E-ISSN: 2443-0765 Available online at http://jiip.ub.ac.id
Price Behavior of Beef and Chicken Meat in Indonesia
Raslea Azalia1), Ketut Sukiyono*2) and Nola Windirah2)
1) Agribusiness Master Degree, Study Program, Faculty of Agriculture, University of Bengkulu, Bengkulu, 38371, Indonesia
2) Department of Agricultural Socio-Economics, Faculty of Agriculture, University of Bengkulu, Bengkulu, 38371, Indonesia
Submitted: 16 February 2023, Accepted: 08 August 2023
ABSTRACT: Stabilization of food prices is still a strategic issue in Indonesia. This is because most Indonesians use their income mostly for food. Various publications reported that Indonesian food expenditure is approximately half of their income. Therefore, maintaining price stability, including beef and chicken meat price, is a significant policy to design.
However, the continuous difficulty in predicting how beef and chicken meat prices behave is another reason for the importance of analyzing the price behavior of these two commodities.
This research aims to respond to these problems by analyzing price fluctuations and price trends in beef and chicken meat commodities in Indonesia. The data used is monthly beef and chicken meat prices in Indonesia from January 2018 until December 2022, or 60 observations.
Analysis of price fluctuations (instability) and trend analysis is applied. The results show that commodity chicken meat has a higher instability (fluctuation) than beef price. Both beef and chicken meat trend show a quadratic pattern, with an increasing trend for beef and a decreasing trend for chicken meat.
Keywords: Price behavior; Fluctuations; Trends; Beef; Chicken
*Corresponding Author: [email protected]
INTRODUCTION
Stabilization of food prices is still a strategic issue for the Indonesian people.
This is only natural considering that most Indonesian people's spending is still used for food. In 2022, the Indonesian population's share of national food expenditure was recorded at 50.14% or half of their income (BPS, 2022).
The share of food expenditure has increased by 1.8% compared to the previous year, namely 2021. When compared between rural and urban areas in the same period, rural areas have an average share of expenditure on food that is larger (57.45%) than urban areas (46.54%) (BPS, 2022).
According to Engle's law, the share of expenditure on food will decrease with increasing income levels (Nicholson, 1995).
The development and dynamics of changes in food commodity prices are very important to maintain stability.
In addition to the impact on the population as food consumers, price uncertainty also has an adverse impact on food producers, both large and small-scale businesses. Price is an indicator that can measure the efficiency of a trade or a business (Pagala et al., 2017). Price uncertainty can affect small-scale farmers who fully depend on their income from farming and have limited ability to postpone the sale of their agricultural products (Ceballos et al., 2016).
Unstable food prices can also hinder investment development in the agricultural sector and reduce agricultural productivity growth, especially for the agricultural sector, which needs better risk management (Ceballos et al., 2016). Thus, price volatility can broadly impact increasing poverty and hampering economic growth.
One of the strategic food commodities to maintain price stability is livestock-origin commodities such as beef and chicken meat.
Beef and chicken meat are in demand by the people of Indonesia in line with changing consumption patterns and people's tastes, as well as public awareness of the importance
of animal protein for growth. Changes in consumption patterns and people's tastes have placed these two commodities as important in meeting the animal protein needs of the Indonesian people (Rusdiana &
Maesya, 2017).
Commodity prices of beef and chicken meat tend to fluctuate. Where the beef commodity always experiences an increase, especially ahead of or facing national religious holidays (HBKN), especially on Eid Al-Fitr, and so on. As for the price of chicken meat, it always fluctuates every year. Based on Catriana (2022) wrote that in the daily data on food commodity prices in 2022, one of the rising commodity prices is the price of hash beef (hamstring).
In addition, the price of broiler meat also experienced a similar thing.The magnitude of uncertain price changes in the short and long term in a market can affect other markets or markets for producers and their derivative products. The continuous and difficult-to-predict increases and decreases in beef and chicken meat prices indicate a tendency for prices to fluctuate.
Erratic price fluctuations can be calculated using the deviation, standard deviation, and coefficient of variation (Dewi et al., 2017).
Apart from that, price trends in previous years can also be seen to illustrate the condition of beef and chicken meat commodity prices.
Based on this description, studying the behavior of commodity prices for beef and chicken meat in Indonesia is important.
Departing from the above discussion, this research analyzes fluctuations and trends in beef and chicken meat prices in Indonesia.
MATERIALS AND METHODS
The type of analysis used in this journal is quantitative analysis. The data used is secondary data, namely monthly time series data for beef and chicken meat prices in Indonesia from January 2018 - December 2022. The data source was obtained from the National Strategic Food Price Information Center. The following data analysis methods are used in this journal:
Price Fluctuation (Instability) Analysis Method
This method is used to examine a portrayal of fluctuations, or it can be called the average deviation, where the fluctuations that occur are the impacts that occur. The variation coefficient is obtained
from a variable's standard deviation divided by its average value (Nidausoleha, 2007).
Sukiyono et al. (2022) also applied this method, among others. The coefficient of variation can be estimated using formula (1):
𝐶𝑉 =𝑆𝐷
𝑟 × 100% (1) Where SD denotes the standard
deviation, and P denotes the price. However, using the coefficient of variation (CV) to measure price volatility is often
overestimated because of its trend component. For this reason, price volatility is estimated as indicated by the Price Instability Index (PII) using formula (2):
PII = CV × √1 − R2 (2) Where R2 represents the coefficient of
determination. Price behavior analysis in this study is also completed by examining the level of price disparity between the highest and lowest prices.
Trend Analysis Method
Trend analysis is an analytical method used to estimate (forecast) the mass of data and determine whether the data trend is increasing or decreasing. In analyzing
forecasting, it takes a lot of data or information and is observed for a certain period to see the magnitude of the current fluctuations and the factors that occur and influence these changes (Bachri, 2019). The following are several methods and equations that can be used to calculate trend analysis (Douglas et al. 2008):
a. Trends linear
Trends linear has the equation that is :
Y = a + bx (3) Where (Y = periodical data
X = time (day, week, month, year), a = constant number
b = regression coefficient).
b. Trends Quadratic
The quadratic trend method is a method for viewing non-linear data trends.
Short or medium-term data trends will
follow a linear pattern. However, in the long run, it can be non-linear. The equation for the quadratic trend is:
Y' = a + bx + cx2 (4) Information:
Y = periodic data
X = time (day, week, month, year) a = constant value
b, c = regression coefficient
c. Trends Exponential
Exponential equations are expressed in terms of the time variable (X) expressed
as a power. To find the values of a and b from data Y and X, the following formula is used:
Y' = a.bx (5) However, in the calculations, the
equation above can be converted into semi- log form, making it easier to find the values of a and b.
Trend Result Accuracy Measure
The accuracy of trend results measures trend error, namely the degree of difference between forecasting results and actual data.
Inaccuracy (error) forecasting can be measured by deviation and bias. In using various trend methods, we must choose the results or methods that are closest to accurate; this can be seen by using error measurements or error calculations to obtain
the best method. Several formulas can be used in setting the standard difference (standard error), including Mean Absolute Deviation (MAD), Mean Square Error (MSE), Mean Error (ME), and Mean Absolute Percentage Error (MAPE) (Nasution, 2008). The four sizes are described as follows:
a) Average Absolute Deviation (MAD) MAD is the average absolute error in a certain period regardless of whether the forecast results are greater or smaller than the reality. MAD is mathematically formulated as equation (6):
𝑀𝐴𝐷 = ∑|𝑌 − 𝑌′| ÷ 𝑛 (6) where:
Y = actual demand in a period –t
Y' = Forecasting demand in the period –t n = Number of forecasting periods involved
b) Mean Square Error (MSE)
MSE is calculated by adding the squares of all forecasting errors in each
period and dividing them by the number of forecasting periods. Mathematically MSE is formulated as follows:
𝑀𝑆𝐸 = ∑(𝑌 − 𝑌′)2 ÷ 𝑛 (7)
c) Average Error (Mean Error =ME) ME is very effective in knowing whether the results of a forecast over a certain period are too high or too low. The ME value will be close to zero if the forecast results are unbiased. ME is calculated by
adding all forecasting errors during the forecasting period and dividing them by the number of forecasting periods.
Mathematically ME is formulated as follows:
𝑀𝐸 = ∑(𝑌 − 𝑌′) ÷ 𝑛 (8)
d) Mean Absolute Percentage Error (MAPE)
MAPE is a measure of relative error.
MAPE is usually more meaningful than MAD because MAPE states the percentage
of forecasting error against actual demand during a certain period, providing information on the percentage that is too high or too low. Mathematically, MAPE is formulated as follows:
𝑀𝐴𝑃𝐸 =100∑|𝑌−𝑌′|÷𝑌
𝑛 (9) The measure of data accuracy is used
to determine the best trend analysis method. In addition, according to Juanda and Junaidi (2012), there is another way that can be used to determine the best equation method for the R-square value (R2). The equation with the highest R2 value is the best equation that can be used to see the trend of an event.
RESULTS AND DISCUSSION
Description of Beef and Chicken Meat Prices
Figure 1 shows an overview of beef and chicken meat prices in Indonesia per month from January 2018 to December 2022.
Figure 1 shows a graph of beef and chicken meat prices development in Indonesia in the monthly data series for 2018-2022. For a statistical description of the figure, see the Table 1.
Based on Table 1, it can be seen that the average price of beef is Rp121,178/
month, and chicken meat is
IDR34,813/month. The minimum price for beef is Rp113,850/ month, and the maximum is IDR134,600/month.
Meanwhile, the minimum price for chicken meat is IDR29,200/ month, and the maximum is IDR39,000/month. In addition, the standard deviation values for beef and chicken meat indicate that these values are far below the average value, which means that the price data for these two commodities varies because the farther the standard deviation values are from the average value, the wider the variation in the data. This shows that data over five years has fluctuated the prices of these two commodities, in line with the research of Priyanti and Inounu (2016), which states that the regional and year variables and their interactions have a significant effect on changes in the prices of beef, chicken meat, and chicken eggs race. In addition, based on Burhani et al. (2013), the volatility of the previous period and the price variance of the previous period also affected the price of beef in Indonesia.
Figure 1. Beef and Chicken Meat Prices in Indonesia (IDR/Month) Source: Secondary data processed, 2022.
Table 1. Description Statistics of Beef and Chicken Meat Prices in Indonesia, 2018-2022
Commodity Average (IDR) Standard Deviation Minimum (IDR) Maximum (IDR)
Beef 121,178 6,231 113,850 134,600
Chicken meat 34,813 2,095 29,200 39,000
Source: Secondary data processed, 2022.
0 20000 40000 60000 80000 100000 120000 140000 160000
Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19 Jul-19 Oct-19 Jan-20 Apr-20 Jul-20 Oct-20 Jan-21 Apr-21 Jul-21 Oct-21 Jan-22 Apr-22 Jul-22 Oct-22
Price (Rp)
Month
Beef Price (Rp) Chicken Meat Price (Rp)
Price Fluctuation (Instability) Analysis Analysis of price fluctuations (instability) is an analysis to see the highs and lows of price variations and the highs and lows of price uncertainty. Table 2 shows the index of instability (PII), CV, Divergence (%), Highest Prices, and Lowest Prices of beef and chicken meat commodities in Indonesia.
The analysis found that the percentage difference (divergence) in beef prices was 15.42%, while the difference in chicken meat prices was 25.13%. For the percentage (CV) of the beef commodity, a value of 5.14% was obtained, while the percentage coefficient of variation (CV) for the chicken meat commodity had a value of 6.02%.
Table 2. Beef and Chicken Meat Price Fluctuations (Instability), 2018-2022
Commodity Divergence(%) CV(%) PII Highest Price Lowest Price
Beef 15.42 5.14 1.41 July 2022 February 2018
Chicken meat 25.13 6.02 5.63 July 2018 April 2020
Source: Secondary data processed, 2022.
Then for beef PII value of 1.41, and the PII for chicken meat of 5.63. Of the three data when compared to chicken meat has a higher level of instability compared to beef.
This is supported by the difference (divergence) value of the chicken meat commodity, which is higher than beef, as well as the coefficient of variation (CV), which indicates that the coefficient of variation of chicken meat is greater than beef.
This means that the chicken meat price data distribution is more diverse and volatile than the more homogeneous beef price data.
This is in line with the opinion of Adzanian et al. (2021), where that year, the Covid-19 pandemic became one of the things that impacted fluctuations in broiler meat prices.
In addition to beef, according to Zainuddin, Based on the analysis results, it can be concluded that the coefficient of variation (CV) is directly proportional to price volatility. Prices that vary will cause prices to be less stable. Because of that, some
researchers often use the coefficient of variation (CV) as an analytical tool to measure price volatility in the description in Nidausoleha (2007). In addition, the highest and lowest prices for each commodity are described; where for the beef commodity, the highest price was in July 2022, and the lowest price was in February 2018, while the highest price for chicken meat was in July 2018, and the lowest price was in April 2020.
Beef and Chicken Meat Trend Prices The best trend method is determined in advance in conducting price trend analysis. The following are the stages of trend analysis carried out on beef and chicken meat commodities:
Trend Result Accuracy Measure
Based on the results of the trend measurement accuracy test results (model test) in the linear, exponential, and quadratic methods of beef and chicken meat price data in Indonesia, the following results are obtained.
Table 3. Measures of Model Accuracy in Beef and Chicken Meat Price Data in Indonesia
Accuracy Size
Trend Method
Beef Chicken meat
linear Exponential Quadratic linear Exponential Quadratic
MAD 638.06 575.05 500.15 853.49 846.23 838.45
MSE 1229952.5 1014700.15 813517.11 2473121.6 2456663.35 2255606.79
ME 216.64 200.52 216.28 -49.43 -6.16 -49.21
MAPE 0.55 0.49 0.43 2.50 2.47 2.45
R-square 0.814 0.827 0.925 0.07 0.07 0.13
Source: Secondary data processed, 2022.
From the elaboration of Table 3, it can be concluded that for the beef commodity, the smallest MAD value found in the quadratic method of 500.15, and the smallest MSE value in the quadratic method is 813517.1109.
Then, the ME value closest to zero is the exponential method of 200.52. For the smallest MAPE value in the quadratic method of 0.43. Moreover, the highest R- square value in the quadratic method is 0.925. So it can be concluded that the best method for trend analysis in beef price data is the quadratic method.
Likewise, based on MAD, MSE, and MAPE values for the chicken meat commodity, the smallest value is found in the quadratic method with consecutive values of 838.45, 2255606.79, and 2.45.
Then the ME value closest to zero is the
exponential method of -6.16. Furthermore, the highest R-square value in the quadratic method is 0.13. Consequently, the best method for trend analysis in chicken meat price data is the quadratic method.
Beef and Chicken Meat Prices Trend in Indonesia
In this study, the quadratic method was obtained after obtaining the best model accuracy measurement results for beef and chicken meat commodities. So then, the specified method is used to perform trend analysis. Following are the results of the trend analysis of beef and chicken meat prices in Indonesia:
Beef Price Trend in Indonesia
Figure the shows the trend analysis results of beef prices in Indonesia in the monthly time series from January 2018 to December 2022 in Figure 2.
Source: Secondary data processed, 2022
Figure 2. Beef Price Trend in Indonesia (IDR/Month) The graph of the data pattern for the
development of beef prices in Indonesia based on Figure 2 shows a quadratic pattern with an increasing trend. Where the data on the price of beef in Indonesia in 5 years (60
months) tends to increase and experience significant fluctuations, this is in line with the results of the trend analysis of beef prices in Indonesia which produces a quadratic trend equation, namely:
y = 7,7012x2- 147,96x + 116217 R² = 0,9253
100000 105000 110000 115000 120000 125000 130000 135000 140000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
Price (Rp)
Month Beef Price in Indonesia (Rp/Bulan)
Poly. (Beef Price in Indonesia (Rp/Bulan))
Y = 116217 – 147.96 X + 7.7012 X2 Interpretation :
a. A constant value of 116217 means that if the value of the time variable (X) is assumed to be constant or equal to zero, then the price of beef (Y) is RP116217/month.
b. The value of the X coefficient is 147.96, meaning that the time variable (X) negatively affects the beef price (Y). If the time variable (X) is increased by one unit of time (1 month), then the price of beef (Y) will decrease by IDR147.96/month, assuming that the other independent variables are fixed.
The explanation above shows that the time variable influences the increasing trend of beef prices. This can be seen from the resulting R-square value of 0.9253, which means that the time variable (X) affects the beef price variable (Y) simultaneously by 92.53%, while the remaining 7.47% is influenced by other variables not analyzed in this journal.
In line with research conducted by Wati et al. (2022), which examined food projections in terms of beef commodity prices as a form of anticipation of price increases, where the time variable greatly determines the price of beef. The results of the foresting obtained show that the price of beef in the Bangka Belitung Islands Province has increased from June 2022 to December 2022. Despite the PMK condition, it has not affected the price of beef in the province, so purchasing power is still high, especially in commemoration of the feast day. In the 60 months (5 years) from January 2018 to December 2022, there were many price changes, but it tends to increase each year. It can be seen in Figure 2 that in the first month of January 2018 to early 2020, the prices were initially low and then increased, but in mid-2020, the price range decreased slightly, but the decrease was not too significant. Then increase again in 2021 until 2022. The increase in beef
prices will continue to increase. Even though there have been changes in a few months, these price fluctuations are still in a quadratic pattern line according to the beef price trend.
In line with the information provided by Rahman (2022), which explains that prices of beef increase amidst the trend of inflation, which is getting faster, and outbreaks of foot and mouth disease (FMD).
The National Strategic Food Price Information Center (PIHPS) noted that the average beef price reached IDR134,100 per kilogram (kg). This means that the price of beef has increased by 7.32% from the beginning of the year. In addition, Hasibuan et al. (2022), who researched Covid-19 and the Disparity in Indonesian Beef Prices, showed that the average price of beef in almost every province in Indonesia had increased during the Covid- 19 pandemic.
This can be seen from the comparison of the average daily price of beef in each province in Indonesia before the pandemic (August 1, 2018, to March 2, 2020) and during the Covid-19 pandemic (March 3, 2020, to August 31, 2021).
Chicken Meat Price Trend in Indonesia Following are the results of the analysis of the trend of chicken meat prices in Indonesia in the monthly time series from January 2018 to December 2022 in Figure 3.
Source: Secondary data processed, 2022.
Figure 3. Chicken Meat Price Trend in Indonesia (Rp/Month) The graphical results of data patterns
on the development of chicken meat prices in Indonesia based on Figure 3 show a quadratic pattern with a downward trend.
Data on the price of chicken meat in Indonesia in 5 years (60 months) tends to
decrease and experience significant fluctuations around the quadratic pattern line. This is in line with the results of the trend analysis of chicken meat prices in Indonesia which produces a quadratic trend equation, namely:
Y = 34938 – 75.76 X + 1.7759 X2 Interpretation:
a. A constant value of 34938 means that if the value of the time variable (X) is assumed to be constant or equal to zero, then the price of beef (Y) is IDR34938/month.
b. The value of the X coefficient is 75.76, meaning that the time variable (X) has a negative effect on the price of chicken meat (Y). If the time variable (X) is increased by one unit of time (1 month), then the price of chicken meat (Y) will decrease by IDR75.76/month, assuming that the other independent variables are fixed.
The explanation above shows that the time variable influences the downward trend of chicken meat prices. This can be seen from the resulting R-square value of 0.1263, which means that the time variable (X) has an effect on the chicken meat price variable (Y) simultaneously by 12.63%, while the remaining 87.37% is influenced by other variables not analyzed in this journal. In contrast to the price trend of beef, the price of chicken meat has a downward trend where the time variable only has a small effect on the price trend, so other factors have a large influence on the fluctuation in
the price of the chicken. This is as explained in Indra's research (2022), where the results of the trend of broiler meat prices in Southeast Sulawesi over 5 years from 2017- 2021 experienced developments obtained from the results of linear trend analysis, with fluctuating prices. This is also influenced because reduced stock. The animal feed also decreased so that the price of chicken soared up so that traders raised prices, including at the agent level as well as the price of production inputs such as broiler DOC which is above the reference price, and the price of animal feed in line with the increase
y = 1,7759x2- 75,76x + 34938 R² = 0,1263 0
5000 10000 15000 20000 25000 30000 35000 40000 45000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
Price (Rp)
Month Chicken Meat Price in Indonesia (Rp/Bulan) Poly. (Chicken Meat Price in Indonesia (Rp/Bulan))
in world corn and soybean prices. Therefore, these factors become influences that might cause a downward trend in this study.
There were many price changes in the period of 60 months (5 years) from January 2018 to December 2022. It can be seen in Figure 3 that in the first month of January 2018 to December 2022, the price fluctuated around the quadratic pattern line according to the trend in the price of chicken meat itself. The lowest price was in April 2020.
The price returned to its original state in the following months, fluctuating around the quadratic pattern line. However, price changes that occur tend to decrease. The picture shows that prices have decreased in certain months in the 5 years. This is in line with research conducted by Apriyanti E et al. (2021), where this research shows that the price of animal food in the form of broilers is influenced by the time of demand for these chickens in Palu City. In addition, Ahdiat (2022) explained that based on data from the Market and Basic Needs Monitoring System (SP2KP) of the Ministry of Trade, in September 2022, the average price for purebred chicken nationally was at the level of RP34,800/kg. This price decreased by around 0.28% compared to the previous month (month-on-month/mom).
As in Indonesia, the price trend for chicken meat in the global market has also shown a downward trend in recent months.
CONCLUSIONS
From the description above, it can be concluded that 1) Chicken meat has a higher level of instability (fluctuation) than beef, and 2) Beef and chicken meat price trend shows a quadratic pattern with an increasing trend for beef and a decreasing trend for chicken meat.
The policy implications of this research are expected to provide information about the trend of beef and chicken meat commodity prices and their relationship with fluctuations in these commodity prices, which are expected to be used as material for policymaking in maintaining the stability of beef and chicken meat commodity prices for
people in Indonesia. As well as specifically being able to display a long-term analysis obtained from this journal, the price trend data that has been analyzed can be used as a basis for estimating commodity prices for beef and chicken meat in the following months.
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