Available online at HABITAT website: http://www.habitat.ub.ac.id
Market Integration Analysis of Cayenne Pepper in East Java Province
Vetty Seily Kurnia Dessy1*, Hery Toiba2, Fahriyah2
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: 28 September 2021; Revised: 7 February 2022; Accepted: 1 April 2022
ABSTRACT
Market integration research has often been used to evaluate agricultural products' marketing performance.
However, the involvement of marketing agencies in chili marketing resulted in a relatively high price difference and impacted market integration. So it is essential to understand how the chili market integration as an effort to improve marketing efficiency and price control policies. The study analyzed the causality and market integration of cayenne pepper prices at the farmers' and retailers' levels in East Java Province.
The study used weekly cayenne pepper price data at the farmers' and retailers' levels from 2016 - 2020. The secondary data was obtained from the Department of Industry and Trade of East Java Province. The analytical methods used in this study were Engle-Granger Causality analysis and Error Correction Model (ECM). The findings showed that retail prices caused the farmgate prices, but not the other way. However, the price of cayenne pepper at the farmer and retail level in East Java Province has been integrated.
Therefore, this study showed that there needs to be an improvement in the farmers' institutional system to improve the farmers' bargaining position to reduce the power of the selling price control at the farmers' level by traders.
Keywords: market; integration; marketing; efficiency; price How to cite:
Seily, V., Dessy, K., & Toiba, H. (2022). Market Integration Analysis of Cayenne Pepper in East Java Province. HABITAT, 33(1), 24–32. https://doi.org/10.21776/ub.habitat.2022.033.1.3
1. Introduction
The study of market integration is closely related to the marketing efficiency concept, one of the critical aspects of agricultural economics (Yuanglong et al., 2007). Market integration combines different market demand, supply, and transaction costs in terms of pricing, simultaneous distribution flows, and the transmission of price shocks from one market to another (Barret and Li, 2002). Market integration in the agricultural sector is essential because the characteristics of agricultural products are perishable, bulky, and concentrated only in the central region. Therefore it can impact the high marketing costs (Sexton et al., 1991). Those conditions would affect agricultural commodity prices. Price is the primary mechanism that links different market levels. The extent of adjustment and speed at which shocks are transmitted between farmer and retail prices is a significant factor that shows the actions of market participants (Abdulai, 2002).
Price information can affect the response of market participants when price changes occur. If the price changes could have been transmitted between farmer and retailer, the market would have been integrated (Khotimah et al., 2016). The changes and magnitude of price transmission on every market level will help the price movements stabilize and influence market participants' welfare (Timmer, 2008). These conditions occur if there are no price distortions which cause resource allocation inefficiencies and decreasing economic welfare (Conforti, 2004; Firdaus and Gunawan, 2012; Dang and Lantican, 2011). However, most farmers have limited capital to respond to price changes (Okoh and Egbon, 2005).
Cayenne pepper is considered one of the most important horticultural commodities. In October 2020, cayenne pepper contributed to the inflation of 8.1% (Ministry of Trade of the Republic of Indonesia, 2020). Based on the Department of Industry and Trade of East Java Province (2020), the average monthly cayenne pepper price at the farmers' level in August 2020
Available online at HABITAT website: http://www.habitat.ub.ac.id was Rp 9.987,90/kg, while the average price at the
retailer level in August 2020 amounted to Rp.
17,399.00 /kg. So, the difference between the farmer and retailer prices in East Java Province can be seen. According to Yustiningsih (2012), the difference between the farmer and retailer prices can be measured using market integration. The high price difference between farmers and retailers indicates the possibility of price distortions that cause vertical asymmetry transmission (Rajendra, 2015). The cause of price transmission asymmetry is uncompetitive behavior between market participants, especially if the traders are in a concentrated market (Vavra and Goodwin, 2005). In general, traders will try to maintain their profit level without increasing or decreasing the price according to the actual price signal (Difah et al., 2020).
Based on previous research, market integration was analyzed to determine long- and short-term relationships (Kusumaningsih et al., 2017) and analysis of engle granger causality between markets (Martey et al., 2020; Deb et al., 2020; Kustiari, 2017; Rajendran, 2015). The studies analyzed market integration between farmers'-level prices to retailers. The results explain that inter-market prices have been cointegrated in the long term but have not been integrated into the short term (Kusumaningsih et al., 2017; Martey et al., 2020; Kustiari, 2017;
Rajendran, 2015). Other study results showed that farmers are more responsive to price declines, while traders are more responsive to price increases (Deb et al., 2020). Although studies about the market integration of horticulture products have been widely investigated, they used monthly prices data sets to estimate the market integration (Kusumaningsih et al., 2017; Martey et al., 2020; Deb et al., 2020; Kustiari, 2017;
Rajendran, 2015). Hence, these studies cannot describe the actual conditions of rapid changes in the price of horticultural products. Therefore, weekly series data is more appropriate to describe it. This study used weekly data on horticultural products to analyze the market integration focusing on cayenne pepper products to fill this gap. This study provided an essential contribution to the literature on the market integration of cayenne pepper in East Java of Indonesia.
2. Theoretical Underpinning
Market performance is an analytical approach to reviewing marketing processes reviewed from market behavior through price,
cost, and production volume to impact society (Kizito, 2008; Abbott and Makeham, 1979;
Cramer et al., 1997). Market performance can be measured by various indicators, one of which is market integration. Some studies used the Error Correction Model to analyze market integration (Kusumaningsih et al., 2017; Khotimah et al., 2016). Some add Granger causality tests to see the related inter-market causality relationship (Zavale and Macamo, 2020; Martey et al., 2020;
Rajendran, 2015). Then, Deb et al. (2020) also added non-linear price transmission using TAR and M-TAR models.
Furthermore, the variables used in spatial market integration analysis in the study of Zavale and Macamo (2020), Martey et al. (2020), and Kustiari (2017) are prices at the wholesale level.
While in the analysis of vertical market integration, the variables used are prices at the level of producers and retailers (Kusumaningsih et al., 2017; Rajendran, 2015). Then, some studies added price variables at the wholesale level (Deb et al., 2020; Khotimah et al., 2016; Kustiari, 2017). Zavale and Macamo's (2020) results found a cointegration relationship that can be interpreted as a long-term relationship between the price of white corn kernels in the Mozambique and Malawi markets but not integrated into the short term. Next, Martey et al. (2020) indicate that local prices respond to long-term price relationships but are only partially integrated into the short term.
Based on causality tests, prices in Ghana are caused by the international price but not the other.
As producers in the area, Ghana's imports from the Americas are so small that they will likely become price takers.
Based on Deb et al. (2020), the farmers' prices react more quickly to the imbalance caused by a decrease in wholesale prices than the shock of an increase in wholesale prices. The results from TAR and M-TAR showed a significant asymmetry in price transmission where the upstream market responds more quickly to price decreases than price increases. The downstream market does the opposite because it responds to price increases more quickly than decreases, and all processes are variable. To the findings of Kusumaningsih et al. (2017), it is revealed that retail rice prices and farmers' grain prices in Indonesia have a long-term and short-term equilibrium relationship. Meanwhile, Khotimah et al. (2016) concluded that the rice market chain in Indonesia is segmented. Rice prices are not fully transmitted from producers to retailers.
Available online at HABITAT website: http://www.habitat.ub.ac.id The results of vertical market integration
research by Kustiari (2017) found that the price of chili at the producer, wholesaler, and consumer levels has a long-term equilibrium. The findings also showed that the relationship between two markets is unidirectional Granger Causality. This result implied that the rural market price determines the price of red chili in urban areas. It was due to inadequate information flow between rural and urban areas. Furthermore, Rajendran's (2015) research found that the Granger Causality Test showed that wholesale prices caused retail prices; wholesale and retail prices show bidirectional causality. In other markets, retail and wholesale prices have no causality. The results of the ECM test were that retail prices respond more quickly when margins are higher.
3. Research Methods
The research was conducted in East Java Province because it is the center of cayenne pepper production. This study was used secondary data on farmer prices and retail prices of cayenne pepper in East Java Province. The price data used was the weekly time-series data from January 2016 until December 2020. So, the data used in this study was 514. The data was obtained from “Sistem Informasi Ketersediaan dan Perkembangan Harga Bahan Pokok (SISKAPERBAPO)” Department of Industry and Trade of East Java Province. The application used to analyze the data was Eviews 10. The analytical method used in this research was causality test and market integration analysis.
The research methods used were a causality test and market integration analysis.
3.1. Market Integration Analysis
Market integration analysis in this study was used the Error Correction Model (ECM).
There were several stages to be able to analyze, including:
i. Stationarity Test
The stationarity of time series data was tested by unit root test. This test was carried out because time series data are not stationary (Pavel and Barry, 2005). Data that is not stationary will result in spurious or spurious regression equations.
The approach to overcoming the spurious regression equation is to differentiate the data (Kustiari, 2018). Price stationarity test is:
∆ = + + ∆ + ∑ ∆ +
(1)
Where ∆ is the difference in the price of cayenne pepper in period t (Rp/Kg), describes
the price of cayenne pepper in period t (Rp/Kg), is the price of cayenne pepper in period t minus the lag value or previous period (Rp/Kg). T is time (January 2016 – December 2020). The null hypothesis is that the integrated y time series data at the first degree is tested based on the t-statistical value of the estimated coefficient a1. If the t- statistic value is greater than the critical ADF value, it rejects H0 so that the price data is stationary.
ii. Cointegration Test
Before continuing the cointegration test, the optimal lag determination must be done. It can be seen from several criteria, including the LR (Likelihood Ratio) criteria, FPE (Final Prediction Error), AIC (Akaike Information), HQIC (Hannah Quin Information), and SBIC ( Schwarts Bayesian Information). Then it can be continued with cointegration testing. A cointegration test was conducted to identify the long-term relationship between cayenne pepper price variables at the farmer and retailer levels. This study was used Johansen's cointegration model. There are two tests to test a long-term relationship between variables: the trace test and the maximum eigenvalue test. The TS and ME tests follow the following equation:
= − ∑ ln(1 − )
(2) ( , + 1) = − ∑ ln(1 − )
(3)
where, e explains the estimated value of the characteristic root (eigenvalue) obtained from the estimated matrix Π, T explains the number of observations, and r is an exponent that indicates the number of cointegration vectors. Johansen and Juselius (1990) and Brooks (2002) explained that the cointegration test criteria where if the trace statistic (TS) and maximum eigenvalue (ME) are more significant than the t-statistic value, then there is a long-term relationship between the variables.
iii. Error Correction Model (ECM) Test The Error Correction Model (ECM) test tests the short-term change or the speed of adjusting the long-term equilibrium between the independent and dependent variables. Short-term equilibrium in the ECM test assumes that if an equilibrium is detected in a period, an error correction will occur in the next period, leading to the equilibrium point within a specific period. The models used in this study are:
∆ = + ∆ + ∆ +
∆ + + (4)
Available online at HABITAT website: http://www.habitat.ub.ac.id where:
∆ : Changes in the price of cayenne pepper at the producer level in the t period (Rp/Kg)
∆ : Changes in the price of cayenne pepper at the retail level in period t (Rp/Kg)
∆ : Changes in the price of cayenne pepper at the producer level in the previous period (Rp/Kg)
∆ : Changes in the price of cayenne pepper at the retail level in the previous period (Rp/Kg)
t : time (January 2016–December 2020) : intersept
: short term coefficient
: speed of adjustment back equilibrium (1/ )
ε : notation for error terms
Based on statistical tests, if the sign is negative, then the model in the study is considered valid. The ECT coefficient value denotes the short-term coefficient of the ECM equation. The ECT value indicates the speed of adjustment back to equilibrium. ECT describes the movement of the price variable to the short-term equilibrium.
ECT is positive, so the price movement is close to short-term equilibrium and leads to a strong short- term integration. If the ECT is negative, it indicates that the price movement is away from the short-term equilibrium and indicates weak market integration in the short term (Rahutami, 2011).
Short-term test results can be significant if the probability value is smaller than the significance value of 0.05.
3.2. Granger Causality Analysis
Granger causality testing stages are used to determine the relationship between variables at a certain lag. The causality test was carried out by comparing the probability value of (0.05). If the probability value > the value of (0.05), then H0 is accepted so that there is no causality between variables. But if the probability value < value (0.05), then H0 is rejected: there is a causality between variables.
4. Results and Discussion 4.1. Market Integration Analysis a. Stationarity Test
The results of the stationarity test on the price of cayenne pepper at the farmer level in East Java Province are presented in Table 1. Table 1 shows that the data series on the variable price of cayenne pepper at the farmer level has an ADF statistical value of -0.108. This value is smaller than the critical value of 5%, -1.942. Furthermore, the probability value of the stationary test at the level is 0.646, which is greater than the error tolerance value (α) of 0.05. So, the results of the stationary test on the variable price of cayenne pepper at the farmer level in the East Java Province level were not stationary. These results make it necessary to carry out a differentiation process until the data is stationary, continue the analysis process, and avoid pseudo-regression.
The results of the stationary test of the cayenne pepper price variable at the farmer level in East Java Province at the first difference level are stationary. It can be seen from Table 1 that the ADF statistic value (-12.795) is greater than the critical value (-1.942), and the probability value (0.000) is smaller than the value of (0.05).
Table 1 shows that the retail price at the level has an ADF statistic of 0.271. This value is smaller than the critical value of 5%, -1.942.
Furthermore, the probability value of the stationary test is at the level of 0.764, which is greater than the error tolerance value (0.05). So that the results of the stationary test on the retail at the level are not stationary. It is necessary to carry out a differentiation process until the data is stationary. The results of the stationary test of the retailer price variable at the first difference level that the ADF statistic value (-9.224) is greater than the critical value (-1.942), and the probability value (0.000) is smaller than the value of (0, 05).
Thus, the retail price is stationary at the first difference level. If each variable data is stationary, the cointegration test can be continued.
Table 1. Stationary Test Results of Cayenne Pepper Prices in East Java Province
Level First Difference
Variable ADF
statistic
Critical
value 5% Prob. Info. ADF
statistic
Critical
value 5% Prob. Info.
Farmer prices
(PP) -0,108 -1,942 0,646 Non-
Stasioner -12,795 -1,942 0,000 Stasioner Retailer
prices (PE) 0.271 -1.942 0.764 Non-
Stasioner -9.224 -1.942 0.000 Stasioner
Available online at HABITAT website: http://www.habitat.ub.ac.id b. Cointegration Test
Before performing the cointegration test, it is necessary to test the optimal lag using available information, including the criteria for Likelihood Ratio (LR), Final Prediction Error (FPE), Akaike Information Criterion (AIC), Schwarz Information Criterion (SC), and Hannan-Quin
Criterion (HQ). If the information criteria refer to a candidate lag, then that lag will be selected to continue the estimation at the next stage. Based on Table 2, the results show that the LR, FPE, AIC, and HQ criteria were used in this study, indicating that the optimal lag is lag three.
Table 2. Optimal Lag Test
Lag LR FPE AIC SC HQ
0 NA 0.011576 1.216965 1.244976 1.228236
1 810.9630 0.000460 -2.008169 -1.924135 -1.974355
2 105.7624 0.000310 -2.404610 -2.264554 -2.348255
3 27.82758 0.000285* -2.486446* -2.290367* -2.407548*
Source: Secondary Data, 2021 (processed) After determining the optimal lag, the next step is the cointegration test. Based on Table 3, it is known that there is a cointegration relationship between the price of cayenne pepper at the farmer and retail level. The trace statistic value explains this condition (178.393 > 15.494) and the maximum eigenvalue (120.7095 > 14.2646), which is greater than the critical value at the 5%
level. The price of cayenne pepper at the farmer and retailer level in East Java Province is cointegrated. This cointegration indicates that a change will follow a change in the price of
cayenne pepper at the retail level in the price of cayenne pepper at the farmer level in East Java Province in the long term.
If there is an increase in the price of cayenne pepper at the retail level, it will be followed by a rise in the price of cayenne pepper at the farmer level in East Java. If there is a decrease in prices at the retail level, it will be followed by a decline in prices at the level of cayenne pepper farmers. In line with Jumiana et al. (2018) and Hanani et al.
(2020), there is a long-term relationship between farmer-level prices and retailers.
Table 3. The results of the regression between the price of cayenne pepper at farmer and retailer levels in East Java Province
Hypothesis Trace Statistic Critical Value 5%
Max-Eigen Statistic
Critical Value 5%
None 70.47157 20.26184 54.32247 15.89210
At most 1 16.14910 9.164546 16.14910 9.164546
Table 4. Error Correction Model (ECM) Test between Price Producer Level and Retailer in East Java Province
Variable Dependent (PP)
Variabel Coefficient Std. Error t-Statistic Prob. R-Squared
C 0.3927 0.1592 2.4663 0.0143
0.5959
PE 1.164 0.0650 17.9141 0.0000
PE(-1) -0.0383 0.0155 -2.4728 0.0141
ECT(-1) -0.3465 0.0560 -6.1866 0.0000
Table 5. Granger Causality Test Results
Null Hypothesis Probability
PE does not Granger Cause PP 0,0213
PP does not Granger Cause PE 8, E-11
c. Error Correction Model (ECM) Test The ECM test is used to determine the short-term relationship between the price of cayenne pepper at producer and retailer levels in
East Java Province. The test is used to describe the existence of vertical market integration. Based on the estimation results of the ECM model between the price of cayenne pepper at the producer and
Available online at HABITAT website: http://www.habitat.ub.ac.id retailer levels in East Java Province in Table 5, it
can be formulated in the form of the following equation,
∆= 0,3927 + 1,1645 ∆
− 0,0383 ∆ − 0.3465 + The ECT probability value based on Table 4 is 0.0000 less than the significance level used (0.05), so the model is significant at an error rate of 5%. The R-Squared value of 0.5959 indicates that the price movement of cayenne pepper at the producer level in East Java Province can be explained by the retail level in East Java Province of 59.59%, and other variables explain the remaining 40.41% outside the model.
The coefficient value∆ explained that if the price of cayenne pepper at the retail level increases by 1%, it will cause an increase in the price of cayenne pepper at the producer level by 1.16%. The increase in the price of cayenne pepper at the producer level tends to be higher than the price increase that occurs at the retailer level in the short term. Otherwise, if the farmer's price is decreased by 1%, it would cause a decrease in the price of cayenne pepper at the producer level by 1.16%. It is indicated that traders well transmit price changes.
4.2. Price Causality Analysis
Based on Table 5, the results of the Granger causality test can be explained that PE does not Granger Cause PP. It has a probability value (0.0213) smaller than (0.05), so the price of chili at the retail level in East Java Province affects the farmer's price. Change in the price of cayenne pepper at the retail level will affect changes in the price of cayenne pepper at the farmer level in East Java Province, but not vice versa.
Then, PP does not Granger Cause PE. It has a probability value (8,E-11) larger than (0.05), so the price of chili at the farmers level does not affects the retailer's price. Change in the price at the farmer level was not followed by a change at the retailer level.
4.3. Market Integration Analysis
Based on results 4.1. this condition described that the farmers would benefit from an increase in prices. However, it would be a loss if the price decreased due to the significant changes responded to by farmers. These results align with Sahara and Wicaksena's (2018) research. Elvina et al. (2017) have explained that the price of chili in
Indonesia has been integrated, and there is no indication of price asymmetry.
According to Kusumah (2018), the farmers have greater sensitivity in responding to price changes. He explained that producers could have a better bargaining position in the chili production in the center area. A high amount of production would increase the selling price in scarcity season.
In addition, producers who sell their products to traders who have been well subscribed have the opportunity for bargaining so that traders will be wiser in determining the price of chili. According to Rindayanti et al. (2010), the proximity of producers and traders would influence determining prices for farmers to be more competitive.
Based on the value of the ECT coefficient, which had a negative and significant sign, it could be said that the specification of the model used was valid. The ECT coefficient value of -0.35 indicated that the price adjustment to the new equilibrium condition takes about three weeks (1/0.35). The greater the value of the ECT coefficient (-1), the faster the adjustment process to return to the equilibrium point (Ekananda, 2016).
Based on the test results, it was known that the price of cayenne pepper at the producer and retailer levels in East Java Province had been vertically integrated. This result differed from Martey et al. (2020) and Rajendran (2015), which showed that the integrated producer-level price was weakly integrated with the retail-level price because the magnitude of price changes at the producer was smaller than at the marketing agency level.
Meanwhile, the results of this study described that price changes at the producer level were more significant than at the retail level in the long and short term. Although it was known that cayenne pepper producers were only price takers, they had easy access to information on the market price of cayenne pepper. The information gained from buyers and traders outside the region through cell phones and social media. However, the role of intermediary institutions cannot be separated in the distribution process of cayenne pepper in East Java Province. According to some farmers' explanations, they cannot directly enter the market because the only collectors who can accommodate their harvests then distribute it to markets both in the area and outside the region before finally reaching consumers.
The existence of information transparency helped farmers and other market players
Available online at HABITAT website: http://www.habitat.ub.ac.id determine the right selling price of cayenne
pepper, so the market participants gained profits in marketing cayenne pepper. If there were obstacles in the price information channel, it indicated that parties had difficulty obtaining information. The behavior of producers and traders who do not have the same information can result in one participant's loss (Anindita and Baladina, 2017).
However, even though it has been vertically integrated, the risk of price uncertainty received by cayenne pepper farmers in East Java Province was still high. Findings Deb et al. (2020) showed that farmers were more likely to respond to price decreases than price increases. It was known that the risk received by cayenne pepper farmers was related to seasonal changes that affected the quantity of their harvest. In addition, the supply of cayenne pepper from outside the region was also a challenge for local chili farmers, especially when entering the harvest season in the region. So it is necessary to strengthen the institutional system of farmers and ease of access to capital to minimize losses at harvest time and respond to price increases rather than price declines.
4.4. Price Causality Analysis
Based on result 4.2. indicated that the marketing of cayenne pepper has not been efficient. It happened because cayenne pepper farmers in East Java Province only followed the prices determined by the collectors based on the prices prevailing in the market. This result was in line with Miftahuljanah et al. (2020) and Hanani et al. (2020), which explained that prices at the retail level affect farmers' prices in the region.
This condition was thought to be impacted by the power of market players in the distribution area in establishing prices so that farmers as producers only act as the price takers (Kustiari, 2017; Martey et al., 2020). Efficient marketing will occur if the transmission of information goes both ways, both from farmers and retailers. Of course, this condition does not reflect efficient marketing if the distribution of information goes in the same direction and the bargaining position of cayenne pepper farmers is still weak because it is very dependent on traders. Therefore, it is necessary to strengthen access to information and strengthen farmer institutions to improve farmers' bargaining position in the distribution of agricultural products so that each market participant gets an equal profit.
5. Conclusion
The idea of market integration is closely related to market participants' responses in the face of price changes on one side. This study analyzed the causality and market integration of cayenne pepper prices at the producer and retailer level in East Java Province. This study was used secondary data obtained from the Department of Industry and Trade of East Java Province. The data used in this study was the price of cayenne pepper at the farmer and retailer level for 2016-2020. This study used causality analysis to analyze the relationship between farmer and retailer prices.
Meanwhile, market integration analysis was used to analyze how the prices of farmers and retailers of cayenne pepper are related. Based on the results, it was found that the price of cayenne pepper at the farmer's level in East Java Province caused the price of cayenne pepper at the retailer level in East Java Province. Furthermore, the market integration analysis results showed that the price of cayenne pepper at the farmer and retailer levels in East Java Province had been integrated.
Even though it had been integrated, this study showed that it was still necessary to improve farmer institutions so that farmers' bargaining position increases significantly to reduce the power of controlling the selling price of cayenne pepper at the farmer level by traders. The flow of information between parties could be well received. Further research is expected to analyze whether farmers are more responsive to price increases or decreases in prices for further study.
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