Stephany Dian Arbella, Purwanto Widodo
1,2 Corresponding Author : Economic Development, Universitas Pembangunan Nasional Veteran, Jl. R.S Fatmawati No. 1, Cilandak, Jakarta Selatan 12450, Indonesia.
Email : 1 [email protected]
ORCID : https://orcid.org/0000-0002-9284-159X
Acknowledgement
All authors have read and agreed to the published version of the manuscript.
Author Contributions: Conceptualization, D.A; methodology, D.A and P.W.validation, P.W; formal analysis, D.A and P.W.; investigation, D.A and P.W.; resources, D.A.; writing—original draft preparation, D.A..; writing—review and editing, D.A and P.W
Funding: This research is independently funded
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions.
Conflicts of Interest: The authors declare no conflict of interest.
ABSTRACT
Agriculture commodities play an important role in international market demand, one of which is tea. Geographical conditions and highlands in Indonesia are very suitable for tea plants, so it would increase its international competitiveness when it develops. This research aims to determine the effect of production, areas, price, exchange rate, and GDP on Indonesia’s tea exports. This research used quantitative data in a time series data of 60 samples from 2006-2020. Furthermore, this research used ARIMA to forecast with econometric aids Eviews 10. The results obtained include a positive and significant effect between production and export, and there is no effect on areas, price, the exchange rate, and GDP to exports. The forecasting results reveal that Indonesia’s tea exports will fall in the next ten years.
Keywords: production, areas, price, exchange rate, and GDP Introduction
International trade is the most critical factor for a country to seek economic prosperity. The number of countries involved in international trade increases competition to provide the best and most profitable products. One of the means used by a country to conduct international trade is through participation in export activities. Kayika (2010) argues that plantations are a sector that plays an important role compared to other industries in contributing to the national economy. One of the potential plantation products is tea. The existence of tea plantations makes it one of the critical sectors and has the
potential to be developed. The resources and conditions of Indonesia's tropical geographical environment are very suitable for growing tea plants, such as in the highlands. Tea is still preserved today, so it can continue contributing significantly through the foreign exchange it generates. In 2020, as much as 84.5% or 42.194 tons of tea products were produced in West Java, while 61% of Indonesia's tea production is exported to provide global market needs; the rest is used as raw materials for domestic and household industries. Indonesia's tea exports are in the form of green tea and black tea. In 2020, black tea exports reached 82.5% or 37.339 tons, with an export value of USD 79.1 million. In addition, the growth of green tea exports is more likely to slow down, and green tea was only able to contribute 17.7% or 7.926 tons with an export value of USD 17.1 million.
Most of Indonesia's tea production is traded internationally, and few are traded domestically. In 2020, as many as 64 countries controlled the market share of Indonesia's tea. Also, this year, the top five tea importers in Indonesia are Russia, which imported 8.048 tons or 17.78% of Indonesia's total tea exports or equivalent to USD 13.5 million.
Next, Malaysia with an export volume of 7.413 tons, with a contribution of 16.38% and an export value of USD 12 million. They were followed by the United States with 7.9%, with an export volume of 3.575 tons and an export value of USD 6.5 million. Pakistan and Taiwan on fourth and fifth. Tea exports to Pakistan amounted to 2.621 tons or about 5.79%, and the export value was USD 5.1 million, while tea export to Taiwan amounted to 2.202 tons or 4.87%, and the export value was USD 5 million. (tolong masukkan referensinya)
Changes in the development of tea exports continue to fluctuate, but there is a significant downward trend, a decrease of 12.25% in 2007 due to the shift in global tea consumption. Furthermore, there was another decline in 2011 by 15.44% due to the European financial crisis. This crisis has dramatically affected the industries that export commodity goods. The lowest drop in tea exports occurred in 2016, by 17.11%, due to limited plantation land. The low productivity makes so many farmers switch to planting new crops, the high cost of production, as well as unmet standardization targets. Therefore, Indonesia must import low-priced, low-quality tea to meet the needs of the national tea industry. The decline in tea exports occurred again in 2019 by 12.7% due to trade wars and political conflicts between the United States and China, which caused a global economic recession. This resulted in a sharp decline in Indonesia's exports and trade balance deficit.
The highest growth in tea exports occurred in 2008, in addition to the financial crisis at that time. Agricultural development continues to increase despite declining GDP with a contribution of 4.5%. The global crisis was supposed to affect the structure of production, in particular, exports. It turns out that the economic situation does not show a signal in that direction. (tolong masukkan referensinya)
Literature Review
Competitiveness
Weak competitiveness in the global market has caused Indonesia's tea exports to fluctuate. The fluctuating contribution of Indonesia's tea exporters is a problem related to the development and competitiveness of Indonesia's tea. Indeed, the liberation of international trade is an excellent opportunity for the tea industry, but serving higher- quality tea products is a big challenge. Improving product competitiveness is the biggest challenge in free trade. Indonesia's tea exports continue to decline, disrupting the function of tea as a source of national income. Due to the increasingly fierce competition, improving product performance in the global market in developing tea products is necessary. This research is based on the theory of comparative advantage by David Ricardo. He posits that countries with no edge can achieve profitable international trade if they may specialize in cheaper products than other countries. (tolong masukkan referensinya)
To increase the competitiveness of Indonesia's tea, the role of various parties is needed, such as the government should not increase the quota on tea imports because oversupply causes domestic prices to fall below the benchmark price, to the detriment of farmers. In stabilizing the rupiah exchange rate, Bank Indonesia may expand its reach and disseminate policy instruments for exporters in conducting foreign exchange swaps for liquidity management, export transactions, and foreign exchange debt by offering more currency. The government must seriously carry out the development of tea production.
This can be achieved if the policy harmony between the government and producers. In addition, it is necessary to develop research institutions and increase human resources to innovate quality tea products that are competitive in the international market. This will definitely be achieved by providing support for facilities and infrastructures and the government's responsibility for developing the tea industry. (tolong masukkan referensinya)
The quality of processed tea can be improved by maximizing production factors to achieve optimal results and quantity of tea production, either in quality or quantity.
Governments and exporters must maintain and strengthen relations between tea-importing countries worldwide so that export trade runs well and the export network can be expanded. Expansion of tea land is essential, but both farmers and the government must focus on input capacity, and adding one input should be balanced by adding another input.
In addition, there is not only the area of tea plantation but also the quality and efficiency of cultivating land for maximum yields. The amount of foreign exchange earned by the Indonesian government through export activities is expected to contribute to the development of the real sector, the agricultural industry, and the maximization of agricultural land, especially tea plantations. (tolong masukkan referensinya)
Analisis Deret Waktu
Para ahli ekonometrika telah mengembangkan berbagai metode peramalan time series dengan tujuan untuk mendapatkan suatu model yang memberikan hasil ramalan yang lebih akurat (Pitaloka, Sugito and Rahmawati, 2019)1. Metode yang sering digunakan antara lain
1 Pitaloka, R.A, Sugito and Rahmawati, R. 2019. Perbandingan Metode ARIMA Box-Jenkins Dengan ARIMA Ensemble Pada Peramalan Nilai Impor Provinsi Jawa Tengah. JURNAL GAUSSIAN, Volume 8, Nomor 2, Tahun
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adalah metode ARIMA Box-Jenkins yang digunakan untuk mengolah time series yang univariat. Salah satu metode yang sering dipergunakan adalah Box-Jenkins (Widodo, 2022)2. Langkah pertama untuk menggunakan metode Box – Jenkins, adalah menguji masalah stationarity data.
Stationeritas data deret waktu
Data time series dikatakan stasioner jika rata-rata dan variansinya konstan sepanjang waktu dan kovarian antara dua data time series hanya tergantung dari lag antara dua periode tersebut (Widarjono, 2018)3. Secara statistic dapat dinyatakan sebagai berikut :
E
(
Yt)
=μ ……… (1)Yt−μ¿2=σ2
Var
(
Yt)
=E¿ ………. (2)γk=E (Yt−μ)(Yt+ k−μ) ………. (3) Pemeriksaan Stationeritas Deret Waktu
Uji Augmented Dickey- Fuller (ADF)
The data stationarity test used the Augmented Dickey-Fuller (ADF) (Widarjono, 2018).
The ADF Test Formulation is:
∆ Yt=γ Yt−1+
∑
i=2 p
βi∆ Yt−i+1+εt ………..……… (4)
∆ Yt=ao+γ Yt −1+
∑
i=2 p
βi∆ Yt −i+1+εt ……….……… (5)
∆ Yt=ao+a1t+γ Yt−1+
∑
i=2 p
βi∆ Yt−i +1+εt ………..………. (6)
Where :
Yt : t-th observation value
Yt−1 : t-th observation value t -1 (previous time)
∆ Yt : Yt−Yt−1 t : time trend Model Box - Jenkin
Model Box – Jenkin merupakan salah satu Teknik peramalan model time series yang hanya berdasarkan perilaku data variable yang diamati. Alasan utama penggunaan model Box – Jenkin karena pergerakan variabel- variable ekonomo yang diteliti seringkali sulit
2019, Halaman 194 – 207. ISSN: 2339-2541. Online di: http://ejournal3.undip.ac.id/index.php/gaussian 2 Widodo, P. 2022. Is the Volatility of the Islamic Stock Index Lower than the Conventional Stock Index during Covid-19 Pandemic? Empirical Evidence in Indonesia Stock Exchange. Journal of Islamic Economics and Finance Studies.
3 Widarjono, A. 2018. Ekonometrika. Pengantar dan Aplikasinya Disertai Penduan Eviews. Edisi Kelima. UPP STIM YKPN.
dijelaskan oleh teori-teori ekonomi (Widarjono, 2018). Model Boc – Jenkin terdiri atas beberapa model yaitu : autoregressive (AR), moving average (MA), autoregressive - moving average (ARMA) dan autoregressive integrated moving average (ARIMA).
Model Autoregressif (AR)
The general form of the autoregressive model (AR) with the order p (AR(p)) or the ARIMA model (p,0,0) is expressed as follows:
Yt=β1Yt −1+β2Yt−2+…+βpYt− p+et ……….……….. (7) Description:
βp=Autoregressive parameter et=Error term
Yt=Observation value
Model autoregressive merupakan model dimana variable dependen merupakan fungsi linear dari pengamatan sebelumnya.
Model Moving Average (MA)
The general form of the moving average model (MA) with the order q (MA(q)) or the ARIMA model (0,0,q) is expressed as follows:
Yt=et+β1et −1+β2et −2+…+βqet −q ……….……… (8) Description:
βq=Moving average parameter et=Error term
Yt=Observation value Model ARMA/ARIMA
Model penggabungan antara model autoregressive dengan moving average, dinamakan dengan ARMA, jika data time series tidak stationer pada level, namun stationer pada first difference, maka dinamakan dengan autoregressive integrated moving average (ARIMA).
RESEARCH METHODS
This research uses quantitive data with objects of export, production, land areas, price, exchange rate, and GDP and utilizes time series data from 2006 to 2020, as many as 60 samples and sourced from the Central Bureau of Statistics, World Bank, Index Mundi, Ministry of Agriculture, and the UPN Veteran Jakarta Economic Science Laboratory. The method dikembangkan oleh Box-Jenkins the Autoregressive Integrated Moving Average (ARIMA) or Box-Jenkins method to show forecasts of dependent variables in the future with significance tests to show the effect of independent variables on dependent variables both partially and simultaneously.
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Untuk analisis model AR, MA, ARMA atau ARIMA, penulis mengikuti prosedur yang dianjurkan oleh Widodo (2022), yaitu :
1. Uji Stationerity
The procedure for determining whether the data is stationary or not by comparing the ADF statistical value with the critical value of the Mackinnon statistical distribution. The ADF statistic value is the comparison between � and the standard error of �t-1. If the absolute value of the ADF statistical value is greater than the absolute value of the critical value of the Mackinnon statistical distribution, it is concluded that the data is stationary. Jika data tidak stationer pada level, maka ditransformasi ke bentuk first difference (Widodo, 2022).
2. Uji signifikasi parameter
Model yang akan dipergunakan baik model AR, MA maupun ARMA/ARIMA variabelnya harus signifikan.
3. Uji white noise
Model AR, MA maupun ARMA/ARIMA variabelnya harus signifikan, kemudian diuji white noise. Pengujian white noise untuk mengetahui apakah hasil analisis dengan model tersebut memiliki rata-rata error term sama dengan nol dan variancenya konstan. Pengujian White Noise menggunakan Uji Box and Pierce, where :
Q=n
∑
k=1 m
ρk2 ……….. (9)
n = number of sample k = length of lag
ρk=¿ ACF (Autocorrelation Function)
Suatu model time series dikatakan white noise, jika nilai Q lebih kecil dari table Chi Square dengan levl significance tertentu dan degree of freedom sebesar k (banyaknya ACF) atau :
Q< χα (df =k)2
4. Uji ARCH atau Heteroskedasticity
Uji Autoregressive Conditional Heteroskedasticity (ARCH) yang dikembangkan oleh Engle (1982) dalam Widarjono (2018). Engle menggunakan error term kuadrat sebagai berikut :
εt2=αo+α1εt −12 +α2εt −22 +…+αpεt− p2 ………. (10) n R2 χα(df = p)2
Jika n R2>χα(df = p )2 berarti terdapat masalah ARCH
5. Pemilihan model
Jika terdapat beberapa model yang memenuhi syarat, maka pemilihan model berdasarkan kriteria AIC (Akaike Info Criterion). The Akaike information criterion (AIC) is an estimator of prediction error and thereby relative quality of statistical models for a given set of data. Suppose that we have a statistical model of some data. Let k be the number of estimated parameters in the model.
Let L hat be the maximized value of the likelihood function for the model.
Then the AIC value of the model is the following.
AIC=2k −2 ln (^L) ……… (11) Model yang dipilih jika memiliki AIC terkecil.
RESULTS AND DISCUSSION
Modeling Techniques
Identification of Data Patterns
Indonesia’s tea export data is not stationary because its average and variance tended to fluctuate, as shown in the following figure.
Figure 1. Plot of Export Data for 2006-2020 Stationary Test
The stationary test is performed to find out that the observation data are stationary using the Augmented Dickey-Fuller Test (ADF Test) and Phillip-Perron Test (PP Test presented in the following table.
Table 1. Augmented Dicky-Fuller Test Results t-statistic Probability Conclusion Level -0.822497 0.8052 Not Stationary 1st Difference -7.967753 0.0000 Stationary
At the level, the data is not stationary with a smaller ADF value than the 5% critical value test, i.e., -0.822497 < -2.911730, and the probability value is greater than the α of 0.8052 > 0.05, so that H0 rejected dan H1 accepted. The first difference shows
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stationary data with the acquisition of calculated ADF value greater than 5% critical value test, i.e., -7.967753 > -2.912631, and the probability value is smaller than the α of 0.0000 <
0.05 so that H0 accepted dan H1 rejected.
ARMA/ARIMA Model Identification
Correlogram Q-statistic test results at the level as shown by the following figure.Date: 07/22/22 Tim e: 13:30 Sam ple: 2006Q1 2020Q4
Included obs ervations : 60
Autocorrelation Partial Correlation AC PAC Q-Stat Prob 1 0.948 0.948 56.689 0.000 2 0.896 -0.027 108.23 0.000 3 0.845 -0.027 154.79 0.000 4 0.793 -0.028 196.54 0.000 5 0.755 0.107 235.07 0.000 6 0.717 -0.023 270.46 0.000 7 0.679 -0.023 302.78 0.000 8 0.641 -0.024 332.14 0.000 9 0.587 -0.162 357.27 0.000 10 0.533 -0.033 378.44 0.000 11 0.480 -0.034 395.92 0.000 12 0.426 -0.035 410.00 0.000 13 0.380 0.003 421.44 0.000 14 0.334 -0.035 430.47 0.000 15 0.288 -0.036 437.32 0.000 16 0.242 -0.038 442.27 0.000 17 0.187 -0.091 445.31 0.000 18 0.133 -0.045 446.87 0.000 19 0.079 -0.047 447.43 0.000 20 0.024 -0.050 447.49 0.000 21 -0.013 0.094 447.50 0.000 22 -0.051 -0.041 447.76 0.000 23 -0.089 -0.043 448.55 0.000 24 -0.126 -0.045 450.19 0.000 25 -0.150 0.156 452.59 0.000 26 -0.174 -0.033 455.90 0.000 27 -0.198 -0.034 460.32 0.000 28 -0.222 -0.035 466.05 0.000
Figure 2. Q-Statistic Test at Level
Based on the ACF and PACF plots, there are some significant lags, and the plots are cutting off, so the AR(1) and ARMA(2,1) models are obtained.
Parameter Estimation
The results of parameter estimation as described in this table.
Table 2. Parameter Estimation Results Model p-value
p q
ARMA(2,1 )
0.0000 0.0000
AR(1) 0.0000 -
The ARMA(2,1) model is significant because at a significance level of 5%, a probability value is produced in both parameters, 0.0000 < 0.05. The next model, AR(1), is also significant to the model because at a significance level of 5%, a probability value of 0.0000 < 0.05 is obtained.
Best Model Selection
The selection of the best ARIMA model is shown in the following table.
Table 3. Comparison of Akaike Info Criterion Values (AIC)
Model AIC
ARMA(2,1 )
16.58188 AR(1) 16.56869
Because there is more than one model that will select the best model based on the smallest Akaike Info Criterion (AIC), it can obtain that the AR(1) model is the best.3.1.6 Model Verification.
Model AR(1) menunjukkan bahwa eksport teh di Indonesia hanya dipengaruhi oleh eksport tahun sebelumnya. Jika dianggap tahun tertentu harga eksport teh cukup baik atau menguntungkan petani, maka pada tahun berikutnya mereka akan mengekspor lebih banyak lagi, demikian pula jika dianggap harga meengalami penurunan, maka petani akan mengurangi ekport.
Correlogram Q-Statistic
Correlogram Q-statistic test results on the AR(1) model as shown in the following figure.
Date: 07/22/22 Tim e: 13:39 Sam ple: 2006Q1 2020Q4 Included obs ervations : 60
Q-s tatis tic probabilities adjus ted for 1 ARMA term
Autocorrelation Partial Correlation AC PAC Q-Stat Prob 1 -0.054 -0.054 0.1820 2 -0.055 -0.058 0.3728 0.542 3 -0.055 -0.062 0.5727 0.751 4 -0.159 -0.171 2.2542 0.521 5 -0.047 -0.078 2.4017 0.662 6 -0.047 -0.086 2.5570 0.768 7 -0.048 -0.095 2.7207 0.843 8 0.206 0.156 5.7672 0.567 9 -0.057 -0.075 6.0061 0.647 10 -0.058 -0.081 6.2569 0.714 11 -0.059 -0.089 6.5199 0.770 12 0.037 0.056 6.6240 0.829 13 -0.051 -0.078 6.8297 0.869 14 -0.052 -0.085 7.0465 0.900 15 -0.053 -0.093 7.2753 0.924 16 0.225 0.171 11.552 0.713 17 -0.051 -0.063 11.781 0.759 18 -0.052 -0.067 12.023 0.799 19 -0.053 -0.071 12.279 0.833 20 0.211 0.243 16.417 0.629 21 -0.031 -0.020 16.509 0.685 22 -0.032 -0.018 16.609 0.734 23 -0.033 -0.017 16.717 0.779 24 -0.081 -0.122 17.394 0.789 25 -0.024 -0.009 17.455 0.829 26 -0.025 -0.006 17.522 0.862 27 -0.026 -0.003 17.596 0.890 28 0.147 -0.032 20.091 0.827
Figure 3. Correlogram Q-statistic Test Results on The AR(1) Model
Each lag produces a significant probability value (probability value > 0.05).
Therefore H0 is accepted, and H1 is rejected. Thus it was decided that there was no residual correlation between lags in the AR(1) model. Nilai Q sama dengan 20.091 lebih kecil dari χ0.01(df =28)
2 =58.62 , sehingga dapat tidak terdapat rwsidual correlation between lags atau sudah white noise.
Heteroscedasticity/ARCH Test
The presence and absence of heteroskedasticity atau ARCH effect can be considered at the probability value of Obs*R-Squared, where the probability value of Obs*R-Squared > α (0.05) means that there is no heteroskedasticity problem.
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Figure 4. Heteroskedasticity Test Results
A probability value 0.3106 > 0.05 was obtained so that H0 was accepted and H1 rejected, and it can be concluded that there is no ARCH effect or heteroskedasticity in the residual.
Forecasting
The AR(1) model identification results are used to forecast Indonesia’s tea export data for the next year and produce visual forecasting, as shown in the following figure.
0 10,000 20,000 30,000 40,000 50,000
06 08 10 12 14 16 18 20 22 24 26 28 30 EKSF ± 2 S.E.
Forecast: EKSF Actual: EKS
Forecast sample: 2006Q1 2030Q4 Adjusted sample: 2006Q2 2030Q4 Included observations: 99
Root Mean Squared Error 7044.122 Mean Absolute Error 5863.620 Mean Abs. Percent Error 42.83003 Theil Inequality Coefficient 0.172358 Bias Proportion 0.676651 Variance Proportion 0.320581 Covariance Proportion 0.002767 Theil U2 Coefficient 11.45664 Symmetric MAPE 31.89276
Figure 5. Indonesia's Tea Exports Forecasting Results for 10 years
The forecasting results show that the volume of Indonesia’s tea exports shows decreased. The Mean Absolute Percent Error (MAPE) value can be used to determine the accuracy of the analysis results. A small MAPE value indicates that the error in the forecasting results tends to be small so that the forecasting results produced by the ARIMA model are declared accurate. The AR(1) forecasting result is at a MAPE value of 42.83003 or 42.83%. This shows that the error rate for forecasting the volume of Indonesia’s tea export data is 42.83%. Thus, it can be said that the use of AR(1) to predict the movement of Indonesia’s tea export volumes has a high level of accuracy.
Table 4. Indonesia's Tea Exports Forecasting Value for 2021 years Year
Volume Export
(Kg)
2021
Q1 11303,52
Q2 11290,84
Q3 11278,18
Q4 11265,53
Forecasting data on Indonesia’s tea exports for the next 2021 year can be obtained for more details, in which the total volume will decline yearly.
The obstacles to the implementation of exports are the existence of tariff policies. In general, tariffs can significantly impact a country’s consumption, production, and economic structure. Market access for developing countries such as Indonesia is still tricky because developed countries have much higher initial rates. Indeed, under the agreement, developed countries are only encouraged to reduce the average tariff by 36%, so the rates are still relatively high even though they have been lowered. With the power of capital in the hands of developed countries, they provide substantial export subsidies to encourage agricultural exports, disrupting the market and making products from developing countries less competitive. European Union countries spend more than 40% of the national budget on subsidizing uncompetitive agricultural products. Another effect of export subsidies for developed countries is the import trap for developing countries such as Indonesia. The flow of imports of food and processed goods are killing local producers of the same kind because they are not competing with imports. Moreover, the influence of trade globalization is like the important role of multinational companies in agro-industrial. Small farmers do not have high hopes of entering the market because of the difficulty of achieving safety and quality standards. Farmers are increasingly dependent on the products of multinational companies, and many companies have begun to transfer their ownership to multinational companies.
CONCLUSION
Hasil penelitian ini menunjukkan bahwa eksport komoditas teh dapat dimodelkan dengan AR(1). Model AR(1) menunjukkan bahwa pergerakan eksport teh di Indonesia hanya dipengaruhi oleh eksport tahun sebelumnya. Akibatnya perkembangan ekspor komoditas teh menjadi fluktuatif, sangat tergantung dari perkembangan harga.
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