JRSSEM 2022, Vol. 01, No. 8, 1026 – 1040
E-ISSN: 2807 - 6311, P-ISSN: 2807 - 6494
DOI : 10.36418/jrssem.v1i8.89 https://jrssem.publikasiindonesia.id/index.php/jrssem/index
FOOD DEMAND SENSITIVITY DURING THE COVID-19
PANDEMIC IN INDONESIA
Riniati1
Chamelia Putri2*
Agus Lutfhi3
1,2,3Master of Economics, Faculty of Economics and Business, University of Jember
*Correspondence: chameliaputri13@gmail.com
Submitted: 15 February 2022, Revised: 02 March 2022, Accepted: 15 March 2022
Abstract. The COVID-19 pandemic that hit Indonesia caused a decline in agricultural production,
rising food prices, restrictions on export-import activities, and a decrease in food and non-food
consumption. The purpose of this study was to determine the demand for staple food during the
pandemic era, to examine household budget allocations and to determine price elasticity and
income elasticity. This study uses expenditure data for consumption of the Indonesian population
based on the results of the March 2020 Susenas. Data analysis uses the AIDS model. The results of
the analysis show that rice is the main staple food with a share of expenditure of 52% in urban
areas and 57% in rural areas, followed by chicken meat, eggs, cooking oil, sugar and milk,
respectively. Comparison of consumption between before and during the pandemic era shows an
increase in consumption for all commodities other than milk and sugar in urban areas, while in
rural areas consumption decreases for rice, milk and sugar. The share of staple food expenditure is
significantly influenced by prices and income in urban areas, while in rural areas prices and incomes
have no significant effect. Both in urban and rural areas, the highest income elasticity is for chicken
meat and eggs, while rice, cooking oil and sugar are considered inferior goods. The pandemic era
is the right moment for the government to promote local food to accelerate food diversification
programs.
Keywords: AIDS; elasticity of demand; staple food; COVID-19 pandemic.
Riniati, Chamelia Putri, Agus Lutfhi | 1027
DOI : 10.36418/jrssem.v1i8.89 https://jrssem.publikasiindonesia.id/index.php/jrssem/index
INTRODUCTION
The COVID-19 pandemic that has hit
since the end of 2019 has at least caused
Indonesia to experience: 1) A 5% decline in
agricultural production due to an increase
in the price of production facilities and the
uneven distribution of production
products; 2) The emergence of panic
buying encourages the need for food to
increase because people want to stockpile
food which results in an increase in food
prices; 3) Realization of imports decreased
because imports were not smooth and
producing countries limited exports
(Hadiutomo, 2020). In addition, BPS noted
that the pandemic had caused an increase
in the number of poor people, namely the
number of poor people increased from
9.22% in September 2019 to 9.78% in
March 2020. Whereas poverty has a direct
effect on household purchasing power
which can be seen from the share of
household expenditure to buy food. The
poor have a high share of food expenditure
and are below the poverty line
(Purwaningsih, Hardiyati, Zulhamdani,
Laksani, & Rianto, 2021). BPS also noted
that during the pandemic the number of
the labor force that was not absorbed in the
labor market (TPT) was twice as large in
urban areas as in rural areas, namely 8.98%
and 4.17% as a result of the closure of
various workplaces (Béné, 2020). Concludes
that the COVID-19 pandemic has caused a
trade off between health and the economy
(Noorbhai, 2020), which can be seen in a
slump on the aggregate demand side,
paralyzed people's purchasing power due
to loss of income sources and stagnation in
the global food supply chain due to lock
down policies and limited direct access. to
food. Meanwhile, on the other hand, there
is a phenomenon of wasted food because
farmers can no longer access markets and
other places to distribute their products
due to the pandemic (Hughes & Haworth,
2013).
Maslow's law states that food is the
most basic need for humans (Wijayati &
Suryana, 2019). Each region has different
consumption and expenditure patterns for
certain food commodities (Sambodo &
Novandra, 2019). Food is defined as all
ingredients that are eaten in daily life to
meet the needs of maintenance, growth,
work and to replace damaged body tissues
(Bigliardi & Galati, 2013), including during
the pandemic era, people still need food. as
a source of energy and to sustain life. BPS
conducted a comparison of spending on
food between March and September 2020,
the results showed a decline in spending on
food, namely the average monthly per
capita expenditure for food of Rp.
613,025.00 in March 2020 decreased to Rp.
588,773.00 in September 2020.
Furthermore, BPS revealed that the decline
in the average consumption expenditure
was greater for people living in rural areas,
namely 5.77%, while in urban areas there
was a decline of 3.51% (Statistik, 2020).
The food commodities to be studied in
this study are based on two considerations,
namely the type of staple food for the
population in Indonesia and the availability
of data on staple food consumption during
the COVID-19 pandemic. So that the staple
foods to be studied include 6 commodities,
namely rice, sugar, cooking oil,
1028 | Food Demand Sensitivity During the COVID-19 Pandemic in Indonesia
purebred/village chicken meat,
broiler/village chicken eggs and milk. The
problem formulation of this research is 1).
How is the demand for staple food in the
era of the COVID-19 pandemic in
Indonesia?; 2). Is there a difference
between the distribution of household
food expenditure share in rural areas and
urban areas during the Covid19 pandemic
era in Indonesia?; 3). How do changes in
prices and income affect staple food in the
era of the COVID-19 pandemic in
Indonesia?
METHODS
Research Design
This research is a quantitative study
using secondary data, namely expenditure
data for population consumption per
province in Indonesia based on the results
of the Susenas in March 2020 sourced from
BPS. The data used are in the form of food
commodity prices, food quantities and
total expenditures for food commodities
where data analysis activities are
distinguished based on regional origin,
namely urban and rural areas.
Data Analysis
The first problem was answered using a
descriptive method by calculating the
amount of consumption of 6 basic food
commodities during the COVID-19
pandemic era, then compared with the
consumption of 6 staple food commodities
before the COVID-19 pandemic. The
second and third problems are answered
with an econometric approach using the
AIDS (Almost Ideal Demand System) model
with the SUR approach. The AIDS model
used is:
Description:
〖sh〗_b : share of rice type food to
total food expenditure
〖sh〗_da : share of chicken meat to
total food expenditure
〖sh〗_t : hare of food, egg type,
total food expenditure
〖sh〗_s : share of dairy foods to total
food expenditure
〖sh〗_mg : share of cooking oil type of
food to total food expenditure
〖sh〗_gp : share of granulated sugar
to total food expenditure
lnp : natural log of the
estimated price of the type of food
x : total expenditure of food
commodities
Riniati, Chamelia Putri, Agus Lutfhi | 1029
p^* : stone price index lnp=∑▒
〖w_(i ) ln〖p_i 〗 〗
α, ,β : alpha, gamma, beta
(regression parameters)
Furthermore, the estimation of the AIDS
model uses several restrictions, namely
adding up, homogeneity and symmetry in
order to meet the nature of the demand.
After the model is formed, it can be further
analyzed to obtain the elasticity value by
reducing the demand function and then
tested for significance with the one-tailed t
test.
RESULTS AND DISCUSSION
Description of Demand for Staple Food
in the Era of the COVID-19 Pandemic
Humans always have a budget
allocation for food consumption, especially
staple food. This staple food is a source of
energy for daily activities, including during
the COVID-19 pandemic.
Table 1. Consumption of staple foods by area of residence in March 2020
Variab
le
Uni
t
Mean
Sd
Min
Max
Mean
Sd
Min
Max
Urban Area
Rural Area
Quantity purchased
Rice
Kg
6,12
0,84
4,71
8,04
7.20
1.00
4.12
8.97
Chicke
n meat
Kg
0,59
0,24
0,14
1,00
0.43
0.19
0.07
0.77
Egg
Ite
m
9.48
2.13
4.59
12.53
7.75
2.18
.14
10.81
Milk
397
gr
0.29
0.11
0.09
0.59
0.29
0.13
0.07
0.51
Cookin
g oil
Lite
r
0.99
0.54
0.69
1.24
1.01
0.16
0.62
1.31
Sugar
On
s
5.41
1.01
2.71
7.07
6.75
1.65
2.46
10.05
Price per unit
Rice
Kg
10337.
85
1162.3
1
8122.0
0
12356.
00
10459.
48
1579.3
1
8368.0
0
15353.
00
Chicke
n meat
Kg
34378.
62
5527.2
8
25403.
00
49447.
00
37832.
58
7849.9
7
28355.
00
56150.
00
Egg
ite
m
1665.5
0
295.66
1364.0
0
2437.0
0
1823.5
8
442.40
1418.0
0
3370.0
0
Milk
397
gr
10410.
26
928.08
8425.0
0
12564.
00
10900.
39
1207.5
7
9475.0
0
15698.
00
Cookin
g oil
Lite
r
12809.
50
1667.2
4
10611.
00
18375.
00
13238.
36
2528.9
5
10314.
00
24796.
00
1030 | Food Demand Sensitivity During the COVID-19 Pandemic in Indonesia
Sugar
On
s
1458.3
2
131.21
1241.0
0
1741.0
0
1538.3
6
236.80
1299.0
0
2637.0
0
Amount of expenditure
Rice
Kg
62807.
89
8299.4
6
48091.
39
86832.
00
74393.
99
9621.3
4
52205.
30
96696.
60
Chicke
n meat
Kg
19899.
82
7919.0
5
6108.0
6
33600.
17
15545.
23
7157.2
0
3633.9
8
29990.
72
Egg
Ite
m
15458.
71
3088.9
4
8663.2
2
20181.
06
13562.
60
3422.7
0
8028.0
5
21944.
30
Milk
397
gr
3074.8
1
1185.0
7
866.97
5794.9
8
3248.4
7
1549.6
1
721.00
6593.1
6
Cookin
g oil
Lite
r
12712.
13
2385.9
9
8856.9
3
21315.
00
13233.
90
2903.2
4
9280.1
6
24300.
08
Sugar
On
s
7891.4
6
1669.4
6
4062.2
9
10977.
12
10428.
03
3025.6
8
3579.3
0
16112.
07
Expenditure share
Rice
0.52
0.06
0.42
0.65
0.57
0.07
0.41
0.71
Chicke
n meat
0.16
0.05
0.05
0.24
0.12
0.04
0.03
0.19
Egg
0.13
0.02
0.08
0.16
0.10
0.02
0.07
0.14
Milk
0.03
0.01
0.01
0.05
0.02
0.01
0.01
0.04
Cookin
g oil
0.10
0.01
0.07
0.14
0.10
0.02
0.07
0.16
Sugar
0.06
0.01
0.03
0.09
0.08
0.02
0.03
0.11
Table 1. provides information on the
staple food commodity consumed by the
Indonesian population the most, namely
rice. It can be seen from the share of
expenditure on rice that exceeds half of the
total expenditure, which is 52% in urban
areas and 57% in rural areas. Where the
highest consumption of rice is in East Nusa
Tengga Province which reaches 8.75
kg/capita/month while the lowest is in
Papua Province with a large consumption
of 4.67 kg/capita/month. Chicken meat is
the second commodity that gets a large
portion of the budget even though the
amount of consumption is small, namely
0.59 kg/month/capita in urban areas and
0.43 kg/month/capita in rural areas. The
Province of the Bangka Belitung Islands is
the area with the highest consumption of
chicken meat, reaching 0.89
kg/capita/month, while the lowest is in
North Maluku Province, which is 0.09
kg/capita/month. Furthermore, the
commodities with the largest share of
expenditure were eggs, cooking oil,
granulated sugar and milk, for both urban
and rural areas.
Riniati, Chamelia Putri, Agus Lutfhi | 1031
Table 2. Consumption of staple food per capita a month before the pandemic
and during the pandemic era based on regional origin
Staple Food
Commodities
Unit
Urban area
Desc
Rural area
Desc
Before the
pandemic
In the
pandemic
era
Before the
pandemic
In the
pandemic
era
Rice
Kg
5.89
5.91
0.02
7.15
7.14
-0.01
Chicken meat
Kg
0.62
0.65
0.03
0.42
0.44
0.02
Egg
Item
10.17
10.26
0.09
7.89
8.24
0.36
Milk
397
gr
0.33
0.31
-
0.02
0.29
0.29
-
0.002
Cooking oil
Liter
0.98
1.00
0.02
0.98
1.01
0.03
Sugar
Ons
4.82
4.73
-
0.09
6.26
6.19
-0.07
Before the pandemic used Susenas data for March 2019, while in the pandemic era using
Susenas data for March 2020. Source: BPS (2019, 2020).
Based on Table 2., staple food
consumption in urban areas has increased
for rice commodities, namely by 0.02
kg/capita/month, chicken meat
commodities by 0.03 kg/capita/month, egg
commodities by 0.09 eggs/capita/month
and cooking oil commodities by 0.02
liters/capita/month. The highest increase
was for the animal protein commodity
group, namely eggs and chicken meat. This
is in line with the results of research
conducted (Atmadja et al., 2020) which
found that most Indonesians are optimistic
that the COVID-19 pandemic can be
controlled by consuming high protein
foods to increase body immunity.
Meanwhile, two other commodities
experienced a decline, namely milk
commodities by 0.02 397gr/capita/month
and sugar by 0.09 ounces/capita/month.
Furthermore, in rural areas the increase in
consumption occurred in commodities
such as chicken meat, eggs and cooking oil.
Interestingly, both in urban and rural areas,
there was a decrease in consumption for
the same two commodities, namely milk
and sugar, and the highest increase
occurred in the same two commodities,
namely eggs and chicken meat.
AIDS Model Restriction Test Results
Staple Food Demand
Consumers have rationality in
determining the choice of goods and
services to be consumed. This rationality
causes demand to have three important
properties, namely adding-up,
homogeneity and symmetry. The nature of
adding-up means that the consumer's
budget is the same as the consumer's
expenditure because the consumer will
spend all the budget he has. The nature of
homogeneity means that prices and
incomes increase at a constant level, so it
1032 | Food Demand Sensitivity During the COVID-19 Pandemic in Indonesia
has no effect on the amount of goods or
services consumed because the demand
function is homogeneous at the zero level
(Devi, Warasniasih, Masdiantini, &
Musmini, 2020). The nature of symmetry
means that when real income is constant,
the substitution effect of commodity x on
commodity y will be the same as the
substitution effect of commodity y on
commodity x. Restriction tests need to be
carried out so that the model fits the
essential nature of the request.
Table 3. Restriction test results of staple food demand models in urban areas
Based on table 3. the restriction
conditions have been met. This can be
known by adding up the intercept
parameters from each food group, the
result of which is equal to 1, which means
that the adding-up restriction is met.
Furthermore, the sum of the parameter
coefficients fromeach equation is equal to
0 which indicates the homogeneity
restriction is met, namely
lpb=lpda=lpt=lps=lpmg=lpgp. Finally, the
estimated coefficient between the
equations is the same which indicates that
the symmetry restriction has been met. So
it can be concluded that the AIDS model
used to estimate the demand for staple
food in urban areas is feasible to use.
Food
Group
Interce
pt
Price
Lexpd
Lnp
Lpb
lpda
lpt
lps
lpmg
lpgp
shb
3.21367
-
0.36161
0.3611
7
0.0393
8
0.0068
3
-
0.0476
6
0.0019
0
-
0.7082
8
-
0.0789
4
shda
-
3.06180
0.36117
-
0.0510
9
-
0.1203
9
-
0.0760
0
-
0.1101
8
-
0.0035
1
0.7989
8
1.3039
7
sht
-
0.35506
0.03938
-
0.1203
9
0.0245
3
0.0188
8
0.0500
3
-
0.0124
3
0.1490
8
0.2948
6
shs
-
0.09082
0.00683
-
0.0760
0
0.0188
8
0.1515
9
0.0249
5
-
0.1262
6
0.0252
2
0.0870
1
shmg
0.40659
-
0.04766
-
0.1101
8
0.0500
4
0.0249
5
0.0576
8
0.0251
7
-
0.0750
9
-
0.2413
4
shgp
0.88742
0.00190
-
0.0035
1
-
0.0124
3
-
0.1262
6
0.0251
7
0.1151
3
-
0.1964
5
-
0.3655
5
Riniati, Chamelia Putri, Agus Lutfhi | 1033
Table 4. Restriction test results of basic food demand models in rural areas
Table 4. provides information that the
restriction requirements have been met in
the AIDS model for basic food demand in
rural areas. This can be known by the value
of the sum of the intercept parameters
between equations equal to 1. The sum of
the parameter coefficients between
equations is equal to 0 and the estimated
coefficients between equations have the
same value. The restriction test that has
been carried out makes the AIDS model in
accordance with the theory of consumer
demand. So it deserves to be used and
analyzed further.
Determinants of Staple Food Demand in
the COVID-19 Pandemic Era
Estimation of demand for staple food
uses the AIDS model with the SUR
approach. The dependent variable is the
expenditure of each staple food
commodity, while the independent variable
is the relative price of each staple food
commodity which is interconnected with
one another.
Table 5. Determinants of demand for staple food in the era of the COVID-19 pandemic
Variabel
Rice
Chicken
Egg
Milk
Cooking
Sugar
Food
Grou
p
Interce
pt
Price
Lexpd
Lnp
Lpb
lpda
lpt
lps
lpmg
lpgp
shb
2.80948
-
0.0198
8
0.1796
4
0.0862
9
-
0.0925
6
-
0.0889
7
-
0.0645
2
-
0.4255
6
0.0470
8
shda
-
1.47174
0.1796
4
-
0.0904
0
-
0.0576
0
-
0.0701
2
0.0012
8
0.0372
0
0.4291
2
0.8056
3
sht
-
0.23463
0.0862
9
-
0.0576
0
-
0.0621
2
0.0376
5
0.0314
1
-
0.0356
2
0.0538
4
0.0008
6
shs
-
0.41996
-
0.0925
6
-
0.0701
2
0.0376
5
0.1754
4
0.0385
8
-
0.0889
9
0.0849
2
0.0291
2
shmg
0.27407
-
0.0889
7
0.0012
8
0.0314
1
0.0385
8
-
0.0700
7
0.0877
6
-
0.0727
0
-
0.3807
1
shgp
0.04278
-
0.0645
2
0.0372
0
-
0.0356
2
-
0.0889
9
0.0877
6
0.0641
8
-
0.0719
3
-
0.5019
7
1034 | Food Demand Sensitivity During the COVID-19 Pandemic in Indonesia
meat
oil
Urban area
Constant
3.2137***
-3.0618***
-0.3551
-0.09082
0.40659*
0.88742***
P Rice
-0.3616**
0.36117***
0.03938
0.00683
-0.04766
0.00190
P Chicken
meat
0.36117***
-0.05109
-
0.12039**
-0.07600**
-0.11018**
-0.00351
P Egg
0.03938
-0.12039**
0.02453
0.01888
0.05004
-0.01243
P Milk
0.00683
-0.07600**
0.01888
0.15159***
0.02495
-
0.12626***
P Cooking
oil
-0.04766
-0.11018**
0.05004
0.02495
0.05768
0.02517
P Sugar
0.00190
-0.00351
-0.01243
-
0.12626***
0.02517
0.11513
R-Square
0.54155
0.71359
0.30650
0.32159
0.55324
0.49978
Rural area
Constant
2.80948***
-1.47174***
-0.23463
-0.41996**
0.27407
0.042780
P Rice
-0.01988
0.17964
0.08629
-0.09256
-0.08897
-0.06452
P Chicken
meat
0.17964
-0.09040
-0.05760
-0.07012*
0.00128
0.03720
P Egg
0.08629
-0.05760
-0.06212
0.03765
0.03141
-0.03562
P Milk
-0.09256
-0.07012*
0.03765
0.17544***
0.03858
-0.08899
P Cooking
oil
-0.08897
0.00128
0.03141
0.03858
-0.07007
0.08776
P Sugar
-0.06452
0.03720
-0.03562
-0.08899
0.08776
0.06418
R-Square
0.46755
0.57243
0.38244
0.44224
0.66215
0.60807
significant at 1% significance level, **) significant at 5% significance level, *) significant at 10%
significance level.
Based on table 5. the estimation results
of staple food demand show that in urban
areas the R-square value ranges from
0.3065 to 0.7136. This means that the
variation in the proportion of expenditure
(budget share) of the studied food groups
can be explained by the model around 30-
71 percent while the rest is explained by
other factors outside the model. In urban
areas, the share of rice expenditure is
significantly influenced by the variables of
rice prices and chicken meat prices. The
share of chicken meat expenditure is
significantly influenced by the variables of
rice prices, egg prices, milk prices and
cooking oil prices. An interesting thing can
be seen from the share of expenditure for
chicken meat which is influenced by almost
all the variables studied except the price of
chicken meat and sugar.
Based on the R-Square value in rural
areas in table 5, it means that the variation
in the proportion of expenditure from the
food groups studied can be explained by
the model around 38-66 percent while the
rest is explained by other factors outside
the model. The low value of R-Square is
Riniati, Chamelia Putri, Agus Lutfhi | 1035
caused by the use of cross section data,
because this data has a wide range of
observations and a high level of diversity.
So that if the R-Square value is low, it is not
a problem (Wijayati & Suryana, 2019);
(Nasution, Krisnamurthi, & Rachmina,
2020). Details of the large variation of
independent variables from share of
expenditure in rural areas are 46.75 percent
for rice commodities, 57.24 percent for
chicken meat commodities, 38.24 percent
for eggs, 44.22 percent for dairy
commodities, 66.22 percent for cooking oil
commodities and 60.81 percent for sugar
commodities. Based on the coefficient
value of the model estimation results in
rural areas, almost all variables do not show
an influence in determining the share of
staple food expenditures during the
COVID-19 pandemic. This can happen
based on the findings of research
conducted (Lestari, Hartati, & Nopianti,
2016) that rural communities have their
own way to meet their basic needs
including involving all family members in
cultivating agricultural land and to get
additional income, diversifying agricultural
land to meet their needs. daily, the use of
the yard to meet the needs of vegetables
and fruit, saving on family expenses made
by the wife.
Self Price Elasticity, Cross Price and
Income Elasticity
Knowing the elasticity value of a
commodity provides information about
how big the response to changes in
consumption of the commodity is if there is
a change in price or change in income.
Table 6. Self-price elasticity, cross-price elasticity and income elasticity of staple food in
urban areas
Commodit
y
Own
price
elasticit
y
Cross price elasticity
Income
elasticit
y
Rice
Chicke
n meat
Egg
Milk
Cookin
g oil
Sugar
Rice
-0.3616
0.3612
0.039
4
0.006
8
-0.0477
0.001
9
-0.7083
Chicken
meat
-0.0511
0.361
2
-
0.120
4
-
0.075
9
-0.1102
-
0.003
5
0.7989
Egg
0.0245
0.039
4
-0.1204
0.018
8
0.0500
-
0.012
4
0.1491
Milk
0.1516
0.006
8
-0.0759
0.018
8
0.0249
-
0.126
3
0.0252
Cooking oil
0.0577
-
0.047
7
-0.1102
0.050
0
0.024
9
0.025
2
-0.0751
1036 | Food Demand Sensitivity During the COVID-19 Pandemic in Indonesia
Sugar
0.1151
0.001
9
-0.0035
-
0.012
4
-
0.126
3
0.0252
-0.1965
Based on table 4.6 in urban areas, the
elasticity value for rice is -0.3616, this value
can be classified as an inelastic commodity.
This means that if there is an increase in the
price of rice by one percent, it will cause a
decrease in demand by 0.3616 percent,
cateris paribus. Furthermore, if there is an
increase in the price of chicken meat by one
percent, it causes a decrease in demand by
0.0511 percent, cateris paribus. When the
egg commodity price increases by one
percent, it causes an increase in the number
of requests by 0.0245, where the prices of
other food commodities are constant.
Meanwhile, for dairy commodities, demand
increased by 0.1516 percent when there
was a one percent increase in price.
Likewise, for cooking oil and granulated
sugar, there was an increase in demand by
0.0577 percent and 0.1151 percent,
respectively, when there was a one percent
price increase, cateris paribus. We can
conclude that from the six staple food
commodities, all of them have their own
elasticity value of less than one, which
means they are inelastic. The interesting
thing is that there is an increase in demand
for eggs, milk, cooking oil and granulated
sugar when there is an increase in prices.
This can be caused by the COVID-19
pandemic, which causes the community's
food supply pattern to tend to be healthy
and balanced (Lestari et al., 2016).
Based on table 4.6 the value of income
elasticity for commodities of rice, cooking
oil and sugar is negative, which means that
the three commodities are classified as
inferior goods, namely goods that are less
desirable when there is an increase in
income. The income elasticity of rice is the
lowest compared to other commodities.
This strengthens the notion that the
number of high-income groups in
Indonesia is increasing. Where the findings
of research conducted (Kusnadi &
Tinaprilla, 2011) the elasticity of rice income
based on Susenas data from 1970 to 2003
in Indonesia shows the opposite result. The
high elasticity of rice income indicates that
rice consumption has not been fulfilled so
that if there is an increase in income, most
of it is used to increase rice consumption.
Besides rice, cooking oil also
experienced a decline in demand. If it is
related to the condition of the COVID-19
pandemic, this can happen because people
tend to change their consumption patterns
towards healthy food. This is appropriate if
it is associated with the value of income
elasticity for the largest chicken meat
commodity, namely an increase in demand
for chicken meat by 0.7989 percent when
there is an increase in income of one
percent. Likewise for egg commodities,
there is an increase in demand by 0.1491
percent if there is an increase in income.
Based on the value of income elasticity,
chicken meat, eggs and milk are included in
the category of necessities for urban areas
during the COVID-19 pandemic era.
Furthermore, rice has a substitution
relationship with the other five
Riniati, Chamelia Putri, Agus Lutfhi | 1037
commodities, namely chicken meat, eggs,
milk and sugar. Meanwhile, chicken meat
has a complementary relationship to all
commodities other than rice. The
interesting thing is that the sugar
commodity has a complementary
relationship to all the commodities studied
other than the rice commodity.
Table 7. Self-price elasticity, cross-price elasticity and income elasticity of staple food in rural
areas
Commodit
y
Own
price
elasticit
y
Cross price elasticity
Income
elasticit
y
Rice
Chicke
n meat
Egg
Milk
Cookin
g oil
Sugar
Rice
-0.0199
0.1796
0.086
3
-
0.092
6
-0.0889
-
0.064
5
-0.4256
Chicken
meat
-0.0904
0.179
6
-
0.057
6
-
0.070
1
0.0013
0.037
2
0.4291
Egg
-0.0621
0.086
3
-0.0576
0.037
6
0.0314
-
0.035
6
0.0538
Milk
0.1754
-
0.092
6
-0.0701
0.037
6
0.0386
-
0.088
9
0.0849
Cooking oil
-0.0701
-
0.088
9
0.0013
0.031
4
0.038
6
0.087
8
-0.0727
Sugar
0.0642
-
0.064
5
0.0372
-
0.035
6
-
0.088
9
0.0878
-0.0719
Table 7. provides information on the
value of price elasticity itself for almost all
commodities with a negative sign, meaning
that when a price change of one percent
causes a decrease in demand for that
commodity. This is consistent with the law
of demand. The commodity of rice
experienced a decrease in demand by
0.0199 percent when the price increased by
one percent, cateris paribus. Likewise for
eggs, there was a decrease in demand by
0.0621 percent when there was a one
percent increase in price. The biggest
decline occurred in chicken meat, which
was 0.0904 percent when there was a one
percent price increase, cateris paribus. An
interesting thing happened to milk and
sugar commodities, where there was an
increase in demand despite an increase in
prices. The COVID-19 pandemic has caused
rural communities to prioritize a complete
and balanced diet. It can be concluded that
1038 | Food Demand Sensitivity During the COVID-19 Pandemic in Indonesia
the six commodities have an elasticity value
of less than one so that the six commodities
are inelastic.
Based on table 7. the value of income
elasticity for chicken meat, eggs and milk
commodities in rural areas is between zero
to one, which means these commodities
are included in the category of necessities.
The interesting thing is that the income
elasticity for commodities of rice, cooking
oil and sugar is less than zero, meaning that
they are classified as inferior goods. The
value of cross price elasticity in rural areas
shows that rice commodities have a
complementary relationship with milk,
cooking oil and sugar, while chicken meat
and eggs have a substitution relationship.
Policy Implication
During the COVID-19 pandemic, the
income elasticity of rice commodities was
negative both in urban and rural areas,
although the share of expenditure
remained the largest. This indicates that
rice is still the main food but people are
starting to substitute it with other
commodities. This fact is the right moment
for the government to promote various
types of local food in terms of nutrition and
nutrition, because diverse foods are
needed to get balanced nutrition so that
the body's immunity is maintained during
the pandemic. So that efforts to accelerate
food diversification carried out by the
government so far can be realized
immediately.
The interesting thing was that both in
urban and rural areas during the pandemic,
chicken meat had the highest income
elasticity value. When there is an increase in
income in the community, the demand for
chicken meat increases because the need
for animal protein sources increases as an
effort to maintain body stamina during the
pandemic. This fact becomes important for
the government to prepare the poultry
industry to face difficult conditions like this.
Based on data from the Basic Needs Market
Monitoring System (Zaks & Kucharik, 2011)
there was a fairly high fluctuation in the
price of chicken meat in the domestic
market during the period September 2019
to September 2020, namely the average
coefficient of diversity reached 8.99%,
which means it is necessary to evaluate
policies for this industry. , so it can be better
at ensuring price stability during the
pandemic.
CONCLUSIONS
Based on the quantitative analysis that
has been carried out, it can be concluded
that: 1) Comparison of staple food
consumption data between March 2019
and March 2020 shows an increase in
consumption for the six commodities
studied, except for milk and sugar, which
experienced a decline in urban areas.
Meanwhile, in rural areas, there are three
commodities that experienced a decline in
consumption, namely rice, milk and sugar.
During the pandemic, the largest share of
expenditure for rice commodities, both in
urban and rural areas, was 52% and 57%,
followed by chicken meat, eggs, cooking
oil, granulated sugar and milk, respectively.
Riniati, Chamelia Putri, Agus Lutfhi | 1039
2) Restriction test shows that the AIDS
model used fulfills the nature of the
demand for adding-up, homogeneity and
symmetry so that it deserves further
analysis. The R-square value shows the
variation of the independent variables of
share of expenditure in urban areas 54.16%
for rice, 71.36% for chicken meat, 30.65%
for eggs, 32.16% for milk, 55.32% for
cooking oil and 49.97% for sugar. The share
of rice is significantly influenced by the
price of rice and the price of chicken meat,
the share of chicken meat is significantly
influenced by almost all other commodity
prices besides the price of sugar. The R-
square value in rural areas shows that the
variation of the independent variables from
the share of rice expenditure is 46.75%,
chicken meat is 57.24%, eggs is 38.24%,
milk is 44.22%, cooking oil is 66.26% and
sugar is 60.81%. 3) The price elasticity of
both urban and rural areas during the
pandemic indicates that the six
commodities studied are inelastic. Income
elasticity shows that rice, cooking oil and
sugar are inferior goods, while chicken
meat and eggs have the highest income
elasticity values. This can be due to the fact
that the pandemic has encouraged healthy
food consumption patterns and contains
lots of animal protein to maintain the
body's immunity.
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