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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.19 No.3 pp.520-526
DOI : https://doi.org/10.7232/iems.2020.19.3.520

Does the World Price of Crude Palm Oil and Total of Production Determine Palm Oil Marketing Margins in Indonesia?

Syahril, T. Zulham, Ishak Hasan, Jumadil Saputra*, Helmi Noviar, Okta Rabiana Risma
Faculty of Economics, Universitas Teuku Umar, Meulaboh, 23681 Aceh Barat, Indonesia
Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
Faculty of Economics, Universitas Teuku Umar, Meulaboh, 23681 Aceh Barat, Indonesia
*Corresponding Author, E-mail: jumadil.saputra@umt.edu.my
May 6, 2020 June 22, 2020 June 29, 2020

ABSTRACT


This study clarifies the issue of low-profit margins obtained by palm oil farmers due to price fluctuations through examining the multivariate relationship between the world price of Crude Palm Oil, Total of Crude Palm Oil (CPO) production, Palm oil price of Fresh Fruit Bunches and Marketing Margin applying the Wald test approach. Then with a cointegration approach trying to explore how the short-term balance and the long-term marketing margin price for the period January 2008 to December 2017. The findings prove that: (i) in the long run the world CPO price, the Indonesian CPO export price, the price of Indonesian palm oil FFB have a significant influence on the palm oil marketing margin price; whereas in the short term, the marketing margin price one month before, the current Indonesian CPO export price and one and four months before, the current price of oil palm (fresh fruit bunches or FFB) and one and four months earlier. (ii) Multivariate causality test shows that Indonesian CPO production has a one-way causality relationship with the volatility of world CPO prices. The results of this study are an effort to encourage an increase in the price of FFB at the level of oil palm farmers.



초록


    1. INTRODUCTION

    The development of oil palm plantations in Indonesia contributes to the development of the national economy in realizing the prosperity and welfare of the people in a just manner (Busyra, 2014;Widyaningtyas et al., 2017). Furthermore, the contribution of oil palm has become one of the main and leading agricultural commodities in Indonesia, both as a source of income for millions of farming families, as a source of foreign exchange, employment providers, a trigger for the growth of new economic centers, and as a driver of growth and development of the industry palm oil-based downstream (Syahril et al., 2019b;Nasution, 2016;Rauf, 2007;Gomes and Romão, 2016;Esfahani et al., 2018;Novikova et al., 2018;Haryadi et al., 2020).

    The role of palm oil commodities in this large development is caused by oil palm having advantages and potential as other world vegetable oils from the aspect of productivity. The productivity of one hectare of oil palm plantations can produce 7 tons of oil, while one hectare of soybean plantations only produces 0.45 tons of oil, especially compared to canola and sunflower, 10 times the productivity of palm oil (Masruroh and and Pahlawan, 2017). Utilizing the comparative advantage of palm oil, Indonesia has the opportunity to develop oil palm plantations in increasing the competitiveness of exports of palm oil and other palm oil derivative products in the world market (Rifai et al., 2014;Purba, 2012;Hadi and Tety, 2012;Sadeghpour et al., 2017).

    Even though palm oil has advantages, fluctuating price movements become uncertainty that is responded to by changes in FFB prices at the farm level. The negative response to the price of FFB so far has resulted in the incomes of oil palm farmers not being able to cover operational costs. Then when the CPO export price drops the factory and the collectors quickly will reduce the purchase price of FFB. Conversely, when the CPO export price rises the factory and the collecting unit slowly raise the purchase price of FFB. The impact of this problem, the profit margin enjoyed by farmers is minimal when compared to the margins obtained by collectors and exporters.

    Studies on marketing margins include those conducted by Jumiati et al. (2013), looking at the large share price in each of the coconut marketing channels and also a study conducted by Tety et al. (2013) looking at marketing margins from PKS to independent smallholders. However, the study has not looked especially at: (1) Is there a short-term and long-term balance between the volatility of world CPO prices, Indonesian CPO production and palm oil marketing margins. (2) is there a multivariate relationship between marketing margins with the volatility of world CPO prices and palm oil production? By further investigating the problems associated with marketing margins in oil palm commodities, the findings of this study can be an essential input for autonomy in formulating policies that protect the sustainability of oil palm businesses, especially at the farmer level.

    In recent years, domestic researchers have researched marketing margins including those conducted by Jumiati et al. (2013), seeing the large share price in each of the coconut marketing channels in Pratiwiyanti and Nuraeni (2018) in the case of shallots and studies (Rizal et al., 2017) in the case of cocoa. Then a study was conducted by Tety et al. (2013) looking at marketing margins from PKS to independent smallholders. Several studies conducted abroad, such as studies on the transmission of agricultural retail prices and the behavior of marketing margins (Kinnucan and Zhang, 2015), and more interestingly, studies on the increase in palm oil have a positive effect on raising farmer incomes but lead to increases in food prices (Kochaphum et al., 2013).

    However, the increase in the price of palm oil is not necessarily the development of this price margin enjoyed by farmers, because this margin could be enjoyed by intermediaries both at the level of traders, palm processing factories and exporters. So the study conducted by Pokhrel and Thapa (2007) has a point that this marketing intermediary is a parasite especially in developing countries, this is in line with the study of Magfiroh et al. (2017) which is that high marketing margins and low farmer prices.

    The application of the VECM model with the cointegration approach sees the balance and the wald test approach sees a multivariate causality relationship (Syahril et al., 2019a), but the variables used in this study are different. Then various studies on marketing margins have not looked at the case of palm oil commodities which emphasizes his study of short-term and long-term equilibrium as well as looking at the multivariate causality relationship between marketing margin variables, world CPO prices and more concrete production.

    2. RESEARCH METHODOLOGY

    This study uses secondary data from several sources, i.e.: Bank Indonesia, Central Statistics Agency (BPS), World Trade Organization (WTO) and Food Agricultural Organization (FAO). Monthly data are used in time series data analysis in the period January 2008 to December 2017 which condition the time series data requirements (Narayan and Narayan, 2005). This research measured marketing margins from changes in world CPO prices and Indonesian CPO production by forming equations to answer the research objectives, can be formulated as follows:

    M M C t = a 0 + a 1 C P V t + a 2 I C P t + ε t
    (1)

    where MMC is the marketing margin, CPV is the volatility of world CPO prices, ICP is Indonesian CPO production, and ε is the error term.

    Starting the data analysis process requires data stationarity test with the Dickey-Fuller (ADF) and Phillips- Perron (PP) Augmented Root units. After all stationary data have been proven, the next step is to prove that there is a short-term and long-term balance using the VECM model with the Johansen Cointegration approach and then examine the multivariate causality relationship using Wald Tests (Syahril et al., 2019a). The VECM equation formulated as follows:

    Δ M M C t = a 0 + a 1 i = 1 n C P V t 1 + a 2 i = 1 n I C P t 1 + E C T 1 t 1
    (2)

    Δ C P V ^ t = a 0 + a 1 i = 1 n M M C t 1 + a 2 i = 1 n I C P t 1 +   E C T 2 t 1
    (3)

    Δ I C P t = a 0 + a 1 i = 1 n M M C t 1 + a 2 i = 1 n C P V t 1 +   E C T 3 t 1
    (4)

    3. RESULTS AND DISCUSSION

    3.1 Stationary Test

    This study analyzes the short-term and long-term balance of the influence of Indonesian CPO exports, Malaysian CPO exports, world crude oil prices, world soybean oil prices and the exchange rate to the volatility of world CPO prices. The testing of this study was initiated by the unit root test to ensure that all data were stationary (Sokhtsaraei, 2018;Cong and Hoang, 2019). Unit root test (unit root test) in the first equation using two methods, namely; Augmented Dickey-Fuller (ADF) and Phillips- Perron (PP) methods. Unit root test using Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) by limiting the level of significance that is equal to 1-10 percent. Unit root tested with level or I (0) or first difference or I (1) using trend and intercept. The unit root test results can be shown in Table 1.

    Table 1 shows the unit root test results using the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) methods that marketing margins (MMC), volatility in world CPO prices (CPV) and Indonesian CPO (ICP) production are not stationary at the level or I (0) while the Kwiatkowski-Philips-Schmidt Shin (KPSS) method is only a stationary marketing margin (MMC) at a level of 10 percent significance. Then the first difference or I (1) is done with the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) method of CPO production in Indonesia (ICP) not stationary and the Kwiatkowski-Philips- Schmidt Shin (KPSS) method of volatility in world CPO prices (CPV) stationary at alpha 5 percent and marketing margin (MMC) and stationary Indonesian CPO production (ICP) at alpha 10 percent.

    3.2 Determination of Lag Length Criteria

    When all data is stationary, then the optimal lag length can be determined. Determination of the optimum lag or lag is one of the most crucial stages in VAR estimation (Nugroho, 2012). This optimal lag length testing aims to eliminate the autocorrelation problem in the VAR system. This study uses a likelihood ratio (LR), final prediction error (FPE), Akaike Information Criterion (AIC), Schwartz Information Criterion (SIC) and Hannan-Quinn Information Criterion (HQ) to determine the optimum lag length (Poetry and Sanrego, 2011).

    The second model, Table 2 shows that the criteria for LR, FPE, AIC, SC and HQ order 1, thus the optimum lag length used is lag 1. It means that all the variables used in this second equation affect each other between variables in the same period but interrelated until one previous period.

    3.3 Cointegration Test

    Cointegration test to get a long-term equilibrium relationship between variables that are not stationary but have a linear combination of stationary (Dirga et al., 2016). In this study, the cointegration test carried out using the Johansen cointegration method. The variables to be tested must be stationary variables of the same degree. For clarity, the results of Johansen’s cointegration test can be seen in Table 3.

    Table 3 shows that the trace statistic value and the maximum eigenvalue are greater than the critical value at the alpha confidence level, which is 5% or the probability value is less than 1 is 5%, it can be concluded that cointegration occurs. This means that in this study all the variables that use cointegrated.

    3.4 The Impact of CPO Price Volatility and CPO Production on Marketing Margins in the Long and Short Term

    Based on the results presented in Table 4, the shortterm marketing margin in the previous period had a positive effect on the marketing margin. It means that if there is an increase in marketing margin in the previous period of Rp 1 per kilogram, it will increase the marketing margin now by Rp 0.3565 per kilogram at a real level of 1 percent. It is consistent with the theory which states that changes in the price of an item are affected by changes in the price itself, in this case influenced by marketing margins of the previous period.

    Furthermore, significant error correction proves that there is an adjustment mechanism from the short run to the long run. The amount of adjustment from short-term to long-term is 0.009, so the length of time needed to return to its equilibrium is 0.009 months or approximately 0.27 days or 6.48 hours (taking the amount in one month is 30 days).

    Table 4 shows that in the long run the volatility of world CPO prices has a positive effect on the marketing margin of Indonesian palm oil, that the volatility of world CPO prices rises to the US $ 1 per metric ton, thus increasing the marketing margin of Indonesian palm oil by Rp 1.6735 per kilogram of CPO. For more clearly the following long-term estimation results are as follows:

    3.5 Analysis of the Multivariate Causality Relationship between Marketing Margins with the Volatility of World CPO Prices and Palm Oil Production

    The multivariate causality test results show that Indonesian CPO production has a one-way causality relationship with the volatility of world CPO prices. This is also in line with the results of bivariate causality, where this relationship shows that Indonesia as the world's largest CPO producer has the potential to influence the global CPO price movements.

    The increase in CPO production in Indonesia has led to an increase in supply for both domestic consumption needs and meeting overseas demand. Following the theory of supply that an increase in world CPO prices encourages increased supply, but in this case an increase in production will be responded negatively by the importing country in which the importing country restricts demand for CPO by the contract, meaning that price stickiness arises. Moreover, this palm oil becomes a threat to the sustainability of other vegetable oil developments (Masruroh and Pahlawan, 2017), this condition becomes an opportunity for them to depress palm oil prices to the lowest level so that the stability of other vegetable oil prices in the short term and the sustainability of its development in the long term.

    4. CONCLUSIONS

    In the short term, the balance marketing margin in the previous month has a positive effect on the marketing margin now. Then the long-term balance in world CPO prices has a positive effect on the marketing margin of Indonesian palm oil. Multivariate causality test results show that Indonesian CPO production has a one-way causality relationship with the volatility of world CPO prices. It shows that Indonesia’s CPO production as the world's largest producer influences the world CPO price movements. Increased production will be responded negatively by the importing country where the importing country limits the CPO demand by the contract, meaning that there is price stickiness. For increasing the price of FFB at the farm level, government policy needed for establishing transparency and implementing it with the principle of consistency, mainly traders and palm oil processing factories (PKS). Then to anticipate the monopoly of FFB both plasma farmers and independent oil palm farmers, the government must simplify permits for the establishment of non-plantation PKS in order to balance the rising FFB prices. Further, an increase in domestic CPO prices is needed to boost the palm oil derivative industry in increasing derivative products of high economic value. Then more effective in increasing domestic demand by optimizing the use of CPO as biodiesel raw material (Mandotori B20 to B100). There needs to be a study of marketing margins along the supply chain of fresh palm fruit bunches at the farm level to the price of palm oil in the world market.

    Figure

    Table

    Roots unit test results

    Note: *, **, *** significance at 1%, 5%, 10%.

    Lagged variable length criteria

    Note: *, **, *** significance at 1%, 5%, 10%.

    Johansen Cointegration test results

    Short-term marketing margin estimation results

    Note: *, **, *** significance at 1%, 5%, 10%.

    Results of long-term marketing margin estimates

    Note: *, **, *** significance at 1%, 5%, 10%.

    Multivariate marketing margin causality

    Note: ( ) is a probability with *, **, *** significance at 1%, 5%, 10%.

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