Food Price and Inflation Volatilities during Covid-19 Period:
Empirical Study of a Region in Indonesia
CEP JANDI ANWAR, INDRA SUHENDRA*, AYU SRIMULYANI, VADILLA MUTIA
ZAHARA, RAH ADI FAHMI GINANJAR, STANNIA CAHAYA SUCI
Department of Economics and Development Studies,
Faculty of Economics and Business,
University of Sultan Ageng Tirtayasa,
INDONESIA
*Corresponding Author
Abstract: - This study aims to analyse the effect of food price volatility on inflation in 34 provinces in
Indonesia using monthly data from January 2018 to December 2021. The dynamic ordinary least squares
(DOLS), fully modified ordinary least squares (FMOLS), and heterogeneous non-causality approaches were
used. The results showed the presence of a long-term relationship between food prices and inflation
volatilities. Furthermore, it was noted that chili, rice, shallot, and garlic prices had a positive impact on
inflation volatility, but chicken prices had a negative effect. The empirical results also suggested that central
and local governments need to stabilize food prices to minimize inflation fluctuation. When the data were
split before and during the Covid-19 pandemic, the results showed there was a significant difference in the
effect of chili, rice, shallot, and chicken prices volatility on inflation volatility.
Key-Words: Inflation volatility, food Price volatility, Panel Cointegration tests, FMOLS, DOLS
Received: May 9, 2023. Revised: July 29, 2023. Accepted: August 8, 2023. Published: August 11, 2023.
1 Introduction
The Covid-19 pandemic has changed the
production and consumption of foods, as well as
affected prices. Recent contributions to food prices
have emphasized the role of certain
macroeconomic factors, such as monetary, fiscal,
trade, and exchange rate policies in the formulation
of agricultural prices. [1], as well as, [2], examined
the role of exchange rates in determining prices.
[3], and, [4], found a substantial effect of monetary
factors on agricultural prices. Meanwhile, [5],
stated that monetary policy indirectly affects the
agricultural sector by contributing to low and stable
inflation expectations, as well as low-interest rates.
Other empirical studies, such as, [6], as well as, [7],
identified a relationship between expected inflation
and changes in the relative prices of some products.
Food price volatility and the underlying factors
have important macroeconomic implications for
inflation. In addition, food inflation has a
significant impact on welfare, specifically for low-
income earners. When the transmission of food
price shocks is strong, as is the case in several low-
income countries, the impact will be crucial on
inflation and welfare levels. [8], found that food
inflation is generally higher and more persistent
than non-food inflation in several countries. This
result is important to developing countries such as
Indonesia and has serious implications for food
security, as food occupies a large portion of the
consumption basket in the country.
In Indonesia, the government has always been
concerned about price stability. Currently, food
price policies have been implemented in the short
term, but not in the long run. Domestic commodity
prices have continued to rise, which can
consequently trigger inflation. Several factors cause
food price fluctuations in developing countries,
including Indonesia, namely variations in
agricultural output between harvests as a result of
diseases and changing weather conditions, inelastic
demand for agricultural products, the longer it takes
for items to respond to price fluctuations, as well as
the increasing incomes and populations in the
world. Volatility in agricultural commodity prices
has worldwide implications, although the impact is
disproportionately significant in developing
countries. It has a direct impact on programs aimed
at eliminating hunger and malnutrition, increasing
food production, stabilizing consumer prices, and
expanding small-scale agricultural output.
Although it affects the whole society, the effect is
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much stronger on the poor because a higher share
of their income is devoted to food.
Indonesia is the world's fourth most populous
country, behind the United States, China, and India.
The population increases every year with about
270.20 million people recorded in September 2020,
an increase from the previous year which was 270.6
million. Therefore, it is necessary to have food
security in various existing commodities to fulfill
the community's needs. During certain periods,
such as the fasting month (Ramadan), Eid holidays,
Christmas celebrations, and other commemorative
days, there is a scarcity of food commodities that
creates volatile price fluctuations.
[9], stated that the increasing demand for food
commodities is due to an increase in population and
people's income. Consequently, when there is an
increase in population, there will be a concomitant
rise in the demand for food and vice versa. The
scarcity of supply and high public demand also
makes prices fluctuate and has an impact on the
economic condition of a region. This can be seen
from its contribution to inflation. However, food
commodities are of concern because they are
included in the ingredients group, which is a fairly
large contributor to inflation.
From 2018 to 2021, it can be seen that the
highest inflation in the food and beverage
expenditure group occurred in 2020, which was
47%. Each year, the ingredients group always
increases its contribution to general inflation. The
commodities analyzed are stapled foods, such as
rice, chili, onions, and chicken, which are the
benchmarks for availability in Indonesia because
they are strategic, [10]. The prices of these
commodities fluctuate and it is important to ensure
stability. Despite the country’s economic downturn
due to COVID-19, agricultural production has been
less affected. This is evident by the increase in
supply and favourable prospects for production,
hence, the stock of staple foods is expected to reach
the highest level. In general, disruptions occurred in
production, processing, and marketing due to
outbreaks, containment efforts, as well as shifts in
consumer demand. This has led to rising prices and
out-of-stock of food products.
A key question is whether food price volatility
has a significant effect on inflation volatility in 34
provinces. Therefore, efforts to mitigate significant
price fluctuations must be directed at the regional
and national levels. As an alternative, when
inflationary fluctuations in developing countries are
mostly caused by other macroeconomic factors, the
most effective policy solution is likely to be at the
national level concerning fiscal and monetary
policies. The reasons for the topic's popularity are
obvious. Also, in developing countries, the
fluctuation of basic food prices is a significant
source of risk. This is especially true for the
nation's low-income residents. The significant
correlation between food cost volatility and
inflation in the country is due to three variables.
Firstly, variations in the prices tend to be higher
during the Covid-19 period, [11]. Secondly, low-
income communities spend a huge portion of their
expenditures on food, frequently more than 60%.,
therefore, price volatility has a major influence on
purchasing power. Thirdly, most of the population
in several provinces use agriculture as their main
livelihood.
2 Literature Review
Inflation is a condition of continuous price
increases, [12]. Three components state that
inflation has occurred, namely rising prices, which
are general and occur continuously. Commodity
prices are believed to have increased when they are
higher than in the previous period. The general
nature means that rising prices also affect other
goods. Keynes's theory states that inflation occurs
due to people's desire to live beyond the limits of
their economic capacity. Therefore, the demand for
goods is greater than the available quantity. The
reason for this is that individuals learn to know
what they want and develop an effective demand
for commodities. Inflation occurs when the quantity
of demand for an item surpasses the maximum
number of items that may be produced at a specific
price level.
The long-term inflation theory is referred to as
the structuralist type because it observes the driver
of the rising price that originates from the structure
of the economy, specifically the distribution of
food ingredients. The production of goods that is
not proportional to demand also leads to an
increase in prices. Consequently, the price of goods
increases evenly, which means inflation has
occurred. This inflation cannot be halted alone by
shrinking the money supply; it must also be
combated by raising growth and innovation in the
food category.
According to, [13], commodity price is a
leading indicator of inflation. This is because the
prices quickly respond to shocks that occur in the
economy in general. For example, the increase in
demand (aggregate demand shock) and prices
respond to non-economic shocks. This includes
natural disasters, such as landslides, floods, and
others that become distribution channels for
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commodities. In general, it is the monetary worth
of an item or service as determined by the amount
of money paid by a customer to achieve the desired
products or services. Food commodities are
strategic because they function to meet the primary
needs of the community which are also part of
activities to fulfill human rights, [14].
Fluctuations occur in commodity prices
because of a mismatch between supply and demand
for consumer needs. The prices will decrease when
there is excess supply and increase with less
supply. Also, the behavior of farmers and traders
has an important role in shaping prices because
they regulate the number of sales in accordance
with consumer needs. Therefore, fluctuations are
the result of farmers' failure to regulate the amount
of supply needed by consumers, [15]. According to,
[16], the demand for commodities can continuously
increase in tandem with the increase in population,
the standard of living, and people's welfare. On the
supply side, food and agricultural commodities are
vulnerable to being disturbed by climatic and
natural conditions, limitations, and changes in the
function of agricultural land, as well as
international geopolitical conditions. This results in
the supply of agricultural commodities
experiencing disruption. Demand develops quite
high and continues to increase without being
accompanied by balanced supply developments
which will cause prices to rise and seek a new
balance. Hence, Cobweb's theory explains how the
price of agricultural products fluctuates every
season. This occurs as a result of the slow reaction
of producers to prices, [17].
[18], showed that in the long-term rice and
chicken prices had a substantial impact on inflation.
Using the same model, [19], showed that in the
long run, the price of red chili had a major
influence on inflation, while in the short term, the
price of red chili, rice, shallots, and garlic had a
significant effect. [20], still with the same model,
stated that in the long run, the price of red chili and
chicken had a significant effect on inflation, while
in the short term, the price of rice had a significant
effect. Furthermore, [21], stated that the rice price
margin had an important impact on inflation. Using
the same model, [22], showed that chili, rice, and
onion prices had a significant effect. [23], also
stated that the price of rice, shallots, and red chilies
affected inflation.
3 Data and Econometric Methodology
3.1 Data
Panel data estimation was used in this work, which
is a mix of time-series data and cross-section data
with monthly data from 2018M1 to 2021M12 for
34 provinces. The prices of chili, rice, shallots,
garlic, and chicken volatilities are the independent
variables, while the dependent is inflation
volatility. Furthermore, data on food prices were
obtained from the National Strategic Food Price
Information. The consumer price index for
calculation inflation was also acquired from the
Central Statistics.
3.2 Econometric Methodology
This study examined the linkage between food
prices and inflation volatilities. The general model
is as follows:
   (1)
where  is inflation volatility.  is food
price volatility.
3.2.1 Estimating Volatility
The volatility of food prices and inflation were
forecasted using GARCH (1, 1). The prices are
denoted as follows: Price of chili, Price of rice,
Price of shallot, Price of garlic, and Price of
chicken. It is defined as the standard deviation of
the log (Pt /Pt - 1), where Pt is the price in time t
and Pt - 1 is the price in time t - 1. Also, inflation is
the growth rate of a consumer price index. The
current growth rate of price is estimated on the
assumption that the past rate influences the current.
The regression equation was run for the lagged
growth rate of prices for selected foodstuffs from
one to the fourth period.
To measure the food price and inflation
volatilities, the GARCH (1,1) model was applied
because it performs best in modelling both
variables. It is relatively simple to set up and
calibrate because it relies on past observations. The
compact representation of the model is specified as
follows:
 
 (2)
 
 
(3)
3.2.2 Cross-sectional Dependence Test and Unit
Root Tests
The existence of cross-sectional dependence was
first examined as it can exist due to regional
inflation and food price volatility. Furthermore,
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[24], cross-sectional dependence test will be used
to check the dependence among cross-sections in
the model. Moreover, the cross-sectional
augmented IPS (CIPS) test developed by, [25], was
used to check the unit root test in the model.
3.2.3 Panel Cointegration Test
The long-term cointegration relationship between
the variables was estimated using the Pedroni panel
cointegration test. This method was established by
[26], [27].
3.2.4 Fully Modified OLS and Dynamic OLS
Tests
Calculations of the long-run elasticity of output
were made using the FMOLS and DOLS methods
of [28], [29]. The following is a representation of
the equation:
    (4)
where Y and X denote inflation volatility and the
related independent variable vector, respectively,
and i, t, and ε denote individuals, time, and
disturbance.
  

 (5)
where Y, X, i, t, and e correspondingly, the
volatility of inflation, the associated vector of
independent variables, specific cross-section,
period, and the disturbance.
3.2.5 Panel Causality Test
The Granger causality panel consists of details
about timing, heterogeneity, and independence,
[30]. Temporality refers to a variable's  prior
values being able to have an impact on .
According to, [31], when  fails to granger-cause
,  is exogenous of , while independence
means there is no causality between the variables.
The standard method to examine causality is the
Granger non-causality test for heterogeneous panel
data models. [32], approach, which is based on
individual country Wald statistics of Granger non-
causality averaged across cross-section units, was
closely followed.
4 Empirical Results
4.1 Descriptive Statistics
Table 1. Descriptive Statistic
Note: This table provides a descriptive statistic of all
variables considered in this paper.
The descriptive statistic of this article is
presented in Table 1. Specifically, the skewness
and kurtosis of each variable’s data suggested that
the distribution was about normal. All variables had
modest coefficients of skewness and were
positively skewed. As the kurtosis value for each
variable was less than the normal distribution’s
median value of 3, this indicated that the data were
close to being normally distributed. Furthermore,
each variable had a mean-to-median ratio close to
1. Between the highest and the minimum, the range
of variance was reasonable. In comparison to the
mean, the standard deviation was quite low,
indicating a tiny coefficient of variation. The
Jarque-Bera test revealed that the variables were
not normal, even though the preceding descriptive
statistics showed that each variable is normal.
However, this does not appear to be a major issue.
4.2 Cross-Sectional Dependence and Panel
Unit Root Analysis
Previous studies on the relationship between the
volatility of food prices and inflation overlooked
the critical issues of heterogeneity and cross-
sectional reliance. Following the assumptions
provided by, [33], as well as, [34], the data used for
the empirical analysis were assumed to have cross-
sectional dependencies. When the data set involves
cross-sectional dependency issues, the traditional
panel unit root tests are inapplicable.
Variable
Inflation
Volatility
Chili
Price
Volatility
Rice
Price
Volatility
Chicken
Price
Volatility
Mean
0.2779
0.0381
0.0361
0.0278
Median
0.2383
0.0323
0.0323
0.0251
Std Dev
0.0397
0.0264
0.0140
0.0070
Min.
4.17E.08
5.62E.09
2.70E-8
2.87E-09
Max.
0.67328
0.2287
0.2619
0.2622
Skewness
2.9653
1.8294
2.6017
1.8135
Kurtois
27.1116
9.1140
15.5955
8.7561
Jarque-Bera
34197.5
2445.3
12214.7
2229.6
Probability
0.0000
0.0000
0.0000
0.0000
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Table 2. Cross-section Dependence and Panel Unit
Root
To address this issue, [24], dependency panel
unit root test, i.e., a cross-sectional (CD)
dependency test, was used. Table 2 presents the
empirical results and it can be seen that the null
hypothesis of cross-sectional independence at the
1% significance level was strongly rejected.
Additionally, [25], cross-sectional augmented panel
root test was used for cross-sectional dependency
data. The empirical findings in Table 2 indicate that
the panel unit root null hypothesis was not rejected
for all sample variables at that level. We then
convert all variables to their first difference, it was
determined that all variables were stationary. This
result provides evidence of the equation’s
integration of all variables in eq. (1).
4.3 Result of Panel Cointegration
After the integration of the variables, a study was
conducted to see if there is a long-term association
between food price volatility and inflation.
Therefore, this study utilized [26], [27] long-term
cointegration test. The Pedroni residual
cointegration test is presented in Table 3. The
proposed cointegration test contains seven test
statistics, namely “Panel v Statistics, rho Panel, PP
Panel, ADF Panel, rho Group, PP Group, and ADF
Group Statistics working under parametric and
non-parametric frameworks”. Table 4 summarizes
the empirical findings and Table 5 presents the Kao
residual cointegration test.
Table 3. Pedroni Residual Cointegration Test
Table 4. Kao Residual Cointegration Test
Table 5. Johansen Cointegration Test
Out of the seven test statistics, the following
six, namely rho Panel, PP Panel, ADF Panel, rho
Group, PP Group, and ADF Group statistics,
demonstrated the existence of long-term
cointegration between variables. It can be
concluded that the volatilities of price and inflation
had a long-term equilibrium relationship. In
addition, two other panel cointegration approaches
were applied, namely, the Kao panel and the
Johansen Fisher-Type panel cointegration test
developed by, [35], [36]. The findings presented by
this test confirmed the existence of a long-term
equilibrium relationship between food prices and
inflation volatilities.
4.4 Estimation Results of Dynamic Ordinary
Least Square
The long-run elasticity of the output was estimated
using FMOLS and DOLS. Endogeneity and serial
correlation are also taken into account with these
methods. The results are presented in Table 6. This
study confirmed the positive impact of food price
volatility on inflation, except for the chicken price
which had a significant negative effect. Based on
Table 6, the estimation model showed the prices of
red chili, onion, garlic, and chicken had a positive
effect on inflation, while the price of rice had a
negative effect in 34 provinces from January 2018
to December 2021. Based on the coefficient of the
determination test result, an R-squared (R2) value
of 0.9179 and 0.9018 was obtained. This showed
the independent variables, namely red chili, rice,
shallot, garlic, and chicken prices, was able to
explain inflation changes in the provinces.
Variables
Inflation
Volatility
Chili
Price
Volatility
Rice
Price
Volatility
Shallot
Price
Volatility
Garlic
Price
Volatility
Chicken
Price
Volatility
Pesaran CD
2.5486**
10.4488***
10.2492***
13.0346***
27.4152***
7.1698
P-value
0.0108
0.0000
0.0000
0.0000
0.0000
0.0000
Unit root test with cross-sectional dependence
CIPS tests
(level)
-1.1845
-0.2668
-1.6359
-1.3886
-1.1753
-0.3882
CIPS tests
(1stdifference)
-14.0875***
-3.3831***
10.7391***
-6.0022***
-6.5611***
-3.2887***
Alternative hypothesis: common AR coefs. (within-dimension)
Statistic
Prob.
Weighted
Statistic
Prob.
Panel v-Statistic
-3.5356
0.9998
-4.4206
1.0000
Panel rho-Statistic
-1.8101
0.0351
-4.3886
0.0000
Panel PP-Statistic
-8.6043
0.0000
-11.7633
0.0000
Panel ADF-Statistic
-7.5153
0.0000
-4.1413
0.0000
Alternative hypothesis: individual AR coefs. (between-dimension)
Statistic
Prob.
Group rho-Statistic
-3.0141
0.0013
Group PP-Statistic
-20.6841
0.0000
Group ADF-Statistic
-5.3433
0.0000
Alternative hypothesis: common AR coefs. (within-dimension)
Statistic
Prob.
Weighted
Statistic
Prob.
Panel v-Statistic
-3.5356
0.9998
-4.4206
1.0000
Panel rho-Statistic
-1.8101
0.0351
-4.3886
0.0000
Panel PP-Statistic
-8.6043
0.0000
-11.7633
0.0000
Panel ADF-Statistic
-7.5153
0.0000
-4.1413
0.0000
Alternative hypothesis: individual AR coefs. (between-dimension)
Statistic
Prob.
Group rho-Statistic
-3.0141
0.0013
Group PP-Statistic
-20.6841
0.0000
Group ADF-Statistic
-5.3433
0.0000
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Table 6. Result of Estimation Panel Cointegration
Note: The dependent variable is Inflation volatility.
The results showed there was a positive effect
of chili price volatility on inflation. It should be
noted that a 1 percentage point increase in chili
price volatility caused an increase in inflation by
1.45-2.98 points. The results of this study are in
accordance with, [18], [19], as well as, [20], which
stated that chili prices had a significant positive
effect on inflation volatility. Also, there was a
positive effect of rice price volatility in the
provinces. This indicated that an increase of 1
percentage point in rice price volatility caused an
increase in inflation of 0.1079-0.1715 points. This
is in accordance with, [21], [22], who stated that
rice price volatility had a significant positive effect
on inflation. This study also showed that there was
a positive effect of shallot price volatility on
inflation in the provinces. An increase of 1
percentage point in shallot price caused a rise in
inflation volatility of 0.0785-1.3452. These results
are in accordance with [19] ,[20], [22], [23], which
stated that shallot price had a significant positive
effect on inflation volatility. Meanwhile, garlic
price positively influenced inflation volatility,
indicating that an increase of 1 percentage point in
the price caused a rise in inflation volatility of
0.0003 - 0.0342 points. These results are in
accordance with, [37], [38], who stated that garlic
prices had a significant positive effect on inflation.
The magnitude of chili price on inflation
indicated that it is a significant driver of volatility
in all provinces. One of the reasons for the large
effect is the high public demand for chili since
there are no commodities or food ingredients that
can substitute for its needs. Besides daily
consumption, it is used as a raw material in the
food industry. The value of its consumption in
Indonesia is relatively large, indicating that an
increase in the price of curly red chili will cause
variations in inflation. Meanwhile, the magnitude
of rice, shallot, and garlic prices volatility on
inflation is not too large, implying that the small
fluctuation of the four commodity prices does not
have a huge effect on inflation fluctuations. These
results are in line with previous studies, such as,
[39], in Africa, [40], in Eastern Europe, [41], in
Indonesia and Thailand, [42], in Turkey, as well as,
[43], in India which stated that the price of food
positively influenced inflation volatility.
In contrast, the results showed there was a
negative effect of chicken price volatility on
inflation in all provinces. There is also a
discrepancy between the theory that has been
explained and the results obtained which showed
chicken price volatility had a significant negative
effect. This can be seen from the development of
chicken prices, indicating that the diversity tends to
be stable in each region. This occurs because
chicken is a staple food consumed by 96% of
Indonesian. Therefore, the prices tend to be stable
and do not follow economic developments,
specifically inflation. These results are contrary to,
[38], [44] who stated that chicken price volatility
negatively influenced inflation.
The data are split into two groups, namely
before and during Covid-19. The results of panel
FMOLS and DOLS estimations are presented in
Table 7. A significant difference was found
between before and during the Covid-19 pandemic.
First, the chili price volatility had a negative and
significant effect on inflation volatility before the
pandemic but was positive during the period. This
implies that the price was more volatile during the
pandemic. In contrast, rice price was less volatile
during the pandemic compared to before. The
magnitudes of the effects of shallot and garlic price
volatility on inflation were higher during the period
compared to before. Finally, the results showed
chicken price volatility had a positive and
significant effect on inflation before the pandemic
but were negative during the period.
Table 7. Result of Estimation Panel Cointegration
Before and the time of Covid-19
Note: The dependent variable is Inflation volatility.
4.5 Non-Causality Test Result
The, [32], approach was applied to conduct a panel
Granger non-causality test for heterogeneous panel
data models. The causation association between
Variable
FMOLS
DOLS
Coefficient
Std.error
Coefficient
Std.error
Chili Price Volatility
1.4551***
0.0164
2.9876***
0.4350
Rice Price Volatility
0.1079***
0.0136
0.1715***
0.0591
Shallot Price Volatility
0.0785***
0.0151
1.3452*
0.7498
Garlic Price Volatility
0.0342***
0.0174
0.0003***
0.0001
Chicken Price Volatility
-3.8270***
0.0168
-6.7644***
1.5751
R Square (R2)
0.9178
0.9018
Variable
FMOLS
2018-2019
2020-2021
Coefficient
Std.error
Coefficient
Std.error
Chili Price Volatility
-1.3147***
0.1729
2.5766***
0.0029
Rice Price Volatility
0.4671***
0.0849
-0.8276***
0.1432
Shallot Price Volatility
0.1683
0.3090
0.7919***
0.0055
Garlic Price Volatility
0.6035**
0.2222
0.4925***
0.0023
Chicken Price Volatility
0.3685***
0.0308
-0.9684***
0.2567
R Square (R2)
0.8804
0.9408
Variable
DOLS
2018-2019
2020-2021
Coefficient
Std.error
Coefficient
Std.error
Chili Price Volatility
-0.7375***
0.2136
0.5226
0.7347
Rice Price Volatility
1.7490**
0.6896
-1.6022***
0.4233
Shallot Price Volatility
0.9657
1.9205
1.6708
1.0789
Garlic Price Volatility
2.4623***
0.7847
0.5425**
0.2359
Chicken Price Volatility
0.3085***
0.1045
-0.3025***
0.0064
R Square (R2)
0.7811
0.7667
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food prices and inflation volatility was also
established using the group mean Wald test
statistic. The non-causality test has the advantage
of being able to be used in integrated or
cointegrated models without the need for pre-
testing for unit roots or cointegration.
The results from the estimation for the two
null hypotheses are presented in Table 8. The first
hypothesis is that chili, rice, shallot, garlic, and
chicken price volatility does not homogeneously
cause inflation. Meanwhile, the other hypothesis is
that inflation does not homogeneously cause price
volatility. The estimation results showed there
were bidirectional causalities between chili price
and inflation, as well as rice price and inflation.
This suggests that the causality runs from chili and
rice price volatilities to inflation. However, there
were unidirectional causalities between the price
volatilities of shallot, garlic, as well as chicken
prices, and inflation.
Table 8. Dumitrescu Hurlin Panel Causality Tests
Note: *** denotes rejection at a 1% significance level
5 Conclusion and Recommendation
In Indonesia, the government's primary concern has
always been price stability. Currently, Food price
policies have been implemented in the short term,
but in the long run, domestic commodity prices
have continued to rise, which can trigger inflation.
Therefore, this study evaluated the impact of food
price volatility on inflation by using monthly
provincial-level data from 2018 to 2021. A
heterogeneous panel approach was applied and the
long-term operating elasticity between the two
variables was determined. The results found that
chili, rice, shallot, and garlic prices volatility had a
positive effect on inflation in 34 provinces, hence,
when the prices are volatile, inflation will be
increased. This is contrary to the chicken price
which had a negative effect on inflation.
These results provided several
recommendations. Firstly, the development of food
price volatility in chili, shallot, and chicken from
2018 to 2021 in the provinces shows a more
volatile trend. Therefore, efforts are needed to
maintain price stability by ensuring the smooth
distribution and stock management of food.
Secondly, as indicated that the prices of food
commodities are subject to inflation volatility, the
local government needs to make efforts to maintain
price stability. Thirdly, more studies are needed on
the factors that influence food prices, and it is
necessary to consider policies and other related
variables.
References:
[1] Bostan, I., Toderașcu, C., & Firtescu, B. N.
(2018). Exchange rate effects on
international commercial trade
competitiveness. Journal of Risk and
Financial Management, 11(2), 19.
https://doi.org/10.3390/jrfm11020019
[2] Singhal, S., Choudhary, S., & Biswal, P. C.
(2019). Return and volatility linkages
among International crude oil price, gold
price, exchange rate and stock markets:
Evidence from Mexico. Resources
Policy, 60, 255-261.
https://doi.org/10.1016/j.resourpol.2019.01.
004
[3] Anzuini, A., Lombardi, M. J., & Pagano, P.
(2013). The Impact of Monetary Policy
Shocks on Commodity Prices. International
Journal of Central Banking, 9(3), 125-150.
https://www.ijcb.org/journal/ijcb13q3a4.pdf
[4] Ahmed, F., Memon, M., & Ali, R. (2019).
Monetary Policy Shocks to Commodity
Prices: Evidence from Pakistan. Signifikan:
Jurnal Ilmu Ekonomi, 8(1), 37-50.
https://doi.org/10.15408/sjie.v8i1.7012
[5] Apergis, N., & Rezitis, A. (2011). Food
price volatility and macroeconomic factors:
Evidence from GARCH and GARCH-X
estimates. Journal of Agricultural and
Applied Economics, 43(1), 95-110.
https://doi.org/10.1017/S107407080000407
7
Null Hypothesis
Wald
Statistics
𝒁 Statistics
Probability
Values
Chili Price Volatility does not
cause Inflation Volatility
1.8397
2.7356
0.0059***
Inflation Volatility does not
cause Chili Price Volatility
1.7029
2.2596
0.0238**
Rice Price Volatility does not
cause Inflation Volatility
13.6819
3.8887
0.0001***
Inflation Volatility does not
cause Rice Price Volatility
14.5873
4.6026
0.0000***
Shallot Price Volatility does
not cause Inflation Volatility
2.5960
5.5552
0.0000***
Inflation Volatility does not
cause Shallot Price Volatility
1.2427
0.6243
0.5324
Garlic Price Volatility does not
cause Inflation Volatility
3.4597
3.2382
0.0012***
Inflation Volatility does not
cause Garlic Price Volatility
2.2605
0.2506
0.8021
Chicken Price Volatility does
not cause Inflation Volatility
19.3513
5.9343
0.0000***
Inflation Volatility does not
cause Chicken Price Volatility
9.1182
-7.7642
0.4447
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[6] Coibion, O., Gorodnichenko, Y., & Hong,
G. H. (2015). The cyclicality of sales,
regular and effective prices: Business cycle
and policy implications. American
Economic Review, 105(3), 993-1029.
http://dx.doi.org/10.1257/aer.20121546
[7] Kaplan, G., & Schulhofer-Wohl, S. (2017).
Inflation at the household level. Journal of
Monetary Economics, 91, 19-38.
https://doi.org/10.1016/j.jmoneco.2017.08.0
02
[8] Furceri, D., Loungani, P., Simon, J., &
Wachter, S. M. (2016). Global food prices
and domestic inflation: some cross-country
evidence. Oxford Economic Papers, 68(3),
665-687.
https://doi.org/10.1093/oep/gpw016
[9] Fróna, D., Szenderák, J., & Harangi-Rákos,
M. (2019). The challenge of feeding the
world. Sustainability, 11(20), 5816.
https://doi.org/10.3390/su11205816
[10] Caroline, L. (2021). Analysis of Food
Inflation in Java and Sumatra
Islands. Indonesian Journal of Economics,
Social, and Humanities, 3(2), 117-129.
https://doi.org/10.31258/ijesh.3.2.117-129
[11] Janssen, M., Chang, B. P., Hristov, H.,
Pravst, I., Profeta, A., & Millard, J. (2021).
Changes in food consumption during the
COVID-19 pandemic: analysis of consumer
survey data from the first lockdown period
in Denmark, Germany, and
Slovenia. Frontiers in nutrition, 8, 60.
https://doi.org/10.3389/fnut.2021.635859
[12] Galí, J. (2015). Monetary policy, inflation,
and the business cycle: an introduction to
the new Keynesian framework and its
applications. Princeton University Press.
https://philpapers.org/rec/GALMPI
[13] Jiménez-Rodríguez, R., & Morales-
Zumaquero, A. (2022). Commodity price
pass-through along the pricing
chain. Review of World Economics, 158(1),
109-125. https://doi.org/10.1007/s10290-
021-00425-2
[14] Margulis, M. E. (2013). The Regime
Complex for Food Security: Implications for
the Global Hunger Challenge. Global
Governance: A Review of Multilateralism
and International Organizations, 19(1), 53-
67. http://hdl.handle.net/1893/21908
[15] Harvey, D., & Hubbard, C. (2013).
Reconsidering the political economy of farm
animal welfare: An anatomy of market
failure. Food policy, 38, 105-114.
https://doi.org/10.1016/j.foodpol.2012.11.00
6
[16] Deaton, A. (2016). Measuring and
understanding behavior, welfare, and
poverty. American Economic
Review, 106(6), 1221-43.
http://dx.doi.org/10.1257/aer.106.6.1221
[17] Mitra, S., & Boussard, J. M. (2012). A
simple model of endogenous agricultural
commodity price fluctuations with
storage. Agricultural economics, 43(1), 1-
15. https://doi.org/10.1111/j.1574-
0862.2011.00561.x
[18] Sativa, M., Harianto, H., & Suryana, A.
(2017). Impact of red chilli reference price
policy in Indonesia. International Journal of
Agriculture System, 5(2), 120-139.
http://pasca.unhas.ac.id/ojs/index.php/ijas/ar
ticle/view/1201
[19] Irawati, D. J., Wibowo, R. P., & Ayu, S. F.
(2019). The impact of fluctuation of the
price of food commodity on inflation in
North Sumatera Province. In IOP
Conference Series: Earth and
Environmental Science (Vol. 260, No. 1, p.
012016). IOP Publishing.
https://doi.org/10.1088/1755-
1315/260/1/012016
[20] Rachmawati, E., Harianto, H., Syaukat, Y.,
& Novianti, T. (2020). Fluctuation and price
responses retail level of red chili, cayenne
pepper, shallot in five major cities in
Java. International Journal of Progressive
Sciences and Technologies, 21(1),168-175.
http://dx.doi.org/10.52155/ijpsat.v21.1.1868
[21] Braun, J. V., & Tadesse, G. (2012). Food
security, commodity price volatility, and the
poor. In Institutions and Comparative
Economic Development (pp. 298-312).
Palgrave Macmillan, London.
https://doi.org/10.1057/9781137034014_16
[22] Dewianawati, D., & Asyik, N. F. (2021).
The impact of climate on price fluctuations
to the income of leek farmers in Sajen
village, Pacet, Mojokerto. International
Journal of Business Continuity and Risk
Management, 11(2-3), 247-262.
https://www.inderscienceonline.com/doi/pdf
/10.1504/IJBCRM.2021.116283
[23] Artika, K., Firdaus, M., & Irawan, T.
(2019). Volatility and transmission of
shallot commodity prices in
Indonesia. International Journal of
Scientific Research in Science, Engineering
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.161
Cep Jandi Anwar, Indra Suhendra,
Ayu Srimulyani, Vadilla Mutia Zahara,
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1846
Volume 20, 2023
and Technology, 6(5), 50-61.
https://doi.org/10.32628/IJSRSET19657
[24] Pesaran, M. H. (2021). General diagnostic
tests for cross-sectional dependence in
panels. Empirical Economics, 60(1), 13-50.
https://doi.org/10.1007/s00181-020-01875-7
[25] Pesaran, M. H. (2007). A simple panel unit
root test in the presence of cross‐section
dependence. Journal of applied
econometrics, 22(2), 265-312.
https://doi.org/10.1002/jae.951
[26] Pedroni, P. (1999). Critical values for
cointegration tests in heterogeneous panels
with multiple regressors. Oxford Bulletin of
Economics and statistics, 61(S1), 653-670.
[27] Pedroni, P. (2004). Panel cointegration:
asymptotic and finite sample properties of
pooled time series tests with an application
to the PPP hypothesis. Econometric
theory, 20(3), 597-625.
https://doi.org/10.1017/S026646660420307
3
[28] Pedroni, P. (2001). Fully modified OLS for
heterogeneous cointegrated panels.
In Nonstationary panels, panel
cointegration, and dynamic panels. Emerald
Group Publishing Limited.
https://doi.org/10.1016/S0731-
9053(00)15004-2
[29] Pedroni, P. (2001). Purchasing power parity
tests in cointegrated panels. Review of
Economics and statistics, 83(4), 727-731.
https://doi.org/10.1162/0034653017532378
03
[30] Anwar, C. J., & Nicholas, O. (2020).
Causality Relationship between Central
Bank Reforms and Inflation: Evidence from
Developing Countries. Signifikan: Jurnal
Ilmu Ekonomi, 9(1), 15-30.
https://doi.org/10.15408/sjie.v9i1.10955
[31] Sims, C. A. (1972). Money, income, and
causality. The American economic
review, 62(4), 540-552.
https://www.jstor.org/stable/1806097
[32] Dumitrescu, E. I., & Hurlin, C. (2012).
Testing for Granger non-causality in
heterogeneous panels. Economic
modelling, 29(4), 1450-1460.
https://doi.org/10.1016/j.econmod.2012.02.0
14
[33] Banerjee, A., Marcellino, M., & Osbat, C.
(2004). Some cautions on the use of panel
methods for integrated series of
macroeconomic data. The Econometrics
Journal, 7(2), 322-340.
https://doi.org/10.1111/j.1368-
423X.2004.00133.x
[34] Breitung, J., & Pesaran, M. H. (2008). Unit
roots and cointegration in panels. In The
econometrics of panel data (pp. 279-322).
Springer, Berlin, Heidelberg.
https://doi.org/10.1007/978-3-540-75892-
1_9
[35] Kao, C. (1999). Spurious regression and
residual-based tests for cointegration in
panel data. Journal of econometrics, 90(1),
1-44. https://doi.org/10.1016/S0304-
4076(98)00023-2
[36] Johansen, S. (1991). Estimation and
hypothesis testing of cointegration vectors
in Gaussian vector autoregressive
models. Econometrica: journal of the
Econometric Society, 1551-1580.
https://doi.org/10.2307/2938278
[37] Miao, X., Yu, B., Xi, B., & Tang, Y. H.
(2011). Risk and regulation of emerging
price volatility of non-staple agricultural
commodity in China. African Journal of
Agricultural Research, 6(5), 1251-1256.
https://doi.org/10.5897/AJAR09.003
[38] Nurliza, N. (2017). The Nature of Food
Commodity Prices Volatility in Driving
Inflation and Policy. Signifikan: Jurnal Ilmu
Ekonomi, 6(1), 103-124.
http://dx.doi.org/10.15408/sjie.v6i1.4523
[39] Fasanya, I. O., & Adekoya, O. B. (2017).
Modelling inflation rate volatility in Nigeria
with structural breaks. CBN Journal of
Applied Statistics, 8(1),175-193.
https://www.econstor.eu/bitstream/10419/19
1694/1/1020627476.pdf
[40] Saman, C., & Alexandri, C. (2018). The
impact of the world food price index on
some East-European economies. Journal of
Business Economics
andManagement, 19(2),268-287.
https://doi.org/10.3846/jbem.2018.5208
[41] Hossain, A. A., & Raghavan, M. (2020).
Time-Variant Sources of Inflation and
Inflation Volatility, Their Interrelations and
Effects on Macroeconomic Fluctuations:
Evidence from Indonesia and
Thailand. Journal of Business and
Economic Analysis, 3(3), 228-267.
https://doi.org/10.36924/sbe.2020.3302
[42] Ozdurak, C. (2021). Major Determinants of
Food Price Volatility in Turkey: Inflation
Surge Aftermath of 2016. Journal of
Business Economics andFinance, 10(3),103-
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.161
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114.
https://doi.org/10.17261/Pressacademia.202
1.1444
[43] Mishra, A., & Agarwal, A. (2021). Food
commodity price volatility and its nexus
with monetary factor: an empirical analysis
of India. International Journal of
Management Practice, 14(1), 88-106.
https://www.inderscienceonline.com/doi/pdf
/10.1504/IJMP.2021.111748
[44] Sitorus, R. S., & Ayu, S. F. (2020,
February). The influence of food price
fluctuation on inflation in Padang
Sidempuan City, North Sumatera Province.
In IOP Conference Series: Earth and
Environmental Science (Vol. 454, No. 1, p.
012023). IOP Publishing.
https://doi.org/10.1088/1755-
1315/454/1/012023
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