The Role of Mass Communications to the Market Interventions of Rice Commodity in Indonesia [Peran Komunikasi Massa terhadap Intervensi Pasar Komoditas Beras di Indonesia]

Kumara Jati, Arie Mardiansyah
| Abstract views: 324 | views: 159

Abstract

The rice is a staple food for the people and significantly contributes to economic development in Indonesia. Occasionally a market intervention should be implemented by the Government of Indonesia during the low harvest season to control and to manage the price of rice and the inflation, so low-income society could meet their basic needs. This study examines how communication aspect is really important as a part of market intervention mechanism to control the price and the stock of rice in Indonesia. Autoregressive and Moving Average, Autoregressive Conditional Heteroskedasticity/Generalized Autoregressive Conditional Heteroskedasticity, and the Structural Time-Series Model are applied with a dummy variable on daily and monthly data of the stock and the price of rice from January 1, 2015 until June 27, 2016. It can be inferred from the data that the form of mass communication by the government to relevant stakeholders (channel distribution and consumers) can run well, especially in order to maintain the supply and the price stabilization of rice. Nevertheless, the ARMA(1,1)-GARCH(1,1) model with dummy variables, inter alia mass communication, and also the number of market operations and rice policy, are not so influential on the price of rice, but more influence on the stock of rice. Then, the Structural Time-Series Model shows that the fluctuation of price and stock is affected by seasonal and cycle components especially more fluctuated in the month of January-March. Therefore, the relevant authorities are expected to maximize the rice policy in order to maintain the price stability in the short term, medium term, and long term.

Keywords: market intervensions, ARMA, ARCH/GARCH, Structural Time-Series Model

Abstrak

Beras merupakan makanan pokok bagi masyarakat dan secara signifikan berkontribusi terhadap pembangunan ekonomi di Indonesia. Terkadang intervensi pasar harus dilaksanakan oleh pemerintah di luar musim panen untuk mengendalikan dan mengelola harga beras dan inflasi, sehingga masyarakat berpenghasilan rendah dapat memenuhi kebutuhan mereka. Penelitian ini mengkaji bagaimana aspek komunikasi sangat penting sebagai mekanisme intervensi pasar untuk mengendalikan harga dan stok beras di Indonesia. Autoregressive and Moving Average and Autoregressive Conditional Heteroskedasticity/Generalized Autoregressive Conditional Heteroskedasticity serta the Structural Time-Series Model digunakan dengan variabel dummy pada data stok dan harga beras, baik harian maupun bulanan, antara 1 Januari 2015 hingga 27 Juni 2016. Hasil analisis menyimpulkan bahwa komunikasi massa oleh pemerintah kepada pihak-pihak yang berkepentingan (pelaku usaha dan konsumen) dapat berjalan dengan baik terutama untuk menjaga pasokan dan stabilitas harga beras. Sedangkan analisis lebih lanjut Model ARMA(1,1)-GARCH(1,1) dengan variabel dummy yaitu komunikasi massa, serta jumlah operasi pasar dan kebijakan beras kurang berpengaruh terhadap harga beras namun lebih berpengaruh terhadap stok beras. Kemudian, the Structural Time-Series Model menunjukkan bahwa naik turunnya harga dan stok beras berasal dari komponen musiman dan siklus terutama lebih berfluktuasi pada bulan Januari-Maret. Oleh karena itu, otoritas terkait diharapkan dapat memaksimalkan kebijakan beras untuk menjaga stabilitas harga dan stok beras dalam jangka pendek, menengah, dan panjang.

Kata kunci: intervensi pasar, ARMA, ARCH/GARCH, Structural Time-Series Model

Keywords

market intervensions; ARMA; ARCH/GARCH; structural time-series model; intervensi pasar

Full Text:

PDF

References

Books:

Berger, A. A. 1995. Essentials of mass communication theory. SAGE Publications, USA.

BPPKP. 2015. Laporan akhir analisis efektifitas operasi pasar beras. Pusat Kebijakan Perdagangan Dalam Negeri. Badan Pengkajian dan Pengembangan Kebijakan Perdagangan (BPPKP). Kementerian Perdagangan.

Gujarati, D.N. 2003. Basic econometric: Fourth edition international edition. McGraw-Hill Higher Education. Singapore.

Harvey, A., & Peters, S. (1993). Structural time series models. In G. S. Maddala, C.R. Rao and H. D. Vinod, eds., Handbook of Statistics, Vol. 11(2), 1–6. Elsevier Science Publishers B.V.

Samovar, L. A., & Porter, R.E. (Eds.). 1985. Intercultural communication: A reader. Belmont,

CA, Wadsworth.

Taylor, S.J. 1986. Modelling financial time series. Chichester, UK: John Wiley and Sons.

Turban, et. al. 2005. Introduction to information technology. John Wiley & Sons, Inc.

Journal:

Bollerslev, T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of

Economics 31 (1986) 307-327, 1986.

Chand, S., Kamal, S., Ali, I. 2012. Modeling and volatility analysis of share prices using ARCH and GARCH models. World Applied Sciences Journal19 (1): 77-82, 2012.

Ederington, L.H. & Guan, W. 2013. The cross-sectional relation between conditional heteroskedasticity, the implied volatility smile, and the variance risk premium. Journal of Banking & Finance, 37 (2013) 3388-3400.

Engle, R.F. 1987. Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4):987-1007, 1987.

Hadikusumah, K.H. 2013. Deskripsi pengambilan keputusan dalam berusaha tani Padi Pandan Wangi (Oryza Sativa L) di kalangan petani. Jurnal Kajian Islam, Sains dan Teknologi, Edisi Juli 2013 Volume VII No.1, Fakultas Sains dan Teknologi, Universitas Islam Negeri Sunan Gunung Djati Bandung.

Harvey, A., & Peters, S. (1990). Estimation procedures for structural time series models.

Journal of Forecasting, 9(2), 89–108. https://doi.org/10.1002/for.3980090203

Indrasari, S.D., Purwaningsih., Apriyati, E., Ardhiyanti, S.D. 2016. Preferensi konsumen pada beras berlabel jaminan varietas untuk Hipa 8, Ciherang dan Inpari 13.Penelitian Pertanian Tanaman Pangan, Vol.35, No.3, 2016.

Jati, Kumara. 2014. Analysis of sugar prices volatility using ARCH/GARCH. International Journal of Trade, Economics and Finance, Vol.5, No.2, April 2014.

Monteiro, N., Altman, I., & Lahiri, S. 2012. The impact of ethanol production on food prices: The role of interplay between The U.S. and Brazil. Energy Policy 41 (2012) 193-199.

Naranpanawa, A. & Bandara, J.S. 2012. Poverty and growth impacts of high oil prices: Evidence from Sri Lanka. Energy Policy xx (2012) xx-xx.

Rahmasuciana, D. Y., Darwanto, D.H., Masyhuri. 2015. Pengaruh pengadaan beras dan operasi pasar terhadap harga beras dalam negeri. Agro Ekonomi Vol. 26/No.2, Desember 2015.

Wijayanti, S., Candra, S., Sarjono, H. 2011. Analisis persediaan beras nasional dalam memenuhi kebutuhan beras nasional pada Perusahaan Umum Bulog. Journal The Winners

, Vol.12, No.1, Maret 2011:82-96.

Wood, B.D.K., Nelson, C.H., & Nogueira, L. 2012. Poverty effects of food price escalation: The importance of substitution effects in Mexican households. Food Policy 37 (2012) 77-85.

Digital Source:

Detik. 2016. Bulog siapkan 150.000 ton beras untuk operasi pasar. Laporan Finance Detik.

com. Diunduh tanggal 28 Juni 2016 dari http://finance.detik.com/read/2016/01/06/171815/

/4/bulog-siapkan-150000-ton-beras-untuk-operasi-pasar

Foodstationjayakarta. 2016a. Harga beras di PIBC. Laporan harga beras di Pasar Induk Beras Cipinang (PIBC). Diunduh pada 28 Juni 2016 dari http://foodstationjayakarta.com/index.php/pusat-informasi-pasar/harga-beras-pibc-palawija

Foodstationjayakarta. 2016b. Stock beras di PIBC. Laporan stock beras di Pasar Induk Beras Cipinang (PIBC). Diunduh pada 28 Juni 2016 dari http://foodstationjayakarta.com/index.php/pusat-informasi-pasar/stock-beras-pibc

Kementerian Perdagangan. 2017. Peraturan Menteri Perdagangan Republik Indonesia

Nomor 46/M-DAG/PER/7/2017 tentang Penyelenggaraan Teknologi Informasi dan

Komunikasi di Lingkungan Kementerian Perdagangan. Diunduh 19 April 2018

dari http://www.kemendag.go.id/files/regulasi/2017/07/11/46m-dagper72017-

id-1504068457.pdf

Republik Indonesia. 2014. Undang-Undang RepublikIndonesia Nomor 7 tahun 2014 tentang

Perdagangan. Diunduh 19 April 2018 dari https://www.google.com/url?sa=t&rct=j&q=

&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwjD_6vGxsXaAhXKM48KHfp

PC2QQFgg8MAI&url=http%3A%2F%2Fwww.kemendag.go.id%2Ffiles%2Fregulasi%2F2014%2F03%2F11%2F7-tahun-2014-id-1398758805.pdf&usg=AOvVaw1keFktfHebn8_OpLEH1d9y

Republik Indonesia. 2015. Peraturan Presiden Republik Indonesia Nomor 71 Tahun 2015

tentang Penetapan dan Penyimpanan Barang Kebutuhan Pokok dan Barang Penting. Diunduh 19 April 2018 dari http://peraturan.go.id/perpres/nomor-71-tahun-2015.html

Republika. 2018. Pasokan beras ke Pasar Beras Cipinang meningkat. Laporan Republika.com. Diunduh 19 April 2018 dari http://www.republika.co.id/berita/

ekonomi/makro/18/02/21/p4hg71423-pasokan-beras-ke-pasar-beras-cipinang-meningkat

Tempo. 2016. Hadapi Lebaran, Bulog Gelar operasi pasar 390 ribu ton beras. Laporan

Tempo.co. Diunduh 27 Juni 2016 dari https://m.tempo.co/read/news/2016/06/08/090777732/hadapi-lebaran-bulog-gelar-operasi-pasar-390-ribu-ton-beras

Other Source:

Adhikari, R. and Agrawal, R.K. 2013. An introductory study on time series modeling and forecasting. Working Paper LAP Lambert Academic Publishing, Germany.

Bulog. 2016. Laporan managerial 10 Juni Tahun 2016 Sore. Laporan Managerial

Badan Urusan Logistik (Bulog).

Durbin, J., & Koopman, S. J. (2001). Time series analysis by state space methods

. https://doi.org/10.1093/acprof:oso/9780199641178.001.0001

FAO. 2011. The State of food insecurity in the world. Report of Food and Agriculture Organization of the United Nations, Rome.

Mensah, J.O. 2011. Examining the behaviour of African financial markets using volatility models. Master Dissertation of Faculty of Business, Economics and Policy Studies, Universiti Brunei Darussalam.

Miniaoui, H., Sayani, H., and Chaibi, A. 2014. The impact of financial crisis on Islamic and

convenctional Indices of the GCC Countries. Working Paper 2014-401, IPAG Business School, Paris, France.

Rahman, Andaleeb. 2012. Characterizing food prices in India. Working Paper 2012-022,

Indira Gandhi Institute of Development Research, Mumbai, India.

Sen, J., & Chaudhuri, T.D. 2016. Decomposition of time series data of stock markets and its

implications for prediction – an application for the Indian Auto Sector. Proceedings of the 2

nd

National Conference on Advances in Business Research and Practices (ABRMP 2016)

, January 8-9, 2016, Kolkata, India.

Shiferaw, Y. A. 2012. Modelling volatility of price of some selected agricultural products in Ethiopia: ARIMA: GARCH Applications. Working Paper School of Mathematical and Statistical Sciences, Statistics Program, Hawassa University, Ethiopia.

Sutrisno. 2007. Tren pemasaran beras di Indonesia. Majalah Pangan Perum Bulog

. Edisi No.48/XVI/Januari/2007. Puslitbang BULOG, Jakarta.

Zulham, A. dan Ferizal, M. 2006. Kebijakan operasi pasar dan pasar beras di Nanggroe Aceh Darussalam. Laporan dari Pusat Aanalisis Sosial Ekonomi dan Kebijakan Pertanian serta Balai Pengkajian Teknologi Pertanian NAD.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Refbacks

  • There are currently no refbacks.