Available online at HABITAT website: http://www.habitat.ub.ac.id
The Impact of Input and Output Prices on Indonesian Coffee Production and Trade Performance
Fatkurrohim1*, Nuhfil Hanani1, Syafrial2
1Postgraduate of Agriculture Economics Program, Faculty of Agriculture, Brawijaya University, Veteran St. (65145), Malang Indonesia
2Department of Socio-Economics, Faculty of Agriculture, Brawijaya University, Veteran St. (65145), Malang Indonesia
Received: 7 December 2021; Revised: 28 January 2022; Accepted: 1 April 2022
ABSTRACT
Coffee is one of the most important agricultural plantation commodities in Indonesia, contributing 1.17 billion USD to the country's foreign exchange in 2017. Indonesia was also the world's fourth-largest coffee exporter from 2011 to 2017. However, the performance of coffee production and trade is very dynamic. In 2018, there was a significant drop in coffee exports by 40.23%, relegating Indonesia to sixth place even until 2020. Coffee production also fluctuates significantly, in contrast to the constantly rising coffee demand. This research aimed to figure out what factors influence Indonesian coffee production and trade performance. A simultaneous equation model using the 2SLS approach was employed as the methodology.
The findings revealed that, in the production block, cocoa prices and interest rates had a significant influence (α 10%) on the coffee land areas. In contrast, domestic coffee prices, rainfall, and trends significantly impacted coffee productivity. Meanwhile, in the trade block, domestic coffee prices and non- tariff dummy barriers significantly influence Indonesia's total coffee exports. As a result, it can be deduced that changes in the input prices (substitution commodity prices, interest rates, rainfall, and trend) have a better impact on coffee production, while the output prices (domestic coffee prices) on the coffee trade performance.
Keywords: coffee; production; trade; simultaneous; equation How to cite:
Fatkurrohim, Hanani, N., & Syafrial. (2022). The Impact of Input and Output Prices on Indonesian Coffee
Production and Trade Performance. HABITAT, 33(1), 33–43.
https://doi.org/10.21776/ub.habitat.2022.033.1.4
1. Introduction
The agricultural sector is the second largest contributor to Indonesia's GDP, after the manufacturing sector. During the COVID-19 pandemic, agricultural GDP growth remained positive at 3.33% in the fourth quarter of 2020, contributing to the national economic recovery (BPS, 2020). Coffee is one of Indonesia's leading agricultural commodities. The value of Indonesian coffee exports reached 1.17 billion USD in 2017 and ranked the fourth largest world coffee exporter (UNComtrade, 2020). Indonesia also has been the world's fourth-largest coffee producer since 2014 (FAO, 2020). However, Indonesian coffee production and trade development is still very dynamic (Nugroho, 2014; Vicol et al., 2018).
In the last five years, the growth trend of Indonesian coffee exports has decreased by an average of -2.27% per year (UNComtrade, 2020).
Moreover, in 2018, coffee exports fell more significantly by -40.23%, bringing Indonesia to sixth place until 2020. Coffee production in 2019/20 was also stagnant with a growth rate of less than 1%, and coffee consumption has continued to increase by an average of 3.31% per year in the last ten years (ICO, 2020).
Furthermore, Indonesian coffee plants' productivity is still relatively low at 0.55 tons per hectare, compared to Vietnam, which has reached 2.42 (FAO, 2020). However, Indonesia has a lot of potential for coffee production geographically (Neilson et al., 2018). Indonesia's coffee land area has reached 1.24 million hectares, while Vietnam only has 619 thousand hectares (FAO, 2020).
Many factors are accused of influencing coffee production and trade performance, such as input and output prices (Bucagu et al., 2013; Ayele
---
*Correspondence Author.
E-mail: [email protected] Phone: +62-81341863753
Available online at HABITAT website: http://www.habitat.ub.ac.id et al., 2021; Ceballos-Sierra and Dall’Erba, 2021;
Kouadio et al., 2021). One method for determining whether factors substantially impact coffee production and trade is the econometric model of the simultaneous equation system (Kumar et al., 2016; Kumar and Datta, 2021; Sarkar et al., 2021).
This model was structured of various endogenous variable equations that interact to produce a simultaneous equation system (Gujarati, 2004).
The endogenous variables that indicate coffee production performance are area and crop productivity, while export and import quantities represent coffee trade performance.
The law of supply and demand between quantity and price is also reflected in the linkage between various endogenous variable equations in the model (Mankiw et al., 2014). These various endogenous variables can be manifested in input and output prices. The input prices include the factors that contribute to the cost of production, such as substitute commodities prices, urea fertilizer prices, and investment interest rates (Komarek et al., 2017; Jouf and Lawson, 2021;
Akber et al., 2022). Meanwhile, domestic coffee prices represent the output price (Sun et al., 2021).
The simultaneous equation model analysis shows the impact of changes in input and output prices on changes in endogenous variables, which will also affect the performance of Indonesian coffee production and trade.
Indonesia has great potential for developing coffee commodities as an income source for the country’s foreign exchange. On the other hand, it is constrained by the highly volatile performance of coffee production and trade. Consequently, it is critical to understand the factors that influence the performance of Indonesian coffee production and trade. So that it can be used to develop appropriate policy strategies for improving production and trade performance. Using a simultaneous equation model with a 2SLS (Two-Stage Least Square) approach, this study aims to determine which factors have a significant (α 10%) impact on the performance of Indonesian coffee production and trade
2. Theoretical Underpinning
Previous studies have widely used the econometric model of the simultaneous equation system with the 2SLS approach, both for researching food crops (Perdana et al., 2013;
Setiawan et al., 2016; Sebayang et al., 2019; Sari, 2020) and plantations (Perdana et al., 2013;
Setiawan et al., 2016; Sebayang et al., 2019; Sari,
2020). (Rifai et al., 2014; Purnomowati et al., 2015; Sinuraya et al., 2017; Rahman et al., 2018;
Suprihanti et al., 2019). This method was primarily used as an analytical tool to identify the impact of economic policy simulations on changes in the value of endogenous variables and determine which variables have a major effect on the model.
Many previous studies have shown that changes in input prices have a much more significant influence on the harvested area and productivity changes. For example, for food crops such as rice and corn, input prices such as substitute product prices, labor wages, and fertilizer prices have a greater impact on changes in harvested area than food crop prices themselves (Setiawan et al., 2016; Sebayang et al., 2019; Sari, 2020). Meanwhile, the technology variable (trend) in maize has a more significant impact on productivity than the price of corn output itself (Sebayang et al., 2019). In plantation crops such as oil palm, cocoa, and sugarcane, input price variables such as substitute product prices and interest rates have a more significant impact on the harvested area than the price of the plantation crops themselves (Rifai et al., 2014; Sinuraya et al., 2017; Rahman et al., 2018).
Furthermore, in terms of trade, imports of food crops such as soybeans and rice are heavily influenced by import prices, domestic prices, and domestic demand (Perdana et al., 2013; Setiawan et al., 2016; Sari, 2020). Meanwhile, the variables of domestic prices, exchange rates, domestic production, and non-tariff policies have a significant impact on the exports of plantation crops such as oil palm (CPO), cocoa, and sugarcane (molasses) (Rifai et al., 2014; Sinuraya et al., 2017; Rahman et al., 2018). In addition, each explanatory variable in the model has a short and long-term elasticity analysis to help interpret the equations of the key parameters that are not significant.
3. Research Methods 3.1. Research Approach
This study was a survey with a quantitative approach. The research locations covered the entire territory of the Republic of Indonesia and were based on secondary time series data collected by various national and international survey institutions.
Available online at HABITAT website: http://www.habitat.ub.ac.id 3.2. Data Collection Techniques
The data used was 31 years of annual secondary data (1989-2020). Several sources for data collection were the Ministry of Agriculture of the Republic of Indonesia (acreage, production, and productivity), Bank Indonesia/BI (interest rate), Central Bureau of Statistics/BPS (labor wages), International Coffee Organization/ICO (consumption and stocks), Food and Agriculture Organization/FAO (domestic prices and exchange rates), United Nations Commodity Trade Statistics (exports, imports, export prices, and import prices), World Bank (world prices and rainfall), United Nations Conference on Trade and Development/UNCTAD (non-tariff policy).
3.3. Model Specification
The simultaneous equation model is divided into two parts: production and trading block. This model is composed of 15 structural equations and 10 identity equations in a simultaneous equation system. In the production block, coffee area and productivity are divided into three land use statuses (large private, large state, and people's plantations). In the trading block, Indonesia's coffee exports also divided into three major export destination countries (America, Japan, and Germany). Meanwhile, Indonesia's coffee imports are the total import data and not broken down further, because Indonesia is a coffee exporter country. Furthermore, the consumption data is the domestic demand for Indonesian domestic coffee.
The simultaneous equation model used in this study is described as follows:
a. Production Block
Area of People's Coffee Plantation (AKR) AKRt= a0+ a1PKDt+ a2PCWt-1+ a3(PUFt-
PUFt-1)+ a4IRIt+ a5AKRt-1 + u1 (1) Expected parameter sign (hypothesis):
a1, a5> 0; a2, a3, a4< 0
Area of Large Private Coffee Plantation (AKS) AKSt= b0+ b1(PKDt-PKDt-1)+ b2PUFt-1+
b PPLt + b IRIt-1+ b AKSt-1+ u2 (2) Expected parameter sign (hypothesis):
b1, b5> 0; b2, b3, b4< 0
Area of Large State Coffee Plantation (AKN) AKNt= c0+ c1PKDt+ c2(PCWt-PCWt-1)+
c3(PUFt-PUFt-1)+ c4PPLt+
c5AKNt-1+ u3 (3)
Expected parameter sign (hypothesis):
c1, c5> 0; c2, c3, c4< 0
Area of Indonesian Coffee Plantation (AKI) AKIt= AKRt+ AKSt+ AKNt (4)
People's Plantation Coffee Productivity (YKR) YKRt= d0+ d1(PKDt-PKDt-1)+ d2PUFt+
d3RFIt+ d4TRN + d5YKRt-1+ u4 (5) Expected parameter sign (hypothesis):
d1, d3, d4, d5> 0; d2< 0
Private Plantation Coffee Productivity (YKS) YKSt= e0+ e1PKDt+ e2PUFt+ e3(RFIt-
RFIt-1)+ e4TRN + e5YKSt-1+ u5 (6) Expected parameter sign (hypothesis):
e1, e3, e4, e5> 0; e2< 0
State Plantation Coffee Productivity (YKN) YKNt= f0+ f1(PKDt-PKDt-1)+ f2PUFt-1+
f3(RFIt-RFIt-1)+ f4TRN + f5YKNt-1+
u6 (7)
Expected parameter sign (hypothesis):
f1, f3, f4, f5> 0; f2< 0
Indonesian Coffee Productivity (YKI)
YKIt= (YKRt+ YKSt+ YKNt) ÷ 3 (8) People's Plantation Coffee Production (QKR)
QKRt= AKRt× YKRt (9)
Private Plantation Coffee Production (QKS)
QKSt= AKSt × YKSt (10)
State Plantation Coffee Production (QKN)
QKNt= AKNt× YKNt (11)
Indonesian Coffee Production (QKI)
QKIt= QKRt+ QKSt+ QKNt (12) Indonesian Domestic Coffee Demand (QDKI) QDKIt = g0+ g1PKDt+ g2(PKPt-PKPt-1)+
g3IRIt+ g4QDKIt-1+ u7 (13) Expected parameter sign (hypothesis):
g2, g4> 0;g1, g3< 0
Domestic Coffee Prices (PKD) PKDt= h0+ h1PKWt+ h2QSKIt-1 +
h3QDKIt-1 + h4PKDt-1+ u8 (14) Expected parameter sign (hypothesis):
h1, h3, h4> 0;h2< 0 b. Trading Block
Indonesian Coffee Exports to America (XKIA) XKIAt= i0+ i1PKXIt+ i2PKDt+ i3QKIt+
i4ERIt+ i5XKIAt-1+ u9 (15) Expected parameter sign (hypothesis):
i1, i3, i , i5> 0;i2< 0
Indonesian Coffee Exports to Japan (XKIJ) XKIJt= j0+ j1PKXIt-1+ j2PKDt+ j3ERIt+
j4DNTJt+ j5(XKPIJt-XKPIJt-1)+
j6XKIJt-1+ u10 (16)
Expected parameter sign (hypothesis):
j1, j3, j6> 0;j2,j4,j5< 0
Available online at HABITAT website: http://www.habitat.ub.ac.id Indonesian Coffee Exports to German (XKIG)
XKIGt = k0+ k1PKXIt+ k2PKDt+ k3QKIt + k4ERIt+ k5XKPIGt + k6XKIGt-1 + u11
(17) Expected parameter sign (hypothesis):
k1,k ,k ,k6>0;k2,k5<0
Indonesian Coffee Export (XKI)
XKIt= XKIAt + XKIJt+ XKIGt + XKIOCt (18) Price of Indonesian Coffee Exports (PKXI) PKXIt= l0+ l1PKWt+ l2XKIt+ l3PKXIt-1 + u12
(19) Expected parameter sign (hypothesis):
l1, l3> 0;l2< 0s
Indonesian Coffee Import (MKI) MKIt = m0+ m1PKMIt+ m2PKDt+
m3QDKIt+ m4ERIt + u (20) Expected parameter sign (hypothesis):
m2> 0;m , m3, m4< 0
Price of Indonesian Coffee Imports (PKMI) PKMIt= n0+ n1PKWt+ n2MTIt+ u14 (21) Expected parameter sign (hypothesis):
n1, n2> 0
Indonesian Domestic Coffee Supply (QSKI) QSKIt= QKIt + SKIt+ MKIt- XKIt (22) World Coffee Export (XKW)
XKWt= XKIt + XKOCt (23)
World Coffee Import (MKW)
MKWt= MKIt+ MKOCt (24)
World Coffee Price (PKW)
PKWt= o0+ o1XKWt+ o2MKWt+o3PKWt-1+
u15 (25)
Expected parameter sign (hypothesis):
o2, o3> 0;o1< 0
3.4. Model Identification
Model identification must be done before entering the variable coefficient estimation process (Koutsoyiannis, 1989). All structural equations in the model were identified using order conditions as mandatory conditions and rank conditions as adequacy conditions. The Indonesian coffee model consisted of 25 equations (G), of which 15 were structural, and 10 were identity equations. The total number of variables in the model is 42 (K), with 25 endogenous variables and 17 exogenous variables. The maximum number of variables (exogenous and endogenous) in a model equation was seven, and the minimum was three (M). In the order condition, if “(K-M) > (G-1)”, the equation was said to be over identified; if “(K-M) = (G-1)”, the
equation was said to be precisely identified; and if
“(K-M) (G-1)”, the equation was said to be under- identified.
Every structural equation in the Indonesian coffee model must be over or exactly identified for the parameters to be estimated using the 2SLS simultaneous equation system. The identification results showed that all structural equations in the model were included in over-identified with the largest notation of (42-7) > (25-1). Furthermore, in the rank condition, all structural equations in the model could be estimated because they had at least one matrix determinant from other structural equation matrices whose value was not equal to zero. As a result of the Indonesian coffee model identification, it had met the requirements of necessity and adequacy conditions to estimate parameters using the 2SLS approach.
3.5. Model Validation
The data stationarity test and serial correlation analysis validated the Indonesian coffee model. Non-stationary data would result in dubious or spurious regression if an econometric equation with time-series data were regressed using non-stationary data (Gujarati, 2004). If a time series variable produced an absolute value of t-statistics greater than the McKinnon critical value at an error level of 1%, 5%, or 10%, it was said to be stationary or did not have a unit root. If the test was still not stationary at the variable level, the test could be repeated at the first difference through the data transformation process to meet the assumption of stationarity.
Serial correlation analysis of each structural equation in the model was also carried out to avoid autocorrelation in time series variables. The Breusch-Godfrey test was used to determine the significance of the probability of the chi-squared coefficient (chi2) generated (Hajria et al., 2018).
The structural equation indicated autocorrelation if the chi-squared coefficient was significant at the error level of 1%, 5%, or 10%, and vice versa.
4. Results and Discussion
The simultaneous equation model of Indonesian coffee had undergone several model re-specification processes to ensure that the resulting parameter estimation values had a logical sign and were consistent with the hypothesis.
Economically, the direction or sign of the parameter estimation results was consistent with expectations, even though several variables were not statistically significant at a certain level of
Available online at HABITAT website: http://www.habitat.ub.ac.id error (α). As a benchmark, the standard magnitude
of the significance error (α)was 10%. The average value of the coefficient of determination (R2) or each equation in the model was approximately 0.6241, indicating that the exogenous variables could explain the endogenous variables by 62.41
percent on average. Furthermore, serial correlation analysis using the Breusch-Godfrey test demonstrated that the estimation results were free of autocorrelation indications with the probability value of the chi-squared coefficient (chi2), which did not exist.
Table 1. Production and Trading Block Parameter Estimation Results
Variable Parameter Pr>|t| Elasticity
Description Short-term Long-term
1. Area of people's coffee plantation year t (AKRt)
Intercept 343665,9 0,078
PKDt 0,002575 0,285 0,0265 0,0266 Domestic coffee price year t PCWt-1 -36,16928 0,068 -0,0601 -0,0016 Cocoa price year t-1
(PUFt−PUFt-1) -0,002792 0,809 -0,0004 -0,0004 Changes in the price of urea fertilizer t IRIt -1361,071 0,779 -0,0163 -0,0001 Investment interest rate year t
AKRt-1 0,764874 0,000 People's plantation coffee area in t-1
2. Area of large private coffee plantation year t (AKSt)
Intercept 41303,66 0,001
(PKDt−PKDt-1) 0,001072 0,165 0,0269 0,0269 Changes in domestic coffee prices year t PUFt-1 -0,001423 0,369 -0,0974 -0,0972 Price of urea fertilizer year t-1
PPLt -0,177243 0,215 -0,1547 -0,1314 Changes in labor wages in year t IRIt-1 -870,1693 0,078 -0,4424 -0,0005 Investment interest rate in year t-1
AKSt-1 0,196471 0,423 Private plantation coffee area year t-1
3. Area of large state coffee plantation year t (AKNt)
Intercept 14793,6 0,011
PKDt 0,000281 0,428 0,1307 0,1307 Domestic coffee price year t
(PCWt−PCWt-1) -2,040958 0,282 -0,0029 -0,0009 Changes in world cocoa prices year t (PUFt−PUFt-1) -0,000018 0,985 -0,0001 -0,0001 Changes in the price of urea fertilizer t PPLt -0,216601 0,120 -0,2051 -0,1686 Labor wages in year t
AKNt-1 0,529293 0,004 State coffee plantation area in year t-1
4. People's plantation coffee productivity year t (YKRt)
Intercept 0,042301 0,662
(PKDt−PKDt-1) 1,93×10-9 0,705 0,0028 0,0028 Changes in domestic coffee prices year t PUFt -3,79×10-9 0,678 -0,1534 -0,1534 Price of urea fertilizer year t
RFIt 0,000052 0,058 0,3075 0,3075 Indonesia's rainfall year t
TRN 0,004313 0,025 0,1421 0,1426 Time trend
YKRt-1 0,481707 0,016 People's coffee productivity in year t-1
5. Private plantation coffee productivity year t (YKSt)
Intercept 0,197141 0,002
PKDt 1,80×10-8 0,019 0,4562 0,4562 Domestic coffee price year t
PUFt -3,01×10-8 0,120 -0,1242 -0,1242 Changes in the price of urea fertilizer t (RFIt−RFIt-1) 2,32×10-6 0,955 0,0001 0,0001 Changes in Indonesia's rainfall year t
TRN 0,003655 0,565 0,1228 0,1232 Time trend
YKSt-1 0,157813 0,401 Private coffee productivity year t-1
6. State plantation coffee productivity year t (YKNt)
Intercept 0,619308 0,001
(PKDt−PKDt-1) 8,64×10-9 0,625 0,0942 0,0942 Changes in domestic coffee prices year t PUFt-1 -7,85×10-9 0,804 -0,2331 -0,2331 Price of urea fertilizer year t-1
(RFIt−RFIt-1) 0,000023 0,700 0,0005 0,0005 Changes in Indonesia's rainfall year t
TRN 0,001802 0,736 0,0446 0,0447 Time trend
YKNt-1 0,008454 0,971 State coffee productivity year t-1
Available online at HABITAT website: http://www.habitat.ub.ac.id Table 1. (Extension)
Variable Parameter Pr>|t| Elasticity
Description Short-
term
Long- term 7. Indonesian domestic coffee demand year t (QDKIt)
Intercept 21562,48 0,444
PKDt -0,002578 0,131 -0,1850 -0,1845 Domestic coffee price year t
(PKPt−PKPt-1) 0,122274 0,971 0,0001 0,0001 Changes in processed coffee prices t IRIt -1159,837 0,401 -0,0972 -0,0001 Investment interest rate year t
QDKIt-1 1,190414 0,000 Domestic coffee demand year t-1
8. Domestic coffee prices year t (PKDt)
Intercept -759150,3 0,686
PKWt 664,6686 0,287 0,1447 -0,0001 Changes in world coffee prices year t QSKIt-1 -6,885510 0,119 -0,1886 -0,0239 Indonesian coffee supply year t-1 QDKIt-1 40,14601 0,009 0,6225 -0,0159 Indonesian coffee demand year t-1
PKDt-1 0,555209 0,002 Domestic coffee prices year t-1
9. Indonesian coffee exports to America year t (XKIAt)
Intercept -37816,6 0,443
PKXIt 11,70991 0,256 0,4084 -0,0381 Indonesian coffee export prices year t PKDt -0,001974 0,213 -0,4473 -0,4464 Domestic coffee price year t
QKIt 0,088202 0,230 1,0501 1,1515 Indonesian coffee production year t ERIt 0.923941 0,596 0,1464 1,9255 IDR exchange rate to USD year t
XKIAt-1 0,588049 0,002 Coffee exports to America year t-1
10. Indonesian coffee exports to Japan year t (XKIJt)
Intercept 40041,8 0,006
PKXIt-1 2,526087 0,445 0,0929 -0,0609 Indonesian coffee export price in t-1 PKDt -0,000904 0,086 -0,2175 -0,2173 Domestic coffee price year t
ERIt 0.902063 0,236 0,1516 1,5483 IDR exchange rate to USD year t DNTJt -13215,97 0,065 -0,0594 -0,0001 Japanese non-tariff dummy year t (XKPIJt− XKPIJt-1) -11,05707 0,126 -0,0075 -0,0006 Change of exports processed coffee to
Japan year t
XKIJt-1 0,216863 0,336 Coffee exports to Japan year t-1
11. Indonesian coffee exports to German year t (XKIGt)
Intercept -47401,68 0,531
PKXIt 14,62341 0,365 0,5186 -0,0381 Indonesian coffee export price in t-1 PKDt -0,003126 0,234 -0,7204 -0,7182 Domestic coffee price year t
QKIt 0,117666 0,261 1,4241 1,6141 Indonesian coffee production year t ERIt 1,421398 0,590 0,2291 -0,5436 IDR exchange rate to USD year t XKPIGt -0,004276 0,849 -0,0289 -0,0288 Processed coffee exports to Germany
year t
XKIGt-1 0,414574 0,077 Coffee exports to German year t-1
12. Price of Indonesian coffee exports year t (PKXIt)
Intercept 399,8227 0,525
PKWt 0,488414 0,251 0,6481 1,2669 World coffee prices year t XKIt -0,001424 0,397 -0,2997 -0,2991 Indonesian coffee exports year t
PKXIt-1 0,464684 0,130 Coffee exports price year t-1
13. Indonesian coffee import year t (MKIt)
Intercept 6051,292 0,764
PKMIt -0,928871 0,917 -0,1106 -0,0574 Coffee import price year t PKDt 0,003383 0,161 2,9685 2,9786 Domestic coffee price year t QDKIt -0,135176 0,461 -1,7839 -1,5715 Domestic coffee demand in year t ERIt -0,548426 0,754 -0,3364 -0,2173 IDR exchange rate to USD year t
Available online at HABITAT website: http://www.habitat.ub.ac.id Table 1. (Extension)
Variable Parameter Pr>|t| Elasticity
Description Short-
term
Long- term 14. Price of Indonesian coffee imports year t (PKMIt)
Intercept -665,7743 0,163
PKWt 0,942125 0,000 1,4027 24,237 World coffee prices in t
MTIt 14,89061 0,593 0,0686 -0,0049 Indonesian coffee import tariffs in t 15. World coffee price year t (PKWt)
Intercept 339,0565 0,670
XKWt -0,000041 0,705 -0,1002 -0,1002 World coffee exports in t MKWt 0,000115 0,558 0,2759 0,2759 World coffee imports year t
PKWt-1 0,696142 0,000 World coffee price in year t-1
4.1. Estimation of Coffee Production
The performance of coffee production was represented by area and productivity. In Table 1, the variables that significantly affected the estimation of the coffee area of smallholder, private, and state plantations at the 10% level were cocoa price lag, smallholder plantation area lag, investment interest rate lag, and state plantation area lag. Additional variables were at the 20%
level, such as changes in domestic coffee prices and labor wages. At the 30% level, there were variables such as changes in domestic coffee prices, changes in labor wages, and changes in cocoa prices. It was consistent with the findings of previous studies, which found that input price factors such as substitute commodity prices and interest rates had a significant negative impact on the area of an agricultural commodity, whether food or plantations (Perdana et al., 2013; Rifai et al., 2014; Purnomowati et al., 2015; Setiawan et al., 2016;).
According to the results, a one-dollar increase in cocoa price lag reduces the area of smallholder coffee plantations by 36.16 hectares.
The rise in the price of substitute commodities (cocoa prices) reduces the incentives received by coffee farmers, causing farmers to shift to growing cocoa (Jouf and Lawson, 2021). Especially given the two plants' nearly identical growing requirements and land suitability criteria.
Furthermore, a 1% increase in the investment interest rate lag reduces the area of private coffee plantations by 870.16 hectares. An increase in interest rates reduces public interest in investing for agricultural purposes, and people are more likely to save their money in bank deposits (Ysmailov, 2021).
In Table 1, the variables that significantly affected people's, private, and state coffee
productivity at the 10% level were rainfall intensity, time trend, smallholder coffee productivity lag, and domestic coffee prices.
Meanwhile, there was another change in urea fertilizer price at the 20% level. This study's findings are consistent with the results of previous studies, which show that output prices, input prices (fertilizers), and time trends (technology) all have a significant impact on the productivity of a food crop or plantation (Sebayang et al., 2019;
Rifai et al., 2014; Sinuraya et al., 2017; Rahman et al., 2018; Sari, 2020).
The interpretation of the results shows that an increase in rainfall of 1 mm/year will increase the productivity of smallholder coffee by 5.2×10-5 tons/ha. Increased rainfall can boost productivity because coffee plants can withstand rainfall intensity of up to 3,000 mm/year but cannot survive at less than 1,000 mm/year (Pham et al., 2020). Furthermore, with the passage of time (years) and the advancement of cultivation technology (trends), the productivity of smallholder coffee plantations will increase by 0.0043 tons/ha/year. An increase in the domestic coffee price of 1 IDR will increase private plantation coffee productivity by 1.8×10-9ton/ha.
The rise in domestic coffee prices will increase coffee farmers' incentives to farm, which is directly proportional to crop productivity (Assa et al., 2021).
Estimation of demand variables and domestic coffee prices were also included in the production block. Domestic coffee demand at a 10% level was significantly influenced by domestic coffee demand lag, while at a 20% level;
it was also influenced by domestic coffee prices.
The price variable was heavily influenced by the lag of domestic coffee demand and the lag of domestic coffee prices in the 10% range.
Available online at HABITAT website: http://www.habitat.ub.ac.id Meanwhile, at 20%, it was influenced by domestic
coffee supply, and at 30%, it was influenced by world coffee prices. A one-ton increase in coffee demand could raise the domestic coffee price by 40.14 IDR. It was consistent with previous research, which indicated that demand and price are related (Setiawan et al., 2016; Purnomowati et al., 2015; Sebayang et al., 2019). Furthermore, domestic prices were influenced by world prices and were shaped by supply and demand (Moncarz et al., 2018; Hansen, 2021).
4.2. Estimation of Coffee Trade
The volume of coffee exports and imports represented the performance of Indonesia's coffee trade. In Table 1, the variables that significantly affected coffee exports to America, Japan, and Germany at the 10% level were the lag of coffee exports to America, the price of domestic coffee, the Japanese non-tariff Dummy, and the lag of coffee exports to Germany. Changes in processed coffee exports to Japan were additional variables at the 20% level, while coffee export prices, national coffee production, and exchange rates were variables at the 30% level. It was consistent with previous research, which found that domestic prices, national production, exchange rates, and non-tariff policies had a significant impact on Indonesia's plantation commodity exports (Rifai et al., 2014; Sinuraya et al., 2017; Rahman et al., 2018). Furthermore, coffee exports to the United States and Japan had an elasticity value greater than one in the long run (elastic). Meanwhile, coffee exports to the United States and Germany were short- and long-term responses to domestic coffee production.
According to the interpretation of the results, an increase in domestic coffee prices of 1 IDR reduced coffee exports to Japan by 9.04×10-4 tons. Because they were substitutes, domestic prices and export volumes had an inverse relationship (Li and Xiong, 2021). Farmers would be more inclined to market their coffee domestically if domestic prices were perceived as more profitable, and vice versa. Furthermore, Japan's non-tariff policies will reduce coffee exports to Japan by 13,215.97 tons. Non-tariff policies such as quotas, trade licenses, and SPS (Sanitary and Phytosanitary) regulations can act as barriers and increase exporters' input costs, reducing export volume (Grubler and Reiter, 2021).
The estimation results of Indonesian coffee imports in Table 1 showed no significant variable
affecting imports at the 10% level and only one variable affecting imports at the 20% level, namely the price of domestic coffee. However, because it had an elasticity value greater than one, the variable of coffee imports in the short and long term was responsive to domestic coffee prices and national coffee demand (elastic). Several previous studies have found that domestic prices, import prices, domestic demand, and population all had a significant impact on agricultural commodity imports (Perdana et al., 2013; Setiawan et al., 2016; Sinuraya et al., 2017; Rahman et al., 2018;
Sari, 2020). Furthermore, there were estimates of several price variables in the model in the trading block, including export prices, import prices, and world prices.
Coffee export prices were significantly affected by the lag of coffee export prices at the 20% level and world coffee prices at the 30%
level. Meanwhile, at a 10% level, world coffee prices considerably impacted coffee import prices.
As a result, a one-dollar increase in the international coffee price could result in a 0.94- dollar increase in the import price of coffee.
Coffee export and import prices were responsive to world coffee prices in the short and long term, with an elasticity value greater than one (elastic).
Furthermore, at the ten percent confidence level, the lag of world coffee prices had a considerable impact on world coffee prices. It was consistent with a prior study, which found that world prices had an enormous impact on export and import prices, whereas the lag variable, as well as the volume of global exports and imports, had a significant impact on world prices (Perdana et al., 2013; Sinuraya et al., 2017; Moncarz et al., 2018;
Rahman et al., 2018).
5. Conclusion
Production and trade are the two main pillars of the Indonesian coffee model. The lag of the plantation area is a variable in the production block that had a considerable impact on the extent of smallholder and state area plantations.
Furthermore, it is the lag of cocoa prices for smallholder plantations and the lag of investment interest rates for private plantations. Meanwhile, rainfall intensity, time trend, and productivity lag of smallholder plantations substantially impact productivity. Still, the price of domestic coffee also had a considerable impact on private plantations. At the 10% level, there is no significant variable in the productivity of state plantations. These findings suggest that input
Available online at HABITAT website: http://www.habitat.ub.ac.id price factors like substitute commodity prices and
investment interest rates significantly impact the coffee plantation area more than output price (domestic coffee prices) considerations.
Meanwhile, the lag variables substantially impact coffee demand and domestic coffee pricing.
Furthermore, the lag of coffee demand affects the domestic coffee price.
The lag of coffee exports to America and Germany significantly impacts coffee exports in the trading block. Meanwhile, domestic coffee prices and non-tariff policies affect coffee exports to Japan. At the 10% level, there is no significant variability in Indonesian coffee imports.
Furthermore, with an elasticity value greater than one, coffee exports to America and Germany are responsive to changes in national coffee output in the short and long run. As a result, the primary variables that determine the performance of Indonesian coffee exports are the output price component (domestic coffee price), non-tariff policy barriers, and national coffee production.
Meanwhile, in the price study, the world price has a considerable impact on the coffee import price.
However, the coffee export price does not have a significant variable at the 10% level. Furthermore, world price lags have a considerable impact on world prices.
The increase in the performance of Indonesia’s coffee export trade has a positive relationship with national coffee production, and the output price factor significantly influences it.
The national production of coffee is divided into two categories: area and plant productivity. Coffee productivity is most influenced by rainfall intensity, while the coffee area is most influenced by the investment interest rate. Furthermore, even at an 80% confidence level, input price considerations such as labor rates and urea fertilizer prices considerably impact area and coffee productivity. As a result, government policy actions such as lowering investment interest rates for coffee farming and subsidizing fertilizer costs for farmers can boost national coffee production and trade performance.
References
Akber, N., Paltasingh, K.R., and Mishra, A.K.
(2022). How can public policy encourage private investments in Indian agriculture?
Input subsidies vs. public investment. Food Policy, 107 (2022), 102210.
Assa, H., Sharifi, H., and Lyons, A. (2021). An Examination of The Role of Price Insurance
Products in Stimulating Investment in Agriculture Supply Chains for Sustained Productivity. European Journal of Operational Reseacrh, 288 (2021), 918- 934.
Ayele, A., Worku, M., and Bekele, Y. (2021).
Trend, instability and decomposition analysis of coffee production in Ethiopia (1993–2019). Heliyon, 7 (2021), e08022.
Badan Pusat Statistik (BPS). (2020). Distribusi Produk Domestik Bruto (PDB) [Internet].
BPS Statistics Indonesia. [accessed on 09 Oktober 2020]. Avalaible from:
https://www.bps.go.id/
Bucagu, C., Vanlauwe, B., and Giller, K.E.
(2013). Managing Tephrosia mulch and fertilizer to enhance coffee productivity on smallholder farms in the Eastern African Highlands. European Journal of Agronomy, 48 (2013), 19–29.
Ceballos-Sierra, F and Dall’Erba, S. (2021).The effect of climate variability on Colombian coffee productivity: A dynamic panel model approach. Agricultural Systems, 190 (2021), 103126.
Food and Agriculture Organization of The United Nations (FAO). (2020). FAOSTAT Data Domains [Internet]. FAO Statistics.
[accessed on 02 November 2020].
Avalaible from: http://www.fao.org/faostat/
Grubler, J and Reiter, O. (2021). Characterising Non-Tariff Trade Policy. Economic Analysis and Policy, 71 (2021), 138-163.
Gujarati, D. (2004). Ekonometrika Dasar. Twelfth print. Jakarta: Erlangga. Translated by Sumarno Zain from Basic Econometrics, 1978.
Hajria, R.B., Khardani, S., and Raissi, H. (2018).
A Power Comparison Between Autocorrelation Based Tests. Statistics and Probability Letters, 143 (2018), 1-6.
International Coffee Organization (ICO). (2020).
Historical Data on the Global Coffee Trade [Internet]. Gray’s Inn Road (London): ICO;
[accessed on 10 September 2020].
Avalaible from: www.ico.org/historical.asp Hansen, H.O. (2021). Agricultural Policy Schemes: Price and Support Systems in Agricultural Policy. Encyclopedia of Dairy Sciences (Third Edition), 703-713.
Available online at HABITAT website: http://www.habitat.ub.ac.id Jouf, G and Lawson, L.A. (2021). European
Farmers’ Responsesto Higher Commodity Prices: Cropland Expansion or Forestlands Preservation. Ecological Economics, 191 (2021), 107243.
Komarek, A.M., Drogue, S., Chenoune, R., Hawkins, J., Msangi, S., Belhouchette, H., and Flichman, G. (2017). Agricultural household effects of fertilizer price changes for smallholder farmers in central Malawi.
Agricultural Systems, 154 (2017), 168–178 Kouadio, L., Tixier, P., Byrareddy, V., Marcussen, T., Mushtaq, S., Rapidel, B., and Stone, R.
(2021). Performance of a process-based model for predicting robusta coffee yield at the regional scale in Vietnam. Ecological Modelling, 443 (2021), 109469.
Koutsoyiannis, A. (1989). Modern Microeconomics. London: MacMillan.
Kumar, A., Mishra, A.K., Saroj, S., and Joshi, P.K.
(2016). Institutional versus non- institutional credit to agricultural households in India: Evidence on impact from anational farmers’ survey. Economic Systems. S0939-3625, (17)30050-X.
Kumar, P and Datta, S.K. (2021). Interdependence between environmental performance and economic growth with a policy approach towards sustainability: Evidence from panel data. Current Research in Environmental Sustainability, 3 (2021), 100105.
Li, M and Xiong, T. (2021) Dynamic Price Discovery in Chinese Agricultural Futures Markets. Journal of Asian Economics, 76 (2021), 101370.
Mankiw, N.G., Quah, E., and Wilson, P. (2014).
Pengantar Ekonomi Mikro: Edisi Asia.
Jakarta Selatan: Salemba Empat.
Moncarz, P., Barone, S., and Descalzi, R. (2018).
Shocks to the international prices of agricultural commodities and the effects on welfare and poverty. A simulation of the ex- ante long-run effects for Uruguay.
International Economics. S2110-7017, (17)30063-X.
Neilson, J., Wright, J., and Aklimawati, L. (2018).
Geographical indications and value capture in the Indonesia coffee sector. Journal of Rural Studies, 59 (2018), 35–48.
Nugroho, A. (2014). The Impact of Food Safety Standard on Indonesia’s Coffee Exports.
Procedia Environmental Sciences, 20 (2014), 425–433.
Pham, Y., Smith, K.R., Mushtaq, S., and Deo R.
(2020). Feedback Modelling of The Impacts of Drought: A Case Study in Coffee Production Systems in Vietnam.
Climate Risk Management, volume 30, article 100255.
Perdana, R.P., Koestiono, D., and Syafrial. (2013).
Dampak Kebijakan Ekonomi Kedelai Terhadap Kinerja Perkedelaian Indonesia.
Habitat, 24(2), 120-132.
Purnomowati, H.D., Darwanto, D.H., Widodo, S., and Hartono, S. (2015). Analisis Permintaan Karet Alam Indonesia di Pasar Internasional. Jurnal Agraris, 1(2), 136- 148.
Rahman, R.Y., Sinaga, N.M., Harianto., and Susilowati, S.H. (2018). Kebijakan Dukungan Domestik Untuk Menetralisir Dampak Negatif Penurunan Tarif Impor Terhadap Industri Gula Indonesia. Jurnal Argo Ekonomi, 36(2), 91-112.
Rifai, N., Syaukat, Y., Siregar, H., andSa’id, E.G.
(2014). Dampak Pengembangan Produk Turunan Minyak Sawit Terhadap Peningkatan Ekspor Produk Minyak Sawit ke Pasar Amerika Serikat. Jurnal Agro Ekonomi, 32(2), 107-125.
Sarkar, A., Azim, J.A., Asif, A.A., Qian, L., and Peau, A.K. (2021). Structural equation modeling for indicators of sustainable agriculture: Prospective of a developing country’s agriculture. Land Use Policy, 109 (2021), 105638.
Sari, N. (2020). Skenario Kebijakan Ekonomi Perberasan Untuk Meningkatkan Kesejahteraan Petani Padi di Sulawesi Tengah. Dr. Thesis. [Malang (ID)].
Universitas Brawijaya.
Sebayang, V.B., Sinaga, B.M., Harianto., and Kariyasa, I.K. (2019). Dampak Kebijakan Domestik Terhadap Ketersediaan Jagung Untuk Bahan Baku Industri Pengolahan di Indonesia. Jurnal Agro Ekonomi, 37(2), 141-155.
Available online at HABITAT website: http://www.habitat.ub.ac.id Setiawan, E., Hartoyo, S., Sinaga, B.M., and
Hutagaol, M.P. (2016). Dampak Kebijakan Input, Output, dan Perdagangan Beras Terhadap Diversifikasi Pangan Pokok.
Jurnal Agro Ekonomi, 34(2), 81-104.
Sinuraya, J.F., Sinaga, B.M., Oktaviani, R., and Hutabarat, B. (2017). Dampak Kebijakan Pajak Ekspor dan Tarif Impor Terhadap Kesejahteraan Produsen dan Konsumen Kakao di Indonesia. Jurnal Agro Ekonomi, 35(1), 11-31.
Sun, Ting-Ting., Su, Chi-Wei., Mirza, N., and Umar, M. (2021). How does trade policy uncertainty affect agriculture commodity prices?. Pacific-Basin Finance Journal, 66 (2021), 101514.
Suprihanti, A., Harianto., Sinaga, B.M., and Kustiari, R. (2019). Dampak Kebijakan Cukai Rokok Terhadap Distribusi Surplus Ekonomi Industri Rokok di Indonesia.
Jurnal Agro Ekonomi, 37(1), 1-23.
United Nations Commodity Trade Statistics Database (UNComtrade). (2020).
Comtrade Commodity Data and Reference [Internet]. UNdata Explorer. [accessed on 25 Desember 2020]. Avalaible from:
https://comtrade.un.org/
Vicol, M., Neilson, J., Hartatri, D.F.S., and Cooper, P. (2018). Upgrading for whom?
Relationship coffee, value chain interventions and rural development in Indonesia. World Development, 110 (2018), 26–37.
Ysmailov, B. (2021). Interest Rates, Cash and Short-Term Investments. Journal of Banking and Finance, 132 (2021), 106225.
