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Analysis of beef market integration between consumer and producer regions in Indonesia

  • Firmansyah EMAIL logo , Pahantus Maruli und Afriani Harahap
Veröffentlicht/Copyright: 11. September 2023
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Open Agriculture
Aus der Zeitschrift Open Agriculture Band 8 Heft 1

Abstract

The research employs secondary data consisting of time series data on beef prices from the consumer regions (Jakarta, Banten, and West Java Provinces) and the producer regions (East Nusa Tenggara, West Nusa Tenggara, Bali, East Java, and Lampung Provinces) obtained from the Center for Information of Strategic Food Prices period January 2018–July 2022. The analytical model utilizes the estimated VAR (vector autoregression)/VECM (vector error correction model). The mean beef price in Indonesia’s producer regions exceeds the normal level (above the reference price), except for East Nusa Tenggara Province, as well as in the consumer regions, which are significantly above the normal level. Beef prices in the producer and consumer regions have a mutual influence in the current and previous periods. The presence of cointegration implies that in the long term, the beef market in the producer regions (East Nusa Tenggara, West Nusa Tenggara, Bali, East Java, and Lampung) is integrated with the consumer regions (Jakarta, Banten, and West Java Provinces) in Indonesia. The most substantial impact of the shock of beef prices in the region of the largest producer is Lampung Province. This research concludes that there is a cointegration of beef prices; in the long term, the beef market in producer regions is integrated with the consumers in Indonesia. Similarly, the beef market will be integrated into producer regions with the consumers in the short term. The VECM is a beef price forecasting model in the producer and consumer regions, which can be considered to have excellent performance.

Keywords: market integration; beef; VAR/VECM; forecasting

1 Introduction

Indonesia’s beef prices are driven by the rising prices of livestock and beef in Australia and New Zealand, which are the exporters of live cattle and beef to Indonesia. Indonesia is highly dependent on these two countries to satisfy its beef demand (live cattle and frozen beef) [1,2,3,4]. The value of Australian live cattle exports to Indonesia in 2021 will be $600 million [5]. Most of the cattle slaughtered at abattoirs in the consumer regions in Indonesia, such as the Province of Jakarta (the capital city of Indonesia), are predominantly imported cattle from Australia. The geographical distance between the consumer regions (Jakarta, Banten, and West Java Provinces) and the producer regions (East Nusa Tenggara, West Nusa Tenggara, Bali, East Java, and Lampung Provinces) is substantial. Indonesia is an archipelagic country, and the transportation of cattle from producer regions to consumer regions entails land and sea transportation [6], which adds to the costs of transportation [7], labor, feed, and body weight loss of cattle due to long journeys [8]. The beef trade in Indonesia involves local and imported beef and various market participants, such as importers, feedlots, abattoirs, wholesalers, and retailers, leading to competition and cartelization [9]. The concern of both producers and consumers is the widening gap between the price of meat from livestock and retail [10], and the price volatility has adverse effects on Indonesian beef production [11].

Beef, as one of the staple goods (the Presidential Regulation of the Republic of Indonesia Number 59 of 2020) [12], must regulate its availability according to the consumption needs of the community during a specific period, with adequate quality and affordable prices throughout Indonesia in both short and long terms. The Central and Regional Governments enact policies to ensure the availability, stabilization, and distribution of beef prices. However, the execution of the beef price stabilization policy in Indonesia has become ineffective and inefficient in its performance because these policies face challenges such as Indonesia’s vast territory and archipelago will hinder the process of distributing cattle, especially between the consumer regions (Jakarta, Banten, and West Java Provinces) and the producer regions (East Nusa Tenggara, West Nusa Tenggara, Bali, East Java, and Lampung Provinces); the dependence on imports from Australia and New Zealand due to the mismatch between domestic production and consumption. The government’s implementation of the beef price stabilization policy will be more effective in integrated markets between producer and consumer regions than in those that are not integrated. Market integration causes needs to be more efficient, but in reality, many markets fail to integrate [13], and losses can occur when commodity markets are not integrated [14]. In an efficient market, prices are transmitted symmetrically down the beef value chain [10]. However, competition for market integration can also encourage an increase in market control by oligarchs [15] and create competition and cartelization in the beef trade [16].

Market integration has been a topic of interest for economists [17,18] and policymakers [19,20] in recent decades. This is because market integration accounts for the variation in price transmission [21], which assists in designing appropriate policies [22]. Market integration plays a crucial role as a benchmark for determining market levels, identifying signs of price manipulation, and promoting structural transformation and adoption of technologies that increase productivity [6]. Spatial integration helps to define the scope of the market, which is the area where the price of a product is determined by the interaction of producers and consumers [23].

2 Materials and methods

This research employs a quantitative descriptive method and a secondary data analysis. The secondary data consist of time series data, namely, the weekly average price of beef in the consumer regions (Jakarta, Banten, and West Java Provinces) and the producer regions (East Nusa Tenggara, West Nusa Tenggara, Bali, East Java, and Lampung Provinces) in Indonesia. The data source is the Center for Information of Strategic Food Prices period January 2018–July 2022 [24]. The data analysis steps were as follows: The first step is to test whether the beef price data in the producer and consumer regions had unit roots or not; the stationarity test was applied. ∆ P t = ∝ 0 + γ P t − 1 + β i ∑ j = 1 m ∆ P t − 1 + ε i [25]. If augmented Dickey-Fuller (ADF) statistic ≥ ADF critical value, then reject H0, meaning that the time series data does not contain a unit root, which implies that the data are stationary; if ADF statistic < ADF critical value, then accept H0, meaning that the data are non-stationary [26].

The second step, determining the optimal lag, is the lag length that gives a significant effect or response [27]. Optimal lag determination for beef price in the producer and consumer regions in Indonesia is based on the following criteria: Akaike information criterion (AIC), Schwartz information criterion (SC), Hannan-Quinn criterion (HQ), likelihood ratio (LR), and final prediction error (FPE). The third step is to test for long-run market integration for beef in producer regions with primary consumers in Indonesia using the cointegration test. The cointegration test method uses Johansen’s cointegration test method; if trace statistic > critical value, then reject H0 or accept H1, which means cointegration occurs. If there is no cointegration between variables, the VARD (VAR in difference) model is used. If there is cointegration, the VAR model is the VECM (vector error correction model).

The fourth step, to examine whether the beef price in producer regions with primary consumers in Indonesia has a reciprocal or significant causal relationship, is to use the Granger causality test [28]. The bivariate causality test in this research used the VAR pairwise Granger causality test. It used a 5% significance level with the formula: unrestricted equation: Y t = ∑ i = 1 m α 1 Y t − i + ∑ i = 1 m β 1 X t − i + ε 1 t , where Y t = value of variable Y at time – t, m = number of lags, α i = coefficient of the ith lag of the variable, Y in the unrestricted model, β i = coefficient of the ith lag of the variable X, X t−i = value of the variable X at the ith lag, where i is greater than 0, e 1t = errors at time – t. Restricted equation: Y t = ∑ i = 1 m γ 1 X t − i + ε 2 t , where e 2t = error at time – t, m = number of lags γ i = coefficient of the ith lag of variable Y in the restricted model Y t−i = value of variable Y at lag i, where i is greater than 0. The statistical hypothesis for testing causality using the Granger approach are as follows: H0: ∑ i = 1 t β it = 0 , meaning that a variable does not affect other variables, H1: ∑ i = 1 t β it ≠ 0 , meaning that a variable affects other variables. The fifth step is estimating the VAR/VECM [29,30,31]. VAR models can be developed after observing stationarity, cointegration, optimal lag, and the suitability of variables to be included in the model. VECM is a restricted form of VAR. This restriction is applied to use data that are non-stationary at the level, but cointegrated. The standard VAR models with lag 1 are as follows:

(1) Y t = α 1 i + ∑ β 1 i Y t − 1 + ∑ γ 1 i X t − 1 + ε t , X t = α 2 i + ∑ β 2 i Y t − 1 + ∑ γ 2 i X t − 1 + ε t

VECM:

(2) ∆ Y t = φ 1 + δ 1 t + λ 1 et − 1 + γ 11 Δ Y t − 1 + … + γ 1 p Δ Y t − p + ω 11 ∆ X t − 1 + … + ω 1 q ∆ X t − q + ε 1 t

and

(3) ∆ X t = φ 2 + δ 2 t + λ 2 et − 1 + γ 21 Δ X t − 1 + … + γ 2 p Δ X t − p + ω 21 ∆ Y t − 1 + … + ω 2 q ∆ Y t − q + ε 2 t .

The final step is the impulse response function, variance decomposition, and forecasting. The impulse response function shows the rate of one variable’s shock to another in a specific period. It can reveal how long the shock of a variable affects other variables until the effect fades or returns to the equilibrium point. Variance decomposition will provide information about the proportion of the movement of the shock effect on a variable due to the shock of other variables in the present and future periods. To test the magnitude of the forecasting error, methods such as root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Theil inequality coefficient (Theil) can be used.

3 Results and discussion

3.1 Beef prices in consumer and producer regions

The average price of beef in the producer regions in Indonesia was the lowest in East Nusa Tenggara Province (period January 2018–July 2022) ($6.84/kg), followed by Bali Province ($7.22/kg), and the highest in Lampung Province ($7.86/kg), followed by West Nusa Tenggara and East Java Provinces, which were $7.65/kg and $7.47/kg as shown in Table 1. In cattle producer regions, namely the Eastern Islands, West Nusa Tenggara and East Nusa Tenggara, farmers may own more than 50 head per farmer due to the abundant land that allows extensive system livestock keeping [1]. Based on the reference price for selling to consumers (Regulation of the Minister of Trade of the Republic of Indonesia Number 07 of 2020) [32] for beef, which is $7.00/kg, the meat price is above normal (above the reference price), except for East Nusa Tenggara Province. Moreover, East Nusa Tenggara Province has the lowest maximum and minimum prices, while Lampung Province has the highest maximum and minimum prices. Despite the high market price of cattle, the farmers do not benefit because of their weak bargaining position [1]. Domestic beef prices in Indonesia are higher than international market prices [3]. In Turkey, red meat prices have soared in recent years, threatening the security of protein-based food, especially for low- and middle-income families [33].

Table 1

Description of beef prices in producer and consumer regions in Indonesia in the January 2018–July 2022 period

Beef prices Consumer area province in Indonesia Producer region province in Indonesia
Jakarta Banten West Java East Java Lampung Bali West Nusa Tenggara East Nusa Tenggara
Mean ($/kg) 8.69 8.13 8.43 7.47 7.86 7.22 7.65 6.84
Median ($/kg) 8.50 7.81 8.17 7.43 7.63 7.25 7.77 6.86
Maximum ($/kg) 10.11 10.52 9.95 7.93 9.20 7.48 8.06 7.22
Minimum ($/kg) 8.25 7.75 7.98 7.19 7.29 6.96 6.84 6.38
Std. Dev. ($/kg) 0.46 0.53 0.46 0.13 0.45 0.10 0.29 0.19
Skewness 1.35 1.46 1.44 1.12 1.18 −0.56 −1.26 −0.41
kurtosis 3.68 4.68 4.24 4.69 3.46 3.88 3.61 4.27
Jarque-Bera 76.74 113.48 98.48 76.74 78.17 57.31 20.08 66.66
Probability 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

The beef prices in the producer regions in Indonesia fluctuated considerably with an upward trend in the January 2018–July 2022 period, where the highest increase occurred in Lampung Province (0.10% per week), while Bali and East Java Provinces experienced the lowest increase (0.02% per week). The beef price is prone to fluctuation because it is an imported commodity, so it is sensitive to price changes domestically and in exporting countries [11].

The Special Capital Region of Jakarta is the central consumption province with the highest average beef price ($8.69/kg). Moreover, Jakarta Province has the highest maximum and minimum prices, while Banten Province has the lowest maximum and minimum prices. Based on the reference price for consumer sales (Regulation of the Minister of Trade of the Republic of Indonesia Number 07 of 2020), the average beef price in the consumer regions in Indonesia is far above normal (above the reference price). The beef prices in the consumer regions in Indonesia fluctuated with an upward trend in the last 5 years, where the highest increase occurred in Banten Province (0.10% per week), while Bali and West Java Provinces experienced the lowest increase (0.08% per week). The demand for beef in Jakarta is around 329.25 tons per day (9877.5 tons per month) [34]; to meet this demand, trade transactions are conducted with several suppliers who only come from producer regions (in the Eastern Islands: West Nusa Tenggara and East Nusa Tenggara) [1]. The Jakarta market and its surroundings account for 70% of Indonesia’s total beef demand [2]. The beef deficit in Indonesia triggers price hikes, which encourage beef business actors to form cartels, resulting in inefficiencies in the supply chain channel [9]. Market inefficiencies in beef supply chains are due to oligopoly caused by cartel involvement in Indonesia [35]. In the US, wholesale pricing of beef by beef packers was consistent with oligopoly and monopoly prices, and retail beef pricing was consistent with oligopoly prices during cartel periods [36].

3.2 Beef market integration in consumer and producer regions

3.2.1 Stationarity test and optimal lag

For the consumer regions (Jakarta, Banten, and West Java Provinces) and the producer regions (East Nusa Tenggara, West Nusa Tenggara, Bali, East Java, and Lampung Provinces) of beef, the unit root test results show that the beef price data are not all stationary at the level but stationary at the first difference, which is a prerequisite for the cointegration test as shown in Table 2.

Table 2

Stationarity test results of beef price in consumer and producer regions in Indonesia

Beef Province Test equation (trend & intercept) t-statistics Augmented Dickey-Fuller test statistics Test critical values Prob.
Consumer regions Jakarta First difference −8.109819 1% level −3.458104 0.0000
Banten First difference −14.36425 1% level −3.458104 0.0000
West Java First difference −13.96975 1% level −3.457984 0.0000
Producer regions East Java First difference −14.53574 1% level −3.457984 0.0000
Lampung First difference −16.64727 1% level −3.457984 0.0000
Bali First difference −20.72545 1% level −3.457865 0.0000
West Nusa Tenggara First difference −14.93273 1% level −3.457865 0.0000
East Nusa Tenggara First difference −14.07050 1% level 3.457865 0.0000

The VAR (vector autoregression)/VECM model starts by calculating the optimal lag to estimate the model. The optimal lag for the beef price is determined based on criteria such as AIC, SC, HQ, LR, and FPE. The optimal lag test results for beef price in the producer and consumer regions are lag 4. This research’s findings indicate that the beef price in the producer and consumer regions in Indonesia affected each other not only in the current period but also in the previous four periods. In Japan, current changes in beef prices are strongly related to changes in beef prices in the last 2 months; prices of different types of meat can be adjusted more efficiently in a short time than their demand in response to exogenous shocks [37].

3.2.2 Long-term beef market integration

Based on Johansen’s cointegration test method, the result is that the trace statistic value and the max-eigen value are both larger than the critical value of 5%, as shown in Table 3, so it can be concluded that cointegration has occurred. This research’s findings imply a long-term relationship between the beef price in the producer and consumer regions. The significant cointegration in the long run of the beef market in the producer regions (East Nusa Tenggara, West Nusa Tenggara, Bali, East Java, and Lampung Provinces) and the consumer regions (Jakarta, Banten, and West Java Provinces) in Indonesia indicates market integration. East Java has the largest cattle population, and Bali is one of the live cattle suppliers for the Jakarta beef market [9]. In a spatially integrated market, there is a movement between surplus and deficit areas [20]. The results of Penone and Trestini’s research in Italy concluded that the non-linear cointegration analysis shows price transmission asymmetry for all agricultural commodity prices in Italy [38].

Table 3

Test results cointegration of beef price in consumer and producer regions in Indonesia

Hypothesis Eigen value Trace statistics 0.05 critical value Prob. Max-Eigen statistics 0.05 critical value Prob.
None 0.452767 573.6059 143.6691 0.0000 140.4710 48.87720 0.0000
1 0.372237 433.1349 111.7805 0.0001 108.4833 42.77219 0.0000
2 0.278383 324.6517 83.93712 0.0000 76.01891 36.63019 0.0000
3 0.252460 248.6327 60.06141 0.0000 67.79537 30.43961 0.0000
4 0.236066 180.8374 40.17493 0.0001 62.74080 24.15921 0.0000
5 0.180987 118.0966 24.27596 0.0000 46.51980 17.79730 0.0000
6 0.159991 71.57678 12.32090 0.0000 40.62175 11.22480 0.0000
7 0.124407 30.95503 4.129906 0.0000 30.95503 4.129906 0.0000

3.2.3 Short-term beef market integration

The significant cointegration in the long run of the beef market in the producer and consumer regions in Indonesia indicates market integration, but in the short term, integration may not occur. Therefore, to examine the integration in a short time, a VECM analysis is conducted with the estimation results of the VECM shown in Table 4. The existence of a significant error correction parameter, namely t-statistics > t-table, that occurs in the central consumption province (Jakarta) proves that there is an adjustment mechanism for beef prices from short-term to long-term in the central consumption province (Jakarta). The beef price adjustment from the short term to the long term in the consumption provinces, especially Jakarta Province, is −0.22%.

Table 4

VECM estimation results of beef price in consumer and producer regions in Indonesia

Error correction D(Jakarta,2) Error correction D(Banten,2) Error correction D(West Java,2)
CointEq1 −0.223053 CointEq1 −0.168787 CointEq1 −0.170925
(0.04774) (0.06133) (0.04681)
[−4.67227] [−2.75189] [−3.65153]
D(Jakarta(−1),2) −0.140401 D(Banten(−1),2) −1.407458 D(West Java(−1),2) −1.598523
(0.10237) (0.15360) (0.29658)
[−1.37157] [−9.16332] [−5.38991]
D(Jakarta(−2),2) −0.256699 D(Banten(−2),2) −0.996194 D(West Java(−2),2) −1.453856
(0.11934) (0.18519) (0.27962)
[−2.15107] [−5.37945] [−5.19948]
D(Jakarta(−3),2) −0.123149 D(Banten(−3),2) −0.689131 D(West Java(−3),2) −0.981621
(0.11581) (0.16721) (0.23993)
[−1.06336] [−4.12147] [−4.09120]
D(Jakarta(−4),2) −0.121188 D(Banten(−4),2) −0.045018 D(West Java(−4),2) −0.778451
(0.10156) (0.12778) (0.20217)
[−1.19321] [−0.35231] [−3.85042]
D(East Java(−1),2) 1.504337 D(East Java(−1),2) 1.362496 D(East Java(−1),2) 1.192360
(0.32465) (0.41710) (0.31832)
[4.63372] [3.26659] [3.74578]
D(East Java(−2),2) 1.024654 D(East Java(−2),2) 1.259206 D(East Java(−2),2) 0.930831
(0.32636) (0.41930) (0.32000)
[3.13966] [3.00315] [2.90888]
D(East Java(−3),2) 0.767781 D(East Java(−3),2) 0.510043 D(East Java(−3),2) 0.609454
(0.30031) (0.38583) (0.29446)
[2.55660] [1.32192] [2.06974]
D(East Java(−4),2) 0.147556 D(East Java(−4),2) 0.009095 D(East Java(−4),2) 0.285409
(0.21346) (0.27425) (0.20930)
[0.69125] [0.03316] [1.36362]
D(Bali(−1),2) 0.313905 D(Bali(−1),2) 0.468773 D(Bali(−1),2) 0.180432
(0.22492) (0.28896) (0.22053)
[1.39566] [1.62225] [0.81817]
D(Bali(−2),2) 0.322672 D(Bali(−2),2) 1.277725 D(Bali(−2),2) 0.414905
(0.31315) (0.40233) (0.30705)
[1.03040] [3.17582] [1.35127]
D(Bali(−3),2) 0.319122 D(Bali(−3),2) 0.938747 D(Bali(−3),2) 0.418298
(0.32445) (0.41685) (0.31813)
[0.98357] [2.25200] [1.31487]
D(Bali(−4),2) 0.129588 D(Bali(−4),2) 0.069943 D(Bali(−4),2) 0.107885
(0.23223) (0.29836) (0.22770)
[0.55801] [0.23442] [0.47380]
D(Lampung(−1),2) 0.812113 D(Lampung(−1),2) 0.181441 D(Lampung(−1),2) 0.477601
(0.28298) (0.36357) (0.27747)
[2.86984] [0.49906] [1.72130]
D(Lampung(−2),2) 0.543958 D(Lampung(−2),2) 0.418392 D(Lampung(−2),2) 0.498003
(0.23079) (0.29651) (0.22629)
[2.35696] [1.41106] [2.20074]
D(Lampung(−3),2) 0.355984 D(Lampung(−3),2) 0.435478 D(Lampung(−3),2) 0.360842
(0.17195) (0.22092) (0.16860)
[2.07022] [1.97118] [2.14019]
D(float(−4),2) 0.165976 D(Lampung(−4),2) 0.400084 D(Lampung(−4),2) 0.232886
(0.10289) (0.13219) (0.10089)
[1.61308] [3.02648] [2.30837]
D(West Nusa Tenggara(−1),2) −0.616460 D(West Nusa Tenggara(−1),2) −1.101197 D(West Nusa Tenggara(−1),2) −0.877668
(0.26662) (0.34255) (0.26142)
[−2.31213] [−3.21475] [−3.35728]
D(West Nusa Tenggara(−2),2) −1.044405 D(West Nusa Tenggara(−2),2) −1.181217 D(West Nusa Tenggara(−2),2) −1.228789
(0.29441) (0.37825) (0.28867)
[−3.54745] [−3.12285] [−4.25671]
D(West Nusa Tenggara(−3),2) −0.474738 D(West Nusa Tenggara(−3),2) −1.046714 D(West Nusa Tenggara(−3),2) −0.778468
(0.28328) (0.36394) (0.27775)
[−1.67589] [−2.87603] [−2.80273]
D(West Nusa Tenggara(−4),2) −0.107618 D(West Nusa Tenggara(−4),2) 0.131564 D(West Nusa Tenggara(−4),2) −0.151634
(0.22329) (0.28687) (0.21893)
[−0.48197] [0.45862] [−0.69260]
D(East Nusa Tenggara(−1),2) 0.165982 D(East Nusa Tenggara(−1),2) 0.410564 D(East Nusa Tenggara(−1),2) 0.320194
(0.23728) (0.30484) (0.23265)
[0.69953] [1.34680] [1.37629]
D(East Nusa Tenggara(−2),2) 0.375308 D(East Nusa Tenggara(−2),2) 0.295883 D(East Nusa Tenggara(−2),2) 0.287368
(0.28030) (0.36012) (0.27484)
[1.33894] [0.82161] [1.04559]
D(East Nusa Tenggara(−3),2) 0.546230 D(East Nusa Tenggara(−3),2) 0.173044 D(East Nusa Tenggara(−3),2) 0.216576
(0.27666) (0.35545) (0.27127)
[1.97435] [0.48683] [0.79838]
D(East Nusa Tenggara(−4),2) 0.261279 D(East Nusa Tenggara(−4),2) 0.016038 D(East Nusa Tenggara(−4),2) 0.094405
(0.22972) (0.29514) (0.22524)
[1.13739] [0.05434] [0.41913]

Other researchers have reported that wholesale prices have a long-term asymmetric effect on retail prices, with a 1% increase in the farm prices leading to a 0.769% increase in the wholesale prices and a 1% decrease in the farm prices leading to a 0.734% decrease in the wholesale prices [19]. The beef price in Jakarta Province was determined by the hard meat situation 2 weeks before in the same province, as well as by the beef prices in the previous 1–3 weeks in East Java and Lampung provinces and the previous 1–2 weeks in West Nusa Tenggara Province. The last three provinces are situated on separate islands, which indicates a lower level of market integration than provinces on the same island, as supported by other research [6]. Spatial market integration refers to the movement and transmission of goods and price information across regions [18]. The slow response of farmers to demand and supply shocks underscores the importance of imports as a temporary solution to meet the beef demand in the short and medium term [11]. The US beef market, on the other hand, exhibits a high degree of integration [39], which can lower market barriers and foster more competition [16].

3.2.4 Granger causality test

The Granger causality test reveals a relationship between beef prices in some producer and consumer regions in Indonesia. The results show that beef price fluctuations in East Java Province (a producer region) significantly affect the beef prices in Jakarta Province (0.0184), Banten (0.0337), and West Java (0.0132) (consumer regions). Similarly, beef price fluctuations in Lampung Province (producer region) significantly affect the beef prices in Jakarta Province (0.0001) (consumer region), as indicated in Table 5. This is due to the proximity and ease of transportation of cattle between these regions, which are on the same island. However, beef price fluctuations in other producer regions (Bali, West Nusa Tenggara, and East Nusa Tenggara) do not have a significant impact on beef prices in Indonesian consumer regions. This is because of the distance and difficulty of the transportation of cattle between these regions, which are on different islands.

Table 5

Results of the Granger causality test of beef price in consumer and producer regions in Indonesia

Causality relationship between producer and consumer regions F-statistics Prob.
East Java → Jakarta 4.06764 0.0184
Bali → Jakarta 0.62503 0.5361
Lampung → Jakarta 9.26204 0.0001
West Nusa Tenggara → Jakarta 0.91279 0.4028
East Nusa Tenggara → Jakarta 2.66163 0.0720
East Java → Banten 3.44127 0.0337
Bali → Banten 0.18183 0.8339
Lampung → Banten 0.98147 0.3763
West Nusa Tenggara → Banten 0.98106 0.3765
East Nusa Tenggara → Banten 1.57487 0.2092
East Java → West Java 4.40557 0.0132
Bali → West Java 0.08185 0.9214
Lampung → West Java 1.65275 0.1938
West Nusa Tenggara → West Java 0.64152 0.5274
East Nusa Tenggara → West Java 1.39495 0.2499

Producers are more adaptive to various market changes, including costs and price volatility [11]. Empirical results in the US show that processors (beef packers) have certain advantages over primary producers (cattle/calf operators, stocker operators, feedlot operators), and retailers have certain advantages over processors; also, end consumers are more likely to suffer a loss in their surplus from a price hike than to gain an increase in their surplus from a drop of a key upstream price. A market is integrated when prices move together and are in equilibrium with prices in spatially distant markets [40,41].

3.2.5 Impulse response function and variance decompositions

The impulse response function analysis is applied to examine the speed of shock beef prices in the producer regions in Indonesia on beef prices in the consumer regions in Indonesia from January 2018–July 2022. The first period shows a shock in beef prices. The Jakarta Province has a positive impact on beef prices. Jakarta Province amounted to 1807.96. The second to tenth period shows a shock of beef prices in the consumption provinces, especially Jakarta Province, which causes a positive and negative response to beef prices in Jakarta Province. Meanwhile, the shock of beef prices in the production province, namely Lampung Province, has a positive and negative effect, and the result is the most significant response to beef prices in Jakarta Province in the second to tenth period, where the most critical effect occurred in the second period of −586.49, as presented in Table 6.

Table 6

Results of impulse response function and variance decompositions of beef price in consumer and producer regions in Indonesia

Response of D(Jakarta) Period
1 2 3 4 5 6 7 8 9 10
D(Jakarta) 1807.22 167.35 −321.09 273.40 44.18 −221.34 −8.52 47.73 −58.76 9.25
D(Banten) 0.00 −374.32 94.72 205.73 −59.10 34.25 75.89 −50.57 −6.29 −2.54
D(West Java) 0.00 −325.71 −82.93 −28.87 165.84 36.38 −114.87 −19.19 30.37 −25.80
D(East Java) 0.00 −97.74 −255.53 −141.81 −194.37 −57.06 6.35 90.83 90.95 −18.71
D(Lampung) 0.00 −586.50 −4.87 235.14 −67.94 26.91 27.34 −54.11 47.85 21.32
D(Bali) 0.00 −81.50 −173.71 −58.45 −89.77 5.37 −73.17 62.41 18.99 −48.39
D(West Nusa Tenggara) 0.00 83.96 −71.34 433.65 193.99 −140.46 −1.77 54.84 −72.36 −52.11
D(East Nusa Tenggara) 0.00 62.23 126.96 208.84 −73.25 −121.72 50.15 −56.27 −36.85 45.80
Variance decomposition of D(Jakarta): Period
1 2 3 4 5 6 7 8 9 10
D(Jakarta) 100.00 84.22 81.92 75.88 73.88 73.52 73.10 72.74 72.49 72.36
D(Banten) 0.00 3.58 3.60 4.18 4.15 4.09 4.19 4.22 4.20 4.19
D(West Java) 0.00 2.71 2.72 2.49 3.01 2.98 3.23 3.22 3.23 3.24
D(East Java) 0.00 0.24 1.80 2.08 2.82 2.84 2.82 2.98 3.13 3.14
D(Lampung) 0.00 8.79 8.30 8.73 8.59 8.44 8.41 8.43 8.43 8.43
D(Bali) 0.00 0.17 0.89 0.88 1.03 1.01 1.11 1.19 1.19 1.24
D(West Nusa Tenggara) 0.00 0.18 0.29 4.38 5.06 5.37 5.34 5.38 5.46 5.51
D(East Nusa Tenggara) 0.00 0.10 0.48 1.39 1.47 1.75 1.79 1.85 1.87 1.91

According to previous research in Indonesia, ex-import/feedlot cattle (98%) account for most of the local beef supply in Jakarta and its neighboring areas, while local cattle contribute only 2%. Lampung is the source of 44.38% of the ex-import cattle [34]. The price transmission between Jakarta and other provinces is asymmetric, with other provinces taking 15 and 12 days, respectively, to adjust to Jakarta beef price changes. However, Jakarta reacts to price changes in other regions within 6 days, indicating that Jakarta is more responsive to other regions than the other way around [6]. Likewise, in Finland, a significant cross-price shock effect demonstrates a strong interdependence between producer and consumer beef prices, implying that information spreads rapidly across markets [42]. In contrast, in the US, retail beef prices show downward rigidity due to low demand for beef, which impacts producer prices more swiftly than upward price movements [10].

The variance decomposition analysis results indicate the proportion of the variation in beef prices in the consumer regions in Indonesia that is due to the shock of beef prices in the producer regions in Indonesia in the current and future periods. The variation in beef prices in Jakarta Province, the producer region in Indonesia, was entirely driven by its own shock (100%) in the first period, and none by the shock of beef prices in the consumer regions in Indonesia (0%). In the second period, the contribution of its own shock reduced to 84.22%, while the shock of beef prices in East Java Province, Lampung Province, Bali Province, West Nusa Tenggara Province, and East Nusa Tenggara Province explained 0.24, 8.79, 0.17, 0.18, and 0.10%, respectively. This pattern continued until the 10th period, when the contribution of its own shock decreased to 72.36%, while the shock of beef prices in East Java Province, Lampung Province, Bali Province, West Nusa Tenggara Province, and East Nusa Tenggara Province accounted for 3.13, 8.42, 1.23, 5.51, and 1.90%, respectively. Market price information should be accessible and transparent at all levels of the supply chain to ensure the full operation of market mechanisms [3]. Spatial market integration enables distant places to accommodate local supply excesses, preventing excessive price drops that would damage local producers’ profitability [23].

3.2.6 Beef Price Forecast Model

Market integration and price forecasting can facilitate price stabilization by eliminating market imperfections such as monopolies and monopsonies and achieving market efficiency [43]. Forecasting methods are assessed using MAPE to determine the most accurate forecasting model [44]. Table 7 displays the results of the evaluation of the beef price forecasting model in the producer and consumer regions in Indonesia. These results indicate that the beef price forecasting model in the producer and consumer regions in Indonesia has excellent performance.

Table 7

Results of evaluation of forecasting models’ beef price in consumer and producer regions in Indonesia

Variables RMSE MAE MAPE Theil
Bali 563.751 290.579 0.268947 0.002603
Banten 2004.803 1232.802 0.991738 0.008196
Jakarta 1674.942 1007.818 0.761905 0.006406
West Java 1561.819 907.163 0.705961 0.006158
East Java 809.653 516.598 0.458661 0.003609
Lampung 2104.782 1123.030 0.938206 0.008899
West Nusa Tenggara 634.980 392.878 0.342110 0.002758
East Nusa Tenggara 515.478 222.544 0.217629 0.002509

Forecasting models’ beef price in the consumer regions in Indonesia (Jakarta is the capital city of Indonesia) are as follows:

D(Jakarta) = 0.44538 × D(Jakarta(−1)) − 0.14529 × D(Jakarta(−2)) + 0.04803 × D(Jakarta(−3)) − 0.05616 × D(Jakarta(−4)) − 0.04197 × D(Banten(−1)) + 0.17458 × D(Banten(−2)) + 0.16927 × D(Banten(−3)) − 0.06853 × D(Banten(−4)) + 0.22599 × D(West Java( −1)) + 0.00459 × D(West Java(−2)) − 0.03533 × D(West Java(−3)) + 0.18097 × D(West Java(−4)) − 0.041344 × D(East Java(−1)) − 0.52967 × D(East Java(−2)) − 0.30742 × D(East Java(−3)) − 0.51203 × D(East Java(−4)) − 0.55052 × D(Lampung(−1)) − 0.131254 × D(Lampung(−2)) − 0.00244 × D(Lampung(−3)) + 0.0058 × D(Lampung(−4)) − 0.17453 × D(Bali(−1)) − 0.31507 × D(Bali( −2)) − 0.28753 × D(Bali(−3)) − 0.32650 × D(Bali(−4)) + 0.13777 × D(West Nusa Tenggara(−1)) − 0.27136 × D(West Nusa Tenggara(−2))) + 0.69452 × D(West Nusa Tenggara(−3)) + 0.48581 × D(West Nusa Tenggara(−4)) + 0.11871 × D(East Nusa Tenggara(−1)) + 0.30963 × D(East Nusa Tenggara(−2)) + 0.30466 × D(East Nusa Tenggara(−3)) − 0.20267 × D(East Nusa Tenggara(−4)) + 55.03231

4 Conclusions

The average beef price in the producer regions in Indonesia is above the normal level (above the reference price), except for East Nusa Tenggara Province, and the average beef price in the consumer regions is far above the normal level (above the reference price). The beef prices in the producer and consumer regions affect each other in the current period and are interrelated in the previous four periods. There is a significant cointegration in the long run between the beef markets in the producer regions (East Nusa Tenggara, West Nusa Tenggara, Bali, East Java, and Lampung Provinces) and the consumer regions (Jakarta, Banten, and West Java Provinces) in Indonesia. The shock of beef prices in the producer regions has the largest impact on Lampung Province, the producer region. The VECM is an accurate beef price forecasting model for the producer and consumer regions in Indonesia.

To stabilize beef prices between producer and consumer regions, the Government of Indonesia can implement control measures such as improving land and sea transportation infrastructure to minimize price gaps between regions. Furthermore, it should also improve the market information system that is fast and up to date. The policies of the Government of Indonesia in the form of the Presidential Regulation of the Republic of Indonesia Number 59 of 2020 and Regulation of the Minister of Trade of the Republic of Indonesia Number 07 of 2020 through price ceilings and price floors have not been effective for stabilizing beef prices, so it is time for the Government of Indonesia to enforce the Law of One Price for beef across regions. Fan et al. found that applying the Law of One Price required information transparency and regulators’ potential monitoring role on social networks [45]. Institutional change has had a major influence on market integration in Germany. Because institutional improvement increases market integration, institutions increase the extent of interdependencies between economies [46].

Acknowledgments

The authors would like to thank the Chancellor of the University of Jambi and the Dean of the Faculty of Animal Husbandry, University of Jambi, as well as the Head of the Institute for Research and Community Service, University of Jambi, who was approved and supported this research.

  1. Funding information: This research was funded by the DIPA PNBP of the Faculty of Animal Husbandry, University of Jambi, in the Applied Research Scheme for the 2022 Fiscal Year Number: SP DIPA-023.17.2.677565/2022 November 17, 2021, by the Research Contract Agreement Letter Number: 333/UN21.11/PT.01.05/SPK/2022 dated May 17, 2022.

  2. Author contributions: F: Preparation, coordination, conceptualization, data analysis, forecasting model, and article drafting; PM: assisting data analysis and forecasting models; AH: helping with data analysis and article drafting.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request.

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Received: 2023-06-07
Revised: 2023-07-29
Accepted: 2023-08-11
Published Online: 2023-09-11

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  71. Control measure of sweet potato weevil (Cylas formicarius Fab.) (Coleoptera: Curculionidae) in endemic land of entisol type using mulch and entomopathogenic fungus Beauveria bassiana
  72. In vitro and in silico study for plant growth promotion potential of indigenous Ochrobactrum ciceri and Bacillus australimaris
  73. Effects of repeated replanting on yield, dry matter, starch, and protein content in different potato (Solanum tuberosum L.) genotypes
  74. Review Articles
  75. Nutritional and chemical composition of black velvet tamarind (Dialium guineense Willd) and its influence on animal production: A review
  76. Black pepper (Piper nigrum Lam) as a natural feed additive and source of beneficial nutrients and phytochemicals in chicken nutrition
  77. The long-crowing chickens in Indonesia: A review
  78. A transformative poultry feed system: The impact of insects as an alternative and transformative poultry-based diet in sub-Saharan Africa
  79. Short Communication
  80. Profiling of carbonyl compounds in fresh cabbage with chemometric analysis for the development of freshness assessment method
  81. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part I
  82. Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy
  83. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part I
  84. Traditional agri-food products and sustainability – A fruitful relationship for the development of rural areas in Portugal
  85. Consumers’ attitudes toward refrigerated ready-to-eat meat and dairy foods
  86. Breakfast habits and knowledge: Study involving participants from Brazil and Portugal
  87. Food determinants and motivation factors impact on consumer behavior in Lebanon
  88. Comparison of three wine routes’ realities in Central Portugal
  89. Special Issue on Agriculture, Climate Change, Information Technology, Food and Animal (ACIFAS 2020)
  90. Environmentally friendly bioameliorant to increase soil fertility and rice (Oryza sativa) production
  91. Enhancing the ability of rice to adapt and grow under saline stress using selected halotolerant rhizobacterial nitrogen fixer
https://doi.org/10.1515/opag-2022-0221
Schlagwörter für diesen Artikel
market integration; beef; VAR/VECM; forecasting
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Artikel in diesem Heft

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  8. Lablab purpureus: Analysis of landraces cultivation and distribution, farming systems, and some climatic trends in production areas in Tanzania
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  17. Meta-analysis of the influence of the substitution of maize with cassava on performance indices of broiler chickens
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  27. Molecular and phenotypic markers for pyramiding multiple traits in rice
  28. Natural product nanofibers derived from Trichoderma hamatum K01 to control citrus anthracnose caused by Colletotrichum gloeosporioides
  29. Role of actors in promoting sustainable peatland management in Kubu Raya Regency, West Kalimantan, Indonesia
  30. Small-scale coffee farmers’ perception of climate-adapted attributes in participatory coffee breeding: A case study of Gayo Highland, Aceh, Indonesia
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  32. Production factors, technical, and economic efficiency of soybean (Glycine max L. Merr.) farming in Indonesia
  33. Economic performance of smallholder soya bean production in Kwara State, Nigeria
  34. Indonesian rice farmers’ perceptions of different sources of information and their effect on farmer capability
  35. Feed preference, body condition scoring, and growth performance of Dohne Merino ram fed varying levels of fossil shell flour
  36. Assessing the determinant factors of risk strategy adoption to mitigate various risks: An experience from smallholder rubber farmers in West Kalimantan Province, Indonesia
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  38. Grade-C kenaf fiber (poor quality) as an alternative material for textile crafts
  39. Technical efficiency changes of rice farming in the favorable irrigated areas of Indonesia
  40. Palm oil cluster resilience to enhance indigenous welfare by innovative ability to address land conflicts: Evidence of disaster hierarchy
  41. Factors determining cassava farmers’ accessibility to loan sources: Evidence from Lampung, Indonesia
  42. Tailoring business models for small-medium food enterprises in Eastern Africa can drive the commercialization and utilization of vitamin A rich orange-fleshed sweet potato puree
  43. Revitalizing sub-optimal drylands: Exploring the role of biofertilizers
  44. Effects of salt stress on growth of Quercus ilex L. seedlings
  45. Design and fabrication of a fish feed mixing cum pelleting machine for small-medium scale aquaculture industry
  46. Indicators of swamp buffalo business sustainability using partial least squares structural equation modelling
  47. Effect of arbuscular mycorrhizal fungi on early growth, root colonization, and chlorophyll content of North Maluku nutmeg cultivars
  48. How intergenerational farmers negotiate their identity in the era of Agriculture 4.0: A multiple-case study in Indonesia
  49. Responses of broiler chickens to incremental levels of water deprivation: Growth performance, carcass characteristics, and relative organ weights
  50. The improvement of horticultural villages sustainability in Central Java Province, Indonesia
  51. Effect of short-term grazing exclusion on herbage species composition, dry matter productivity, and chemical composition of subtropical grasslands
  52. Analysis of beef market integration between consumer and producer regions in Indonesia
  53. Analysing the sustainability of swamp buffalo (Bubalus bubalis carabauesis) farming as a protein source and germplasm
  54. Toxicity of Calophyllum soulattri, Piper aduncum, Sesamum indicum and their potential mixture for control Spodoptera frugiperda
  55. Consumption profile of organic fruits and vegetables by a Portuguese consumer’s sample
  56. Phenotypic characterisation of indigenous chicken in the central zone of Tanzania
  57. Diversity and structure of bacterial communities in saline and non-saline rice fields in Cilacap Regency, Indonesia
  58. Isolation and screening of lactic acid bacteria producing anti-Edwardsiella from the gastrointestinal tract of wild catfish (Clarias gariepinus) for probiotic candidates
  59. Effects of land use and slope position on selected soil physicochemical properties in Tekorsh Sub-Watershed, East Gojjam Zone, Ethiopia
  60. Design of smart farming communication and web interface using MQTT and Node.js
  61. Assessment of bread wheat (Triticum aestivum L.) seed quality accessed through different seed sources in northwest Ethiopia
  62. Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan
  63. Agronomic performance, seed chemical composition, and bioactive components of selected Indonesian soybean genotypes (Glycine max [L.] Merr.)
  64. The role of halal requirements, health-environmental factors, and domestic interest in food miles of apple fruit
  65. Subsidized fertilizer management in the rice production centers of South Sulawesi, Indonesia: Bridging the gap between policy and practice
  66. Factors affecting consumers’ loyalty and purchase decisions on honey products: An emerging market perspective
  67. Inclusive rice seed business: Performance and sustainability
  68. Design guidelines for sustainable utilization of agricultural appropriate technology: Enhancing human factors and user experience
  69. Effect of integrate water shortage and soil conditioners on water productivity, growth, and yield of Red Globe grapevines grown in sandy soil
  70. Synergic effect of Arbuscular mycorrhizal fungi and potassium fertilizer improves biomass-related characteristics of cocoa seedlings to enhance their drought resilience and field survival
  71. Control measure of sweet potato weevil (Cylas formicarius Fab.) (Coleoptera: Curculionidae) in endemic land of entisol type using mulch and entomopathogenic fungus Beauveria bassiana
  72. In vitro and in silico study for plant growth promotion potential of indigenous Ochrobactrum ciceri and Bacillus australimaris
  73. Effects of repeated replanting on yield, dry matter, starch, and protein content in different potato (Solanum tuberosum L.) genotypes
  74. Review Articles
  75. Nutritional and chemical composition of black velvet tamarind (Dialium guineense Willd) and its influence on animal production: A review
  76. Black pepper (Piper nigrum Lam) as a natural feed additive and source of beneficial nutrients and phytochemicals in chicken nutrition
  77. The long-crowing chickens in Indonesia: A review
  78. A transformative poultry feed system: The impact of insects as an alternative and transformative poultry-based diet in sub-Saharan Africa
  79. Short Communication
  80. Profiling of carbonyl compounds in fresh cabbage with chemometric analysis for the development of freshness assessment method
  81. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part I
  82. Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy
  83. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part I
  84. Traditional agri-food products and sustainability – A fruitful relationship for the development of rural areas in Portugal
  85. Consumers’ attitudes toward refrigerated ready-to-eat meat and dairy foods
  86. Breakfast habits and knowledge: Study involving participants from Brazil and Portugal
  87. Food determinants and motivation factors impact on consumer behavior in Lebanon
  88. Comparison of three wine routes’ realities in Central Portugal
  89. Special Issue on Agriculture, Climate Change, Information Technology, Food and Animal (ACIFAS 2020)
  90. Environmentally friendly bioameliorant to increase soil fertility and rice (Oryza sativa) production
  91. Enhancing the ability of rice to adapt and grow under saline stress using selected halotolerant rhizobacterial nitrogen fixer
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