Elsevier

Energy Policy

Volume 38, Issue 1, January 2010, Pages 445-451
Energy Policy

Food versus fuel: What do prices tell us?

https://doi.org/10.1016/j.enpol.2009.09.034Get rights and content

Abstract

Sorting out the impacts of biofuels on global agricultural commodity prices is impossible without turning to data and distinguishing between the short-run versus the long-run impacts. Using time-series prices on fuels and agricultural commodities, the aim is to investigate the long-run cointegration of these prices simultaneously with their multivariate short-run interactions. Results indicate no direct long-run price relations between fuel and agricultural commodity prices, and limited if any direct short-run relationships. In terms of short-run price movements, sugar prices are influencing all the other agricultural commodity prices except rice. With sugar the number one world input for ethanol, results indicate increased ethanol production is potentially influencing short-run agricultural commodity prices. Overall, results support the effect of agricultural commodity prices as market signals which restore commodity markets to their equilibria after a demand or supply event (shock).

Introduction

As early as 1983, research indicated the potential of fuel ethanol over the next decades to be very disruptive to global agricultural commodity prices (Barnard, 1983). Biofuel may compete for renewable and nonrenewable resources and thus impact its sustainability and that of food (von Urff, 2007). Over time, an expanding biofuel market will provide commodity producers a choice of producing food for people or fuel for automobiles. Economics suggests they will produce whichever is more profitable (Brown, 1980). This food versus fuel trade-off emerged on a global scale with the 2007–2008 world agricultural commodity prices crisis (Monbiot, 2004). The crisis in price spikes was due to a number of mutually reinforcing factors in global agricultural markets: a sharp increase in biofuel demand, rapid economic growth, droughts in key grain-producing regions, high oil prices, a weak US dollar, speculation, and export restrictions (Collins and Duffield, 2005; Dewbre et al., 2008; Headey and Fan, 2008; Muhammad and Kebede, 2009; Rajagopal et al., 2007; Senauer, 2008).

Research into this food versus fuel issue is generally investigated by employing economic models including computable general equilibrium (CGE) models and incorporating mathematical simulation. However, in many cases such relationships are established exogenously based on economic theory and expert opinions with assumed elasticities and parameter specifications. This leaves the models determining the magnitude of long-run agricultural commodity price impacts of fuel-price shocks. They take into account interactions with other markets, but do not capture short-run price dynamics including the recent price spikes. Detailed studies of specific crops may include the short-run dynamics, but often exclude the impact on other markets (Mitchell, 2008). As an example, long-run analysis using a CGE indicates that fuel-price shocks govern rising food prices (Arndt et al., 2008; Rosegrant et al., 2008; Tyner and Taheripour, 2008; Yang et al., 2008). Differences in the estimates of the impact of biofuels on agricultural commodity prices depend largely on how broadly the food basket is defined and what is assumed about the interaction between fuel and food prices. For example, the Council of Economic Advisors estimate that retail food prices increased only around 3% in 2007 due to ethanol production (Lazear, 2008). In contrast, investigations have concluded the large increases in biofuel production in the United States and Europe are the main reason behind the recent spike in global food prices (Collins, 2008; Mitchell, 2008), and could result in the US becoming a net importer of food as opposed to an importer of oil (Reilly and Paltsev, 2007).

Sorting out the impacts of biofuels on global agricultural commodity prices is naturally an empirical one. One such example is Balcombe and Rapsomanikis’ (2008) investigation of the sugar–ethanol–oil nexus in Brazil. They determined oil prices are the long-run drivers of ethanol and sugar prices, and sugar prices Granger-cause ethanol prices. Although they found various price pairs to be cointegrated, their adjustment to long-run equilibria differs. Their work suggests it is simply impossible to determine the impacts of biofuels on agricultural commodity prices without turning to data and distinguishing between the short-run versus the long-run impacts.

Prior to the recent global downturn, most projections concluded that food prices would remain relatively high for many years to come because of expanded biofuel production, high oil prices, and increased international demand (Diao et al., 2008). In contrast, Senauer (2008) was correct in stating a short-run bubble in food prices existed in the first half of 2008 which deflated in the second half. In the long-run, productivity gains and acreage response to prices can mitigate the volatility in agricultural commodity prices (Diao et al., 2008; Daschle, 2007; Kerckow, 2007; Perlack et al., 2005; Prabhu et al., 2008; Webb, 1981). This body of literature yields the following hypothesis that short-run fuel and agricultural commodity market volatilities are a consequence of a different set of factors than long-run market volatilities. Such a hypothesis would explain some of the shortcomings of previous results by not distinguishing between short-run and long-run price volatility.

Using time-series prices on fuels (ethanol, gasoline, and oil) and agricultural commodities (corn, rice, soybeans, sugar, and wheat), the aim is to provide support for this hypothesis. Specifically, the long-run cointegration of these prices is investigated simultaneously with their multivariate short-run interactions. Results indicate no direct long-run price relations between fuel and agricultural commodity prices, and limited if any direct short-run relationships. Consistent with the hypothesis, relationships among the prices do differ in terms of long-run versus short-run price adjustments. In terms of short-run price movements, sugar prices are influencing all the other agricultural commodity prices except rice. With sugar the number one world input for ethanol, results indicate increased ethanol production is potentially influencing short-run agricultural commodity prices through its impact on sugar prices (Koizumi, 2003). Although possibly more plausible, sugar prices as a leading indicator of economic growth are serving as a growth surrogate. Sugar production contributes 20% of GDP and employs 30% of the workforce in African, Caribbean and Pacific Group of States (ACP) sugar-producing countries (FAO, 2003). Economic growth, in general, is then the driver of short-run agricultural commodity price fluctuations.

These results have major policy implications in terms of addressing the food versus fuel issue. However, of greater significance for the fuel versus food security issue, results support the effect of agricultural commodity prices as market signals which restore commodity markets to their equilibria after a demand or supply event (shock). Such shocks may in the short-run lead to agricultural commodity price inflation, but decentralized freely operating markets will mitigate the persistence of these shocks. Consideration may then be directed toward shifting agricultural policy for mitigating such short-run commodity-price inflation with commodity buffers for supplementing supplies in years of insufficient harvests. Such commodity buffers could blunt food price spikes caused not only by possible biofuel shocks but also shocks associated with weather, conflicts, and terrorism.

Section snippets

Data

Monthly price data for the agricultural commodities corn, rice, soybeans, sugar, and wheat along with energy prices for ethanol, gasoline, and oil were collected from March 1989 through July 2008. Table 1 lists the data sources and accompanying units of measurement. Summary statistics, listed in Table 2, indicate the volatilities of these price series and the high degree of skewness and kurtosis, but less so for the log price changes. The Jarque–Bera test statistics reject the hypothesis of

Cointegration estimation

Price series are cointegrated if they move together in the long-run. Although there may be short-run shocks causing price series deviation, if they are cointegrated there is a long-run linear relation which ties the prices together. Johansen trace tests were applied in a stepwise procedure for indicating the long-run relations among the eight price series (Juselius, 2006). As indicated in Table 4, only the three fuel-price series were first tested, leading to two long-run relations at the 1%

Vector error corrections model (VECM)

A VECM specifies the short-run dynamics of each price series in a framework that anchors the dynamics to long-run equilibrium relationships (cointegrates). Granger-type causality tests with a one period (month) lagged error correction term along with the Final Prediction Error and Akaike's statistics for determining the lag length are employed. These statistics indicate a lag length of four with model estimation for alternative lag lengths yielding robust results and nearly identical estimated

Implications

Agricultural commodity price volatility negatively impacts all society by causing macroeconomic instability, but particularly impacts the impoverished that spend a large portion of their resources on food and fuel. Food prices are more volatile in developing countries where people living in poverty devote over half of their income to food (Brown, 1980; Senauer, 2008). The spillover effect of volatile food prices has renewed interest in the establishment of food-market restrictions. Recently,

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