metadata: authors: 0: author: Hochman,Gal
1: author: Rajagopal, Deepak
2: author: Timilsina,Govinda R.
3: author: Zilberman,David
count: World
docna: 0: docna: The role of inventory adjustments in
            quantifying factors causing food price inflation
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Crops and Crop Management Systems,Rural and Renewable Energy,Food Security,Energy Demand,Energy and Environment,Energy and Mining,Renewable Energy,Commodity Risk Management
teratopic: Macroeconomics and Economic Growth,Industry,International Economics and Trade,Finance and Financial Sector Development
abstracts: cdata!: The food commodity price increases
            beginning in 2001 and culminating in the food crisis of
            2007/08 reflected a combination of several factors,
            including economic growth, biofuel expansion, exchange rate
            fluctuations, and energy price inflation. To quantify these
            influences, the authors developed an empirical model that
            also included crop inventory adjustments. The study shows
            that, if inventory effects are not taken into account, the
            impacts of the various factors on food commodity price
            inflation would be overestimated. If the analysis ignores
            crop inventory adjustments, it indicates that prices of
            corn, soybean, rapeseed, rice, and wheat would have been,
            respectively, 42, 38, 52, and 45 percent lower than the
            corresponding observed prices in 2007. If inventories are
            properly taken into account, the contributions of the above
            mentioned factors to those commodity prices are 36, 26, 26,
            and 35 percent, respectively. Those four factors, taken
            together, explain 70 percent of the price increase for corn,
            55 percent for soybean, 54 percent for wheat, and 47 percent
            for rice during the 2001-2007 period. Other factors, such as
            speculation, trade policy, and weather shocks, which are not
            included in the analysis, might be responsible for the
            remaining contribution to the food commodity price increases.
repnme: repnme: The role of inventory adjustments in
            quantifying factors causing food price inflation
display_title: The role of inventory adjustments in
            quantifying factors causing food price inflation
listing_relative_url: /research/2011/08/14698450/role-inventory-adjustments-quantifying-factors-causing-food-price-inflation
topicv3: Agriculture and Food Security m1326205 435,Energy and Extractives m1326595 507,Biofuels Energy m1326600 532,Crop Production m1326627 174,Commodity and Resource Prices m1503849 3034
publishtoextweb_dt: 2011-08-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,International Economics and Trade,Finance and Financial Sector Development
no_of_pages: 61
author_keys: 0: author_key: 1271716
1: author_key: 940446
2: author_key: 931774
model_extractions: WPS5744 Policy Research Working Paper 5744 The Role of Inventory Adjustments in Quantifying Factors Causing Food Price Inflation _Gal Hochman Deepak Rajagopal Govinda Timilsina David Zilberman_ The World Bank Development Research Group Environment and Energy Team August 2011
Policy Research Working Paper 5744

# **Abstract**

The food commodity price increases beginning in 2001 and culminating in the food crisis of 2007/08 reflected a combination of several factors, including economic growth, biofuel expansion, exchange rate fluctuations, and energy price inflation. To quantify these influences, the authors developed an empirical model that also included crop inventory adjustments. The study shows that, if inventory effects are not taken into account, the impacts of the various factors on food commodity price inflation would be overestimated. If the analysis ignores crop inventory adjustments, it indicates that prices of corn, soybean, rapeseed, rice, and wheat would have been, respectively, 42, 38, 52, and 45 percent lower than the corresponding observed prices in 2007. If inventories are properly taken into account, the contributions of the above mentioned factors to those commodity prices are 36, 26, 26, and 35 percent, respectively. Those four factors, taken together, explain 70 percent of the price increase for corn, 55 percent for soybean, 54 percent for wheat, and 47 percent for rice during the 2001–2007 period. Other factors, such as speculation, trade policy, and weather shocks, which are not included in the analysis, might be responsible for the remaining contribution to the food commodity price increases.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gtimilsina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The Role of Inventory Adjustments in Quantifying Factors Causing Food Price Inflation<sup>*****</sup> Gal Hochman<sup>†</sup> , Deepak Rajagopal<sup>‡</sup> , Govinda Timilsina<sup>§</sup> , David Zilberman<sup>**</sup> _JEL classifications:_ Q1 _Keywords:_ Inventories; Stock-to-use; Food commodity prices; Economic Growth; Agriculture productivity

> _* The views and findings presented here should not be attributed to the World Bank. We thank Will Martin, John Beghin, Mike Toman, Caesar Cororaton and Robert Townsend for their valuable comments. We acknowledge financial support from the Knowledge for Change Program (KCP) Trust Fund._

> _† Energy Biosciences Institute, University of California, Berkeley, email: galh@berkeley.edu._

> _‡ Institute of Environment, University of California, Los Angeles, email: rdeepak@ioe.ucla.edu._

> _§ Development Research Group, The World Bank, email:gtimilsina@worldbank.org_

> _** Department of Agricultural and Resource Economics, University of California, Berkeley, email: zilber11@berkeley.edu._
# **1. Introduction**

Food and fuel commodity prices, which had been rising since 2001 (see Figures 1 and 2), reached record levels by mid-2008 [21, 52, 65]. According to the International Monetary Fund (IMF) primary commodity price database, world food commodity prices increased 100% or more from 2001 to 2008 (in 2005 US $), with prices increasing by almost 300% for rice (see table inset in Figure 2).

The period between 2001 and 2008 was also the period during which production of biofuels such as ethanol and biodiesel produced from food crops grew several fold. During this time, global ethanol production from maize and sugarcane more than doubled from 30 billion liters to 65 billion liters, while biodiesel production from edible oil seeds such as soybean, oil palm, and rapeseed expanded six fold from 2 billion liters to 12 billion liters [46]. The increase in biofuel demand, which was concentrated in the United States and the European Union (EU), was primarily a response to government mandates and subsidies.<sup>1</sup> This has led to the popular opinion that biofuel policies in the high-income countries are one of the principal causes for the inflation in food commodity prices.

Biofuels reduce demand for oil and increase demand for agricultural goods.<sup>2</sup> With crops comprising a small share of the final cost of food in high-income countries, the impact of biofuels on food consumers is small. To low-income countries, where expenditure on raw grains and vegetable oils comprises a much larger share of the household food budget, a given increase in crop prices will have a much larger impact on food consumers.

This paper aims to identify the main factors affecting food commodity prices, and to also quantify the contributions of these factors. A distinguishing feature of our analysis is taking into account adjustments in inventories of agricultural goods in response to these various factors. Although conceptually an important component of food commodity markets, to the best of our knowledge, it is not explicitly incorporated into existing empirical/computational models.

> _1Growth in domestic biofuel demand in Brazil, a large biofuel producer, also increased, but was less significant relative to growth in demand in the U.S. and EU countries._

> _2Biofuels reduce the Organization of Petroleum Exporting Countries’ (OPEC) market power, and therefore reduce energy prices [32,33]._

2
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include stagnation of productivity growth due to cumulative underinvestment in agricultural research and technology, as well as infrastructure such as irrigation [5, 10]. All these supply factors resulted in slow or negative growth in production [2, 3, 21, 66]. Some factors affect both demand and supply. These factors include trade policies such as export bans on grains (especially the ban on rice exports by several countries in Asia, such as Bangladesh, Vietnam, and India [23])<sup>3</sup> and import tariffs on non-grain biofuels (especially the U.S. import tariffs on cane ethanol from Brazil, but also on rice in Indonesia [64]). The depreciation of the U.S. dollar relative to major world currencies has also been a contributing factor to commodity price increases [2,59], as were energy prices [33].

The rest of the report is structured as follows. In section 2, we present a review of the literature on recent increase in food commodity prices and the effect of biofuels on food commodity prices. We briefly survey historical trends in section 3. Following this, the effect of introducing an empirical model of inventory into the partial equilibrium model is also illustrated in section 4. In section 5 we extend the partial-equilibrium analysis to a multi-market multi-region framework. Section 6 describes the results from the numerical simulation of the multi-market model. This section demonstrates the importance of understanding the market for inventory to better predict the effect of any large supply or demand shock on commodity prices. Section 7 concludes the report.

# **2. Literature review**

Economic equilibrium models have a long tradition of use for predicting the effects of one or more policies on prices, welfare, and a variety of other economic variables [19]. These models can be classified as partial and general equilibrium models. Partial equilibrium models are essentially the aggregation of supply and demand equations that represent economic behavior of agents in one or more markets of interest. Examples of prominent partial equilibrium models include IMPACT, AGLINK/COSIMO, FAPRI, and FASOM.

The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) is a partial-equilibrium model that has often been used by the International Food Policy Research Institute (IFPRI) for projecting global food supply, food demand, and food security to 2020 and beyond. Using this model, Msangi et al. simulate the impact of biofuel under different scenarios on the price of food in different regions [48]. In one of the scenarios,

> _3For a comprehensive list, see the Food and Agriculture Organization’s (FAO) summary on policy measures taken by governments to reduce the impact of soaring prices http://www.fao.org/giews/english/policy/2.htm. Also seeftp://ftp.fao.org/docrep/fao/012/i0854e/i0854e04.pdf_

4
which focused on rapid global growth in biofuel production under conventional conversion technologies, the price increase for major crops ranges between 30% and 76% by 2020. There is significant increase in malnutrition in many developing country regions with Sub-Saharan Africa being the hardest hit. Using the AGLINK and COSIMO models, the OECD predicts the impact of achieving the stated policy targets (as of 2006) for biofuels in several countries [42]. It finds that compared to a situation with unchanged biofuel quantities at their 2004 levels, crop prices could increase by between 2% in the case of oilseeds and almost 60% in the case of sugar by the year 2014.

Partial models have several limitations, such as lack of acknowledgement of the finiteness of resources such as land, labor, and capital; no explicit budget constraint on households; and no check on conceptual and computation consistency of the model [30]. These limitations can be overcome by using a general equilibrium approach. Computable general equilibrium (CGE) modeling is a numerical technique that combines the theoretical framework of Walrasian general equilibrium formalized by Arrow and Debreu [6] with real world economic data to determine the levels of supply, demand, and price that support equilibrium across a specified set of markets [69]. These models, which were initially developed to analyze the impact of changes in trade policies and public finance, have subsequently found wide application in the analysis of relationship between energy and the macro economy, the impact of greenhouse gas policies, and most recently in the context of biofuel policies [10, 13, 31]. GTAP, LINKAGE, and USAGE are some prominent general equilibrium models that were used to analyze biofuels.<sup>4</sup> Dixon, Osborne, and Rimmer [18] use a dynamic CGE model called USAGE to quantify the economy wide effects of partial replacement of crude petroleum with biofuels in the United States. They forecast the impact of the current biofuel policies on the U.S. economy in 2020 [18]. Although there is no direct discussion of the impact of these policies on the global price of food, the model predicts a reduction in agricultural exports and an increase in the export prices. Gohin and Moschini assess the impacts of the European indicative biofuel policy on the EU farm sector with a farm-detailed CGE model and predict positive income effects on farmers in the EU [26]. Birur, Hertel, and Tyner use the GTAP-E model to study the impact of six drivers of the biofuel boom, namely, the hike in crude oil prices, replacement of methyl tertiary butyl ether (MTBE) by ethanol as a gasoline additive in the United States, and subsidies for ethanol

> _4 GTAP – model developed at Purdue University; LINKAGE – The World Bank’s model, and USAGE – model developed at Monash University, Australia._

5
and biodiesel in the United States and EU [12]. They find that between 2001 and 2006 these drivers were responsible for a 9% increase in the price of U.S. coarse grains, 10% increase in price of oilseeds in the EU-27 region, and 11% for sugarcane in Brazil. Similar impacts were observed on energy-exporting countries in Latin America and Sub-Saharan Africa. The main drawbacks of a CGE model are the large data requirements and the high degree of complexity.

The food crisis of 2007-08 has spawned a large body of literature examining the causes for the spike in food commodity prices. Interest in the food crisis can be motivated by the impact of an increase in food commodity prices on food-insecure and poor households, which is substantial [67]. De Hoyos and Medvedev use domestic food consumer price data to show that the 5.6% increase in average food commodity price between January 2005 and December 2007 implied a 1.7 percentage point increase in the extreme poverty headcount at the global level, with significant regional variation [16] (see also Ivanic and Martin [39]). Nearly all of the increase in extreme poverty is reported to occur in South Asia and Sub-Saharan Africa. Furthermore, Regmi et al. show that when faced with higher food commodity prices, the poor switch to foods that have lower nutritional value and lack important micronutrients [57].

Although the IMF Global Food Index during the 12 months preceding March 2008 increased 43%, the U.S. food Consumer Price Index increased only 4.5%. The global food price index assigns greater weight to raw grains unlike the U.S. food Consumer Price Index where the basket places greater weight on processed foods. The reason for the smaller increase in food commodity prices is that Americans tend to consume highly processed foods. When U.S. consumers purchase foods from supermarkets, convenience stores, or restaurants, a large fraction goes to cover labor associated with preparing, serving, and marketing the food.<sup>5</sup> Similar patterns are observed in other developed countries. This is not the case in developing countries. The poor spend a larger fraction of their income on food, whereas the typical American spends slightly less than 14% of total expenditures on food.<sup>6</sup> In contrast, Africans spend 43% of their expenditures on food,<sup>7</sup> and those subsisting on less than one dollar per day in Sub-Saharan Africa may dedicate as much as 70% of their expenditures to food.<sup>8</sup> We need a statement that the global food index assigns greater weight to raw grains unlike the U.S. CPI where the basket is processed foods. And we should refer to rich nations in general

> _5USDA, http://www.ers.usda.gov/Data/FarmToConsumer/Data/marketingbilltable1.htm 6U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, 2006. ftp://ftp.bls.gov/pub/special.requests/ce/share/2006/income.txt. Typical American refers to an individual at the median income level._

> _7Federal Reserve Board Staff calculation, IMF, and World Bank._

> _8The International Food Policy Research Institute 2020 Discussion Paper No. 43, “The World’s Most Deprived.”_

6
rather than just the U.S.

Global (food) commodity price inflation equaled 43% during the 12 months ending on March, 2008. While this rate is high, it is not unprecedented. Similar increases in (food) commodity prices were observed between 1971 and 1974 and between 1994 and 1996 [52]. While biofuels were unique to the recent crisis, other (important) factors were common to one or both crises. Furthermore, each of the three periods of peak prices has been marked by a below-normal ratio of stocks to use. An IMF report assessing the impact of rise in food and fuel price on macroeconomic indicators such as balance of payments, overall inflation, and poverty also concludes that biofuels are one among several factors, which coincided to cause the food commodity price inflation [38]. This report also contends that restrictive trade policies were the major reason for the run-up in the price of rice.

**Table 1. Quantitative estimates of impact of biofuel on food commodity prices**

|**Source**|**Estimate**|**Commodity**|**Time period**|
|---|---|---|---|
|Mitchell[47]|75%|global food index|Jan 2002 to Feb 2008|
|IFPRI[59]|39%|corn|2000to 2007|
||21-22%|rice and wheat|2000 to 2007|
|OECD-FAO [51]|42%|coarsegrains|2008to 2017|
||34%|vegetable oils|2008 to 2017|
||24%|wheat|2008 to 2017|
|Collins[15]|25-60%|corn|2006 to 2008|
||19-26%|U.S. retail food|2006 to 2008|
|Glauber[25]|23-31%|commodities|Apr 2007 to Apr 2008|
||10%|global food index|Apr 2007 to Apr 2008|
||4-5%|U.S. retail food|Jan to April 2008|
|CEA[42]|35%|corn|Mar 2007 to Mar 2008|
||3%|global food index|Mar 2007 to Mar 2008|
|Rajagopal et al. [54]|15-28%|global cornprice|2007 to 2008|
||10-20%|global soy price|2007 to 2008|
|Hoyos and<br>Medvedev[16]|6%|global food index|2005 to 2007|

Abbott, Hurt, and Tyner, through a review of several reports on the food crisis, conclude that there are several key drivers of food commodity price increases: the depreciation of the dollar, global changes in production such as weather shocks, changes in patterns of food consumption, and the role of biofuels in commodity price increases [2]. They do not, however, present quantitative estimates of percentage contribution to the total price rise that is attributable to a specific factor such as biofuel consumption. The FAO in its State of Food and Agriculture 2008 Report also states that growing demand for biofuels is only among several factors driving 7
increases in agricultural commodity prices [21]. A USDA report describing the factors leading to the food commodity price rise concludes that the run- up in commodity price reflects a trend of slower growth in production and more rapid growth in demand that led to a tightening of world balances of grains and oilseeds over the last decade [66].

Biofuels are considered to be one among several demand-side and supply-side factors responsible for the increase in crop and food commodity prices in recent years [15, 21, 25, 43, 47, 51, 59]. Quantitative estimates of the impact of biofuels on grain prices range from 20% to 60% (see Table 1). The most pessimistic estimate ascribed 70 to 75% of the price rise between 2002 and 2008 to biofuels [47]. This report uses historical data to estimate the elasticity of world prices of agricultural commodities with respect to the price of energy and related inputs to agriculture and with respect to changes in the value of the dollar. Using these elasticities, this report estimates that between 2002 and 2007, higher prices of energy increased export prices of major U.S. food commodities by about 15 to 20 percentage points, and the depreciating dollar increased food commodity prices by about 20 percentage points. These together, it is argued, translate into a 25 to 30% increase in total price. The author argues that depletion of stocks, shifting for food cropland for production of energy crops, government response in the form of food export bans, and speculative activity, which caused prices to rise, were the consequences of the shocks considered with demand for biofuels being the main cause.

Rosegrant estimates the effect of biofuels using a simulation-based approach [59]. He simulates the market equilibrium under two different scenarios, one without high growth in biofuel and another with high growth in biofuel. For the former, he simulates a scenario in which biofuel grows at a rate which was observed between 1990 and 2000. This is the period before the rapid takeoff in demand for bioethanol. For the latter, he simulates actual demand for food crops as a feedstock for biofuel, from the years 2001 through 2007. Based on these simulations, he estimates that weighted average grain price increased by an additional 30% under the high biofuel scenario, i.e., the actual situation. The increase was highest for maize (39%) and lower for wheat and rice (22% and 21%, respectively). Using a similar approach, Rajagopal et al. estimate that U.S. ethanol production in 2007 may have been responsible for a 15% to 28% increase in the world price of maize and 10% to 20% increase in the world price of soy [54].

Global estimates of both the increase in food commodity prices and the contribution of biofuels to this increase hide variations at the regional level. Mabison and Weatherspoon argue that in South Africa food is processed and then transported several miles before it reaches the 8
consumer [44]. Thus, a large percentage of the price of food is a result of high fuel costs, which may not be true in other regions in the world. Increase in energy prices was therefore a major contributor to the increase in food commodity price in those regions. Yang, Zhou, and Liu argue that the current level of bioethanol production in China, which consumed 3.54% of total maize production of the country, reduced market availability of maize for other uses by about 6%. It is projected that depending on the types of feedstock, 5 to 10% of the total cultivated land in China would need to be devoted to meet the biofuel production target of 12 million metric tons for the year 2020 [70]. The associated water requirement would amount to 3272 per year, approximately equivalent to the annual discharge of the Yellow River. The net contribution of biofuel to the national energy pool could be limited due to generally low net energy return of conventional feedstocks. The current biofuel development paths could pose significant impacts on China’s food supply, trade, and therefore food commodity prices (see also [56]). The impact of India’s biofuel program, if successful, on food and water supply is also likely to be minimal as its policies intend to promote the cultivation of a non-edible and drought-tolerant biofuel crops such as Jatropha curcas on nonagricultural land [53].

Data also show that wheat and rice crops, which have not been utilized to a significant extent as biofuels, are the crops that recorded the highest percentage increase in price in recent years (refer to Figures 4(a), 4(b), and 4(c)). This clearly suggests that in addition to being region specific, the analyses need to be crop specific. Goldemberg and Guardabassi show that impact on food commodity prices is minimal in the case of ethanol produced from sugarcane in Brazil, which is cheaper and less intensive in inputs such as land, water, fertilizer and energy compared to corn and biodiesel [27].

Recent papers highlighted the food commodity price increases as one among several key negative impacts of first-generation biofuels [40, 41, 55]. These papers argue that some of these environmental and societal costs may be ameliorated or reversed with the development and use of next-generation biofuel feedstocks, especially cellulosic biomass from different types of wastes (agricultural, forestry, and municipal) and energy grasses such as switchgrass and Miscanthus. Certain types of biofuels do represent potential sources of alternative energy, but their use needs to be tempered with a comprehensive assessment of their environmental impacts. When evaluating the causes of food commodity price spikes, not only regional differences should be modeled, but also technological differences. Moreover, policy and differences in policy among nations should also be addressed.

Historically, agricultural commodity prices were low, and markets were characterized by 9
excess supply [24]. A recent spike in energy prices challenged this. Globalization, accompanied by capital flows, led to increase in energy demand and made biofuel a viable alternative [35, 36]. These changes challenge existing policy, as well as the (poor) response by policy practitioners in developing countries to the 2007-08 food crisis. To this end, farm support in higher income countries is a testament to the fundamental social economic and political importance of agriculture, and it leads to a very different set of issues with respect to the fuel-versus-food debate. Baka and Roland-Holst argue that the advent of biofuels offers a new opportunity for agriculture to contribute to society in Europe, and do so in a way that reduces trade rivalry and improves energy security [8]. Holding current agricultural production constant, they find that the EU has the potential to reduce oil imports between 6% and 28% by converting eligible agricultural crops into biofuels under two differing conversion scenarios.

During the 2007-08 food crisis, many countries took steps to try to minimize the effects of higher prices on their populations. Argentina, Bolivia, Cambodia, China, Egypt, Ethiopia, India, Indonesia, Kazakhstan, Mexico, Morocco, Russia, Thailand, Ukraine, Venezuela, and Vietnam are among those that have taken the easy option of restricting food exports, setting limits on food commodity prices, or both. For example, China has banned rice and maize exports; India has banned exports of rice and milk powder; Bolivia has banned the export of soy oil to Chile, Colombia, Cuba, Ecuador, Peru, and Venezuela; and Ethiopia has banned exports of major cereals. These policies contributed to the severity of the food crisis and caused contraction of the global food markets. Other nations, however, have contributed to the expansion of the global food market. Some net food-importing developing countries reduced import barriers. Morocco, for instance, cut tariffs on wheat imports from 130% to 2.5%; Nigeria cut its rice import tax from 100% to just 2.7% [67]. Although tariff reductions, in theory, may contribute to the increase in world food prices, it does reduce domestic prices in those countries.

Differences in institutions and the competitive setup lead to differences in regulation of agricultural biotechnology, where the regulatory framework ranges from promotional to preventive, and subsequently to differences in the rate of innovation [34]. These differences also lead to differences among nations in utilization of agricultural biotechnology. Although agricultural biotechnology introduces an indirect effect on yield by reducing crop losses and improved control of damage and diseases, and therefore contributes to food security, political economic considerations prevent its adoption on a global scale [28, 29, 34, 49].

Another emerging line of research uses a time-series tool to investigate the links between the 10
prices of various commodities. Serra et al. [61] used nonlinear time-series models to assess the price relationship within the U.S. ethanol industry. They used daily data on ethanol corn and crude oil prices and identified equilibrium relationships between these prices. They found that when corn prices are high relative to fuel prices, ethanol prices are mostly affected by the price of corn. When the price of corn is low relative to the price of fuel, then ethanol prices are likely to follow the price of fuel. Similarly, Balcombe and Rapsomanikis [9] established a consistent long-term equilibrium between ethanol prices and the price of sugarcane and oil using data from Brazil.

In summary, the literature suggests that one has to contend with several factors in order to explain the causes for the food crisis. On the demand side, another major factor is rapid economic growth in emerging economies, which increased demand for meat, a highly grain-intensive product. On the supply side, bad weather in key grain-producing regions (especially wheat-growing regions such as Australia), stagnation of productivity growth (due to underinvestment in agricultural research and technology and infrastructure such as irrigation), and increase in production costs (due to high energy prices) have resulted in slow or negative growth in production. Prices spiraled even further as a result of policies such as export bans on grains and import tariffs on non-grain biofuels (especially the U.S. import tariffs on cane ethanol from Brazil) and on account of speculative activity in reaction to such policies. Lastly, the depreciation of the U.S. dollar relative to major world currencies has also been a contributing factor to commodity price increases. Historically, when the dollar is weak, commodity prices tend to be higher and, when the dollar is strong, commodity prices tend to be lower. However, with different countries adopting different policies toward biofuels and trade, assessing the country-level impacts of these factors require case-by-case analysis.

With several such factors at play, identifying the contribution of any one factor such as biofuel is a challenging task. The estimates of the impact of biofuels that can be found in the literature are wide ranging, ranging between 3% and 75%. One reason why the optimistic estimates may be an underestimate is because of a lack of representation of the market for inventory. We are not aware of any standard equilibrium models including those mentioned earlier that incorporate an explicit representation of the market for inventory.

# **3. Historical trends**

Historical trends in production, consumption, inventory, and price at the global level for four major crops, namely, maize, wheat, rice, and soybeans, are shown in figures 3(a), 3(b), 3(c), 11
<!-- Start of picture text --> Corn<br>900,000 160<br>€ 800,000 140 _<br>Z 700,000 120 @<br>éS$ 600,000500,000 100 =&<br>= so 2<br>% 300,000 =<br>100,000 20<br>00<br>05 10 15 20 25 30 35 40<br>Time period - 1971/72 to 2007/2008<br><!-- End of picture text --> <!-- Start of picture text --> Wheat<br>700,000 300<br>ec<br>z 600,000 250<br>500,000 8<br>8= 400,000 200 &£<br>3 150 2<br>Hy 300,000 §<br>200,000 100 Fi<br>100,000 50<br>00<br>05 10 15 20 25 20 a5 40<br>Time period - 1971/72 to 2007/2008<br><!-- End of picture text -->
<!-- Start of picture text --> Rice<br>450,000 450<br>e 400,000 400<br>< 350,000 350 @<br>r=<br>=Cal 250,000 250 4<br>- 150,000 150 ;<br>§ 100,000 100<br>50,000 50<br>00<br>05 10 15 20 25 30 35 40<br>Time period -1971/72 to 2007/2008<br><!-- End of picture text --> <!-- Start of picture text --> 250,000 Soybean 350<br>= 200,000 300 =<br>< 20 8<br>§ 150,000 200 £R<br>= 2<br>3 400,000 150 é<br>3 100 3<br>50,000 a<br>50<br>00<br>05 10 15 20 25 30 35 40<br>Time period -197 1/72 to 2007/2008<br><!-- End of picture text -->
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<!-- Start of picture text --> 450<br>y = 2E+06x -1.058<br>400 R2 = 0.99955<br>350<br>y = 3E+06x -0.84<br>300 R2 = 0.54417<br>Rice<br>250<br>200 y = 4E+07xR2 = 0.62125 -1.041 Wheat<br>Maize<br>150 y = 1.0093x 0.509<br>R2 = 0.26548 Rapeseed<br>100<br>50 y = 5022.5x -0.325 Soybean<br>R2 = 0.04127<br>0<br>0 50000 100000 150000 200000 250000<br>Year ending stock (in Thousand metric tonnes)<br>World price in $/tonne<br><!-- End of picture text -->

**Figure 6. The observed correlation between price and inventory**

The spike in food commodity prices was not instantaneous, but resulted from a steady but gradual decline in stock-to-use. On the supply side, sluggish growth in world food production between 1995 and 2003, and a decline of stock-to-use ratio of world grain and oilseed stocks from 35% in 1985 to less than 15% in 2005 [66] – Stock-to-use ratio declined by more than 50%. Low food commodity prices over the last several decades reduced incentives for maintaining food stockpiles and for funding research and development to increase yields. Regulation in key regions also hampered research and development of yield-enhancing technologies.

The sluggish growth in food production, coupled with rapid growth in manufacturing production, causes biased expansion of the production possibility frontier toward manufacturing goods.<sup>10</sup> Agricultural output in the emerging markets for the last two decades has been at most about half that of GDP growth. In China, 20% of humanity and the world’s largest consumer and producer of food, non-agriculture productivity has been growing 3-5 times faster than agriculture.

This bias suggests higher food prices. We illustrate this graphically in figure 7. Assume the world is producing food, denoted , and manufacturing, denoted , with increasing opportunity costs, and homothetic preferences. In addition, normalize the price of manufacturing to . Then, curve in Figure 7 depicts the world production possibility frontier before biased growth. The equilibrium price equals , and the amount of food and manufacturing produced and consumed are and , respectively. Introducing growth that

> _10Although Mitra and Martin [45] found that agriculture and manufacturing growth rates are converging, but the productivity of several commodities like wheat and soybeans have been lagging because of regulation that did not enable adopting of new biotechnologies (Alston et al. [4], Sexton et al. [62] and Graff et al. [27])._

16
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not hold all other factors constant, and the correlation between consumption and GDP/capita does not identify causation, it does suggest a strong positive linear relation between consumption and income at the world level for corn, soybean, rapeseed, and oil palm, and correlation coefficient of about 0.75 for rice and wheat (Table 2). The positive correlation computed above suggests that income is an important factor affecting consumption, and thus prices. Note that although globally the correlation between consumption and income is positive, in some regions it may be negative. Most notably, corn, rice, and wheat consumption in China declined during 2001 to 2007. Below we use income elasticity of demand from existing literature to incorporate income growth into our analysis.

**Table 2. Correlation between income and consumption of major agricultural commodities in major**

**regions** **<u>during 2001 to 2007</u>**

|**Region **|**Corn**|**Soybean**|**Rapeseed**|**Rice**|**Wheat**|**Oilpalm**|
|---|---|---|---|---|---|---|
|**Argentina**|0.87|0.99||0.39|-0.04||
|**Brazil**|0.89|0.89||-0.13|0.80|0.97|
|**China**|-0.53|0.97|0.10|-0.94|-0.44|0.93|
|**EU27**|0.68|-0.63|0.96|0.80|0.16|0.97|
|**India**|0.78|0.90|0.59|0.51|0.66|0.28|
|**US**|0.82|0.53|0.81|0.71|-0.50|0.89|
|**ROW**|0.98|0.35|0.98|0.98|0.86|0.99|
|**World**|**.98 **|**.98 **|**.94 **|**.74**|**.76 **|**.99 **|

Figures 4a-4f depict world consumption of various coarse grains and oil crops.

1. It illustrates the upward trend in global consumption from 2001 to 2007 for the various crops. From 2001 to 2007, demand grew, and the demand curve for the different crops shifted up and to the right. For some crops, however, the growth rate was larger than for others. Whereas corn demand grew by about 30%, rapeseed demand grew by almost 100% from 2001 to 2007.

2. In addition, growth was not symmetric across regions. Whereas globally consumption of all crops increased with income (at the world level, income is positively correlated with consumption, and world income grew throughout the period investigated), in some regions consumption of certain crops decreased. For example, corn, rice, and wheat consumption in China went down by 12.7%, 23.7%, and 20.9%, respectively, although global consumption increased by 24.0%, 3.2%, and 4.3%, respectively.

18
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The increase in food commodity prices over the last several years is part of a general increase in global commodity prices, including minerals, metals, and energy. Although the food commodity price index rose to a historic high in 2007-08, the price indices for all commodities, and for crude oil in particular, have significantly outpaced it. In fact, between January 2002 and July 2008, the IMF price index for food commodities rose 130%, compared with 330% for all commodities and 590% for crude oil.

Another factor to consider is the increase in energy prices [33]. To this end, the energy impact on food commodity prices should be divided into two factors: the allocation of land to biofuel crops (which reduces food and feed availability and increases the aggregate demand for food commodities), and the increase in energy prices (which increases production costs and reduces the supply of food commodities). See also Hochman et al. [32]. First-generation biofuels, which are derived primarily from corn and sugarcane, compete with food and feed, resulting in higher demand for agricultural commodities and thus in higher prices. The introduction of biofuels, however, also lowers fuel prices [54]. Yet, the literature fails to recognize that lower fuel prices affect farm-level costs. Introducing energy markets, with all its complexity, to our multi-market framework reduces the impact of biofuels on food commodity prices further.

To reiterate, the data show that successive years of positive excess demand led to the gradual depletion of inventory, which reached an historical low in 2008.

The following section discusses the implications of inventory and describes one approach for modeling the demand for inventory in a multi-market equilibrium framework.

# **5. An analytical framework with inventory demand**

The peak of the food crisis marked the depletion of stored grain stocks to historically low levels that had not been witnessed since the 1970s [52, 68]. For storable goods, the ability to adjust the level of inventory can play a crucial role in maintaining price stability and reducing price volatility when there is a supply or demand shock [68]. During periods of excess supply, demand from storers protects producers from rapidly descending prices, while during periods of scarcity; supply from inventory protects consumers from rapidly ascending prices.

We do not focus on the theoretical underpinnings of speculative inventory, which is dynamic and forward looking. Anticipation of future inventory decisions affects current ones, and this complexity is augmented by the inventory constraints, i.e., one cannot borrow from the future or that inventory cannot be negative [68]. Instead we assume that one can estimate an 25
empirically derived inventory demand function using historical data on prices and inventory. Formally harvest, , is a function of past period crop prices , where and such that denotes the set of crops and denotes a region. Assuming prices follow a random walk, then suggests that the end of period inventory is only a function of current and past prices, as well as the beginning stocks in period , i.e., . Consumption demand for crops comprises of demand for food ( and demand for biofuel production ( . Both demand for food/feed and demand for biofuels are a function of the price of crops ( and the price of energy ( . In addition, demand for food and feed at region at time is a function of GDP per capita, .

With inventory, the equilibrium price does not need to equate harvest, , plus imports, , with consumption, , plus exports, . However, it should equate world supply, , plus global beginning stock, , with world demand, , plus global ending stocks, : The left-hand side can be called total availability, , at time . The equilibrium condition can now be written as Knowing , and the shape of demand functions , one can determine the effect of different levels of biofuel mandates on crop prices.

26
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<!-- Start of picture text --> Staple crops output prices<br>“ 7a<br>land and energy prices<br><!-- End of picture text -->
<!-- Start of picture text --> Energy (fuel) consuming<br>sectors<br>Intermediate input<br>(e.g., biofuel)<br>Staple crop A<br><!-- End of picture text -->
accompanied by an increase in demand of crop B. As the price of maize increases, maize for feed may be substituted with soybeans. Similarly, as the price of rice increases, wheat consumption increases. On the other hand, the nutritional characteristics of soybean for feed may complement those of maize—the two crops may then be complements, not substitutes.

Identifying the dominant forces between the different staple crops is crucial for correctly disaggregating the horizontal structure. In sum, the horizontal structure is influenced by (i) factors affecting derived demand for inputs (especially land and energy), and by (ii) factors influencing demand for the different crops, especially growth in disposable income and biofuels (ethanol and biodiesel) consumption. Many of these factors are not specific to the horizontal structure of crop production, and are also affected by the vertical structure. Therefore, and to better understand these cross-market effects, we next disaggregate the vertical structure.

# _6.2. Vertical structure_

Demand for staple crops is affected by the introduction of new markets, namely, ethanol and biodiesels. More specifically, production of ethanol and biodiesels created new demand for staple crops, which further increased the price of the feedstock (the staple crops used to produce biofuels). Increasing demand for staple crops triggers an increase in land allocated to crops used to produce first-generation biofuels, i.e., less land allocated to food and feed and more allocated to energy production. This effect is augmented when the price of crude oil—a main source of transportation fuel— increases [36]. The surge in crude oil prices during 2007 created a very profitable (although temporary) environment for crops used to produce biofuels [35]. Many farmers switched to crops used to produce ethanol and biodiesel, and limited the amount of crops sold to the food markets. The competition between uses (food, feed, and feedstock) within a feedstock market can become intense, much more than between food grain and feed grain. The vertical structure is influenced by input markets (especially energy), and by demand for the different end products, especially food and feed versus biofuel (ethanol and biodiesel) consumption.

# _6.3. The numerical model_

We now use the horizontal and vertical structure discussed above to model our empirical 30
model. Our model extends the single-region, single-crop model with inventory discussed above in section 3, to a multi-region framework, where demand for each crop is composed of food/feed, inventory, and where applicable, demand for biofuels. We apply the model for five major crops, namely, corn, soybean, rapeseed, rice, and wheat. With the exception of rice and wheat, all the other crops are currently being used to produce biofuel.

Biofuel from corn, soybean, and rapeseed is jointly produced along with a co-product that is itself a substitute for the raw grain or the oilseed. For instance, in the case of corn, 1 bushel (56 pounds) of corn yields approximately 2.75 gallons of ethanol and 18 pounds of distiller grains, which is a substitute for corn grain. A fraction of the quantity of original crop used for biofuel is replaced in the form of co-product. Therefore, for these three crops, we compute an effective demand of the particular crop for biofuel, which equals the crop consumption for biofuel minus the quantity of a co-product. In the case of corn, the effective demand of corn is bushels per 2.75 gallons of ethanol. We assume that biofuel production function is of Leontief (fixed-proportion) type.

We divide the world into seven major regions, namely, Argentina, Brazil, China, European Union (EU-27 countries), India, United States, and an aggregate that represents the rest of the world (ROW), and focus on the time period between the year 2001 and the year 2007.

Let be harvest of crop in region at time , is demand of crop in region at time for food/feed consumption, is demand for crop in region at time for biofuel production, and is global demand for inventory of crop at time . Let and denote constants which are determined through calibration of the supply and demand functions, denotes exchange rate of currency in region at time with respect to the US$, is world price of crop at time , and is world price of energy at time .

With the exception of the demand for inventory, we assume a linear structure for supply and demand. The linear structure generally serves as a good approximation for small disturbances or shocks. Supply, which is the sum of harvest and imports of crop in region at time , is modeled as, Demand, which is the sum of domestic demand for food/feed consumption and the demand for 31
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with demand for inventory and once without. The calibrated demand and supply parameters are used to numerically calculate the effect of each of the different shocks on the observed price in a given year.

**Table 3. Range of elasticities contained in the literature cited in FAPRI database**

|||**Supp**|**ly (1) **|**Dema**|**nd(2) **|**Incom**|**e(3) **|
|---|---|---|---|---|---|---|---|
|**Commodity **|**Region**|**min**|**max**|**min**|**max**|**min**|**max**|
|**Corn**|**Argentina **|0.65|0.75|-0.4|-0.3|0.35|0.45|
||**Brazil**|0.37|0.47|-0.4|-0.1|0.35|0.45|
||**China**|0.08|0.18|-0.14|-0.6|0.75|1|
||**EU**|0.01|0.13|-0.44|-0.24|0.1|0.2|
||**India**|0.16|0.26|-0.28|-0.22|0.75|1|
||**U.S.**|0.45|0.55|-0.24|-0.1|0.05|0.1|
||**ROW**|0.45|0.55|-0.43|-0.21|0.4|0.6|
|**Soybeans **|**Argentina**|0.27|0.37|-0.3|-0.2|0.35|0.45|
||<br>**Brazil**|0.29|0.39|-0.21|-0.11|0.35|0.45|
||**China**|0.4|0.5|-0.25|-0.15|0.75|1|
||**EU**|0.14|0.24|-0.3|-0.2|0.1|0.2|
||**India**|0.31|0.41|-0.35|-0.25|0.75|1|
||**U.S.**|0.18|0.28|-0.48|-0.31|0.05|0.1|
||**ROW**|0.18|0.28|-0.48|-0.31|0.4|0.6|
|**Rapeseed **|**Argentina**|0.53|0.63|-0.35|-0.03|0.35|0.45|
||**Brazil**|0.53|0.63|-0.35|-0.03|0.35|0.45|
||**China**|0.21|0.31|-0.35|-0.25|0.75|1|
||**EU**|0.23|0.33|-0.13|-0.03|0.1|0.2|
||**India**|0.29|0.39|-0.3|-0.2|0.75|1|
||**U.S.**|0.53|0.63|-0.35|-0.03|0.05|0.1|
||**ROW**|0.53|0.63|-0.35|-0.03|0.4|0.6|
|**Rice**|**Argentina**|0.27|0.37|-0.43|-0.38|0.35|0.45|
||**Brazil**|0.27|0.37|-0.43|-0.38|0.35|0.45|
||**China**|0.27|0.37|-0.71|-0.54|0.75|1|
||**EU**|0.27|0.37|-0.43|-0.38|0.1|0.2|
||**India**|0.27|0.37|-0.43|-0.38|0.75|1|
||**U.S.**|0.27|0.37|-0.87|-0.77|0.05|0.1|
||**ROW**|0.27|0.37|-0.43|-0.38|0.4|0.6|
|**Wheat**|**Argentina**|0.36|0.46|-0.39|-0.28|0.35|0.45|
||<br>**Brazil**|0.38|0.48|-0.38|-0.27|0.35|0.45|
||**China**|0.04|0.14|-0.18|-0.07|0.75|1|
||**EU**|0.07|0.17|-0.33|-0.26|0.1|0.2|
||**India**|0.24|0.34|-0.37|-0.32|0.75|1|
||**U.S.**|0.43|0.53|-0.35|-0.25|0.05|0.1|
||**ROW**|0.43|0.53|-0.35|-0.25|0.4|0.6|

1. Own price elasticity of supply

2. Own price elasticity of demand 3. Income elasticity of supply 33
Key parameters in the calibration of these functions are elasticities of supply and demand, i.e., the sensitivity of a relative change in quantities supplied or demanded to a given relative change in (energy) prices. Given the wide range of elasticities reported in the literature and the sensitivity of the simulation to elasticities, for each crop we chose to sample 100 times from within a range of elasticities. The range of elasticities is shown in Table 3. The elasticity of supply and demand with respect to energy price is assumed to lie within the range and , respectively. This reflects the assumption that demand is less responsive than is supply to energy prices.

Note that our specification does not include cross-price elasticities on the supply or the demand side. The reason for this is to overcome computational constraints. We chose to investigate the robustness of the results through a sensitivity analysis with respect to own-price, income, and energy elasticities and by employing alternative specifications of the demand function. Our computational capacity did not allow us to introduce cross-price elasticities to this numerical exercise. This limitation will be addressed in future work.

**Table 4. Inventory demand parameters**

|**Parameter**|**corn**|**soybean**|**rice**|**wheat**|
|---|---|---|---|---|
||0.0186|0.0186|0.0186|0.0186|
||-0.8004|-.5096|-.5096|-1.4676|

Following Carter et al. [14] we estimated the inventory demand parameters using instrumental variable techniques, because inventory is correlated with the disturbance, whereas harvest is uncorrelated with these disturbances but correlated with inventory (harvest is both exogenous and relevant). We estimated the inventory demand function, while using harvest as an instrumental variable. We tested alternative specifications and also introduced crop-specific dummy variables (Table 4). In all cases, however, we could not reject the hypothesis that the specification chosen is correct.

Given the relevant elasticities for each region, we calibrate the parameters a, b, c, d, e, f, g, h, k, and l as follows: 34
where denotes the year chosen for calibration, is own-price elasticity of supply of crop in region , is elasticity of supply of crop in region with respect to energy price, is own-price elasticity of demand of crop in region for food/feed consumption, is own-price elasticity of demand for crop in region for biofuel production, is elasticity of crop demand for biofuel production with respect to energy price for crop in region , and is the income elasticity of demand of crop in region .

# _6.5. Description of the shocks_

To calculate the impact of each of the various factors contributing to the change in average yearly price for each commodity, we shock the system (in our case the equation that represents 35
the market-clearing condition for each commodity) and calculate the counter-factual world price that would have prevailed for the commodity. We simulate four different types of shocks, namely, a biofuel shock (due to biofuel mandates), a food/feed demand shock (due to economic growth), an exchange rate shock, and an energy price shock. We simulate one shock at a time. For each type of shock, we set the value of the shocked parameter equal to its value in the base year. To give an example, the biofuel shock for corn in the year 2005 is computed as the ratio of the world corn biofuel production in 2001 ( billion gallons) and world corn biofuel production in 2005 ( billion gallons), which is equal to . We simulate each of these shocks for six years, 2002 through 2007. Furthermore, we simulate the shocks twice, once with a market for inventory and once without, where inventories are added to aggregate supply and demand. We perform 100 simulations of the scenario for the various shocks for each crop and for each time period. The reported value is the mean of these outcomes.

1. **Income shock** : To simulate the income shock, we multiply the income coefficient of the demand for food/feed for that region by a scalar. The scalar takes a value greater than one in the case of positive income shock and a value less than one with a negative shock. Mathematically, the market-clearing identity is now represented as,

where, and is a scalar quantity used to simulate a shock. Although the counterfactual scenario assumes no income growth, the income elasticity remains positive.

2. **Biofuel demand shock** : The biofuel shock is used to simulate a condition with no biofuel production. Mathematically, the market-clearing identity is now represented as, 36
where is a binary variable. When it is zero, it simulates a counterfactual scenario with no biofuel.

3. **Exchange rate shock** : To simulate changes in the exchange rate over time, we multiply all prices within a given region by the annual exchange rate. Mathematically, the market-clearing identity becomes, where is a scalar quantity used to simulate an exchange rate shock that affects the domestic price in a region. Since in our model this is the only path through which prices can differ between regions, this work overestimates the exchange rate effect.

4. **Energy price shock** : To simulate changes in energy prices over time, we multiply energy prices within a given region by the annual change in energy prices. Mathematically, the market-clearing identity becomes, 37
where is a scalar quantity used to simulate an energy price.

# _6.6. Numerical scenarios_

Given the cumulative change in a variable with respect to the year 2001, we use the market-clearing condition to derive a counterfactual equilibrium world price for each crop for the various shocks for each year. We do so for four different alternative scenarios which either differ in the assumed range for elasticities used in calibration of supply and demand functions, or differ in the specification of the demand for food/feed (whether GDP per capita is explicitly represented in demand) or differ in parameters of the inventory demand function. Given the challenge of estimating a point estimate for the various elasticities, as well as the inventory parameters, we simulated these alternative scenarios to determine the robustness of our results.

The first scenario, which we henceforth refer to as the _baseline scenario,_ is one in which we use the range of price and income elasticities reported in the literature, namely, that mentioned in the USDA’s database of elasticities and in the FAPRI database. Under this scenario, the parameters for the inventory demand function are those that we estimate ourselves using the specification of Carter et al. [14]. As mentioned earlier, we perform 100 simulations of this scenario for the various shocks for each crop and for each time period but report the mean value of these outcomes.

In the second scenario, the _inelastic scenario_ , we assume a narrower range for elasticities, which is on average more inelastic compared to the baseline scenario and follows Gardner [24]. This scenario further differs from the baseline in that we employ a demand specification that does not include income. The reason for excluding income is that some of the elasticities reported in the literature were based on models that did not include income.

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**Table 5. Sources of data**

|**Data**|**Source**|
|---|---|
|Production, consumption and stocks<br>in each region|<br>U.S. Department of Agriculture’s Production, Supply<br>and Distribution Data base(1)|
|Domestic price of grains, sugar and<br>oilseeds|Food and Agriculture Organization of the United<br>Nations(FAO) (2)|
|World energy price|International Monetary Fund Primary Commodity<br>Prices(3)|
|Biofuelproduction and consumption|Renewable Fuels Association(4)|
|Exchange rates|U.S.Federal ReserveStatistical Database(5)|
|Price and income elasticities of<br>supplyand demand for crops|Food and Agricultural Policy Research Institute<br>ElasticityDatabase and USDA elasticitydatabase(6)|

1. http://www.fas.usda.gov/psdonline

2. http://faostat.fao.org/ 3. http://www.imf.org/external/np/res/commod/index.asp 4. http://www.ethanolrfa.org/industry/statistics/#E 5. http://www.federalreserve.gov/releases/G5A/ 6. http://www.fapri.iastate.edu/tools/outlook.aspx Finally, to test the robustness of the inventory demand parameters, we simulate a fourth scenario using Carter et al.’s estimates for the inventory demand function as opposed to our own. Note that different from us, Carter et al. [14] estimate the inventory demand based on U.S. data for 2006 through 2008, while we use world data for 2001 through 2008.

# _6.7. Sources of data_

The various data sources are shown in Table 5. Data on production, consumption, beginning and ending stocks, imports, and exports for each region are obtained from the U.S. Department of Agriculture’s Production, Supply and Distribution database. Data on crop prices within each region are obtained from the FAO database. A key set of parameters in simulation models is the elasticities of crop supply and crop demand. Our specification of supply and demand requires information on elasticities of supply and demand with respect to own-price elasticities of supply and demand with respect to energy price and the income elasticity of demand. The range of elasticities contained in FAPRI database and in the literature cited by the USDA database is shown in Table 3.

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# **7. Results**

We report two different price changes:

1. Reduction of commodity price if key variables would have stayed at their 2001 levels, . Technically, it is the percentage difference between the actual price in a given year and the counter-factual price for the same year, and

2. The increase of the commodity price attributed to a change in one of the variables (income, biofuel mandate, exchange rate , energy prices) between 2001 and the specific year, , where . Technically, it is the percentage difference between counter-factual price for a given year and the price in 2001.

The simulations compute . We then compute as follows: let denote the total percentage price change between the year and year 2001; then, Total change in price from year to year 2001 that is explained by our model equals the sum of over all the shocks. The figures depict – namely, the food commodity price reduction attributed to a shock that eliminates one of the factors that caused prices to change after 2001, whereas the tables show – namely, the increase in commodity prices from 2001 attributed to one of the factors that caused prices to change after 2001. In both cases we report the mean outcome of 100 simulations, where for each run we draw a number from a range of plausible values (for price, income, and supply elasticities) and compute the counterfactual outcome. When presenting prices for different crops, we distinguish between two different specifications: one with inventory demand function and another without inventory demand. For each crop, we show the impact of these shocks one at a time.

The analysis includes five simulated scenarios for each of the five crops, namely, corn, soybeans, rapeseed, rice, and wheat. The baseline scenario’s outcome is contrasted with alternative specifications to evaluate robustness of the relative and absolute value of the numerous shocks. The alternative scenarios illustrate the robustness of the results presented with respect to relative impact, but the absolute impact usually becomes larger as elasticities become smaller. Some but not all scenarios include an income term in the demand specification 40
<!-- Start of picture text --> Annual market prices<br>o<br>8<br>@<br>os J<br>‘= ci See oil<br>oO<br>2002 2003 2004 2005 2006 2007<br>Period<br>—*— Corn<br>—*— Soybeans ——®*— Rice<br>——*—— Wheat<br><!-- End of picture text -->
<!-- Start of picture text --> Corn Soybeans<br>200000 200000<br>© 1500004¢ © 450000<br>E iit E<br>3 100000 3 100000<br>F 50000 F 50000 a eee<br>2002 2003 2004 + 2005 + 2006 = 2007 2002 2003 2004 +=—2005 «=.2006 = 2007<br>Period Period<br>Rice Wheat<br>200000 200000 ®<br>©z3oc 150000100000 e ©z3 150000100000 —r)<br>= e e oF<br>50000 50000<br>2002 2003 2004 + 2005 += 2006~=Ss 2007 2002 2003 2004 «2005 «= 2006S 2007<br>Period Period<br><!-- End of picture text --> <!-- Start of picture text --> Stock to use and prices<br>Cor Soybean<br>e<br>8 R40<br>.<br>3, 6 -*——e—__» 8<br>°o o<br>A2 14 16 18 2 22 14 AG 18 2<br>stock_use stock_use<br>8 Rice 3 Wheat<br>e<br>‘<br>8 8<br>co} iJ<br>AS 2 25 3 15 2 25 3<br>stock_use stock_use<br><!-- End of picture text -->
(i.e., between 2006 and 2007). One explanation for the observed price fluctuation in corn and soybeans is that consumption of corn for biofuel became significant around 2006, when the federal government began implementing biofuel mandates. Although biofuel subsidies have been in effect for several decades, mandates are the main cause for the recent increase in biofuel production. Furthermore, land allocated to corn replaces soybean land, resulting in higher soybean prices (not modeled explicitly in the report, because we do not have data on land use). This complements the upward pressure on soybean prices attributed to biodiesel production. On the other hand, economic growth results in structural changes to demand in countries like China, where increased demand for feed led to larger demand for soybeans [66] (considerable growth (around 20%) between 2000 and 2008 was also observed for pork).

Because we assume rice and wheat are not utilized for biofuels in any significant quantities, and since land growing rice and wheat do not generally compete with corn, sugarcane and oilseeds, we conclude that the prices of rice and wheat are not influenced by biofuels. However, a general equilibrium framework, in contrast to the multi-market framework presented here, may identify indirect linkages between biofuel production and rice and wheat [47].

When the market for storage is excluded, higher price fluctuations are documented (figures 21 and Table 6). We plotted the standard deviation of prices for five crops for a represented shock, and show that the shock caused prices to fluctuate more when inventory is not modeled explicitly. This picture emerges for all shocks. Inventory specification matters.

Introducing inventory demand alters outcomes. Then, because we do observe inventories and introducing inventory to the numerical model makes a difference, we focus below on simulations with inventory demand.

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<!-- Start of picture text --> standard deviation Corn Standard deviationao Soybeans<br>88<br>88<br>83<br>g_—+ 8<br>> |¢#—_e+$F — o /¢—_—_e——_+ + + —=<br>2002 2003 2004 2005 2006 2007 2002 2003 2004 2005 2006 2007<br>Period Period<br>e Inventory ¢ No inventory e Inventory ¢ No inventory<br>———_ Fitted values ——— Fitted values ———_ Fitted values ———_ Fitted values<br>standard deviation standard deviation<br>Rice Wheat<br>88<br>id©id©<br>33<br>ScN=i —_——_4—_——= — —_@—_—_9< & N=o ¢—__+ina ++— —=a=o -4<br>2002 2003 2004 2005 2006 2007 2002 2003 2004 2005 2006 2007<br>Period Period<br>e Inventory ¢ No inventory e Inventory a No inventory<br>——— Fitted values ——— Fitted values ——— Fitted values ———._ Fitted values<br><!-- End of picture text -->
# _7.2. Decomposing the change in crop prices_

Decomposing changes in crop prices involves numerous factors, some affecting demand whereas some affect supply. We limit our analysis to four such factors; namely, biofuel, income, exchange rate, and energy prices. Other factors that need to be considered, but are outside the scope of this work, are productivity shocks, trade policy, and speculation. We report below the change in prices (due to the shocks) in a certain year compared to actual or observed prices in that same year.

## _7.2.1. Corn_

The results of four different shocks, applied one at a time for the period 2002-2007, are depicted in Figure 22. In particular, the figure presents the decline in the price of corn, if demand for corn did not grow because of the GNP growth, biofuel was not mandated, energy prices did not increase, and the US$ was not devalue. Note that the results in table 6, and different from the figures, project the inverse relationship, the increase in food commodity price because of economic growth, introduction of biofuel, higher energy prices and the depreciation of the US$. Throughout the text, as mentioned above, the figures and the tables present the results of our simulated scenarios from two vantage points. The table shows the price increase because of a change in factor _i_ (i.e., ), whereas the figures represent the price reduction if the observed change in factor _i_ would not have happened (i.e., ) – equation (21) shows the relation between and .

Two dominant factors affect corn prices: The introduction of biofuels and economic growth (see Figure 22 and Table 6). Whereas in the absence of an increase in demand for corn, prices would have been 15% lower in 2007, prices would have been 10% lower without the increase in biofuel production (Table 6). If we ignore the fact that the co-product of corn ethanol production, namely, distillers grains, is a substitute for corn, then biofuels appropriate a larger quantity of corn that is traditionally consumed as feed and, as a result, become responsible for about 12% of the price increase in 2007. The single largest use of corn is feed grain for animals, which is used for meat and dairy. Furthermore, meat consumption tends to increase with income, resulting in higher demand for corn in emerging economies. As per FAO statistics, in China, which witnessed average growth rate of 8.5% between 1990 and 2003, per capita meat consumption increased 150% from approximately 20 kg per person per year in the year 1985, to approximately 50 kg per person per year by the year 2000. Furthermore, increase in per 45
<!-- Start of picture text --> Corn<br>N<br>me<br>8<br>F i<br>So a ss __<br>= ccna<br>2002 2003 2004 2005 2006 2007<br>Period<br>—?— Biofuel —?— income<br>——®— Exchange rate<br>——®— energy prices<br><!-- End of picture text -->
<!-- Start of picture text --> Soybeans<br>N<br>=<br>3<br>EO _—_®<br>a<br>no]<br>N<br>2002 2003 2004 2005 2006 2007<br>Period<br>—?— Biofuel —?— income<br>——®— Exchange rate<br>——®— energy prices<br><!-- End of picture text -->
<!-- Start of picture text --> Rice<br>wo<br>=)<br>i4<br>o<br>E<br>$3<br>a2)<br>2002 2003 2004 2005 2006 2007<br>Period<br>—#— income<br>——®— Exchange rate ~—®— energy prices<br><!-- End of picture text -->
interest in rice farming.

The move towards free trade in agricultural production begun with the Uruguay Round of General Agreement on Tariffs and Trade (GATT), will affect the sustainability of rice farming in these countries. There will be economic incentives for the movement of land, water, and labor out of rice to other economic activities. Second, the potential for increased productivity for the irrigated ecosystem, created by the dramatic technological breakthrough in genetic enhancement of seeds that initiated the green revolution, has almost been exploited, while improved varieties for the unfavorable ecosystems expected from the ongoing gene revolution are still on the horizon. As such, the worldwide situation with genetically modified (GM)rice is basically development as opposed to distribution—the problem is not regulatory constraints on distribution but lack of varieties with required traits.<sup>13</sup> Currently, several dozen varieties of GM rice are underdeveloped or are undergoing field testing. Between 1982 and 1997, 160 patents were granted or pending. In 2001, the mapping of the rice genome was completed, spurring further GM development.

In the absence of exchange rate shock, rice price would have been 6% lower from the actual price observed in 2007 (Figure 24). Although China is the largest rice producer (its share in global rice production is approximately 33%), and its currency only marginally fluctuates relative to the US$, many other countries that produce rice saw their currency strengthen relative to the US$.

In response to rising food prices, different countries adopted a range of different short-term measures. An FAO report [17] classifies these measures into three main groups, namely, trade-oriented policies such as reducing import tariffs and export restrictions, consumer-oriented policies such as food subsidies price controls and policies reducing inventory, and thirdly, producer-oriented policies such as input subsidies. Based on information obtained from 81 countries, they report the two most widely applied measures are reduction of tariffs, as reported by 43 countries, and releasing grain from public stocks, as reported by 35 countries. While tariff reductions are easy to implement, the efficacy of the latter policy depends on the level of reserves. In an attempt to shore up domestic supply, several major grain-exporting nations also imposed export restrictions and in some cases banned them altogether in response to the food price inflation. Examples of nations with such restrictions include Argentina, Cambodia, China, Egypt, India, Kazakhstan, Pakistan, Russia, Ukraine, and Vietnam. However, world prices escalated as a result of such restrictions. The

> _13http://beta.irri.org/index.php/Home/Welcome/Frontpage.html_

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most severe impact of export restrictions has been on world rice market, which is traditionally thin in trade. In our report, the impact of trade policy restrictions are indirectly captured through exchange rate shocks, and magnifies the impact of the exchange rate on rice prices.

Energy prices contributed about 3% to the price increase. The spike in crude oil and the impact it had on energy prices caused prices to increase albeit only by a few percentage points.

Explicit consideration of storage demand has a statistically significant effect on model predictions for rice. Similar to corn and soybeans, we reject the hypothesis that the dynamic price path of rice with an inventory demand function is not different from a model without an inventory demand function.

Using equation (21), we find that income shock contributed 29.6% to the increase in rice price in 2007 relative to 2001, exchange rate shocks contributed 13.0%, and energy shocks contributed at least 6.7%.

# _7.2.4. Wheat_

**The main contributor to the increase in the price of wheat is the demand shock.** In 2007 world production of wheat was 607 million tons, making it the third most-produced cereal after maize (784 million tons) and rice (651 million tons). Wheat also supplies much of the world’s dietary protein and food supply, with China consuming in 2007 nearly 30% of global wheat consumption. Therefore, the impact of an income shock dominates the other effects (figure 25). It contributed more than 21% to the increase in wheat prices during 2007 (Table 6 and Figure 25).

The depreciation of the US$ resulted in wheat prices being 10% higher. Finally, and similar to other crops, we reject the hypothesis that price dynamics does not depend on inventory at a 1% significant level.

Using equation (21), we find that income shock contributed 34.4% to the increase in wheat price in 2007 relative to 2001, exchange rate shocks contributed 19.5%, and energy shocks contributed at least 8.6%.

As pointed out above, although biofuel is an important factor contributing to the price spike, demand growth due to income and probably population growth is the main factor. Other scholars have also arrived at the conclusion that demand growth is a key factor affecting food prices. Employing a partial equilibrium framework, Subramanian and Deaton [63] argued that 50
<!-- Start of picture text --> Wheat<br>ioSoO ° eT a<br>a<br>no}<br>E an<br>N<br>2002 2003 2004 2005 2006 2007<br>Period<br>—#— income<br>—®— Exchange rate —-®— energy prices<br><!-- End of picture text -->
Pricel <!-- Start of picture text --> 0<br>cB<br> Pie SP BeSS BE<br><!-- End of picture text --> Quantity
for inventory, are challenging but are key step to accurately measuring the factors causing the food inflation of 2007-08, two additional scenarios were numerically simulated to further check the robustness of our conclusion.

# _7.3.1. Inelastic scenario_

Key parameters in our analysis and in simulation-based models in general are the elasticities, which are used to calibrate the demand and supply curves. The alternative specification, denoted the _inelastic scenario_ , assumes lower elasticities. The elasticities used in the baseline scenario were obtained from well-known and widely used sources such as the FAPRI elasticity database and the USDA elasticity database.<sup>14</sup> However, according to several other researchers, the elasticities of supply and demand for agriculture are more inelastic than those reported in the above databases (For instance, see Gardner [24] for a discussion of supply and demand elasticities for agricultural commodities). In order that the elasticities are on average lower than those in the baseline scenario and also conservative, we chose own-price supply elasticities in the range 0.2 to 0.3 and own- price demand elasticities in the range -0.3 to -0.2. Employing these elasticities, we find that the main qualitative conclusions regarding the importance of the different shocks from the baseline scenario hold.<sup>15</sup> Comparing the baseline scenario to the inelastic scenario results in the price changes summarized in Table 8. This comparison emphasizes the importance of obtaining good elasticity estimates. Elasticity matters, and the more inelastic scenario result in a larger impact.

> _14http://www.ers.usda.gov/Data/InternationalFoodDemand/_

> _15To this end, using world data on four major crops, namely, corn, soybeans, wheat, and rice from 1960 to 2007, Roberts and Schlenker estimate that short-term, own-price elasticity of supply and demand for calories from these crops is less than 0.15 and greater than -0.1, respectively [58]._

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**Table 8. Comparison of results between main and sensitivity analysis** (% change as compared to the counterfactual scenario in 2007)

|**Shock**|**Crop**|**Main Analysis**|**Sensitivity analysis**|
|---|---|---|---|
|**Biofuel**|**Corn**|9.8%|12.7%|
||**Soybean**|3.4%|3.7%|
|**Income growth**|**Corn**|15.3%|20.3%|
||**Soybean**|14.7%|16.0%|
||**Rice**|16.1%|17.2%|
||**Wheat**|21.1%|25.8%|

## _7.3.2. Price effect of shocks using inventory specification of Carter et al. [14]._

Finally, we simulate the model using the inventory demand parameters estimated in Carter et al. [14]. Results confirm the conclusions derived for the baseline scenario. The price effect now is marginally smaller for all shocks. This is because the estimates of the parameters of the inventory demand employed in the elastic scenario imply an inventory demand function that is on average more elastic compared to that suggested by parameters estimated by Carter et al.

# **8. Conclusion**

This report has focused on four major factors widely agreed to be responsible for food commodity price increases – economic growth, biofuel expansion, exchange rate fluctuations, and energy price change. The study also captures the effect of inventory adjustments. Incorporating an empirically estimated inventory demand function into the market-clearing condition shows that the impact of inventory on prices increases as the level of inventory diminishes. We find that in the absence of shocks attributable to the four factors mentioned above, in 2007 the prices of corn, soybean, rapeseed, rice, and wheat would have been 26% to 36% lower than the observed prices in that year. On the other hand, if inventory demand were to be ignored, in 2007 the prices would have been 38% to 52% lower than the observed prices in that year. Abstracting from considerations of inventory responses leads to predictions of larger price changes.

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Because key parameters in our analysis are the elasticities which are used to calibrate the demand and supply curves, we performed several sensitivity analyses on these values. In these alternative scenarios we introduced more inelastic curves and compared our results to those obtained if, instead of our estimated inventory parameters, we used the inventory parameters from Carter et al. [14]. We conclude that although the percentage changes vary between scenarios, the main conclusion that inventory matters does not change. The relative magnitude of the various shocks also does not change.

From a policy standpoint, the food crisis emphasizes both the importance of a proactive inventory management policy, and the need for mechanisms that either compensate the poor when prices rise to abnormally high levels or more directly mitigate spikes in food prices. Such mechanisms may include biofuel mandates that adjust automatically to the situation in food markets, as well as inventory management policies. Expanding agricultural supply through investment in research and development, and introducing policies that would allow more effective utilization of existing technologies as well as investment in outreach and infrastructure that will enhance productivity, also reduce the likelihood of a food price spike.

One limitation of this paper is that some important crop-specific factors, such as weather and productivity shocks (especially for wheat) and trade policies (especially for rice), are not considered. Another factor not considered in this report is speculation. The reasons for exclusion of these factors are data as well as model limitations. Another limitation is that we looked at each market separately, rather than in an integrated manner. No cross-price elasticities were introduced, which may lead us to underestimate the impact of the different factors on prices.

Although our conclusions are robust to a broad range of assumptions about the price elasticity of supply and demand for crops and parameters of the inventory demand function, an important area of future work is the empirical estimation of these parameters. Identifying correctly the inventory demand curve is a challenge, and is a key step to accurately measuring the factors causing the food inflation of 2007-08. In future work we plan to further investigate these relationships, and to introduce cross- price elasticities. Moreover, the study does not cover the 2008-2010 period, which was characterized by strong commodity price volatility. Thus, a further study is imperative to generate more policy insights by extending this study, incorporating the factors excluded here and also covering the 2008-2010 period.

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