Heliyon 9 (2023) e19518
Contents lists available at ScienceDirect
Heliyon
journal homepage: www.cell.com/heliyon
Households food consumption pattern in Pakistan: Evidence from
recent household integrated economic survey☆
Naveed Hayat a, Ghulam Mustafa a, *, Bader Alhafi Alotaibi b, **, Roshan K. Nayak c,
Muhammad Naeem a
a
Department of Economics, Division of Management and Administrative Science, University of Education, Lahore, 54000, Pakistan
Department of Agricultural Extension and Rural Society, College of Food and Agriculture Sciences, King Saud University, Riyadh, 11451, Saudi
Arabia
c
Division of Agricultural and Natural Resources, University of California, 2801 2nd Street, Davis, CA, 95616, USA
b
A R T I C L E I N F O
A B S T R A C T
JEL classification:
D12
The analysis of food consumption pattern is a primary concern of any developing country because
it is related with food security. Pakistan is one of the emerging nations of the developing world.
Due to the similarities and differences in households’ food consumption behavior, income
distribution, the effects of alternative tax structures, cost-benefit analyses, and the choice of cost
of living index, the study of households’ food consumption pattern is crucial for a developing
nation like Pakistan. Furthermore, for Pakistan’s food security in the present and the future, an
analysis of food consumption pattern is crucial. The main objective of this study is to analyze the
households’ food consumption pattern. Linear Approximation Almost Ideal Demand System (LA/
AIDS) is applied using data from Household Integrated Economic Survey (HIES) for the year
2018–19. This study makes a significant contribution by estimating household age composition
elasticities, which were absent from earlier studies. Results from the income elasticities reveal
that milk, meat, and fruits are luxuries food items. Similarly, on the basis of inelastic income
elasticities we declared cereals, pulses, vegetables, sugar, and ghee as necessity food items. Re­
sults from the compensated own price elasticities show that the eight food commodity groups
have inelastic own-price elasticities. This implies that these food commodities are integral food
items of household diet. Results from the compensated cross-price elasticities shows that cereals
and pulses, cereals and vegetables, pulses and vegetables, milk and fruits, meat and fruits, and
milk and ghee are gross substitutes. On the other hand, pulses and meat, pulses and fruits, and
ghee and meat are gross complements. According to the findings of the household age compo­
sition elasticities, adding children to a household significantly increases its sugar consumption
while significantly reducing its fruit consumption. Any increase in the size of the household by an
adolescent, adult, or a person in their middle age results in a significant increase in the con­
sumption of cereals and a significant drop in the consumption of fruits. Finally, any increase in the
size of the households brought about by an elder resulted in a significant rise in the consumption
of cereals and a significant drop in the consumption of vegetables.
Keywords:
Households
Food consumption
Food demand
LA/AIDS
HIES 2018–19
Elasticities
☆
The authors extend their appreciation to The Researchers Supporting Project number (RSP2023R443) King Saud University, Riyadh, Saudi
Arabia.
* Corresponding author.
** Corresponding author.
E-mail addresses: ghulam.mustafa@ue.edu.pk (G. Mustafa), balhafi@ksu.edu.sa (B.A. Alotaibi).
https://doi.org/10.1016/j.heliyon.2023.e19518
Received 25 December 2022; Received in revised form 21 July 2023; Accepted 24 August 2023
Available online 25 August 2023
2405-8440/© 2023 Published by Elsevier Ltd.
This is an open access article
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
under
the
CC
BY-NC-ND
license
Heliyon 9 (2023) e19518
N. Hayat et al.
1. Introduction
The analysis of households’ food consumption pattern is the most popular research topic in microeconomics [1]. The food con­
sumption varies across persons, households, incomes, preferences, intellectual traditions and local prices [2]. Consumption theory has
been generally used to find out the households’ food consumption pattern, because a major portion of the households’ nominal income
goes to food consumption. There is hardly an angle of economic literature or research that does not contain information on household
food consumption pattern. Literature on food consumption patterns assist to grant superior understanding of how the demand for food
reacts to alterations in food prices as well as variations in household income. This information is necessary for estimating the welfare
outcomes of various sorts of economic shocks on the well-being of household at various income levels [3,4].
The analysis of food consumption pattern is a primary concern of any developing country because it is related with food security.
Food is one of the most important basic necessity for the survival of mankind because it provides energy, stimulate body growth and
maintain a healthy life [5]. Proper nourishment of a good quantity and quality is very important for sustaining a healthy life. Un­
dernourishment causes deprived body growth and deprived health, which further causing poor productivity capacity in terms of work
at an individual level, and consequently, lower GDP at national level. For that reason, the accessibility and availability of proper food
items at reasonable prices and adequate consumer purchasing power are essential for guaranteeing food security [6]. Currently, the
households’ consumption pattern is changing in both developed and developing countries due changes in economic, demographic, and
epidemiological factors [7]. Pakistan is one of the emerging nations of the developing world. There are two main sources of food in the
country namely, animal based foods and crop based foods. Cereals, pulses, fruits, sugar, and oil seeds etc. Are the most common crop
based foods in the country whereas meat, milk, chicken meat, clarified butter, egg and fish etc. Are the common examples of animal
based foods in the country. Since independence of the country from British rule in 1947, policymakers of the new country developed
policies for getting self-sufficiency in crop based foods. Consequently, the history witnessed a boom in the country’s agriculture
productivities in 1960’s and the research named it “Green Revolution in Pakistan”. This Green Revaluation in the country reduced the
nation’s dependence on imported foods. Though, the country, did not maintain the boom in crop based foods and agriculture pro­
ductivities could not exceed a certain threshold. Thus, growth rate of agriculture productivities lowering growth rates in population.
Due to such a huge jump in population growth and a gradual decline in the agriculture growth, the country faces shortage of several
essential food items and started to import that food items from abroad. Overtime the country faced huge fluctuations in the production
of various food items and hence turned the country food deficient [8].
The food security situation in Pakistan is also affected from a continuous food price inflation, climate change, and regular natural
disasters in the country. According to Pakistan National Nutritional Survey (NNS) 2018, 39.6% households in the country are food
insecure. The province Baluchistan and Sindh experiences severe food insecurity where 50% and 47% of the households are food
insecure. On the other hand, Khyber Pakhtunkhwa and Punjab experiences moderate food insecurity where 29% and 32% of
households are food insecure [9]. Furthermore, political instability, militancy, and energy crises condense the capacity of the agri­
culture, industrial, and services sectors to provide employment to growing population of the country. This further reduces the incomes
of the households, and reduced the purchasing power of the households and leads to put more households within the poverty line. The
rapid rural-urban migration in the country is another factor that affect the household food consumption pattern in the country.
Like many developing countries, Pakistan also faces the situation of an increase in poverty, hunger and food insecurity. There is
strong evidence that an increase in the price of food items and a decrease in real wages greatly impact poverty and food security
through the channel of consumption pattern in the country. One of the main elements of household food consumption is the demand
elasticities that provide valuable information for policymakers to understand the pattern of growth in food consumption. Demand
elasticities are affected by both the level of income attained and the quantities of food consumed. Correct estimation of demand
elasticities is essential in getting price and expenditure elasticities. These elasticities also act as prerequisite for the design of various
policies; for example, policy design for indirect taxation and subsidies requires [10]. For a developing country like Pakistan, the study
of households’ food consumption pattern is very important due to similarities and dissimilarities in households’ food consumption
behavior, distribution of income, the impact of the alternative tax structure, the cost-benefit analysis and the choice of cost of living
index. Moreover, the analysis of food consumption pattern is also very important for Pakistan’s food security at the present and in the
future. Consequently, the major purpose of the present study is to examine the food consumption pattern of households in Pakistan by
employing LA/AIDS model. This study’s main contribution is that it fills a gap in earlier research by estimating household age
composition elasticities (children, adolescents, adults, middle aged, and elders’). This paper will assist policymakers in formulating
proper food and nutrition policies for the country. More specifically the food demand elasticities provide by this study may be used to
setting administered prices and in designing subsidy and tax policies for various food commodity groups.
2. Literature review
In Sub-Saharan African countries, numerous studies have been conducted recently by various researchers on the issue of food
consumption pattern, For an instant, Ansah et al. [11] examined the food demand characteristics of three categories of consumers in
Ghana based on fourteen selected food commodity groups. The study used Ghana Living Standard Survey for the year 2012–2013 and
applied the Quadratic Al-most Ideal Demand System (QUAIDS). The study found that fish and cereal products take close to half of the
food budget of the average Ghanaian household. The study also found that female-headed households spent a higher proportion on
food budget than their male counterparts. In the same year, Onyeneke et al. [12] used same model and analyzed the demand for
imported rice, local rice, maize, and other cereals for Nigeria. The study used data of the Nigerian Living Standard Measurements
2
Heliyon 9 (2023) e19518
N. Hayat et al.
Survey. Results of the study indicated that the imported and local rice are proved to be normal goods. However, imported rice is a
luxury item while local rice is a necessity food item. The estimates of uncompensated cross-price elasticity showed that imported rice
and local rice are complements in Nigeria. Ojogho and Imade [13] also used same model but just focused on Edo State of Nigeria. The
study estimated a complete food demand system and estimated the price and income elasticities using micro-data obtained from 252
households. Results of the study showed no strong complementarity and substitutability relationship among majority of the food
commodities in the State. However, potatoes and meat were luxury food commodities in Edo state. In the next year, Ojogho and Ojo
[14] used same model and presented households food consumption pattern in south-eastern Nigeria using micro-data from 790
households. The study found that most of the food commodities like chicken, rice, yam, tomato, and pepper are luxury food com­
modities. Salman et al. [15] employed same model for same country and investigated food consumption pattern using National Living
Standard Survey (HNLSS) data for year 2009. Results of the study showed that staples and animal protein were normal goods while
fats, fruits, and vegetables were luxury goods. Staples and animal proteins were demand inelastic while fats, fruits, and vegetables are
demand elastic. Cross price elasticity results suggested substitutability amongst most of the food groups in the country.
Various researchers extended the issue of food consumption pattern to Indonesia. For an instant, Mulyana and Yamin [16] analyzed
the food consumption patterns of agricultural households using data for the Indonesian National Socioeconomic Survey for 2013 by
employing QUAIDS model. Results of the study showed that the expenditure elasticities of agricultural and nonagricultural households
are significantly different only in milk, meat, fruits, rice, and other foods items. In the next year, Kharisma wt al [17]. used same model
and determined the effect of demographic and socio-economic factors on household animal-sourced food in West Java Province. The
study used cross-section data sourced from the National Socio-Economic Survey of West Java Province in 2017. Results of the study
showed that the demand for household animal-sourced food in West Java was influenced by price, income, and socio-demographic
factors. All groups of animal-sourced food were categorized as normal goods. The nature of the commodity determined that all
animal-sourced food groups except eggs are luxury goods. The own-price elasticity also showed meat as the most responsive com­
modity to price increases compared to fish, poultry milk, and eggs. The cross-price elasticity of most animal-sourced food commodity
groups achieved negative elasticity values, which indicated that the related animal-sourced food commodity groups were comple­
mentary in nature. Khoiriyah et al. [18] employed same model and analyzed the impact of the price change, the income, and the
household size on the demand for five commodity groups, i.e. eggs, chicken, beef, fish and powder milk. The study utilized data from
Indonesian National Socio-Economic Survey 2016. Results of the study showed that all of the price elasticities was negative and the
income elasticities was positive. The effect of income caused the demand for animal foods in Indonesia more elastic rather than other
commodities with the highest demand elasticity i.e. beef, powder milk, fish, meat, and eggs.
Some researchers extended the issue of food consumption pattern to South Asian and other Asian countries. For an instant, Vu [19]
analyzed food consumption patterns of Vietnamese households by using Almost Ideal Demand System (AIDS) model on the Vietnamese
household survey data in 2006. Results of the study indicated that food consumption patterns in Vietnam are affected by income, price,
as well as socio-economic and geographic factors. All food commodities have positive expenditure elasticities and negative own-price
elasticities. Using the estimated elasticities, the study found that when rice prices increase by 20% points, average household welfare
rises by 1.3% points. Two year later, Altayeb and Daoud [20] used the same model and analyzed food demand for Jordan. The
estimated expenditure elasticities indicated that the demand for food is inelastic whereas the demand for all the other commodity
groups is elastic. The uncompensated own-price elasticities indicated that demand for food items is elastic. In same year, Dilanka and
Wijetunga [21] investigated the effects of food price and income variation on households’ food consumption patterns in the Uva
province Sri Lanka using Household Income and Expenditure Survey 2016. The study applied the Linear Approximation of the Almost
Ideal Demand System (LA/AIDS) model. Results of the study showed that all food items are normal goods because all food items have
positive expenditure elasticity. Expenditure elasticity for rice, milk powder, and coconuts were greater than one implies these food
items were luxury goods. The own-price elasticities were negative for all food items and less than one in absolute value indicating price
inelastic demand in these goods. Besides, almost all cross-price elasticities showed negative values imply that they are complementary
goods.
In Pakistan, numerous studies have been conducted recently by various researchers on the issue of food consumption pattern. For
an instant, Haider and Zaidi [22] examined the changes in household consumption patterns in Pakistan for eleven food commodity
groups. The study used Household Integrated Economic Survey (HIES) with seven consecutive rounds spanning over the period
2000–01 to 2013–14. Results from Quadratic Almost Ideal Demand System (QUAIDS) support the hypothesis that food consumption
patterns are not only different across regions but are also different among provinces. In the next year, Ullah et al. [23] used Linear
version of Almost Ideal Demand System (LA/AIDS) and examined the food consumption decisions of households in Pakistan. The study
used comparatively old data of HIES for the year 2011–2012. The study found that the uncompensated own price elasticity of demand
for milk, meat, fruits, rice, other cereals and backed products are more elastic to food expenditures and can be categorized as luxury
commodities. Therefore, imposition of any income tax on household personnel income could reduce consumption of these food
commodities. After a one year break, Akram [24] analyzed the household’s food demand in Pakistan by estimating the QUAIDS on
HIES for the year 2015–16. The study found that hoteling, fruits & dry fruits, soft drinks & bottled water, bakery products, beef &
mutton, chicken & seafood are luxury food items whereas eggs, sugar & sweets, pulses, tea & coffee, vegetables, rice, and edible oil &
ghee are necessity food items whereas the spices, milk & yogurt and wheat are sticky food items. Furthermore, the demand for beef &
mutton, seafood, eggs, and soft drinks is relatively elastic as compared to other food commodity groups. In the next year, Hameed and
Salam [25] used the same model and same data set and found that demand for most of the food commodity groups except for fruit,
meat, sugar and other products is income inelastic. Similarly, cross price elasticities from the study showed that fruit, milk and meat
3
Heliyon 9 (2023) e19518
N. Hayat et al.
are complementary products as compared to wheat, pulses, and vegetables. In the same year, Hina et al. [10] investigated the demand
elasticities and household consumption behavior at the provincial and national level. The study also projected the food demand for the
country from 2015 to 2040. The study applied AIDS model on the HIES data for the year 2015–2016 for demand analysis. Results of the
study showed that socioeconomic factors such as household size, profession and literacy of head play a significant role in determining
the pattern of food demand along with prices and income. The results from per capita household projected demand from 2016 to 2040
showed that Khyber Pakhtunkhwa will exceed Punjab province in terms of total per capita consumption of all food items with the
passage of time.
From the review of the above studies we concluded that the previous studies in South Asian and other Asian countries on
households’ food consumption pattern used AIDS model and Linear approximation of AIDS model whereas studies in Sub-Saharan
African countries and Indonesia used Quadratic version of AIDS model. Furthermore, these studies used old data sets for various
years like for 2005–2006, 2008–2009, 2012–13, 2015–2016, and 2016–17. Similarly, from the review of the above studies we
concluded that the previous studies in Pakistan on households’ food consumption pattern used old HIES data sets for various years like
for 2011–12, 2013–14, and 2015–16. However, the current study used most recent information from HIES for the year 2018–19.
Similarly, most of the previous studies did not estimate household age composition elasticities. Although some studies included
specific age composition variables like children, adolescents, and adults. However, they ignored the middle aged and elders’ family
members in the households. Therefore, in the current study along with children, adolescents, and adults, we also include middle aged,
and elders’ categories. Furthermore, in terms of food and nutrition security, this study estimates the food consumption pattern in
Pakistan by employing the Linear Approximation Almost Ideal Demand System (LA/AIDS) in order to understand the behavior of
household consumption at national levels and to enable policymakers to put in place better food security policies.
3. Data and its sources
In Pakistan, Household Integrated Economic Survey (HIES) is widely used to examine the household consumption pattern across
the country. HIES data provide information on the expenditure, income, and living conditions of households. For this study, we took
data from Household Integrated Economic Survey (HIES) for the year 2018–19.1 This dataset is collected by Pakistan Bureau of
Statistics (PBS). This data set is cross-sectional in nature and provides information regarding the quantities consumed and expenses
made by households on different food commodity groups such that, cereals (wheat, wheat flour, rice, rice flour, maize, barley, jawar,
millet, maida, suji, besan, and vermicelli), pulses (gram, pulse gram, pulse mash, pulse masoor, pulse moong, beans, and other pulses),
milk (fresh milk tetra packed milk, lassi, and yogurt), ghee (vegetable ghee, cooking oil, desi ghee, mustard oil, and other fats and oils),
meat (beef, mutton, chicken meat, fish, and other poultry), fruits (apple, peach, grapes, mango, dates, water melon, husk melon, musk
melon, plum, apricot, guava, and other fresh fruits), vegetables (potato, tomato, onion, cabbage, cauliflower, bitter gourd, lady finger,
brinjal, cucumber, tinda, pumpkin, bottle gourd, turnip, radish, carrot, peas, moongra, turai, arvi, green papper, and other vegetables),
and sugar (sugar, gur, and, shaker). Besides, this dataset also provides information on households’ age composition like children,
adolescents, adults, middle aged, and elders. The sample size of the data is 24,809 households. However, for some households, the
quantities of various commodities they consumed and expenditures the made on these commodities were missing; therefore, after
arranging the data we used data of 24,620 households. The frequency of food items expenditure data in HIES 2018–19 is of two types i.
e. fortnightly and monthly. The 14 days’ data is first converting into monthly information and then these groups are joined to make the
household total food expenditures during the month. According to Burney and Khan [26] income data usually experience measure­
ment errors and may also comprise a transitory component of income. Therefore, instead of using household income in this study, we
used the households’ expenditures on the eight food commodity groups as a proxy for household income.
4. Estimation method
For estimating the households’ income/expenditure elasticities, uncompensated and compensated own-price and cross-price
elasticities, and household age composition elasticities, we used Linear Approximation Almost Ideal Demand System (LA/AIDS)
[27,28]. The estimation for the model is carried out for eight food commodity groups such as cereals, pulses, milk, meat, fruits,
vegetables, sugar, and ghee. Since we have eight food commodity groups, therefore; we estimated a set of eight budget share equations,
such as one budget share equation for each food commodity group. In support of ith food commodity group, we used the flowing budget
share equation (1) for empirical estimation:
( ) ∑
∑
X
wi = αi +
+
θih Zh + μi
γ ij ln Pj + βi ln
(1)
P
where wi is the budget share for ith food commodity group computed as:
pi qi
wi = ∑
pi q i
(2)
1
HIES is a major initiative by the Pakistan Bureau of Statistics (PBS). The HIES data set is available for various years however, for the current
study we used the most recent data of HIES for the year 2018–2019.
4
Heliyon 9 (2023) e19518
N. Hayat et al.
where pi and qi in equation (2) represents the price paid and quantity consumed by households for ith food commodity group, pi qi
∑
represents the expenditure made by households on ith food commodity group and pi qi households total expenditures on all the food
commodity groups. The HIES data set did not provide any information on the price of ith food commodity group. However, we
computed the price of ith commodity group using equation (3) as:
pi =
pi qi
qi
(3)
X is the per capita expenditure on all food commodity groups competed in equation (4) as [29–31]:
∑
pi q i
X=
n
where n represents household size. P is Stones’ price index estimated by using equation (5) as:
∑
P=
wj ln Pj
(4)
(5)
Zh is the number of household members of type h, where:
h = 1, children (aged ≤9 years).
= 2, adolescents (aged 10–19 years).
= 3, adults (aged 20–39 years).
= 4, middle age (aged 40–59 years).
= 5, elders (aged ≥60 years).Pj is the aggregate price of the jth food commodity group where, i,j = 1,2,3, … ….N. αi , γij, γ ij and θih
are the parameters and μi is the error term. The LA/AIDS model has the advantage that it automatically gratifies three restrictions
∑
∑
namely, the adding-up, homogeneity, and symmetry. The first restriction involves γ ij = 0, βi = 0, the second restriction involves
∑
γij = 0 and the third restriction involves γ ij = γ ji . After estimating equation (1), one can easily compute the uncompensated ownprice (eii ), cross-price (eij ), expenditure (Єi ) and household age composition elasticities (φi ) by using equation (6), (7), (8), and (9)
[6,32]:
( )
β
Єi = i + 1
(6)
wi
(
eij =
)
γij − βi wj
– σ ij
wi
(7)
In case of the own price elasticity, the values of σij are one while in case of cross-price elasticity its value is zero. With the help of Slutsky
equation we estimated compensated own and cross-price elasticities with the help of equation (8):
(8)
eHij = eij + wj Єi
The household age composition elasticity can be computed by using equation (9) as:
[
( )]
θih Zh − βi Znh
φih =
wi
(9)
The demand equations for eight food commodity groups2 in LA/AIDS model is estimated using Seemingly Unrelated Regressions
(SUR) with homogeneity and symmetry restrictions imposed. In SUR, generalized least square (GLS) is applied to each equation
separately. The coefficients obtained in SUR are equivalent to the maximum likelihood coefficients. The adding up property of the
model states that the sum of the entire budget shares must be equal to one. Resultantly a singular system of equations formed that
cannot be computed directly. Consequently, one of the share equations has to be dropped randomly to make the system of demand
equations non-singular. The βi in the model captures the effect of alteration in real income on the budget share of ith food commodity
group and decide whether this food commodity group is luxury, necessity or inferior. When an estimated equation for a food com­
modity group the value of βi > 0 and the Єi > 1 and wi increases with rising total income, one treat the food commodity group as a
luxury. On the other hand, when βi < 0 and the 0 < Єi > 1 and wi diminishes with increasing X, one treat the food commodity group is
a necessity. Furthermore, when βi < − 1 and the Єi < 1, one treat the food commodity group as inferior. Additionally, with the help of
the cross-price elasticities of the model one can easily inspect complementary and substitutive relationships among different food
commodity groups. The two commodity groups are said to be gross substitutes if the cross-price elasticities are positive (eij > 0)
whereas the same commodity groups are said to be gross complements if the cross-price elasticities are negative (eij > 1). Finally, the
household age composition elasticities (φih ) provide the impact of a change in family composition on the households consumption
pattern for various food commodities groups. Household age composition elasticities may be positive (φih > 0) or negative (φih < 0), it
2
We selected eight food commodity groups because a significant portion of the households’ nominal income goes to consumption of these
commodities.
5
Heliyon 9 (2023) e19518
N. Hayat et al.
also may be elastic (φih > 1) or inelastic (φih < 1). A positive age composition elasticity for a specific food commodity group show that
the households’ consumption on that food commodity group increases if the household size increases by a specific age person and vice
versa. Similarly, the elastic age composition elasticity shows that the increase in the size of household by a specific age category cannot
bring substantial changes in the consumption of a specific food commodity group and vice versa.
5. Results and discussion
5.1. Descriptive statistics
Descriptive statistics about the budget shares of eight food commodity groups, the prices of these food commodity groups, and
households age composition are presented in Table 1. It is observed that households spent major shares of their incomes on milk (31%)
and cereals (23%), respectively. After spending a major share on milk and cereals, the households spent the remaining incomes on
meat (13%), vegetables (11%), and ghee (10%), respectively. On the other hand, the households spent a smaller share of their incomes
on fruits (0.05%), sugar (0.05%), and pulses (0.03%), respectively. Comparing the coefficient variations of the budget shares of eight
commodity groups, we observe highest variations in the budget share of meat (69%) and least variation in the budget share of cereals
(35%). Comparing the prices of the eight food commodity groups, we observe that the household on average paid higher prices for
meat (PKR313/kg), ghee (PKR167/kg), and pulses (PKR134/kg) whereas comparatively lower prices for fruits (PKR94/kg), milk
(PKR85/kg), sugar (PKR62/kg), cereals (PKR41/kg), and vegetables (PKR40/kg). The coefficients of variations for prices of the eight
food commodity groups ranged between 0.09 and 0.34. The largest variation is observed for the price of cereals (34%) and lowest
variation is observed in the price of sugar (9%). This is reasonable because in Pakistan the sugar prices are fix by the governmental
authorities at the federal level. Furthermore, the average household size is approximately seven members and on average households
are composing of about two children, one adolescents, two adults, one middle aged person, and one elder person.
5.2. Results of the LA/AIDS model
The results of the estimated LA/AIDS models for eight food commodity groups are given in Table 2. However, there is a standard
econometric role that one cannot interpret the results of the estimated LA/AIDS models, instead the elasticities generated on the basis
of the estimated coefficient of the models are interpreted. The results of the diagnostic tests of the eight models are reported in the last
panel of Table 2. In our eight regression models, the R squared is in the range of 0.09–0.56 whereas the root mean square error (Root
MSE) is in the range of 0.02–0.08. The highest R squared is observed for milk whereas the lowest R squared is observed for pulses. It is
observed that except for milk, all other food commodity groups have comparatively lower R squared. However, this is common because
in cross-sectional data sets with a large number of observations usually the researchers get lower R squared for the estimated models.
Table 1
Descriptive statistics.
Variables
Dependent:
Budget shares on food commodity groups
Cereals
Pulses
Milk
Meat
Fruits
Vegetables
Sugar
Ghee
Explanatory:
Price of commodity groups
Cereals (PKR/Kg)
Pulses (PKR/Kg)
Milk (PKR/Kg)
Meat (PKR/Kg)
Fruits (PKR/Kg)
Vegetables (PKR/Kg)
Sugar (PKR/Kg)
Ghee (PKR/Kg)
Households age composition
Household size
Children (Age ≤9)
Adolescent (age 10–19)
Adults (age 20–39)
Middle age (age 40–59)
Elder (age ≥60)
Mean
Standard deviation
Coefficient of variation
0.23
0.03
0.31
0.13
0.05
0.11
0.05
0.10
0.08
0.02
0.12
0.09
0.03
0.04
0.03
0.04
0.35
0.67
0.39
0.69
0.60
0.36
0.60
0.40
41
134
85
313
94
40
62
167
15.1
18.2
19.1
107.4
31.2
9.6
5.8
54.2
0.37
0.14
0.22
0.34
0.33
0.24
0.09
0.32
6.7
1.4
1.3
1.6
1.3
1.1
1.1
0.48
0.46
0.70
0.48
0.28
0.16
0.34
0.35
0.44
0.37
0.25
Source: Estimated by authors’ based on HIES data for the year 2018–2019.
6
N. Hayat et al.
Table 2
Results of the LA/AIDS model.
(1)
(3)
(3)
(4)
(5)
(6)
(7)
(8)
Dependent variables:
Cereals
Pulses
Milk
Meat
Fruits
Vegetables
Sugar
Ghee
0.0210***
(0.00122)
0.0170***
(0.00355)
− 0.0101***
(0.00216)
− 0.0496***
(0.00151)
− 0.0232***
(0.00141)
− 0.0466***
(0.00191)
− 0.0184***
(0.00515)
− 0.0853***
(0.00232)
− 0.00313**
(0.00151)
0.0181***
(0.000972)
0.0355***
(0.00101)
0.00864***
(0.000664)
0.0159***
(0.000979)
0.00894***
(0.00160)
1.033***
(0.0269)
0.00302***
(0.000296)
0.0250***
(0.000861)
0.0108***
(0.000524)
− 0.00396***
(0.000369)
− 0.00145***
(0.000341)
0.00118**
(0.000464)
− 0.00228*
(0.00125)
− 0.000089
(0.000569)
− 0.00502***
(0.000354)
− 0.000162
(0.000237)
0.00187***
(0.000245)
− 0.000691***
(0.000161)
0.000258
(0.000237)
− 0.000401
(0.000387)
− 0.0872***
(0.00648)
− 0.0113***
(0.00140)
− 0.0357***
(0.00404)
− 0.0231***
(0.00247)
− 0.0632***
(0.00172)
0.00167
(0.00161)
− 0.0494***
(0.00217)
− 0.0266***
(0.00587)
− 0.00143
(0.00268)
0.199***
(0.00124)
0.0192***
(0.00111)
− 0.00499***
(0.00115)
0.00475***
(0.000757)
− 0.00434***
(0.00112)
0.00791***
(0.00182)
− 0.0662**
(0.0305)
0.0224***
(0.00116)
− 0.0226***
(0.00336)
0.000614
(0.00205)
0.123***
(0.00153)
0.00368***
(0.00134)
− 0.0116***
(0.00182)
− 0.0677***
(0.00488)
− 0.0542***
(0.00220)
0.0873***
(0.00125)
0.00310***
(0.000927)
− 0.00296***
(0.000959)
0.000410
(0.000630)
− 0.00377***
(0.000928)
− 0.000972
(0.00151)
− 0.520***
(0.0253)
0.00299***
(0.000516)
− 0.00493***
(0.00150)
− 0.00383***
(0.000913)
− 0.00351***
(0.000642)
0.0195***
(0.000596)
0.0149***
(0.000811)
0.0320***
(0.00218)
− 0.00119
(0.000992)
0.00516***
(0.000612)
− 0.00506***
(0.000413)
− 0.00747***
(0.000427)
− 0.00298***
(0.000281)
− 0.00459***
(0.000413)
− 0.00240***
(0.000674)
− 0.173***
(0.0113)
− 0.0108***
(0.000651)
0.00944***
(0.00189)
0.00170
(0.00115)
− 0.0236***
(0.000807)
− 0.00809***
(0.000749)
0.0627***
(0.00103)
0.0171***
(0.00274)
− 0.0319***
(0.00124)
− 0.0176***
(0.000789)
0.000451
(0.000520)
0.000115
(0.000537)
− 0.00218***
(0.000354)
− 0.00221***
(0.000521)
− 0.00651***
(0.000849)
0.250***
(0.0143)
0.00201***
(0.000418)
− 0.0257***
(0.00121)
− 0.00780***
(0.000740)
− 0.00377***
(0.000520)
− 0.00560***
(0.000481)
− 0.000954
(0.000655)
0.0523***
(0.00176)
− 0.0161***
(0.000801)
− 0.00675***
(0.000500)
0.00388***
(0.000334)
0.00234***
(0.000345)
− 0.000336
(0.000227)
− 0.000106
(0.000335)
− 0.000841
(0.000546)
0.154***
(0.00916)
− 0.000868
(0.000589)
0.0170***
(0.00171)
0.00112
(0.00104)
− 0.0157***
(0.000730)
− 0.00781***
(0.000677)
− 0.00628***
(0.000922)
− 0.0108***
(0.00248)
0.0665***
(0.00116)
− 0.0239***
(0.000729)
− 0.00104**
(0.000471)
0.00443***
(0.000486)
0.000728**
(0.000320)
0.00220***
(0.000471)
− 0.000707
(0.000768)
0.0165
(0.0129)
24,620
0.23
514***
0.07
24,620
0.09
163***
0.02
24,620
0.56
2185***
0.08
24,620
0.45
1144***
0.07
24,620
0.11
220***
0.03
24,620
0.19
436***
0.04
24,620
0.11
219***
0.02
24,620
0.16
325***
0.03
Explanatory Variables:
Cereals Price (ln)
Pulses Price (ln)
Milk Price (ln)
Meat Price (ln)
Fruits Price (ln)
Vegetables Price (ln)
Sugar Price (ln)
7
Ghee Price (ln)
Stones’ price index
Children (Age ≤9)
Adolescents (age 10–19)
Adult (age 20–39)
Middle aged (age 40–59)
Elders (age ≥60)
Constant
Observations
R-squared
F statistics
Root MSE
Source: Estimated by authors’ based on HIES data for the year 2018–2019. Standard errors in parentheses***p < 0.01, **p < 0.05, *p < 0.1
Heliyon 9 (2023) e19518
Model:
Heliyon 9 (2023) e19518
N. Hayat et al.
Furthermore, the higher Root MSE is observed for milk whereas the lower Root MSE is observed for pulses and sugar. However, we
have observed lower Root MSEs for all the eight regressions, this indicates to the accuracy of our estimation techniques. Finally, results
of the F statistics show that the regression models are overall statistically significant at 1% level for each of the eight regressions.
5.3. Results of the estimated elasticities
The estimated income/expenditure elasticities, uncompensated/Marshallian own and cross-price elasticities and household age
composition elasticities are presented in Table 3 whereas the compensated/Hicksian own and cross-price elasticities for the eight food
commodity groups are presented in Table 4. From Tables 3 and it is observed that estimated income elasticities for milk, meat, and
fruits are greater than one (Єi > 1), implying that these food commodities are luxury food items for households. This indicate that
milk, meat, and fruits are more income elastic than other food commodities such that when the households’ incomes rise then their
consumption on these food commodities also rises. These results are consistent with the findings of previous studies [22,23]. They
reported that milk, meat, and fruits are luxury food items. In other study, Akram [24] found that beef, mutton, chicken meat, seafood,
fruits and dry fruits are income elastic whereas milk and yogurt are income inelastic. In a similar fashion, Hameed and Salman [25]
revealed that meat and fruits are luxury food items however, they declared dairy as necessity food item. Furthermore, Hina et al. [10]
found that milk and meat are income elastic than other food items.
On the other hand, the estimated income elasticities for cereals, pulses, vegetables, sugar, and ghee are less than one (Єi < 1),
implying that these food commodities are necessity food items for the households. This indicate that these food commodities are less
income elastic such that when the households’ incomes rise then their consumption on these food commodities rises with a small
proportion. These results are consistent with the findings of Akram [24]. He reported that cereals, pulses, vegetables, sugar, and ghee
are necessity food items. In other study, authors found that wheat, rice, pulses, vegetables, and oil are income inelastic whereas sugar is
income elastic [25]. In a similar fashion, Ullah et al. [23] revealed that wheat & wheat flour, pulses, vegetables, sugar, and oil & fats are
necessity food items however, they declared rice and other cereals as luxury food items. Furthermore, previous found that pulses,
vegetables, sugar, and clarified butter are income inelastic while food grains are income elastic [10]. In addition, Haider and Zaidi [22]
reported that rice and other food commodities are necessity food items while wheat, pulses, vegetables, sugar, and oil are luxury food
items.
We are estimated the uncompensated and compensated own and cross-price elasticities for the eight food commodity groups.
However, the compensated own and cross-price elasticities provide most accurate picture of own price elasticities and cross-price
complements and substitutes. Therefore, we have discussed only the own and cross-price effects of compensated elasticities.
Comparing the compensated own price elasticities given in Table 4, we observe that the eight food commodity groups have inelastic
compensated own-price elasticities. This implies that these food commodities are integral food items of household diet. Any change in
the price level of these commodities cannot bring a considerable change in the consumption of these commodities. These results are
consistent with the findings of previous studies [23,25]. They reported that wheat & wheat flour, rice, other cereals, milk, meat, fruits,
pulses, vegetables, sugar, and oil & fats are integral food items. In other study, Hina et al. [10] found that food grains, pulses, milk,
meat, vegetables, sugar and, clarified butter are price inelastic. In a similar fashion, Haider and Zaidi [22] revealed that wheat, pulses,
dairy, meat, fruits, vegetables, sugar, and oil & fats are integral food items however, they declared rice and other cereals as price
elastic.
On the basis of compensated cross-price elasticities, we observe nine pairs of gross-substitutes and five pairs of gross-complements.
However, some of these pairs adhere to microeconomics theory while others do not. We thus addressed any complimentary or sub­
stitutive relationships between commodity groupings that are supported by microeconomics theory. Results of the compensated crossprice elasticities shows that cereals and pulses and cereals and vegetables are gross substitutes. This indicates that when the price of
cereals goes up the households divert their consumption from cereals to pulses or vegetables and vice versa. Pulses, cereals, and
vegetables are already commonly consumed and provide an exceptional amount of nourishment for people. They are suitable for usage
because they are gluten-free and high in protein. Additionally, they have similar nutritional qualities and are abundant in vitamins and
minerals. As a result, when cereal prices rise, households switch their consumption from cereal to pulses or vegetables, and vice versa.
Similarly, it is observed that pulses and vegetables are gross substitutes. This indicates that when the price of pulses goes up the
households substitute it with vegetables and vice versa. In the developing countries like Pakistan the households with lower purchasing
power heavily rely on low price food commodities like pulses and vegetables. However, the recent increase in prices of pulses in
Pakistan, diverted the households from pulses to vegetables consumption. In similar fashion, we observe a substitutive relationship
among the luxury food commodity groups like milk and fruits and meat and fruits. This shows that in situation of increasing prices of
milk and meat the households decide to consume more fruits and vice versa. The cost of fruits and milk is lower for Pakistani
households compared to the cost of meat. Therefore, households can readily swap out these items for one another when they notice a
price increase. Moreover, milk and ghee are also emerged as gross substitutes. This means that the household replace milk with ghee
when they observe a hick in the prices of milk and vice versa. Several dairy products can be replaced with ghee. Ghee is aptly referred
to as a versatile dairy product, since it may be used in cooking as cooking oil, as a flavor enhancer, or even in its raw form as a spread. In
each of these situations, ghee takes the place of other goods like dairy butter, brown butter, etc. Consequently, we can state that ghee
and milk are gross substitutes. These results are different from the results of Akram [24]. He found a substitutive relationship among
beef and mutton and chicken, seafood and milk & yogurt. Similarly, Ullah et al. [23] indicated that rice & fruits and rice & beverages
are gross substitutes. On the other hand, pulses and meat and pulses and fruits are gross complements. This indicates that when the
price of pulses goes up the households’ consumption on meat or fruits goes down and vice versa. Households in underdeveloped
nations like Pakistan with lesser purchasing power rely significantly on low-cost food staples like pulses. The recent raise in the price of
8
N. Hayat et al.
Table 3
Estimated income, uncompensated own and cross-price elasticities and household composition elasticities.
9
Food
commodity
group
Income
elasticities
Own and cross-price elasticities
Household age composition elasticities
Cereals
Pulses
Milk
Meat
Fruits
Vegetables
Sugar
Ghee
Children
Adolescents
Adults
Middle
aged
Elders
Cereals
Pulses
Milk
Meat
Fruits
Vegetables
Sugar
Ghee
0.986314
0.822615
1.642557
1.683099
1.111688
0.834897
0.862245
0.769305
¡0.90505
0.641275
− 0.17957
− 0.54433
− 0.52771
− 0.39939
− 0.34401
− 0.7706
0.013592
¡0.11159
0.016688
− 0.05032
− 0.03455
0.015742
− 0.04263
0.00567
− 0.04517
− 1.20655
¡1.27359
− 0.70608
0.001557
− 0.41228
− 0.50019
0.057643
0.099694
− 0.77592
− 0.08014
¡0.12486
0.06538
− 0.08772
− 1.36403
− 0.49368
0.013706
− 0.16601
− 0.04205
− 0.05902
¡0.58308
0.147403
0.659426
− 0.00083
− 0.04576
0.352478
− 0.06301
− 0.25748
− 0.18701
¡0.39422
0.363664
− 0.28332
0.009459
− 0.89944
− 0.05667
− 0.06297
− 0.12668
− 0.00086
¡0.94803
− 0.1441
− 0.00238
0.619084
− 0.06295
− 0.19362
− 0.18062
− 0.04181
− 0.20614
¡0.33421
0.533024
− 0.0025
0.285509
0.024465
− 0.75638
0.061714
0.558371
− 0.02064
1.042691
0.477488
− 0.23389
− 0.28907
− 1.1052
0.039588
0.346959
0.331712
0.256395
− 0.12111
− 0.05113
− 0.14211
− 0.45891
− 0.09747
− 0.01295
0.102333
0.468508
0.096097
− 0.22073
− 0.33249
− 0.6877
− 0.10631
0.012699
0.187817
0.264165
− 0.06567
0.065102
− 0.16367
− 0.36648
− 0.38192
− 0.09226
− 0.00766
Source: Estimated by authors’ based on HIES data for the year 2018–2019.
Heliyon 9 (2023) e19518
Heliyon 9 (2023) e19518
N. Hayat et al.
Table 4
Estimated compensated own and cross-price elasticities.
Food commodity group
Cereals
Pulses
Milk
Meat
Fruits
Vegetables
Sugar
Ghee
Own and cross-price elasticities
Cereals
Pulses
Milk
Meat
Fruits
Vegetables
Sugar
Ghee
¡0.67948
0.829407
0.196088
− 0.15941
− 0.27346
0.743557
− 0.14681
− 0.59466
0.041505
¡0.08831
0.063172
− 0.00269
− 0.00309
0.039369
− 0.01823
0.027441
0.26029
− 0.95178
¡0.76489
− 0.18482
0.345847
− 0.15371
− 0.23316
0.295897
0.225745
− 0.67079
0.129783
0.090241
0.207454
0.018982
− 1.25383
− 0.39537
0.059274
− 0.128
0.033833
0.018735
¡0.53172
0.185975
0.699261
0.034714
0.059377
0.440169
0.112089
− 0.07806
− 0.02216
¡0.30522
0.45558
− 0.20132
0.057789
− 0.85913
0.023814
0.019501
− 0.07221
0.040051
¡0.90578
− 0.00543
0.099805
0.704307
0.107216
− 0.01925
− 0.06545
0.044688
− 0.11681
¡0.25451
Source: Estimated by authors’ based on HIES data for the year 2018–201
pulses in Pakistan, however, caused households to reduce eating high-cost food staples like meat or fruits. Similarly, it is observed that
ghee and meat are gross complements. This indicates that when the price of ghee increases the households’ consumption on meat
decreased. Any recipe made with meat should include ghee as a key ingredient. Consequently, households consumed less meat as a
result of rising ghee prices. In other study, Ullah et al. [23] revealed that wheat & wheat flour and oil & fats and wheat & wheat flour
and vegetables are gross complements.
Comparing the household age composition elasticities given in Table 3, for children we observed positive and inelastic age
composition elasticities for cereals, milk, meat, vegetables, sugar, and ghee whereas negative elasticities for pulses and fruits. This
indicates that if the number of children in the households increases then their consumption on cereals, milk, meat, vegetables, sugar,
and ghee increases while their consumption on pulses and fruits decreases. These results are different from Hina et al. [10]. They
revealed that the addition of child to the family decrease the household expenditures on food grains, meat, and milk. Similarly, for
adolescents positive age composition elasticities are observed for cereals, pulses, vegetables, sugar, and ghee whereas negative
elasticities are observed for milk, meat, and fruits. However, the elasticities for cereals and fruits are elastic. This indicates that any
increase in the households’ size by adolescents’ bring a substantial increase in their cereals consumption and a substantial decrease in
their fruits consumptions. These results are in line with Hina et al. [10]. They found that the addition of adolescent to the household
decrease their expenditures on meat, and milk. Furthermore, for adults’ family members positive and inelastic age composition
elasticities are observed for cereals and ghee whereas negative elasticities for pulses, milk, meat, fruits, vegetables, and sugar. This
shows that an increase in the household size by an adult increase their consumption on cereals and ghee. Hina et al. [10] exposed that
the addition of adults to the household decrease their expenditures on food grains, meat, and milk. In addition, for middle aged family
member, we observe positive and inelastic age composition elasticities for cereal, pulses, sugar, and ghee while for other food com­
modity groups we observe negative elasticities. This show that when the households size grow by one middle age person then their
consumption on cereals, pulses, sugar, and ghee increases while their consumption on other food commodities decreases. Finally, for
elders we observe positive and inelastic age composition elasticities for cereals and milk whereas negative elasticities are observed for
all other commodities. This indicates that when the number of elders in a households increase their consumption on cereals and milk
increases while for all other food commodities their consumption decreases. Of Pakistan’s 212 million people, nearly 36.01% are under
the age of 14, 19.3% are between the ages of 15 and 24, 34.7% are between the ages of 25 and 54, 5.55% are between the ages of 55
and 64, and 4.44% are 65 and older. With a 2.4% yearly growth rate, the population doubles every 29 years. Male and female require
the appropriate amounts and types of food for their age and stage of development in order to grow and thrive. This puts enormous
strain on food demand, which is worsened by Pakistan’s rapid population growth [33]. In order to produce food for growing popu­
lation, this placed a significant burden on the agriculture subsectors of livestock, dairy, fishery, horticulture, staple crops, and cash
crops as well as on the ghee and cooking oil manufacturing sector. The demand for food items including milk, meat, fruits, cereals,
pulses, vegetables, sugar, and ghee would rise in the future due to the population growth across all age groups. The production in
horticulture, livestock, and dairy farming must be focused on boosting the availability of these food products.
In nut shell, the pattern of food consumption that has emerged from this empirical analysis suggests that changes in the prices of
milk and meat will bring major changes in the diet of the households. On the other hand, a significant increase in the demand of milk,
meats, and fruits can be expected following an increase in the household income. A change in the household age composition brings
significant changes in the quantities of various commodities consumed.
6. Conclusion and recommendations
This study analyzes the households’ food consumption pattern for Pakistan. The study also projects the future food demand of the
country. Linear Approximation Almost Ideal Demand System (LA/AIDS) is conducted using household level data from HIES for the
year 2018–19. The income elasticities, the uncompensated and compensated own and cross-price elasticities, and the household age
composition elasticities are estimated from the parameters of LA/AIDS model. Results of the study show that the households in
Pakistan with low income and low purchasing power are still unable to properly purchase the high nutritional efficient food item like
milk, meat, and fruits. Therefore, the study recommends that a large income may be a better option to improve the food consumption
and nutritional status of the country as compared to other product-based subsidy policies. An increase in household income through
poverty alleviation programs that provide direct cash transfer, such as the recently introduced Ehsaas (subsidy) Program of the
10
Heliyon 9 (2023) e19518
N. Hayat et al.
Government of Pakistan, would be successful in achieving an increase in consumption of the high nutritional food commodities at the
household level in Pakistan. Besides, as the demand for milk, meat, and fruits are more income elastic thus, imposition of any income
tax on household personnel income could reduce their consumption of these food groups. Such income tax policies could result food
security problems for households in Pakistan. Moreover, household consumption pattern that is emerged from this study reveal that
Pakistan is a food deficit country because the high level food commodities like milk, meat, and fruits are still luxuries food items
whereas the low value food commodities like cereals, pulses, vegetables, sugar, and ghee are still necessity food items for households.
Hence, the development of a strong connection between the federal food security ministry and the food security ministries of the four
provinces of the country on the issues of food security will also be helpful. The overall collaboration will improve the food consumption
pattern of households.
Furthermore, from the results, we observe that the eight food commodity groups have inelastic compensated own-price elasticities.
This implies that these food commodities are integral food items of household diet. Therefore, it is suggested from the results that
government should compensate the consumer when it observes a rise in the prices of cereals, pulses, milk, meat, fruits, vegetables,
sugar, and ghee to maintain the same welfare level in case of price change. Similarly, imposition of any sale tax could create huge loss
in consumption for these commodities. Therefore, any increase in prices of these commodities should be backed with price subsidi­
zation policies. The government can also consider the results of cross-price elasticities of various food commodities in its key decision
regarding households. For instance, our results show that if a tax is imposed on pulses, households will substitute for pulses into
something that they consider a good substitute like vegetables and cereals. According to our results, household age compositions are
one of the most important determinants of the food consumption; therefore, diverse population control measures may improve the
food consumption standard of Pakistani households. Since, household age composition has a significant effect on food consumption
therefore, food aid and donor agencies should target the larger and poor households for food security interventions. Finally, household
income, price of food commodities, and household age composition are important drivers of food consumption. Therefore, the findings
from our study will inform policymakers and researchers about how household food consumption is evolving and thus help policy­
makers to design an effective food security policy for the country.
Although the findings of this study are credible, significant limitations needs to be made clear. First, the current study estimated the
elasticities for eight food commodity groups (i.e. cereals, pulses, milk, ghee, meat, fruits, vegetables, and sugar) only and includes only
one demographic factor age composition (i.e. children, adolescents, adults, middle aged, and elders’). Another possibility would be to
estimate elasticities for other commodity groups like dry fruits, condiments, spices, mineral water, soft drink, juice, readymade foods,
coffee, and tea etc. Or to incorporate additional socioeconomic factors such as education, occupation, gender, region, material status,
and household size, etc. In the model. Second, the current study estimated the household food consumption pattern at a national level.
However, future research can further narrow and can incorporate consumption patterns at provincial and district level. Third, the
current study utilized conventional factors (such as income, relative prices, and own prices) to analyze the food consumption patterns
of households. There are numerous contemporary issues that can have a big impact on a household’s food consumption, including
dietary needs, nutritional requirements, information about food safety, food labelling, and environmental consumption aspects. Future
researchers can use similar contemporary characteristics to produce more reliable results.
Author contribution statement
Ghulam Mustafa; Naveed Hayat: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted
the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Bader Alhafi Alotaibi: Conceived and designed the
experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Roshan K.
Nayak: Conceived and designed the experiments; Performed the experiments; Contributed reagents, materials, analysis tools or data;
Wrote the paper. Muhammad Naeem: Contributed reagents, materials, analysis tools or data; Wrote the paper.
Data availability statement
Data will be made available on request.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
References
[1] M. Gostkowski, Elasticity of consumer demand: estimation using a quadratic almost ideal demand system. Econometrics, Ekonometria. Adv. Applied Data
Analytics 22 (1) (2018) 68–78.
[2] P. Pinstrup-Andersen, D.D. Watson II, Food Policy for Developing Countries: the Role of Government in Global, National, and Local Food Systems, Cornell
University Press, 2011.
[3] L.V. Hoang, Estimation of Food Demand from Household Survey Data in Vietnam, 2009.
[4] F.B. Türkmen-Ceylan, Economic crisis and consumption: an almost ideal demand system estimation for Turkey with time-varying parameters, Dev. Studies Res.
6 (1) (2019) 13–29.
[5] S. Sary, X. Shiwei, Y. Wen, S. Darith, S. Chorn, Household food consumption in rural, Cambodia almost ideal demand system analysis, J. Phys. Conf. Ser. IOP
Publishing 1176 (2019) 042077, https://doi.org/10.1088/1742-6596/1176/4/042077.
11
Heliyon 9 (2023) e19518
N. Hayat et al.
[6] N. Hayat, A. Hussain, H. Yousaf, Food demand in Pakistan: analysis and projections, S. Asia Econ. J. 17 (1) (2016) 94–113.
[7] M.N. Lokuge, S. Zivkovic, K. Lange, B. Chidmi, Estimation of a censored food demand system and nutrient elasticities: a cross-sectional analysis of Sri Lanka, Int.
Food Agribus. Manag. Rev. 22 (1030–2019-3343) (2019) 717–730.
[8] M. Ahmad, U. Farooq, The state of food security in Pakistan: future challenges and coping strategies, Pakistan Dev. Rev. (2010) 903–923.
[9] Gop, National Nutrition Survey 2018. Nutrition Wing, Ministray of National Health Services, Regulation and Coordination, 2019. Islamabad, Pakistan.
[10] H. Hina, S.T. Abbas, M. Afzal, A. Nazir, M.T. Majeed, I. Shezadi, F. Naz, Demand projections and consumption pattern analysis at provincial level in Pakistan,
Pakistan J. Appl. Econ. 31 (1) (2021) 1–30.
[11] I.G.K. Ansah, E. Marfo, S.A. Donkoh, Food demand characteristics in Ghana: an application of the quadratic almost ideal demand systems, Scientific African 8
(2020), e00293.
[12] R.U. Onyeneke, C.C. Emenekwe, M.U. Amadi, J.O. Munonye, C.L. Njoku, C.U. Izuogu, Demand analysis of rice in Nigeria: application of quadratic almost ideal
demand system model, Asian J. Agric. Rural Dev. 10 (1) (2020) 364–378.
[13] O. Ojogho, O. Imade, Households food consumption pattern in Edo State, Nigeria: application of alternative demand model, J. Res. Forestry, Wildlife and
Environ. 12 (3) (2020) 162–171.
[14] O. Ojogho, M. Ojo, Quadratic almost ideal demand system of food consumption pattern IN SOUTH-eastern Nigeria, Ife Journal of Agriculture 33 (1) (2021)
21–35.
[15] K.K. Salman, R. Salawu, M. Salawu, O. Osawe, Food demand in Nigeria, J. Rural Econ. Dev. 23 (1623–2021-1369) (2021) 12–22.
[16] A. Mulyana, M. Yamin, Agricultural households’ food demand: evidence from Indonesia, Asian J. Agricul. Dev. 16 (1362–2019-4203) (2019) 45–60.
[17] B. Kharisma, A.S. Alisjahbana, S.S. Remi, P. Praditya, Application of the quadratic almost ideal demand system (QUAIDS) model in the demand of the household
animal sourced food in West Java, Agris on-line Papers in Econ. Inform. 12 (665–2020-1222) (2020) 23–35.
[18] N. Khoiriyah, R. Anindita, N. Hanani, A.W. Muhaimin, Animal food demand in Indonesia: a quadratic almost ideal demand system approach, Agris on-line
Papers in Econ. Inform. 12 (665–2020-1237) (2020) 85–97.
[19] L.H. Vu, Estimation and analysis of food demand patterns in Vietnam, Economies 8 (1) (2020) 11.
[20] S. Altayeb, H.-E. Daoud, Application of the almost ideal demand system (AIDS) model in the demand of the household commodity group in Jordan, Jordan J.
Econ. Sci. 9 (1) (2022) 1–15.
[21] W. Dilanka, C. Wijetunga, Households Demand for Selected Food Commodities in Uva Province of Sri Lanka, 2020.
[22] A. Haider, M. Zaidi, Food Consumption Patterns and Nutrition Disparity in Pakistan, 2017.
[23] I. Ullah, A.U. Jan, S.A.U. Shah, M. Ishaq, G. Ali, Food Consumption in Pakistan: Application of Linear Approximate Almost Ideal Demand System, Biomedical
Journal of Scientific and Technical Research, 2018.
[24] N. Akram, Household’s demand for food commodities in Pakistan: issues and empirical evidence, Estud. Econ. 47 (1) (2020) 127–145.
[25] A. Hameed, A. Salam, Estimating food consumption patterns in Pakistan by using ideal demand system, Sarhad J. Agric. 37 (1) (2021).
[26] N.A. Burney, A.H. Khan, Household consumption patterns in Pakistan: an urban-rural comparison using microdata, Pakistan Dev. Rev. (1991) 145–171.
[27] A. Deaton, J. Muellbauer, An almost ideal demand system, Am. Econ. Rev. 70 (3) (1980) 312–326.
[28] A.P. Barten, International evidence on consumption patterns, J. Econ. Lit. 30 (1) (1992) 194.
[29] R. Stone, The Measurement of Consumers’ Expenditure and Behavior in the United Kingdom, 1920-1938, vol. 1, University Press, 1954.
[30] R. Green, J.M. Alston, Elasticities in AIDS models, Am. J. Agric. Econ. 72 (2) (1990) 442–445.
[31] G. Moschini, Units of measurement and the Stone index in demand system estimation, Am. J. Agric. Econ. 77 (1) (1995) 63–68.
[32] U. Farooq, T. Young, M. Iqbal, An Investigation into the Farm Households Consumption Patterns in Punjab, Pakistan, The Pakistan Development Review, 1999,
pp. 293–305.
[33] UNICEF, Pakistan Anuual Report 2020. United Nations Children’s Fund (UNICEF) Pakistan 2021. Human Design Studios, United Nations Children’s Fund
(UNICEF) Pakistan, Islamabad, 2021.
12