 bushels each )
<--Short/Selling Long/Boying -->
Combined Index &
Hedge Fund
Speculation
Commodity Index
Swaps
Producers and
Wholesalers
Managed Money
(Hedge Funds)
Figure 3. Combined CFTC data on net futures contracts for CBOT Corn, 2006–2014. The pink vertical
lines denote when global food prices, measured by the FAO’s (2015) food price index, ‘‘spiked.’’ The first
food price spike occurs in 2008 and lasts several months. The second spike occurs in 2011 and a third spike
occurs in 2013.
Source: CFTC (2015a, 2015b, 2015c; available at: http://www.cftc.gov/files/dea/history/fut_disagg_xls_hist_
2006_2014.zip).
Field 15
contracts the ability to absorb large quantities of speculative money. Marx ([1867] 1990) and
Harvey ([1982] 2006) show that while credit creation may be physically unbounded, its
creation and destruction is not without material consequences. The relationship between
credit creation–destruction and its material consequences is evidenced by the US Mortgage
crisis, for example, which transformed the US suburban landscape and lives of millions
foreclosed middle-class Americans (Aalbers, 2008, 2009). In the present case, the effect of
financial speculation did not materialize in the building and foreclosure of homes, but
instead materialized as higher food prices that acutely changed some consumers’ food
access by making it unaffordable. Index swap dealers and hedge funds encouraged
investors to speculate on commodities using financial derivatives that purportedly allow
investors to distil the benefits of commodity speculation without the cost, messiness, and
moral hang-ups of speculating on food. Mainstream perspectives on food speculation and
the work of Irwin et al. (2009, 2011), in particular, legitimated commodity index speculation
by providing evidence that it is unrelated from material commodity prices.
The results of the regression analysis coincide with the stylized circuits of capital outlined
in the conceptual framework. Index swap dealers, the model shows, enter US agricultural
supply chains through US commodity futures markets. The regression results measure the
strength of the relationship between the financial and agricultural circuits where these
circuits intersect. An important addition to this conceptual framework in the future
would be the inclusion of hedge funds. The empirical analysis shows that hedge funds
influence commodity prices as much as index swap dealers do. This finding is interesting
because the majority of the literature on the link between speculation and food prices focuses
on the index swap dealers.
Conclusion
The mainstream academic literature’s treatment of the link between financial
speculation and food price volatility is conceptually and empirically problematic. This
article responds to this literature by outlining Marxian analytical framework organized
around capital circuits and by presenting new empirical evidence on the link. The findings
of this study are that financial speculation by index swap dealers and hedge funds have
significantly contributed to the price volatility of food commodities between June 2006 and
December 2014. This evidence contradicts the predominant neoclassical perspective of Irwin
et al. (2009, 2011), who contend that no connection between financial speculation and price
volatility exists.
The causal origins of financialized food price volatility in the global north and the impact
on poor in countries in North Africa, the Middle East, and Asia is not without significance.
This geographical pattern exposes the hierarchy of global power relations where between
financial corporations, U.S. regulators, foreign governments, and global populations are all
connected (Clapp, 2009, 2012). Geography is central, not only because it is the basis for the
exchange and circulation of commodities (Harvey, [1982] 2006) but also because it is part of
the material history of power relations under global capitalism that places index swap
dealers and hedge funds near the top of the hierarchy and the food vulnerable global
poor near the bottom.
Acknowledgments
I would like to thank Meagan Crane for her feedback on all of the various drafts of this manuscript.
I would like to thank Dr. Betsy Donald for supervising the dissertation project from which this manuscript
16 Environment and Planning A 0(0)
stems and Dr. John Holmes for the discussions on Marx that influenced the theoretical framework.
I also would like to thank the editors of EPA and the anonymous reviewers for their incisive and
thoughtful feedback.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or
publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or
publication of this article: This study was supported by Social Sciences and Humanities Research
Council (SSHRC).
Supplemental Material
The online appendix is available at http://epn.sagepub.com/supplemental.
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... For instance, during the food price spikes observed between 2008 and 2011, hedge funds were implicated in exacerbating price volatility through speculative trading in futures markets. This financialization of food markets has had profound implications, particularly for vulnerable populations in developing regions, where food security is critically impacted by price volatility driven by financial actors (Field 2016). The interconnectedness of global financial markets means that actions taken by hedge funds can ripple through to affect local food prices, often disconnecting them from the underlying physical supply and demand conditions. ...
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This study explores the financialization of agricultural commodities, focusing on how financial derivatives and index funds impact the volatility of agro-food markets. Using a Dynamic Conditional Correlation (DCC) GARCH model, we analyze volatility spillovers among key agricultural commodities, particularly maize, and related financial assets over a sample period from 2007 to 2020. Our analysis includes major financial assets like Exchange-Traded Funds (ETFs), the S&P 500 index, and agribusiness corporations such as ADM and Bunge and the largest corn flour producer, GRUMA. The results indicate that financial speculation, especially via passive investments such as ETFs, has intensified price volatility in commodity futures, leading to a systemic risk increase within the sector. This study provides empirical evidence of increased market integration between the agro-food sector and financial markets, underscoring risks to food security and economic stability. We conclude with recommendations for regulatory actions to mitigate systemic risks posed by the growing financial influence in agricultural markets.
... Since the financial crisis of 2008, speculation has become a primary reason for the frequent spikes in food prices since the turn of the century (Bredin et al. 2021). Meanwhile, agricultural transnational corporations and capital from developed countries are increasingly concentrated in the global food and agriculture system, leading to developing countries becoming more reliant on imports and more susceptible to the impact of food price shocks (Anderson 2014;Field et al. 2016). Developing countries' agricultural financial markets lag behind, with weaker resilience to risk (Ivanic et al. 2012), making the negative impact of commodity futures speculation on their food security more significant (Sosoo et al. 2021). ...
... Simpson (2019), Labban (2008), and other authors highlight what storage does. They emphasise the temporal-spatial dimensions of storage facilities, the vital role that storage plays in facilitating the circulation of capital, how storage preserves the value of commodities from one time period to another, and how futures markets allow storage to be speculated on and hedged (Field 2016;Working 1949Working , 1953. Applying the West African lens of "spirited matter" to the ethnographic exploration of US residential storage, by contrast, Sasha Newell (2014Newell ( , 2018 unsettles functionalist notions of storage to reveal how items being stored can carry moral weight, be entangled with notions of personhood and memory, and be the fountainhead of imaginative possibilities. ...
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Drawing on ethnographic field research that I conducted in Houston, Texas since late 2018, I explore subterranean storage arrangements utilised by the US hydrocarbon industry. I argue that storage is vital not only to its pluri‐temporal strategies but to the outward projection of good governance. Natural gas, I show, has evolved from excess nuisance, to liability, to potential asset turned commodity in ways that parallel unfolding understandings and treatments of carbon dioxide. Governance and subterranean carbonous storage arrangements, I argue, are tied to the materiality of liquid versus gaseous hydrocarbons and to how understandings of this materiality have changed. Paying attention to what these storage spaces mean and to whom can lend insights into why storage is utilised and to what effect.
... The financialization of agricultural commodity markets is frequently cited as contributing to rising market volatility (Field, 2016). Bruno et al. (2017) demonstrate the existence of a serial correlation between peak grain prices and financial activity. ...
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In recent years, food insecurity has worsened despite decades of progress. Agrifood systems must evolve to address population expansion and climate change simultaneously. A problem of this magnitude necessitates the development of technologies that simultaneously increase (nutritious) food production and protect the environment. Based on an a diagram analysis of the economic model of technological change, this article explores various technological paths that can emerge in response to rising food prices and the need to preserve land. This article indicates, through the use of specialized literature, that the hypotheses derived from the analysis of the technological change model already exist. In addition, we investigate the potential geopolitical effects of these technological shifts on the global agri-food industry. Changes aimed at conserving land must encourage urban food production, systematic resource optimization on abundant high-quality land, and regenerative practices. In addition to rising food prices and population growth, the increasing significance of urban consumers and the internalization of environmental values should drive these various developments.
... The Gregory-Hansen procedure, reported in Figure 1, CBOT futures prices were increasing in that specific period, as opposed to Euronext futures prices and spot prices for all the selected EU MS. The literature has broadly examined US corn price volatility during the first decade of the twenty-first century, linking it both to financial speculation and the increasing demand for corn for biofuel production (McPhail et al., 2012;Field, 2016). ...
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Price transmission between futures and spot prices is a relevant issue, dealing with derivatives exchange for price management practices and efficient price discovery. Indeed, due to the increased market orientation of the Common Agricultural Policy, the development of new market strategies is of utmost importance for European farmers. In this context, this study examines the degree of transmission for the corn commodity between global futures price in either the Chicago Board of Trade or Euronext and the spot prices for a selection of Member States of the European Union. This study provides critical insights into the shape of the futures–spot price transmission, confirming a long‐run relationship and a cointegrating behaviour of price sets. [EconLit Citations: Q02, Q14, E3].
... model Swinnena i Vandeplasa z 2010 roku), jak równie powinien odnosi si do wspierania uczestników a cucha w zarz dzaniu ryzykiem (wymaga to podejcia interdyscyplinarnego w kszta towaniu narz dzi polityki publicznej). 36 Globalizacja wzmacnia konieczno stosowania instrumentów zarz dzania ryzkiem dochodowym [Hamulczuk, 2016;Kowalski, Rembisz, 2016]. 37 Indstry 4.0, 4 th Revolution, Industry Revolution 4.0 (IR 4.0). ...
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Celem monografii jest uogólnienie wyników badań prowadzonych w temacie pt. „Źródła wzrostu oraz ewolucja struktur i roli sektora rolno-spożywczego w perspektywie po 2020 roku” w czterech zadaniach badawczych. Wyprowadzone prawidłowości mogą stanowić podstawę do oceny i projekcji procesów strukturalnych w układzie rolno-spożywczym.
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The Russia-Ukraine conflict has severely impacted global food security. This may increase the risk of supply chain disruption in low-income countries that rely heavily on grain imports. This study used production and trade data for wheat, barley and maize from 1995 to 2021 to construct longitudinal trade networks. On this basis, a cascading failure network model of shock propagation was used to identify the direct or indirect dependence of other countries on grain exported from Russia and Ukraine and the impact caused by trade shocks. The results revealed that the interruption of grain exports from Russia and Ukraine has resulted in an increasing impact on the global grain trade year by year and that the wheat trade is the most vulnerable to shock propagation, but it is also the most resilient. Russia and Ukraine interrupt exports of grain, causing more than 50% reduction in direct imports to 30 countries, including Eritrea, Seychelles, Kazakhstan and Mongolia. A shock propagation model that considers indirect dependence yields divergent results, with lower middle income (LM) countries in North Africa, Southeast Asia and West Asia facing supply shocks from reduced imports because they are unable to fully exploit the trade channels to balance grain supply and demand. Under the COVID-19 pandemic, this indirect dependence on imports is more prominent. It is worth noting that Eastern and Southern European countries often act as intermediaries to spread shocks during cascading failures. In the process of shock propagation, the main suppliers of grain include the United States, Canada, France, Argentina and Brazil. After the outbreak of COVID-19, the import demand faced by Australia increased significantly. We also examined how nodal characteristics relate to shock propagation dynamics and country vulnerability, finding that high import diversity, low import dependence and regional characteristics are effective in buffering countries from supply shocks. This study contributes to our understanding of the external supply risks for grain arising from the Russia-Ukraine conflict in a pandemic context, highlights the issue of accessibility in food security and provides trade policy recommendations to mitigate national vulnerability to food insecurity, thereby creating a resilient food trade system.
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Globally, food systems have become heavily industrialized and are currently threatening both environmental sustainability and human health. Feeding a growing world while remaining within safe social-ecological planetary boundaries, as dictated by the UN Social Development Goals and the Paris Climate Agreement, is feasible but requires a paradigmatic shift in agricultural value chains and their financing: a “Great Food Transformation.” Tracing today’s agri-food main global developmental and financial trends, this paper proposes a set of financially-oriented public policies to accelerate this transition with a focus on advanced and large emerging market economies. Suggested measures include public lending, insurance and guarantee schemes to aid the transition; financial training schemes; changes to prudential regulation to account for financial risks of non-sustainable farming; alongside a bolder approach to ESG investment of public funds and steps to expand green and sustainable bond markets.
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The global food crisis is a stark reminder of the fragility of the global food system. The Global Food Crisis: Governance Challenges and Opportunities captures the debate about how to go forward and examines the implications of the crisis for food security in the world's poorest countries, both for the global environment and for the global rules and institutions that govern food and agriculture. In this volume, policy-makers and scholars assess the causes and consequences of the most recent food price volatility and examine the associated governance challenges and opportunities, including short-term emergency responses, the ecological dimensions of the crisis, and the longer-term goal of building sustainable global food systems. The recommendations include vastly increasing public investment in small-farm agriculture; reforming global food aid and food research institutions; establishing fairer international agricultural trade rules; promoting sustainable agricultural methods; placing agriculture higher on the post-Kyoto climate change agenda; revamping biofuel policies; and enhancing international agricultural policy-making. © 2009 by Wilfrid Laurier University Press. All rights reserved. Link here: https://www.cigionline.org/sites/default/files/the_global_food_crisis.pdf
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This paper provides a new perspective on the political implications of intensified financialization in the global food system. There has been a growing recognition of the role of finance in the global food system, in particular the way in which financial markets have become a mode of accumulation for large transnational agribusiness players within the current food regime. This paper highlights a further political implication of agrifood system financialization, namely how it fosters ‘distancing’ in the food system and how that distance shapes the broader context of global food politics. Specifically, the paper advances two interrelated arguments. First, a new kind of distancing has emerged within the global food system as a result of financialization that has (a) increased the number of the number and type of actors involved in global agrifood commodity chains and (b) abstracted food from its physical form into highly complex agricultural commodity derivatives. Second, this distancing has obscured the links between financial actors and food system outcomes in ways that make the political context for opposition to financialization especially challenging.
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The objective of this study is to explore empirical evidence on the quantitative importance of supply, demand, and market shocks for price changes in international food commodity markets. To this end, it distinguishes between root, conditional, and internal drivers of price changes using three empirical models: (1) a price spike model where monthly food price returns (spikes) are estimated against oil prices, supply and demand shocks, and excessive speculative activity; (2) a volatility model where annualized monthly variability of food prices is estimated against the same set of variables plus a financial crises index; and (3) a trigger model that estimates extreme values of price spikes and volatility using quantile regressions. The results point to the increasing linkages among food, energy, and financial markets, which explain much of the observed food price spikes and volatility. While financial speculation amplifies short-term price spikes, oil price volatility intensifies medium-term price volatility.
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The dynamics of food inflation appear to have changed; the 1996 grain price shocks had a smaller impact than shocks in the past.
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Commodities are key for developing countries' economic integration. This article distinguishes two types of financial investors in commodities and emphasises differences in position taking motivation and price impacts. Index trader positions are positively correlated with roll returns, while money managers emphasise spot returns. During 2006–2009, index trader positions had a price impact for some agricultural commodities, as well as oil. During 2007–2008, money managers impacted prices for non-agricultural commodities, especially copper and oil. The financialisation of commodity markets may make it more difficult for developing countries to manage their resource sectors for sustained economic development.