Oil And Food Commodities Prices. Oil prices effect on agricultural commodity prices in Latin American Nations - Term Paper Example

? of the Oil prices effect on agricultural commodity prices in Latin American Nations Abstract The objective of this paper is to establish the co-movement of macroeconomic index particularly the crude fossil oil and agricultural food prices based on Latin America preferably Colombia, Uruguay, Argentina and Brazil. The analysis and investigation was undertaken using Principal component analysis (PCA) to comprehensively analyze the impacts of the macroeconomic index (fossil fuel prices) on the values of agricultural food products. To achieve this objective, this paper investigated the agricultural food prices for seven major farm products namely meat, oilseed, egg, rice, wheat, milk and sugar. On the other hand, the macroeconomic investigated were fossil crude oil prices, agricultural food prices indexes, GDP and consumer price index around Latin America as from 1963 to 2007. The study applied the use of Scree test and the magnitude of the variance method for measuring the optimal value of the common factors. The relationship coefficient that existed between the obtained principal function or component and the macro-economic index fluctuates between 0.87 for Latin America GDP and 0.36 for the consumer price index (CPI). According to the findings, agricultural food production index has the largest impacts on the macro-economic index and similarly the crude fossil oil has the greatest influence on the agricultural food production index and as a consequent, the prices of crude has a direct impacts and influence on the agricultural food prices in Latin America. 1.0 Introduction In the economic advancement process, food supply and its security are imperative issues. Therefore, food prices are an essential effective variable that have a direct effect on its supply and demand. Ever soaring food prices and cases of food riots across the globe and more important in Latin America have heightened concerns about the world food supply and food security. Food prices of major arable agricultural products such as cereals, oil seed products, sugar, dairy products and wheat have risen in volatility during the past few years across Latin America. On the other hand, global crude oil prices in general have similarly risen in volatility in the past few years. This simultaneous rise in food and oil prices has reinforced a growing concern towards the effect of crude oil prices on food prices across the globe preferably in Latin America. According to Cruse et al., (2010) increasing crude oil and its products prices , inadequate source of fossil fuel and heightened concerns on global warming have generated a growing demand for alternative source of fuel and energy notably biofuels. Production of these fuels is greatly dependant on the agricultural products. However, it is practical that production of biofuels can obviously mitigate consumers from the adverse effect of skyrocketing global oil prices, but could also lead to increasing food prices. In Latin America, fossil oil prices are known to have a direct impact on agricultural prices particularly in cases involving intensive large scale farming. This is because the present global food system is greatly fuel -and transport-depended. This implies that when fuel prices become unaffordable, the highly-dependent agricultural production system consequently become less secure which subsequently makes the agricultural food prices be less affordable. In is therefore imperative to support food self sufficiency and lessen the need for fuel contributions to the food systems at all cost. The relationship between cost of agricultural products and oil prices in Latin America is always systematic, and the costs of both products have shoot more or less in cycle for the past recent years. Present agricultural system uses fuel oils as a source of energy to operate most the farm’s machinery, to ferry other farm’s inputs to the farm and to transport the agricultural products to the their final consumers. Moreover, oil as a source of fuel is often also used as input in agricultural chemicals. Subsequently, any increase in oil price must impose pressure on every of these aspects of commercial agricultural and food systems. Hence, presently there is a growing concern in the whole Latin America that ever soaring and volatile cost of crude oil may cause food prices to continue escalating (Cruse et al. 2010). Furthermore, increase in fossil fuel prices implies a growing demand for the biofuels which hitherto, are the known alternative non-fossil fuels that can substitute the petroleum products used for by combustion engines or motor vehicles. However, given that bio-fuels are also made from agricultural products such as corn. Therefore, as the demand for bio-fuel grows, so does its price and the prices of corns, pushing the costs of food to be more and more costly. Export oriented agricultural policies is also known to have increased the globe’s susceptibility to sky-scraping oil prices. For example, majority of donor agencies have directed the developing countries mainly from black continent and Latin America to focus on the massive production of cash crop at the expense of local staples for local consumptions. As a consequence, the citizenry from Latin America are forced to rely heavily on the subsidized imported cereals or those donated by the Food Aid Agencies. Nonetheless, given that these food imports similarly depend heavily on fuel especially during their transportation, any increase global fuel prices consequently increases the food cost, making even these imported foods to be less affordable. The costs of fuel normally account for approximately 60% of the overall costs of ship operation. For example, Garber (2008) clarifies that from early 2007 to mid 2008, when the fuel prices increased, the cost of transportation of food aid to Latin America escalated by $50 for every tone of food transported and this translated to approximately 30% increase, based on the (USAID) United States Agency for International Development. Meanwhile, majority of poor farmers of Latin America who cannot afford agricultural machineries for large scale production, commercial farm inputs and cost of fuels are not favored in the world food economy or agri-business. This is also justified by the agricultural policies in the developed food-exporting nations, where, farmers through subsidies are encouraged to produce more and the surplus damped into the developing countries such as Brazil, hence squeezing the agribusiness market for the poor small scale farmers of that nations, and pushing them out of the business. Since most of these importing nations, focus more on cash crop production at the expense of local stable food production, its citizenry will continue to wallow in hunger as the cost of food perpetually become less affordable. 2.0 Existing Literature So far, numerous studies have been conducted on the relationship between the fossil oil prices and the agricultural food prices. Gohin and Chantret (2010) examine the long-run correlation between world prices of certain agricultural food commodities and the prices of the crude fossil fuels as source of energy through the application of the global workable general equilibrium model. Based on their study, their outcome was that there is a positive direct relationship between food prices and the prices of crude oil and this observation is attributed to the cost-push impact. Zhang et al (2010) upon the application of the time series prices of both fossil fuels and agricultural commodity prices, he examined the long-run combination and integration of these prices. According to their analysis, there are no direct long-run price correlations that exist between the fuel prices and the agricultural commodity prices. On the other hand, Chen et al (2010) examined whether there is any correlation between the crude and the world grain prices of maize, wheat and soybean. According to them, the empirical outcome shows that the variation of every grain price is notably affected by the fluctuations of the fossil crude oil price and the prices of other grain during the period running from the 3rd week in 2005 to the 20th week IN 2008. This implies that grain products are in a competition with the resultant demand for biofuels when soya bean or maize are used to generate ethanol and biodiesel during the period of lofty crude oil prices in the past recent years. Conventional agricultural production system in the industrialized nations heavily depends on the fossil energy (Cruse et al., 2010). According to Xiaodong and Hayes (2009) there is an evidence of volatility spillover that exist in crude oil, maize and wheat markets, that could be exhaustively justified by extreme inter-dependence that exist among the markets generated by ethanol production. On the other hand, Abdel and Arshad (2008) found out in their study that there is long run causality from cereal to petroleum prices as that of vegetable oil prices or the biodiesels are affected by the petroleum prices. Meanwhile, Tokgoz (2009) found out that the effect of fuel energy prices on EU agricultural sector is escalating with materialization of the biodiesel sector, showing the significance of trade policy with regard to increased crude oil and cereal prices. Increasing food and fuel costs have inconsistence effects on the people of Latin America as a whole, particularly the economically challenged group. America on the other hand, averagely spend utmost 12.5% of their total earning on food and this shows that they are comfortably absorb the increased food prices than the world’s 2 billion economically powerless people, who spend almost their entire income on food alone. 2.1Reasons for increase of oil prices Speculative venture in commodities contributes a role, although, there is a persuasive situation to be concluded that furl prices would be soaring even if future anticipations were wholly curtailed. Hence the trend in oil industry is ever dynamic and rapid. Presently, fossil oil exploration and production are increasingly becoming less affordable and costly, prompting greater environmental hazards, while scrambling for accessibility for new oil prospective zones are generating geopolitical tension. Based on the report by International Energy Agency, the rate of global crude oil production attained its peak in 2006 and so even the International Monetary Fund (IMF) supported the observation made by the energy industry analysts that shortage and soaring prices of oil is perpetual. This effect of unending high fuel price has triggered a detrimental effect on majority of lives in Latin America and the rest of the globe since most country’s economic developments are fuel-dependent notably food security. A decline in global demand for oil as a result of a sharp decrease in world’s economic activity can cause oil prices to reduce as in the case of what took place in 2008. However much this is possible, most farmers from Latin America still are still not able to afford fuel prices, limiting their scale of production and increasing the price of their commodities. This is because is theoretically practicable for the world to contain oil demand through regulations and legislations that lessen consumption and is also pragmatic that some startling technological advancement can rapidly generate a cheap, effective option to fossil fuel. Nonetheless, these two mitigation options are rather unlikely to take place. Hence there is no possibility scenario in which oil services such as transportation of farm inputs, driving of heavy agricultural machinery and in application in farm chemicals would be more affordable to farmers across the globe, preferably in Latin America. This observation also has a ripple effect on the ultimate consumers of the agricultural products who as long as the fuel prices continue to soar, will always carry the burden of heavy food prices. Even though some of the consumers in Latin America can withstand the inflated food agricultural food prices, spontaneous interference of fuel availability which can be as a result of geographical events such as war or natural calamity or a noteworthy gradual shortage of fossil energy reserves due to un ending depletion and exhaustion of hydrocarbon storage can result into an interruption of the food production system very level within Latin America, from its farmers to processors , distributors, retailers and finally to the ultimate consumer. The ever escalating prices of food and oil in Latin America can also be attributed to global inequalities of demand and supply, but from the individual’s point of view based on the economies in the region, those price increases symbolize external shocks. However, these shocks have been partly solved by the appreciation of the local currencies in most situations and one has to consider similarly domestic or local financial and monetary policies to comprehend the increases of inflation rates. In essence, the propagation impacts of primary shocks rely on the real local environment in the context of domestic absorption with respect to production volume. However, the prevailing context poses some unique and particular challenges for the present fine-tuning of macro-economic guidelines in the region. Generally, towering oil prices have contributed adversely to the prices of agricultural products in Latin America. This is because basically, our present world food system is exceedingly fossil oil-dependent, albeit the price of petroleum and products becoming less and less affordable each day. Extreme whether events such as global warming, and other natural calamities such as earth quakes and landslide have similarly contributed to high agricultural food prices in Latin America as most of these events generated from anthropogenic global warming are fossil fuel-related. Hence there is no clear antidote for the Latin America aggravation food crisis within present energy and agricultural structures. As it stands, what the Latin America requires as a major redressing of the structures governing both food and energy. For example, the one of the mitigation options that managers or the government s of Latin America should implement is reducing the overdependence on fossil energy contribution in agri-business, while also minimizing GHG discharges from land-use activities. Realizing this goal would mean increasing local stable food production for self-sufficiency and discouraging the intensive application of fossil fuel and petrochemical in agricultural food production. Owing to the fact that the current scale at which the modern agricultural system has relied heavily on fossil fuels, various proposal and suggestions delinking food systems from fossil fuels seem to be radical. Nonetheless, attempts to this end must criticized or arbitrated not by the scale or level at which they foster the existing importance of the world food system, but by their capacity to solve the major predicament facing the Latin America and whole world which is basically the desire to ensure food security to the larger world’s population with the dying supply of fossil fuel that is highly essential for tilling the lands , irrigating fields, supplying fertilizers to the field, harvesting and transportation of the agricultural products to the final consumer. Besides, farmers majorly from Latin America should lessen their dependence on fossil fuel as the only source of energy to drive their agricultural operations but should start thinking of other alternatives source of energy to construct resilience against future shortage and price volatility 2.2 The Macro-economic Factors are Driving Price Rises: Many economists and policy makers have concluded that the world is currently undergoing a broad commodity boom. Crude oils and minerals, and agricultural products are constantly on the rise in a similar trend and this reveals that large macro-economic variables such as consumer price Indexes, crude oil prices and food consumption Indexes are the driving factors for this boom. Another justification is pegged with the rapid decrease of the value of US$. Due to the fact that most of the world’s food commodity market are run by the US dollars, the diminishing strength of the US dollar is makes all the commodities to be universally affordable especially the food prices hence boosting up the demand, supply and their prices. For instance, the August 2008 increase of the US dollar which was associated with declining farm commodity prices seemed to verify this observation. 2.3 Consequences of Price Increase of Food commodities due to Increase in Crude Oil Price particularly in the developing countries Consequences of price increase particularly in agricultural commodities as a result of soaring crude oil prices are both detrimental and positive. Agricultural large scale farmers especially from those nations where global prices are passed to the framers are favored in spite of the fact that the escalating price of petroleum products significantly increases the production and marketing expenditure. On the other hand, the consequences or the success for small-scale farmers in under developed countries depends on whether global food prices are passed to them or whether they outsmart the world’s surplus food producers. Meanwhile, consequences for poor consumers in third world countries are indisputably adverse in the short due. In the longer duration, increased farm incomes tend to escalate both urban and rural wages which to certain level, could contain or mitigate the adverse impacts longer-term prices At the national rank, the consequences for poor developing states are detrimental. Increasing foreign exchange for import costs especially in food commodities usually hit hard, especially if the state in not an exporter of mineral or oil. 2.4Empirical model The analysis of this work is done with respect to PCA model. The overall purpose of factor analytical methods is to establish a way of condensing the data contained in various original variables into a lesser set of new composite aspects, within few cases on loss of information. According to Rao (1964) PCA is the most reliable method based on the factor analysis techniques. Since the data set with p numerical variables, it is easier to calculate p principal functions or components. Every principal component is linear function of the original variables, with coefficients equivalents to covariance matrices or eigenvectors of correlation. Rao (1964) further explains that principle components have diverse imperative properties and features. The general formula for calculating the first principal component (PCI) IS C1 = b11 (x1) + b12 (x2) +……b1p (xp) Where, C1 is the first principal component b1p depicts the regression coefficient for the eigenvector of covariance matrix of the Pth variable between the variables and xp. There are different techniques of computing the optimum number of factors such as proportion of variance, priori hypotheses, Scree test and analysis of residuals. With regard to this work, Kaiser-Guttman rule will be applied due to its simplicity and availability in most of computer packages (Kaiser, 1960) The Kaiser-Guttman rule stipulates that the number of factors to be obtained should be equivalent to the amount of factors containing eigenvalue exceeding one. The basis of deciding on this specific value is that a factor must contain variance at least as big as that of a sole consistent original variable. 2.5 Granger casualty test Engle and Granger (1987) stipulate that a linear permutation of two or more non-stationary series with similar order of combination may be fixed and in case such stationary combination exists, then the series is recognized to be co-integrated and a long-run equilibrium correlation also exist (Esmaeili & Shokoohi 2011). The linear permutation or combination can be written as follows: Zt = xt – a0-a1y, where, Zt is stationary while a1 and a0 are constants. This equation depicts that the connectivity that exist is the long-run balance or equilibrium correlation and Zt determines the variation with regard to the equilibrium figure. Integrating these co-related features, a vector error relationship model (VECM) can be designed to examine and evaluate Granger Causation of the sequence in at-least single direction. Based on this work, VECM is particularly applied to test and assess the Granger causality between the obtained principal component and the macro-economic index (Esmaeili & Shokoohi 2011). 2.6 Data The Latin America food prices this paper examined are for meat, milk, eggs, rice, oilseeds, wheat and sugar. This paper took these food prices for seven main agricultural products sampled from Brazil, Uruguay, Argentina, Chile and Colombia for a period of 1963-2007 from FAO nad macro-economic variables from obtained from both World Bank 2010 and Nation Master Sites. Representative prices for every of these agricultural products or products groups featuring in the balance sheet are measured by their input to overall calorific consumption. The prices of different food commodities can always be obtained from the FAO website (www,fao.org). FAO food prices index is very significant tool since it shows a measure of the monthly or annual variation in the international prices of food commodities comprising of cereals, dairy products, sugar, meat and oilseeds (Esmaeili & Shokoohi 2011) 3.0 Principal Component Analysis (PCA) To begin, the amount in numbers of components was computed via the Kaiser-Guttman Rule. Table 1 depicts the eigenvalue magnitudes of variance for choosing the optimal value of components. According to Principal Component Analysis, the total eigenvalue of the matrix depicting correlation were equivalent to the overall value of variables being assessed. This is because, each variable accounted for single unit of variance to the data (Esmaeili & Shokoohi 2011) Based on the KAISER-Guttman Rule, only single factor can be maintained because only the initial factor contains an eigenvalue exceeding one as shown in Table 2. With respect to Table 2, it illustrates the computed eigenvectors for the main seven farm products and by these eigenvectors PC1 is calculated using equation 1. 3.1 Correlation and Granger causality test Before perfuming regression analysis, the correlation between the test for causality and the variables was determined Number of Table 1 factors (agricultural commodities analyzed) Eigenvalue 1x 10-2 Difference /variation 1x 10-2 Proportion 1 x 10-2 Cumulative 1x 10-2 1 0.0599 0.0527 85.5 85.48 2 71.43 61.48 10.2 95.68 3 9.951 2.019 1.42 97.11 4 7.933 2.018 1.13 98.24 5 5.915 1.988 0.85 99.08 6 3.928 1.443 0.56 99.65 7 2.485 0.350 0.35 1 Source : (Esmaeili & Shokoohi 2011) Table 2 Eigenvectors for agricultural food products Variable Factor 1 1x 10-2 Egg 96.06 Wheat 95.90 Oilseed 96.19 Meat 95.77 Sugar 87.21 Rice 95.02 Milk 79.70 Source : (Esmaeili & Shokoohi 2011) Table 3 Matrix showing the relationship between PCI and macro-economic index PCI CPI Crude oil price Food production index GDP GDP 0.8791 0.9675 0.5122 0.774 1 Food production index 0.7416 0.7416 0.7416 1 0.774 Crude oil price 0.4378 0.8665 1 0.7306 0.5122 CPI 0.3648 1 0.8665 0.9725 0.9675 PC1 1 0.3648 0.4378 0.7416 0.8791 Source : (Esmaeili & Shokoohi 2011) Table 4 Unit root test findings of PC1 and macro-economic index series Variables ADF test statistical data Level First difference t- statistics Probability t-statistic Probability Food production 84.3541 X 10-2 88.81 X10-2 -4.564.172 0.00 CPI 3,256,963 1.000 -4.614.500 0.00 Crude oil price 1,573,901 0.969 -3,846,808 4 X 10-4 PCI -1,295.335 0.620 -3.167.209 4 X 10-4 GDP 1.1142 0.620 -2.732 4 X 10-4 Source: (Esmaeili, & Shokoohi 2011) Table 5 Granger casualty test Source of causation Dependent variable GDP Crude oil price CPI PCI Food production index GDP - 1.59 0.24 0.128 0.982 Crude oil price 1.135 - 0.525 1.912 8.02b PC1 3.74 1.44 0.316 - 2.03 CPI 8.59b 2.54 - 4.96 6.44b Food production index 2.85 4.306 22.12a 6.281 - Source: (Esmaeili, & Shokoohi 2011) a- shows statistical meaning at the 5 per cent level b- shows statistical implication at the 1% level The results showing the approximation of matrix revealing the relationship that exist between different indicators (price of fossil fuel, CPI and FPI) and the principal function are depicted in Table 3 The outstanding correlation is between FPI and CPI. Therefore it is anticipated that FPI has a notable impact on Consumer Price Index. This relationship coefficient between crude oil price and PCI is therefore found to be 43% To further justify these correlations, Granger casualty test was applied. Table 4 shows the Dickey –Fuller test. According to Stock and Watson, (1988) stationarity is imperative because the Granger casualty test is extremely sensitive to the stationarity of the series. A series is considered to be non-stationary if it has a variable mean, auto-covariance and variance over a period of time (Esmaeili, & Shokoohi 2011). If a non-stationary series has to vary with d times to become static, then in this case, it is said to be incorporated to the order of d. Static test illustrates the presence of a single unit root at the scale of time series data, on the other hand, initial differencing the series produce an apparent devoid of a unit root, at every level of the series. To determine the presence of variability in each variable, Granger casualty test is undertaken based on the transformed data which in this case, is the initial difference. The findings of the Granger Casual test are depicted in Table 5 which composes of the chi-square for every variable (Esmaeili, & Shokoohi 2011). Justification of the causality is got from the projection of FPI and CPI impacting PCI, which means that PC1 is as a result of FPI and CPI. Table 5 also illustrate that the price of crude oil does not cause any direct impact on the PC1 but has a significant effect on the FPI. Subsequently, the price of crude oil causes an indirect effect on PC1. Moreover, the outcome shows that the price of crude oil affects indirectly the Latin America GDP and the entire globe, through its effects on food production index. Food production index is the basis of causation for GDP and CPI. Conclusion Establishing the relationship between agricultural food and crude oil prices with respect to recent variations in the entire Latin America and the global oil prices is of a significant interest to Latin America policy makers and economists. An effort has been made to evaluate and determine the effect of crude oil price on the world food price variation through the application of PCA. The relationship co-efficient that exists between the obtained principal component and the macro-economic indicator (index) fluctuates from 0.87 for the Latin America GDP and 0.36 for the CPI. The outcome of \Granger Causality test fossil oil price on index has a direct impact on FPI (Esmaeili, & Shokoohi 2011). As a consequence, the fossil crude oil price indirectly and positively affects the PC1. According to Hamilton (2009) the prices of fossil fuel have presently shot up as a result of political unsteadiness in most of the giant-oil exporting countries and increase in oil demand in most of major nations such as China, India and other developing countries. Higher prices of fossil oil is the pushing factor to apply the use of food crops in production of biofuels as an alternative source of fuel but still this is not an amicable solution as this will escalate the costs of food production and consequently increasing the agricultural food prices for the ultimate consumers (Von Braun and Pachauri, 2006). The result of this paper shares a common observation made by Zhang et al (2010) who purported that there is no long-run price correlation between that exist between the prices of agricultural food commodities and the crude fossil oil. The positive correlation between the prices of the agricultural products and the fossil oil is similarly justified by the analysis made by Gohin and Charter (2010) and those made by Chen et al (2010). These results further suggest that the impact of crude fossil oil price on the prices of agricultural commodities in Latin America should be further and intensively examined. The implication for this research work for policy formulation and management is the observing the oil prices and its effects on the agricultural food prices not only in Latin America but in the entire globe. References Abdel, H.A., Arshad, F.M., 2008. The impact of petroleum prices on vegetable oils prices: evidence from cointegration tests. Paper presented at the International Borneo Business Conference on Global Changes, Malaysia, December 2008. Chen, S.T., Kuo, H.I., Chen, C.C., 2010. Modeling the relationship between the oil price and global food prices. Applied Energy 87, 2517–2525. Esmaeili, A., & Shokoohi. (2011). Assessing the effect of oil price on world food prices: Application of principal compinent analysis. Energy Policy, 1022-1025. Hamilton, J.D., 2009. Understanding crude oil prices. Energy Journal 30, 179–206. Gohin, A., Chantret, F., 2010. The long-run impact of energy prices onworld agricultural markets: the role of macro-economic linkages. Energy Policy 38, 333–339. Engle, R.F., Granger, C.W.J., 1987. Co-integration and error correction: representation, estimation and testing. Econometrica 55, 251–276. Kshirsagar, A.M., 1972. Multivariate Analysis. Marcel Dekker, Inc., New York. Kaiser, H.F., 1960. The application of electronic computers to factor analysis. Educational and Psychological Measurement 20, 141–151. Movil, E., 2004. A report in energy trends, greenhouse gas emissions and alternative energy. /http://www.esd.lbl.govS. (accessed 5th July 2008). Rao, C.R., 1964. The use and interpretation of principal component analysis in applied research. Sankhya A 26, 329–358. Tokgoz, S., 2009. The impact of energy markets on the EU agricultural sector. Working Paper 09-WP 485, Iowa State University. Stock, J.H., Watson, M.W., 1988. Testing for common trends. Journal of the American Statistical Association 83, 1097–1107. Von Braun, J., Pachauri, R.K., 2006. The Promises and Challenges of Biofuels for the Poor in Developing Countries. IFPRI, Washington, DC. Xiaodong, D., Hayes, D., 2009. The impact of ethanol production on us and regional gasoline markets. Energy Policy 37, 3227–3234 Zhang, Z., Lohr, L., Escalante, C., Wetzstein, M., 2010. Food versus fuel: what do prices tell us? Energy Policy 38, 445–451. Read More