Crude Palm Oil Domestic Production of Major Food Commodities in Indonesia

3 LITERATURE REVIEW An increasing number of studies have investigated the relationship between crude oil and food prices. These studies show a divergence in findings indicating that the effect of crude oil price volatilities on food commodity prices is not universal. This has motivated researchers worldwide to further investigate such correlations. The majority of previous studies have highlighted the existence of volatility spillover from crude oil prices to food commodity prices, an effect that is becoming stronger as biofuel production increases. Such research concurs with crude oil price volatilities being transmitted onto food commodities prices through two essential elements, namely production inputs transportation and electricity costs, fertiliser and pesticide prices and biofuel production agricultural commodities as raw materials. Research investigating volatility spillovers from crude oil onto the food market through production inputs has focused on agricultural commodities used for staple food and found a strong relationship between them. Research by Baffes 2007 was based on 35 non-energy commodities traded globally, such as food rice, maize, soybeans, wheat, and sugar, beverages cocoa, coffee, and tea, raw materials cotton, rubber, and timber, fertilisers, and metals silver, aluminum, copper, nickel, zinc. This author used annual price data from 1960 to 2005 and analysed data using an Ordinary Least Squares OLS regression method. Results indicate that linkages between crude oil prices and food commodities are stronger compared to other commodities. Furthermore, research results of Balcombe 2011 also suggest a positive correlation. This author’s findings, acquired using the time varying and panel approach, were based on smaller samples covering 19 agricultural commodities, such as rice, soybeans, maize, wheat, meat, cheese, cocoa, and sugar from 1957 to 2009. A more recent study by Obadi and Korček 2014 investigated the causality, long-run and short-run linkages between price of crude oil and food products, such as palm oil, wheat, corn, sugar, rice and barley. Data used in this study were monthly commodity prices over the period January 1975 to September 2013 and by adopting a Granger causality and a VECM model, a long- run relationship between oil and food prices was identified. In addition, in the long- run, causality is transmitted from oil prices to all food commodity prices, except for barley which has a two-way causality. Similarly, in the short-run, the direction of causality runs from oil prices to each of the food commodities, except for sugar. These findings, therefore, suggest that the price of agricultural food commodities used as sources of food react significantly to oil price fluctuations as oil is a primary input in energy intensive farmin g Obadi and Korček 2014. In order to investigate the impact of the biofuel boom on volatility transmission between oil and other commodity prices, previous studies have concentrated on agricultural products being used as raw materials for biofuels and divided analyses into two time periods, namely prior to and after the 2008 global food crisis. Serra 2011 used weekly price data of international crude oil and the weekly price of ethanol and sugar from July 2000 to November 2009 to investigate volatility transmission between crude oil, ethanol and sugar prices. The methods used in this study were the semiparametric GARCH and parametric MGARCH. Results indicate that in the long run, crude oil, ethanol and sugar price levels are connected by an equilibrium parity, suggesting a strong volatility linkage between them. Research by Ji and Fan 2012 uses daily log returns data covering various crops soybean, wheat, corn, and sugar and metals gold, silver, aluminum between 7 July 2006 and 30 June 2010. By adopting an EGARCH model, these authors suggest that the nexus between crude oil prices and agricultural and metal commodity prices has become stronger since the 2008 crisis. The work of Bakhat and Würzburg 2013, derived from an ECM model, is based on three categories of samples which are food commodities used to produce biofuel, such as soybean oil, sunflower oil, palm oil and sugar, other food commodities which cannot be converted into biofuels, such as rice, wheat, and beef and agricultural commodities which are not edible such as rubber, coffee and wool. By analysing monthly data for the period January 2000 to April 2011, these authors found that increasing the use of biofuels induces stronger linkages between crude oil and food commodities, especially for commodities used as raw materials for biofuel production. Similarly, recent research by Tadesse, Algieri, Kalkuhl and Braun 2014 which used monthly and annual data of oil and food prices, such as wheat, corn and soybeans, from 1986 to 2009, indicates that in recent years there is an emerging link between food, energy and financial markets. Thus, such results indicate an indirect transmission between oil price volatility and farming commodities through increased demand for biofuel. Other researchers state that results of investigations into volatility transmission from oil onto food commodity prices are not statistically significant at all times. A study by Du, Yu and Hayes 2011 shows that linkages between oil, maize and wheat only exist after 2006. They analysed the weekly data of these commodities by following a stochastic volatility model and a Bayesian econometric method for estimating the parameters of the model. Similar results were obtained by Nazlioglu, Erdem and Soytas 2013 who adopted the Hafner and Herwartz test approach and divided the data into two time periods of observation: before the food crisis 01 January 1986-31 December 2005 and after the food crisis 01 January 2006-21 March 2011. The data used in the study were daily price data of agricultural commodities, such as wheat, maize, soybeans and sugar. The results indicated that during the pre-crisis period there was no volatility spillover between crude oil and agricultural commodity prices. In contrast, there was significant volatility transmission during the post-crisis period. Hence, these findings show that the existence of volatility spillovers may depend on a particular time period marked by the biofuel boom phenomenon after the crisis Nazlioglu, Erdem and Soytas 2013. Other studies suggest an insignificant effect of volatility spillover between the oil and biofuel markets. Zhang, Lohr, Escalante and Wetzstein 2010 used a VECM model to show that there is no direct long-run relationship between fuel and agricultural commodity prices asserting that if there were any direct short-run relationships, then they would be finite, except for sugar because it can affect agricultural product prices through rising biofuel production. These results were obtained by analysing monthly price data for energy commodities, such as gasoline, ethanol and oil, and farming products, such as rice, maize, soybeans, sugar and wheat, covering the period March 1989 through July 2008. Similar results were reported by Kaltalioglu and Soytas 2011, who expanded the sample to include a greater range of commodities. These authors used the monthly price data of oil, food products fruits, vegetables, meat, poultry, fish, grocery food and non- alcoholic beverages and agricultural raw commodities timber, cotton, wool, rubber, and hides between January 1980 and April 2008. By adopting a Granger causality in variance approach, they found that there is no volatility spillover from energy market to food and agricultural raw commodity markets. Gardebroek and Hernandez 2013 used weekly price data of crude oil, ethanol and corn from September 1997 to October 2011. Based on the results obtained from an MGARCH model, there is a higher correlation between ethanol and corn markets, particularly after 2006. However, these authors did not find evidence that energy price volatilities stimulate corn prices in the US. A recent study by Abdelradi and Serra 2015 adopted parametric and semiparametric methods suggests similar results. They used weekly price data for the period 06 November 2008 through 14 June 2012. Results indicate that the biofuel industry in Europe does not trigger an increase in food prices. These contradictory findings have motivated researchers to further analyse the relationships between crude oil price volatilities and food commodity prices in order to shed light on the existence of volatility transmission in different countries. 4 DATA AND METHODOLOGY This section presents data and explores the econometric methods used to investigate the relationship between oil prices in the world market and food commodity prices in the Indonesian market from 2002 to 2015. To measure such a linkage this study adopts the VECM model and the Granger Causality test.

4.1 Data

To investigate volatility spillovers between energy and food markets, this study uses a series of individual prices for commodities rather than price indices in order to circumvent aggregation bias and the weighting rule when making them into indices Arezki, Hadri, Loungani and Rao 2013. The data set includes a monthly average price series for world crude oil, world food commodities and Indonesian food prices from January 2002 to August 2015, consisting of 164 observations. The West Texas Intermediate WTI crude oil price is used to represent world oil prices. Food commodities selected in this study are rice, maize, wheat, soybeans, sugar and cooking oil. Both crude oil and world food commodity prices are compiled from the World Bank database. Domestic food commodity prices are obtained from the Indonesian Ministry of Trade and converted into US. All commodity prices are in real rates 2010=100 and expressed in natural logarithms. Details of the commodity data are presented in Table 4.1 below. Table 4.1 Data of Commodities Commodities Variable Description Unit Source Crude Oil CO Crude oil, US, West Texas Intermediate WTI 40` API US Dollars per barrel USbbl World Bank World Rice WRC Rice Thailand, 5 broken, white rice US Dollars per metric tonne USmt World Bank World Maize WMZ Maize US, no. 2, yellow US Dollars per metric tonne USmt World Bank World Wheat WWHT Wheat US, no. 1, hard red winter US Dollars per metric tonne USmt World Bank World Soybean WSOY Soybean US US Dollars per metric tonne USmt World Bank World Sugar WSGR Sugar US US Dollars per kilogram USkg World Bank World Palm Oil WPLM Palm oil Malaysia, 5 bulk US Dollars per metric tonne USmt World Bank Domestic Rice LRC Medium quality rice US Dollars per metric tonne USmt Ministry of Trade Domestic Maize LMZ Local maize US Dollars per metric tonne USmt Ministry of Trade Domestic Wheat LWHT Segitiga biru US Dollars per metric tonne USmt Ministry of Trade Domestic Soybean LSOY Local soybean US Dollars per metric tonne USmt Ministry of Trade Domestic Sugar LSGR Local sugar US Dollars per kilogram USkg Ministry of Trade Domestic Cooking Oil LCOOK Yellow palm oil US Dollars per metric tonne USmt Ministry of Trade Domestic Petroleum PO Domestic Petroleum US Dollars per barrel USbbl Ministry of Energy and Mineral Resources

4.2 Methodology

This study uses four basic models to analyse linkages between price of crude oil and the price of the following domestic food commodities: rice, maize, wheat, soybeans, sugar and cooking oil. The first model is used to test whether fluctuations in crude oil prices can directly affect domestic food prices, as represented by the equation: � � = + � � + � where � � is the price of the domestic food commodity � at time � ; � � is the price of crude oil at time � ; and � is the error terms. The second model tests whether crude oil prices influence change in domestic food prices through domestic petroleum prices, given that domestic petroleum production uses crude oil as a primary input, as expressed by the following equations: � = + � � + � � � = + � + � where � is the price of the domestic petroleum at time � ; � � is the price of crude oil at time � ; � � is the price of the domestic food commodity � at time � ; � and � are the error terms. The third model is used to investigate whether volatilities of crude oil prices are transmitted through to food commodities in the world market, expressed as: � = + � � + � � � = � + � � + � where � is the price of the world food commodity � at time � ; � � is the price of crude oil at time � ; � � is the price of the domestic food commodity � at time � ; � and � are the error terms. To investigate existence of a stable linear steady-state link between these variables, this study follows a Unit Root test, Co-Integration test, Vector Error Correction Model and Granger Causality test.

4.2.1 Unit Root Test

Economic time series data is generally characterised by stochastic properties and strong trend behaviour. According to Hatanaka 1996, these properties can violate stationarity conditions which require a constant mean, constant variance and constant covariance over time. Granger and Newbold 1974 state that if non- stationary time series data is used in a regression model it can generate a spurious regression result leading to false existence of a significant economic relationship from unrelated variables. Therefore, before doing a time series analysis it is important to test for the stationarity properties of the time series data. The Unit Root test can be used to determine the stationarity characteristics of time series data by detecting the presence of a unit root. This study adopts a commonly used non-stationarity test known as the Augmented Dickey-Fuller ADF test. The null hypothesis of this test is that the series has a unit root, meaning that it is not stationary Obadi and Korček 2014. This hypothesis can be rejected if the value of the t-statistic is lower than the ADF critical values, which are substantially higher in absolute terms than those of standard regression.

4.2.2 Cointegration Test

It is believed that two or more price series are cointegrated if these prices move simultaneously in the long run. Engle and Granger 1987 point out that if a linear combination of two or more variables, which are integrated of the same order � � , generate a stationary series then the series are cointegrated. This means that a long run equilibrium relationship between economic variables may exist even though they deviate in the short run. This study uses the Johansen Cointegration test to analyse the existence of a long run relationship between variables. Before carrying out the test it is important to determine the order of integration of variables and ascertain that these variables share the same order of integration. The null hypothesis of this test is that there is no cointegration relationship between variables.

4.2.3 Vector Error Correction Model VECM

The existence of cointegration between variables indicates a long term equilibrium relationship between them. However, this does not specify short run effects of the cointegrated variables. Thus, VECM can be applied to determine the short run dynamics of each variable in a frame that binds such dynamics to their long run equilibrium relationships Zhang, Lohr, Escalante and Wetzstein 2009.