1130349
research-article2022
JAS0010.1177/00219096221130349Journal of Asian and African StudiesParveen et al.
JAAS
Original Research Article
Impact of Demographic
Vulnerabilities and
Socio-Economic Resilience
on Food Insecurity in Pakistan
Ayesha Parveen1, Saira Tufail1
Verda Salman2
Journal of Asian and African Studies
1­–24
© The Author(s) 2022
Article reuse guidelines:
sagepub.com/journals-permissions
https://doi.org/10.1177/00219096221130349
DOI: 10.1177/00219096221130349
journals.sagepub.com/home/jas
and
1
Department of Economics, Fatima Jinnah Women University Rawalpindi, Pakistan
Department of Economics, School of Social Sciences and Humanities, National University of Sciences & Technology,
Pakistan
2
Abstract
Despite being an agrarian economy, food insecurity is pervasive in Pakistan. This study examines the impact
of multiple dimensions of household vulnerability and socio-economic resilience on food insecurity. The
results of ordinary least squares and Logistic regression based on data from Pakistan Standard and Living
Measurement Survey (2018–2019) and Food Insecurity Experience Scale showed that vulnerabilities like
age and employment dependency, household size, and gender composition play a significant role in food
insecurity. The key policy interventions that can prevent households from experiencing food insecurity
would be investments in human capital, empowering women, and reduction in rural–urban disparity.
Keywords
Demographic vulnerabilities, socio-economic resilience, women empowerment, Food Insecurity
Experience Scale, PCA
Introduction
Centuries of technological progress in almost every domain of human life should have already
ensured the provision of the basic requirement for human sustenance and fundamental human
right, that is, food. However, in the highly “enlightened” 21st century, deficiencies in food provision continue to plague the world, resulting in an unacceptable level of hunger and food insecurity.
According to the Global Hunger Index (GHI, 2020), the southern Sahara and South Asia have the
highest levels of hunger with 27.8 and 26.0 GHI scores, respectively. Correspondingly, 21% of
individuals in Africa and 13% in South Asia are food insecure. Since 2014, the situation has deteriorated as a growing trend of food insecurity has emerged in the modern world, leaving even
developed countries vulnerable (World Health Organization, 2020). COVID-19 has resulted in an
Corresponding author:
Saira Tufail, Department of Economics, Fatima Jinnah Women University Rawalpindi, The Mall, Rawalpindi 54000,
Pakistan.
Email: sairatufail@fjwu.edu.pk
2
Journal of Asian and African Studies 00(0)
upsurge in the vulnerable population, placing a quarter of a billion people at risk of starvation
(Mouloudj et al., 2020; Torero, 2020).
Hunger and food insecurity, in addition to their ethical implications, wreak havoc on economies
and have negative ramifications for the livelihoods and economic capacity of vulnerable groups. In
terms of missed productivity, health, and well-being, as well as lower learning ability and human
potential, the costs to society are considerable. Food insecurity is intertwined with many other
aspects of a broad sustainable development agenda that addresses issues such as inclusive economic growth, population dynamics, decent employment, social protection, access to clean water,
energy, health, sanitation, natural resource management, and ecosystem protection. Furthermore,
empowering women and addressing inequalities—particularly gender and rural–urban divides—
are as important to combating hunger and ensuring food security as they are to achieving universal
sustainable development. Because of its critical importance, food security has become a prerequisite for propelling economies on the path to achieving sustainable development goals (SDG) within
the timeframes specified.
Though the discourse on food insecurity has been going on since 1930, it is widely believed that
the issue gained prominence at the World Food Conference in 1974, which directed global attention
toward fighting hunger and malnutrition. Food availability and stable food prices at the national
level were initially regarded as sufficient conditions for food security. Nonetheless, in the 1980s, the
Food and Agriculture Organization (FAO) considered individual food equality to be necessary for
achieving food security. The concept was further developed at the World Food Summit in 1996,
where the parameters for food security were expanded to include several dimensions in food security. Particularly, food security is defined as a situation “when all people, at all times, have physical,
social, and economic access to sufficient, safe, and nutritious food which meets their dietary needs
and food preferences for an active and healthy life” (Food and Agriculture Organization of the
United Nations (FAO), 1996a, 1996b). Following that, in 2000, the United Nations Millennium
Summit approved eight Millennium Development Goals (MDGs) with its first goal of “eradicating
Extreme Poverty and Hunger.” Later, at the World Summit on Food Security in 2009, four elements
were added to define food security: availability, accessibility, utilization, and food stability. All four
of these domains must be met for complete food security (Berry et al., 2015).
Despite global agreement on the importance of food security, empirical evidence on what determines food security has remained inconclusive. Earlier literature, in this regard, focused on macroeconomic determinants of food security such as cereal production, food imports, and food prices
(see, for instance, Ahmad and Farooq, 2010). In a recent study also by Aziz et al. (2021) the issue
is discussed at aggregate level. However, hunger, malnutrition, and chronic food insecurity have
recently been recognized as a global issue caused by a lack of access and redistribution at the
household level, rather than a global or national food deficit (Drammeh et al., 2019). Even if a
country is self-sufficient in food at national level, a large proportion of its citizens may face food
insecurity as a result of unequal food distribution (Cleaver, 1993; Greenhill et al., 2000). This is
especially evident in Pakistan and India, which are major cereal producers in South Asia but are
still unable to ensure food security for the masses.
For a variety of reasons, Pakistan is a unique case study for the study of food insecurity. First,
the issue of food insecurity seems to be counter-intuitive for a country like Pakistan that started
with a strong agriculture base and is endowed with a diverse natural resource base spanning multiple climatic and ecological zones. Pakistan ranks eighth in the world in farm output and is one of
the top 10 producers of major staple crops. Literature suggests that food availability and supply are
sufficient to feed the country. Estimated caloric availability from major food items is 2485 kcal per
person/day and it exceeds per capita daily requirement of 2350 kcal (Government of Pakistan,
2017). Concurrently, Pakistan is ranked 88th in GHI 2020 and 75th in the Global Food Insecurity
Parveen et al.
3
Index (Allee et al., 2021). Because of this startling contradiction between national food sufficiency
estimates and the widespread and intensifying incidence of food insecurity, this issue must be
investigated in Pakistan and, more importantly, at the local level instead of the national level. It
becomes more pertinent with the evidence that food insecurity is primarily a problem of food
access and utilization rather than food supplies in Pakistan (AKU, 2017; Bagriansky and Voladet,
2013).
Another observation renders Pakistan an exceptional case for the study of food insecurity. On
one hand, there is widespread recognition of the emergence and intensification of food insecurity
over the last two decades, but there was no standard yardstick at the national level to measure the
extent of food insecurity until recently. The 11th Five-Year Plan (2013–2018) did not include
food security targets and instead made only an objective statement about reducing food insecurity. Similarly, Vision 2025 could not find any appropriate benchmark for food security, and
reported 58% food insecurity, which was obtained from National Nutrition Survey (NNS, 2011),
although this was not computed using standard methodology and nor NNS was not intended to
determine food insecurity in the country. Moreover, empirical endeavors about food insecurity
prevalence in Pakistan (see, for reference, Bashir et al., 2012a, 2012b; Guha-Khasnobis and
Hazarika, 2006) have used diverse indicators for food insecurity at the household level. These
studies, while extremely insightful, have a limited horizon for the composition of national food
policy enabling food systems for food security, improved nutrition, and affordable healthy diets
for all.
Given this background, the objectives of the present study are twofold. First, to examine the
demographic composition of households and the role of demographic factors in determining the
extent of food security. Second, to ascertain the role of socio-economic resilience in determining
food insecurity in Pakistan, both at the national and provincial levels. Both of these objectives are
highly pertinent in the current situation of food insecurity in developing countries for several reasons. For instance, previous research has revealed that the composition of households is a significant correlate of food insecurity. A household’s vulnerability to financial hardships and food
insecurity may be attributed to the characteristics of individuals in the household. However, the
association may not be as straightforward as it seems intuitive. Balistreri (2012) documented that
even vulnerable households vary in their experiences of food insecurity, giving rise to a food insecurity paradox. It might be due to the use of a single indicator of household demographic vulnerability or the lack of a collective assessment of the household’s vulnerability and its socio-economic
status. Therefore, it is argued that how household composition correlates with food insecurity and
how socio-economic resources modify the experience of food insecurity among vulnerable populations are relatively less explored areas of research.
Given the background and research gap, the study contributes to the literature in the following ways: First, the study adopts a holistic approach by incorporating the role of household
demographic vulnerabilities and socio-economic resilience to examine their potential and relative impact on food insecurity. Household vulnerability refers to the extent to which households
and individuals can be negatively impacted by an external shock, which could be either physical or socio-economic. The extent of demographic vulnerability of the household is gauged
based on the household’s disadvantaged positions in the labor market, their lower economic
productivity, and/or physical or functional limitations (Lamidi, 2017). In this regard, households with higher age and employment dependency are considered more vulnerable. Moreover,
in countries like Pakistan, where female labor force participation is quite low, a higher femaleto-male sex ratio may also push households into vulnerable populations. Greater vulnerability
to food insecurity among households that have children, elderly inactive persons, more women,
and big household size can be conceptualized as the resultant effect of a high dependency
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Journal of Asian and African Studies 00(0)
ratio—the ratio of the total number of dependents to the total number of working-age adults in
each household. Different studies are based on a single indicator of demographic vulnerability,
providing preliminary and partial insights regarding the demography of food insecurity. By taking into consideration many indices of household demographic vulnerability, this study offers
more in-depth insight in this regard.
Second, household resilience to food insecurity may induce heterogeneity in a household’s
experience of food insecurity even facing the same level of vulnerability. Resilience is defined by
households’ current economic status, their possession and value of human, physical, and financial
assets, and household opportunity set. Households’ capability to diversify their incomes, their geographical and regional advantages, and access to public and local safety nets, all define the household opportunity set. In recent years, households’ resilience to shocks is increasingly associated
with their scale on gender parity. Women empowerment, in this regard, has earned an essentially
important role in expanding household opportunity set with its positive impacts on household food
security (Arimond et al., 2010; Malapit and Quisumbing, 2015; Verhart et al., 2016). There is also
growing evidence to support the fact that income in the hands of women contributes more to
improving household food security as compared with men (Olumakaiye and Ajayi, 2006). It has
been labeled as the most effective step in realizing the right to food and governments are usually
urged to adopt transformative food strategies by focusing on the redistribution of roles between
men and women (De Schutter, 2013). In this study, we have taken multiple indicators of household
socio-economic resilience to understand the underlying process and mechanism through which
households experience food insecurity.
Another contribution of the study lies in its use of a novel Food Insecurity Experience Scale
(FIES). The FIES included in HIES and Pakistan Social and Living Standard Measurement Survey
(PSLM) (2018–2019) is the first official source of data to compile the SDG indicator 2.1.2 for
Pakistan. The data of over 100,000 households with proportional representation from provinces
and districts, as well as rural and urban areas, are expected to provide an in-depth analysis of the
major determinants of food insecurity in Pakistan. Demographic factors provide a broad overview
of the diverse characteristics of a household lying at different levels of food insecurity. These data
are critical and valuable for service planning and interventions for vulnerable groups, and addressing the issue may assist in minimizing food disparities.
Furthermore, the study is also innovative regarding its use of multiple strategies for inferential
analysis. The study constructs the indices for household vulnerability and resilience by clubbing
the representative indicators of similar dimensions. The inferential statistics are then derived using
these indices. To extend the insights from the study deeper, the households are further divided on
a 5-point scale of low to high vulnerability and resilience, and categorical inferential analysis is
then conducted on these scales. As a result, the study can be used to provide detailed information
to health care providers, authorities, and policymakers about the situation and challenges of food
insecurity in Pakistan faced at the household level.
The rest of the study is organized as follows. The literature review is presented in the second
section. The third section presents the methodology. In the fourth section, results and discussion
are presented. The fifth section concludes the study.
Literature review
Food insecurity is a multifaceted experience influenced by many demographic, economic, and
social factors, and its determinants differ across regions, nations, social groups, and time (Riely
et al., 1999). Akbar et al. (2020) documented that household’s income, employment, agricultural
income, donations, parental education level, and some households’ characteristics are important
Parveen et al.
5
factors for improving food security in Pakistan. In another study, Hameed et al. (2020) examined
the socio-economic determinants of household food insecurity at regional and national levels for
Pakistan. The study identified education and household’s involvement in agricultural activities
reduce the likelihood of food insecurity significantly.
The empirical evidence regarding the indicators of household vulnerability and the likelihood of food insecurity has focused mostly on the size of the household as an indicator of
dependency ratio. The study by Babatunde et al. (2007a) in Nigeria found that households with
large sizes are more likely to be food insecure than small-sized households due to a decrease in
per capita calorie consumption. Ihab et al. (2015) also advocated that large families with older
and/or school-aged children are more likely to be food insecure (Shariff and Lin, 2004) due to
their economic inactivity. A recent study on Nigeria by Lamidi et al. (2019) showed that a significantly higher risk of severe food insecurity occurred among households with children and
those with disabled elderly persons. On the other hand, Abdulai and CroleRees (2001) have
found that the size of the household is significant for food security in rural farming areas
because it allows for more labor and increases the likelihood of diversification of a portfolio of
income-earning activities.
Household resilience to food insecurity is attributed to several factors. Ciani and Romano (2012)
showed that factors such as income and asset diversity, education, strategic handling of resources,
and household livelihood strategies can significantly enhance the resilience of households. The current income of a household determines whether it is impoverished or not. Similarly, household
income diversification makes it more immune to shocks that could force it into food poverty. Several
studies including Hakeem et al. (2003), Titus and Adetokunbo (2007), Jacobs (2009), and Aidoo
et al. (2013) concluded that food insecurity is more prevalent in households with low income. Such
families are food deficient both in terms of quantity and quality of food (Maskooni et al., 2013),
emphasizing the need of eradicating poverty as a prerequisite for hunger relief.
Human capital accumulation is widely acknowledged as a vital resource for breaking the intergenerational cycle of poverty and building a strong resilience capacity for food security. The
expenditures on schooling, medical care, and food are an indication of the human capital formation
activities of households (Evenson and Mwabu, 1998). In this regard, it is evident that households
with low food expenditures, hence malnourished, are likely to rank high on the food insecurity
scale (Mohammadi et al., 2012). This significantly higher dietary quality is linked to greater overall food expenditure despite controlling for income and livelihood (Lo et al., 2012). A trade-off
between human capital expenditure and food security is also often evident in poor families, as
Ahmed et al. (2017) reported that an increase in health expenditure reduces food security. This
forces family members to buy less healthy, cheaper food, leading to household food insecurity,
culminating in chronic diseases and further food insecurity (Berkowitz et al., 2014).
Being the important components of household resilience, physical and financial assets are also
believed to be strong predictors of food security. Studies by Gebre (2012) and Ahmed and Abah
(2014) in urban households of Ethiopia and Nigeria, respectively, showed that household asset
possession and access to credit have a significant and negative impact on a household’s food insecurity. Households with assets are expected to be more resilient to supply or price shocks that result
in food shortages.
Furthermore, households having access to public and local safety nets are said to be better protected against food insecurity. According to many studies, favorable government involvement has
a positive impact on household food security (e.g. Bahta et al., 2019; Ngema et al., 2018; Van der
Veen and Gebrehiwot, 2011). Also, when social welfare programs are in place during times of food
scarcity, households are not pushed to sell their animals, preventing asset depletion and allowing
people to stay in their communities (Welteji et al., 2017). Hassan et al. (2016) found that the
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Journal of Asian and African Studies 00(0)
majority of the Benazir Income Support Program income received by households was spent on
food, and it was a substantial source of food security. The favorable impact of the government’s
interventions on food security and the food-for-work programs has also been validated by Van der
Veen and Gebrehiwot (2011) and Ngema et al. (2018).
Household resilience is also strongly associated with opportunities set, which may be
expanded by women empowerment. In this regard, several studies have examined food security
in female-headed households, along with female income, employment, and education. Kennedy
and Peters (1992) found that the level of income dominated by women has a positive and significant impact on household caloric intake. The female education related to food led to a lower
chance of being food insecure (Chinnakali et al., 2014). Furthermore, women’s freedom to
access the market, women’s literacy, and access to the media were all positively related to food
security (Harris-Fry et al., 2015). Aziz et al. (2021) is a notable and recent contribution relating
food insecurity and women empowerment in Pakistan. By specifying different dimensions of
women empowerment, the study showed that women with access to legal rights, social networking, information and communications technology services and familial empowerment are better
food secure. The similar findings are reported by Aziz et al. (2021) for the relationship between
women agricultural empowerment and food insecurity in rural household of Azad Jammu and
Kashmir. Contrary to these studies, Kabbani and Wehelie (2004) found that the situation of food
security is more fragile in households headed by a female (Ahmadi and Melgar-Quiñonez, 2019;
Bawadi et al., 2012).
The region of the households also influences the household’s food insecurity. The study by Iram
and Butt (2004) in Pakistan showed that the food security situation is satisfactory in urban areas.
The lower economic ability of rural households for food supply, high rates of unemployment, and
illiteracy in rural regions make them vulnerable to food insecurity (Akbarpoor et al., 2016; Tumaini,
2017). However, the findings of Sultana and Kiani (2011) and Bashir et al. (2010) are contrary to
these studies.
Research on food insecurity can be enriched by examining the relative importance of household
vulnerabilities and socio-economic resilience. The brief review of the literature demonstrates that
studies taking into account a wide range of household characteristics for determining the extent of
food insecurity are scarce, emphasizing the contribution of the current study in the literature.
Moreover, none of the studies is based on FIES, thus limiting their scope in capturing the multifaceted nature of food insecurity.
Data and methodology
Model specification
This section provides a framework to evaluate the impact of demographic vulnerabilities and
socio-economic strength of a household on food insecurity. Symbolically, the general form of the
model is given as follows
FII i = f ( HVi , HRi )
(1)
Equation (1) shows that food insecurity (FII ) is the function of household vulnerabilities (HV ),
and household socio-economic resilience ( HR) , where FI is based on FIES devised by FAO and
HV and HR are vectors depicting household characteristics clubbed as household vulnerabilities
and resilience, respectively
Parveen et al.
7
Current Economic Status 


Human Capital


 Ownership of Assets 


Household Resilience = 
Value of Assets

 Region and Provinces 


 Women Empowermennt 
 Social Safety Nets 


Age Dependency




Sex Ratio

Household Vulnerability = 


Household Size


 Employment Dependency 
Given the above vectors of variables, the following model is constructed to analyze the relationship between household vulnerabilities, resilience, and food insecurity.
The final form of the model substituting the dimensions of household resilience is given as
follows
FII i = β 0 + β1CESI i + β 2VAI i + β3OAI i + β 4 HCI i + β5 HVI i + β 6 Provi +
β 7 Regi + β8WEmpi + β9 SSN i + vit
(2)
where
FII i = Food Insecurity Index
CESI = Current economic status of household measured by primary and secondary income
VAI = Value of the asset index
OAI = Ownership of asset index
HCI = Human capital index
HVI = Household demographic vulnerability index
Prov = Province of the household
Reg = Region of the household
WEmp = Women Empowerment
SSN = Social Safety Nets including pension, government income maintenance program (Govt),
and Zakat (Local).
As a benchmark model, Equation (2) is estimated by taking all variables in the form of indices
computed from principal component analysis (PCA). Equation (2) is initially estimated for the
overall data set, then for rural and urban regions, and finally for all provinces. As a variant of the
benchmark model, equation (2) is also estimated using an ordered logit model by transforming all
indices into ordered categories. The details of variables, indices, and categories are given in the
next section.
Data and description of variables
The empirical analyses of the study are performed on the latest cross-sectional, microdata taken
from the PSLM 2018–2019. The household survey is conducted by the Government of Pakistan
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Journal of Asian and African Studies 00(0)
and the Pakistan Bureau of Statistics, which offers reliable and detailed data on household-related
indicators, collected from four provinces of Pakistan, as well as from urban and rural regions.
Indices containing information regarding different demographic characteristics and the food insecurity status of households are constructed using PCA.
The Food Insecurity Index is computed on the FIES, which assesses individual or family food
insecurity using an experience-based approach. The FIES is a statistical scale that contains a set of
eight questions about people’s access to sufficient food concerning both quantity and quality.
Table 1 presents the scale and distribution of the respondents based on their responses to FIES.
Along with the average value of the index, its categories are also constructed to present the extent
of food insecurity experienced by the participants, from food secure to extremely food insecure.
Table 2 provides information on the different variables constituting households’ socio-economic
resilience and household demographic vulnerability. Households’ human capital generation activities are linked with specific expenditures on medical care, food, and education (Evenson and
Mwabu, 1996). The present study used expenditures on education, food, and health incurred by
each household and constructed a human capital expenditure index, and categorized it from very
low to very high human capital.
The household demographic vulnerability index is computed from household size, sex ratio,
employment dependency, and age dependency. It is further divided into five categories, ranging
from very low to very high. These categories present the magnitude of the dependency ratio of the
households.
The value of the asset index is based on the value of assets owned by households, such as
property value, financial assets and credit, and the value of livestock. The ownership of asset
index is computed based on ownership of property, livestock, and financial assets. For both of
these indices along with average value and standard deviation, categories are also constructed to
depict the percentage of households falling into different categories.
The next index is the women’s empowerment index and is computed on women’s responses
to questions regarding marriage, education, job, family planning, and also decisions about the
purchase of consumption items, that is, food, clothing, footwear, medical treatment, recreation,
and travel. The five categories are made, which represent the extent of freedom of women in
households, ranging from very low to very high.
The last index is the social safety net index. It is computed over the responses of participants
that they received either zakat and a pension or any financial assistance from the government. The
categories range from very low to very high. Table 2 also provides information regarding the
geographical and regional distribution of the households.
The present study used three techniques for statistical analysis. For the construction of indices,
PCA is conducted. For estimation, the ordinary least squares (OLS) technique is used, while
ordered logistic regression is used to assess the robustness of results. A brief description of each
technique is given below.
Current situation of food insecurity in Pakistan
Figure 1 illustrates the situation of household food insecurity in Pakistan, as assessed by the current
study using data from the PSLM 2018–2019. According to the survey, 63% of the households are
food secure and the remaining 37% are food insecure. While the severity of food insecurity varies
from slightly insecure to extremely food insecure, that is, almost 15.07% of households are slightly
insecure, 13.67% are moderately insecure, 3.60% are highly insecure, and around 4.74% are
extremely food insecure.
Parveen et al.
9
Table 1. Distribution of respondents on FIES scale.
Description
Dummy
Percentage
You or others in your household worried about not
having enough food to eat because of a lack of money
or other resources?
Still thinking about the last 12 MONTHS, was there
a time when you or others in your household were
unable to eat healthy and nutritious food because of
a lack of money or other resources?
Was there a time when you or others in your
household ate only a few kinds of foods because of a
lack of money or other resources?
Was there a time when you or others in your
household had to skip a meal because there was not
enough money or other resources to get food?
Still thinking about the last 12 MONTHS, was there
a time when you or others in your household ate
less than you thought you should because of a lack of
money or other resources?
Was there a time when your household ran out of
food because of a lack of money or other resources?
Was there a time when you or others in your
household were hungry but did not eat because there
was not enough money or other resources for food?
Was there a time when you or others in your
household went without eating for a whole day
because of lack of money or other resources?
0 = Food secure
1 = Food insecure
81.82
18.18
0 = Food secure
1 = Food insecure
66.87
33.13
0 = Food secure
1 = Food insecure
68.39
31.61
0 = Food secure
1 = Food insecure
90.38
9.62
0 = Food secure
1 = Food insecure
84.66
15.34
0 = Food secure
1 = Food insecure
0 = Food secure
1 = Food insecure
92.84
7.16
92.58
7.42
0 = Food secure
1 = Food insecure
94.85
5.15
Food Insecurity
Categories
Percentage
Categories
0 = Food secure (0 on
FIES scale)
1 = Slightly food insecure
(1–2 on scale)
2 = Moderately food
insecure (3–4 on scale)
3 = Highly food insecure
(5–6 on scale)
4 = Extremely food
insecure (7–8 on scale)
62.93
Mean
Standard
deviation
0.158
0.257
FI
15.07
13.67
3.60
4.74
FIES: Food Insecurity Experience Scale; FI: Food Insecurity Index.
Figure 2 depicts the food insecurity situation across the regions and provinces, which illustrates that
rural households of Punjab are more food secure (24%) as compared with rural households of Khyber
Pakhtunkhwa (KPK) and Sindh, which are 6% food secure, while rural households of Baluchistan are
4% food secure. Moreover, rural households in Sindh are less food secure as compared with urban
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Journal of Asian and African Studies 00(0)
Table 2. Description of independent variables.
Human capital
expenditures
Mean
Total education 29765.29
expenditures
Total health
8145.745
expenditures
Total food
5163.13
expenditures
HCI
0.006
Categories
Categories
Very low
0.0 <= HCI < 0.2
in index
Low
0.2 <= HCI < 0.4
Standard
deviation
Vulnerability
Index
70511.84
Household size
Mean
8.04
36947.84
Female–male
1.23
ratio
3430.77
Employment
−1.28
dependency
0.017
Age dependency 0.38
Percentage HVI
0.160
99.88
Categories
Categories
0.11
Very low
Standard
deviation
4.09
0.90
2.85
0.49
0.085
Percentage
0.0 <= HVI < 0.2 in
index
0.2 <= HVI < 0.4
0.4 <= HVI < 0.6
0.6 <= HVI < 0.8
0.8 <= HVI <= 1
77.04
Moderate
High
Very high
0.4 <= HCI < 0.6
0.6 <= HCI < 0.8
0.8 <= HCI <= 1
0.01
00
0.00
Low
Moderate
High
Very high
Ownership of
Asset
Dummies
Percentage
Women
empowerment
Dummies
Percentage
Agricultural
land
0 = No
1 = Yes
92.39
7.61
Education
decision
Commercial
building
0 = No
1 = Yes
97.60
2.40
Employment
decision
Non0 = No
agricultural land 1 = Yes
96.69
3.31
Marriage
decision
Residential
building
0 = No
1 = Yes
13.86
86.14
Livestock
0 = No
1 = Yes
97.64
2.36
Birth control
methods
decision
Family planning
decision
Index
Mean
Standard
deviation
OAI
0.038
Categories
Categories
Very small
0.0 <= OAI < 0.2
in index
0 = No
1 = to some extent
2 = to large extent
0 = No
1 = to some extent
2 = to large extent
0 = No
1 = to some extent
2 = to large extent
0 = No
1 = to some extent
2 = to large extent
0 = No
1 = to some extent
2 = to large extent
0 = No
1 = to some extent
2 = to large extent
0 = No
1 = to some extent
2 = to large extent
0 = No
1 = to some extent
2 = to large extent
0 = No
1 = to some extent
2 = to large extent
84.13
8.39
7.48
83.06
8.41
8.53
86.84
11.57
1.59
54.97
42.32
2.71
57.86
40.07
2.07
58.48
14.64
26.29
64.29
20.11
15.60
65.61
24.92
9.48
70.39
22.60
7.01
0.116
Food decision
Clothing
decision
Percentage Medical
treatment
decision
94.16
Recreation and
travel decision
21.00
1.82
0.09
0.04
(Continued)
Parveen et al.
11
Table 2. (Continued)
Human capital
expenditures
Mean
Standard
deviation
Vulnerability
Index
Mean
Small
0.2 <= OAI < 0.4
2.18
Index
Mean
Moderate
Large
Very large
VAI
0.4 <= OAI < 0.6
0.6 <= OAI < 0.8
0.8 <= OAI <= 1
Mean
Wemp
Categories
Very low
Low
Property value
Financial assets
and credit
Value of
livestock
VAI
Categories
485005.20
78294.09
3.08
0.41
0.17
Standard
deviation
5437031.00
217039
Standard
deviation
0.214
0.176
Categories
Percentage
0.0 <= Wemp < 0.2 52.40
0.2 <= Wemp < 0.4 30.75
Moderate
High
0.4 <= Wemp < 0.6 12.91
0.6 <= Wemp < 0.8 3.83
52193.13
265931.50
Very high
0.8 <= Wemp <= 1
0.005
Categories
0.015
Social welfare
Percentage Zakat
Very small
0.0 <= VAI < 0.2
99.95
Small
0.2 <= VAI < 0.4
0.04
Moderate
Large
Very large
Region
Region
0.4 <= VAI < 0.6
0.6 <= VAI < 0.8
0.8 <= VAI <= 1
Dummies
0 = otherwise
1 = rural
0 = Otherwise
1 = Punjab
0 = Otherwise
1 = Sindh
0 = Otherwise
1 = Baluchistan
0.00
0.00
0.00
Percentage
34.82
65.18
57.18
42.82
75.60
24.40
88.30
11.70
Punjab
Sindh
Baluchistan
Standard
deviation
0.11
Pension and
Others
Index
Dummies
0 = No
1 = Yes
0 = No
1 = Yes
Mean
Percentage
99.34
0.66
98.95
1.05
Standard
deviation
0.102
Percentage
98.95
00
0.01
SSN
Categories
Very low
Low
Moderate
0.012
Categories
0.0 <= SSN < 0.2
0.2 <= SSN < 0.4
0.4 <= SSN < 0.6
High
0.6 <= SSN < 0.8
0.00
Very high
0.8 <= SSN <= 1
1.05
HCI: Human Capital Index; HVI: Household Demographic Vulnerability Index; OAI: Ownership of Asset Index;
VAI: Value of the Asset Index; SSN: Social Safety Nets.
Source: Authors’ construction from PSLM 2018–2019.
households in Sindh. Moreover, the severity of food insecurity ranging from slightly insecure to
extremely food insecure is also high in rural Sindh as well as in urban households in Sindh.
This is because several Sindh districts suffered from drought in 2018. Agriculture and livestock
are the primary sources of livelihood in the province’s districts’ rural areas, with many residents
dependent on small-scale farming. The 2018/19 drought wreaked havoc on their livelihoods, leading to low food/cereal and livestock growth and a huge number of people becoming food insecure.
Results and discussion
Table 3 presents the empirical results of OLS estimation. Table 4 contains provincial estimations.
The probability value of F-statistics indicates that the overall model is significant with a reasonable
value of R2 for cross-sectional data. The coefficients reported are corrected for heteroscedasticity.
12
Journal of Asian and African Studies 00(0)
Figure 1. Food insecurity in Pakistan.
Figure 2. Food insecurity situation across regions and provinces.
A household’s vulnerabilities, determined by its age, gender, and employment composition,
are detrimental to its food security. Our results conform to the resource dilution model of Downey
(1995), where a higher dependency ratio in the form of more children and economically inactive
members lowers the level of per capita resources making households highly susceptible to poverty and food insecurity. Recent empirical evidence, also identifies a strong role of household
composition in explaining food insecurity. However, among the socio-economic resilience factors, the value of physical and human assets reduces a household’s probability of being insecure
substantially. The coefficient of the regional dummy also has a positive association with food
insecurity, which means the food insecurity in rural households is greater compared with urban
households. In this regard, our results are contrary to Lamidi et al., (2019) for Nigeria, showing
that for Pakistan, urban households are less vulnerable or more economically resilient to food
insecurity. A recent study by Ishaq et al. (2018) conducted for Pakistan also conforms to this proclivity of food insecurity in rural areas. They reported that economic access to food is primarily
determined by households’ differences in land holdings, employment, and education in rural
areas. Own production only cushions landlords against food shocks, whereas a significant number
of rural cultivators and landless households are net buyers of food and vulnerable to food
insecurity.
Parveen et al.
13
Table 3. Overall and regional regression on food insecurity.
Variables
Human Capital Index
Ownership of Asset Index
Women Empowerment Index
Region
Current Economic Status
Social Safety Net
Vulnerability Index
Value of Asset Index
Punjab
Sindh
Baloch
Overall regression
Rural
Coefficient
Coefficient
SE
−0.825***
0.046
−0.050***
0.005
−0.098***
0.004
0.047***
0.001
−0.072**
0.001
−0.059***
0.004
0.048***
0.008
−1.142***
0.131
−0.025***
0.002
0.012***
0.002
−0.011***
0.002
Prob > F = 0.0000
R2 = 0.1133
Urban
SE
−1.053***
0.083
−0.060***
0.006
−0.114***
0.005
–
–
−0.071***
0.001
−0.073***
0.006
0.026**
0.011
−2.611***
0.188
−0.012***
0.002
0.049***
0.003
−0.005
0.003
Prob > F = 0.0000
R2 = 0.1042
Coefficient
SE
−0.673***
0.046
−0.010
0.007
−0.071***
0.005
–
–
−0.067***
0.001
−0.040***
0.006
0.088***
0.011
−0.455***
0.088
−0.058***
0.003
−0.056***
0.003
−0.031***
0.004
Prob > F = 0.0000
R2 = 0.1062
SE: standard error.
Source: Author’s own calculations based on PSLM data.
***and ** indicate 1% and 5% level of significance, respectively. The region dummy is categorized as rural = 1 and urban = 0,
lincome is the log of total income of the household.
Province dummies (Punjab, Sindh, Baloch) categorized as 1 = living in that specific province, 0 = otherwise.
Table 4. Provincial regression on food insecurity.
Variables
Human Capital Index
Ownership of Asset Index
Women Empowerment
Current Economic Status
Value of Asset Index
Social Safety Net
Vulnerability Index
Region
Punjab
Khyber
Pakhtunkhwa
Baluchistan
Sindh
Coefficient SE
Coefficient SE
Coefficient SE
Coefficient SE
−0.755*** 0.066
−0.127*** 0.007
−0.137*** 0.005
−0.073*** 0.001
−0.460*** 0.089
−0.076*** 0.005
0.005
0.016
0.042***
0.002
Prob > F = 0.0000
R2 = 0.1082
−0.730*** 0.059
0.027***
0.007
0.022***
0.007
−0.052*** 0.001
−2.163*** 0.135
−0.034*** 0.009
0.001
0.011
−0.007*** 0.003
Prob > F = 0.0000
R2 = 0.1058
−0.670*** 0.177
−0.122*** 0.012
−0.111*** 0.016
−0.080*** 0.003
−5.323*** 0.325
−0.024
0.028
0.135***
0.021
0.022***
0.004
Prob > F = 0.0000
R2 = 0.1125
−1.020*** 0.121
0.080***
0.024
−0.096*** 0.009
−0.072*** 0.002
−5.342*** 0.219
−0.046*** 0.012
0.144***
0.022
0.102***
0.003
Prob > F = 0.0000
R2 = 0.1703
SE: standard error.
Source: Author’s own calculations based on PSLM data.
***and ** indicate 1% and 5% level of significance, respectively. The region dummy is categorized as rural = 1 and urban = 0,
lincome is the log of total income of the household.
Province dummies (Punjab, Sindh, Baloch) categorized as 1 = living in that specific province, 0 = otherwise.
As far as the provincial dummies are concerned, KPK is taken as the benchmark category. The
result showed that households in Punjab and Baluchistan are more food secure as compared with
KHK, while the contrary holds for Sindh. According to Sindh dummy, food insecurity is 1.2%
higher in Sindh than in KPK. The higher food insecurity in Sindh is due to the drought in 2018,
14
Journal of Asian and African Studies 00(0)
which correlates with the time considered for this study. According to the report of Sindh Drought
Needs Assessment (SDNA) 2019, the most common kind of loss experienced by households was
livestock loss, which was recorded by 29% of households, followed by livelihood loss (21%), crop
damages (19%), and loss of food stocks (7%).
Table 3 also contains estimates across rural and urban regions. The regional results are consistent with overall results as far as the direction of variables is concerned. However, households in
the rural and urban regions are affected by different variables with different intensities. Results
indicated that an increase in the demographic vulnerability index increases food insecurity less in
rural households (2.6%) as compared with urban households (8.8%). This is attributed to the
higher cost of living in urban regions as compared with rural ones. Ravallion (2007) stated the fact
that the cost of living in cities is 30% greater than that of rural regions exacerbating the world’s
growing levels of urban poverty, and that, with limited economic opportunities, impoverished
urban people’s capacity to move out of poverty remains constricted. Value of assets happened to
be most instrumental in reducing rural food insecurity followed by human capital, while for urban
households, the human capital accumulation is most effective.
Furthermore, food insecurity is 4.9% more in rural households and 5.6% lower in urban households of Sindh against the rural and urban households of KPK, respectively. This highlights that
Sindh is performing worse than KPK on urban food insecurity grounds. Food insecurity is 1.2%
lower in rural Punjab and 0.5% lower in Baluchistan than in rural KPK. In urban households, it is
5.8% lower in Punjab and 3.1% lower in Baluchistan compared with urban households in KPK.
Food security is a major issue in KPK, since the province does not produce enough quantities of
food to meet the demand and must rely on subsidized imports from neighboring provinces. It is
emphasized that advanced techniques and tools should be used to enhance agricultural yields to
close the gap between food demand and supply (Zhou et al., 2019).
Due to heterogeneities in the demographic implications of food insecurity at the provincial level,
provincial regressions are also conducted and reported in Table 5. Several interesting observations are
underscored. For instance, human capital is most substantial in reducing food insecurity in Sindh. The
wealth status of households also has the most pronounced effect on food insecurity in Sindh, followed
by Baluchistan. Demographic vulnerabilities also add to food insecurity more in Sindh as compared
with other provinces.
Table 5 presents the empirical results of ordered logistic regression, which are consistent with
the estimates obtained from OLS regression. The odd ratios showed that women’s empowerment
is important in pushing the households into a lower category of food insecurity. Devine et al.
(2006) and Martin and Lippert (2012) showed that females have greater food-management skills.
Particularly, mothers adopt a wide range of approaches such as financial management and shopping strategies to shield their families from hunger. Contrary to it, the vulnerability of a household
is highly influential in taking households to a higher level of food insecurity. All variables are
significant at 1%, except ownership of assets. Outcomes of regional dummies indicate that food
insecurity in rural households is 0.356 times more than that in urban households. The marginal
effect shows that rural households are in a higher category of food insecurity by 0.07% as compared with urban households.
The regional regression also highlighted the importance of women empowerment, social safety
nets, and current economic status in transiting households from a higher to a lower category of food
insecurity.
Table 6 depicts the empirical results of OLS and ordered logistic regression across the provinces, respectively. The human capital index, total household income, asset holding index, and
social welfare index have significant negative relationships with food insecurity across the provinces. Women empowerment is influential for food security except in KPK, showing cultural
Parveen et al.
15
Table 5. Ordered logit estimates of overall and regional regression dependent variable food insecurity
(categories).
Variables (categories)
Human Capital
Ownership of Asset
Women Empowerment
Region
Current Economic Status
Social Safety Net
Vulnerability
Value of Asset
Punjab
Sindh
Baloch
Overall regression
Rural
Coefficients
Coefficients
Marginal
effects
−0.411***
−0.090***
−0.028
−0.006
−0.934***
−0.204***
0.356***
0.076***
−0.521***
−0.114***
−0.574***
−0.125***
0.934***
0.204***
−0.751***
−0.164***
−0.877***
−0.185***
−0.299***
−0.063***
−0.551***
−0.111***
Prob > χ2 = 0.0000
Pseudo R2 = 0.089
Urban
Marginal
effects
−0.334***
−0.080***
−0.11**
−0.027**
−0.934***
−0.223***
–
–
−0.495***
−0.118***
−0.694***
−0.166***
0.559***
0.133***
−0.825***
−0.197***
−0.715***
−0.167***
−0.034*
−0.008*
−0.477***
−0.108***
Prob > χ2 = 0.0000
Pseudo R2 = 0.073
Coefficients
Marginal
effects
−0.524***
−0.086***
0.331***
0.055***
−0.979***
−0.161***
–
–
−0.580***
−0.096***
−0.399***
−0.066***
1.870***
0.308***
−0.601***
−0.099***
−1.297***
−0.198***
−0.899***
−0.132***
−0.738***
−0.102***
Prob > χ2 = 0.0000
Pseudo R2 = 0.109
Source: Author’s own calculations based on PSLM data.
***** and * indicate 1%, 5%, and 10% level of significance, respectively. The region dummy is categorized as rural = 1 and
urban = 0; lincome is the log of total income of the household.
Province dummies (Punjab, Sindh, Baloch) categorized as 1 = living in that specific province, 0 = otherwise.
restrictions on the ability of women to fully participate in food production activities in certain
regions of Pakistan (Kabeer, 1990). As a result of social restrictions, women have unequal access
to productive resources. Male-headed households have considerably greater off-farm income,
total household income, crop yield, and available labor hours than female-headed households
(Babatunde et al., 2008). The regional dummy indicates that rural households of the provinces
have more food insecurity than urban households, except in KPK. The rural households of KPK
are more food insecure than the urban ones.
Discussion
According to the findings, 37% of households in Pakistan experience some level of food insecurity,
ranging from mild to severe levels. The empirical findings demonstrated that human capital provides significant resistance to food insecurity. The theoretical presumption that educated, healthy
people are more productive (Babatunde et al., 2007; Cuesta, 2015), along with the supporting
evidence from our empirical findings, makes a compelling argument for allocating greater resources
to the development of human capital at the household and national levels. However, there are two
important implications of the relationship between food insecurity and human capital accumulation. First, it is necessary to consider the possibility of a trade-off between expenditures on various
forms of human capital. For instance, greater medical costs associated with ill health may push
family members to choose less nutritious, less expensive food, resulting in food insecurity within
the home (Berkowitz et al., 2014). Second, it is important to acknowledge that food insecurity and
a low level of human capital can reinforce one another, trapping households in a vicious cycle.
In addition to human capital, physical capital also entails a significant contribution to a household’s food insecurity. Asset holdings like livestock ownership allow direct access to livestock
−0.648***
−0.100***
−0.982***
−0.152***
−1.162***
−0.180***
−0.493***
−0.077***
−0.778***
−0.121***
−0.859***
−0.133***
0.741***
0.115***
0.363***
0.054***
Prob > χ2 = 0.000 Pseudo
R2 = 0.0917
Marginal
effects
−0.852***
−0.205***
0.748***
0.180***
−1.214***
−0.292***
−0.536***
−0.129***
−1.056***
−0.254***
−0.723***
−0.174***
1.360***
0.327***
0.726***
0.170***
Prob > χ2 = 0.0000
Pseudo R2 = 0.104
Coefficient
Coefficient
Marginal
effects
Sindh
Punjab
Marginal
effects
−0.978***
−0.219***
−0.661***
−0.148***
−1.585***
−0.354***
−0.485***
−0.109***
−1.30***
−0.290***
−0.339
−0.076
2.029***
0.454***
0.068**
0.015**
Prob > χ2 = 0.000
Pseudo R2 = 0.093
Coefficient
Baluchistan
Marginal
effects
−0.211***
−0.053***
0.496***
0.124***
0.203***
0.050***
−0.480***
−0.120***
−0.598***
−0.149***
−0.302***
−0.075***
0.111
0.028
−0.059**
−0.015**
Prob > χ2 = 0.0000 Pseudo
R2 = 0.075
Coefficient
KPK
KPK: Khyber Pakhtunkhwa.
Source: Author’s own calculations based on PSLM data.
***and ** indicate 1% and 5% level of significance, respectively. The region dummy categorized as rural = 1 and urban = 0, lincome is the log of total income of the household.
Human Capital
Ownership of Assets
Women Empowerment
Current Economic Status
Value of Asset
Social Safety Nets
Vulnerability
Region
Variables (categories)
Table 6. Ordered logistic regression estimates for provinces dependent variable: food insecurity (categories).
16
Journal of Asian and African Studies 00(0)
Parveen et al.
17
products, giving financial revenue through the sale of livestock and contributing to enhanced
crop yields through improved productivity from dung. It is an organic fertilizer resource that is
highly useful to small farmers, since it helps them to increase their income and, as a result, their
consumption (Sansoucy et al., 1995). Similarly, operational land holding is one of the most
important components in the production of food, fiber, and other goods and services. It is not just
an important indicator of household well-being, but it also plays a significant part in most households’ financial portfolios (Byerlee et al., 2005). This implies that having physical and financial
assets is a crucial defense against food insecurity and other sorts of material distress (Beverly
et al., 2003; Carroll, 1997). Households with a sizable asset base are predicted to weather the
crisis, stay safe, and sustain their per capita spending more than families with less capital in the
event of a shock or budget shortfall. The findings of this study are consistent with those of Gebre
(2012) and Zhou et al. (2019).
In addition to asset possession and the value of assets, the current income of the household is a
significant correlate of food insecurity. It raises people’s financial standing, enabling them to
obtain more food and lower the likelihood of food insecurity. Higher-income households consume
much more nutrients in the form of dairy products, meat, and fruit than lower-income households
do (Birthal, 1996). This association of total household income was also proved by several studies
like Arene (2008) and Jacobs (2009).
Food security is positively correlated with women’s empowerment, which includes their freedom of access to the market, literacy, and the media (Harris-Fry et al., 2015). The implications of
this outcome of our study are very strong and are consistent with the widespread narrative regarding the role of women empowerment for food security. For instance, Barker (2005) and Burchi
et al. (2011) demonstrated that a greater sense of empowerment in women is translated into better
entrepreneurial skills, resulting in a stream of financial resources, adding to household resilience
from food insecurity. With higher command over a household’s financial resources and participation in decision-making, women are in a position to retain a higher proportion of the household
budget for the family’s dietary requirements (Clement et al., 2019; Engle, 1988; Guyer, 1980;
Olumakaiye and Ajayi, 2006). Mother has a superior understanding of dietary requirements based
on the family’s age, gender, and workload. Global policymakers’ viewpoints concur with our
results that women’s participation in decision-making reduces food insecurity and malnutrition. In
a presentation to the United Nations in March 2013, Olivier de Schutter (2013), a Human Rights
Council special rapporteur on the Right to Food, contended that the shortcut to lowering undernourishment and malnutrition is to share power with women and that this is the strongest step
toward recognizing the right to food.
Furthermore, the place of residence also plays an important role in the household food insecurity status. The statistical result indicates that rural regions are more food insecure than urban
regions in Pakistan. This brings to light the major problems of rural food insecurity, which are not
widely studied in current research, most likely because rural regions are assumed to have enough
food supplies. This suggests that although the majority of rural residents are involved in farming
and raising animals, they still face different issues from urban households. For instance, showed
that the cost of irrigation utilizing tube wells and fertilizers also rises as a result of the ongoing
increase in energy prices. Small farmers, who are crucial to ensuring food security in a nation,
therefore experience inflation and food insecurity.
Another important implication of the study arises from the role of assistance either from the
government or society for food security. Unconditional cash transfers have a positive impact on the
psychological well-being of household members and their consumption (Haushofer and Shapiro,
2013). In addition, households will not be pressured to sell their animals if direct transfer programs
and other transfer schemes are in place during times of food scarcity, limiting asset depletion and
18
Journal of Asian and African Studies 00(0)
enabling people to stay in their communities (Welteji et al., 2017). The result is supported by
research, such as Zakari et al. (2014) and Agidew and Singh (2018), and highlights the role that
government and society can play in reducing food insecurity in the community.
It is a well-known fact that the dependency ratio of the household substantially leads to food
insecurity, as it increases the per capita food consumption (Sultana and Kiani, 2011). Members of
large families compete over the limited available resources. They eat in limited quantities, with little
concern for the quality of their food. Similarly, age dependency significantly contributes to food
insecurity. People of a specific age, that is, below 15 years or above 50 years, also put a burden on
household expenditures. Food insecurity is more frequent among elderly people, which may be due
to a reduction in earnings, making it difficult to afford a nutritious diet (Fernandes et al., 2018).
Aged people also experience bad health, which increases their health expenditures. Similarly, families with a large number of children or unemployed members increase the burden. The reason is that
such people become economically inactive and cannot contribute to the household’s total income.
The empirical results show that Punjab has a relatively better food security status than the other
provinces. It is the country’s most developed region. Punjab contributes staple and cash crop output,
that is, 76%, 70%, 68%, and 69% of wheat, rice, sugarcane, and cotton, respectively. As a result, the
population of the country is heavily reliant on Punjab for its food needs (Mahmood et al., 2016).
Punjab also has a better place in terms of infrastructure as well as the provision of secure, efficient,
and accessible transportation services, which has the potential to improve productivity and reduce
hunger. Every year, the Punjab government allocates a substantial amount of funds to infrastructure
development. The allocation pattern for infrastructure development indicates a significant rise in the
yearly allocation for infrastructure development, which has grown to 112,960 million rupees in 2015
from 68,313 million rupees in 2014 in Punjab (Government of Punjab, 2013), followed by 126,106
million rupees in 2016 and 117,200 million rupees in 2017 (Government of Punjab, 2016). Although
the allocation was lower in 2017 than that in 2016, it still accounted for 29% of Punjab’s overall
budget in 2017 (Government of Punjab, 2016). Punjab is blessed with abundant natural resources.
The land is plain, with five rivers flowing throughout the year, providing enough water for irrigation
and also providing silt, making Punjab’s land highly fertile; substantially increasing the agricultural
production of the province as compared with other provinces. Because it is agrarian, several agrobased industries have emerged and developed in and around it. Punjab has a far higher level of development than the rest of the provinces. It has considerably less terrorism than KPK or Karachi, and it
has practically none of Karachi’s frequent shutdowns (Zaidi, 2013).
Conclusion and policy implications
In this research, we have explored the determinants of food insecurity with a particular focus on
demographic vulnerabilities and socio-economic resilience by utilizing the FIES scale for Pakistan.
The analysis of the study is based on multiple empirical exercises and is conducted for both national
and disaggregated levels. According to the empirical findings, human capital expenditure, asset
possession, value, and women empowerment are important resilience factors to prevent households to fall into food insecurity. The results further reveal that in rural regions, food insecurity is
high as compared with urban regions.
As food insecurity is a multifaceted phenomenon, one policy to tackle this problem will not
suffice. The formulation of policies is required for both short-term and long-term solutions to the
problem of food insecurity on a number of grounds. Based on the study’s findings, a targeted policy framework including cash transfers and strategic social assistance is recommended to address
food insecurity immediately. This will stabilize the stream of income for impoverished households
further enhancing their resistance to food insecurity.
Parveen et al.
19
For long-term solutions, the findings of the study are expected to be directly relevant to the
Ministry of National Food Security and Research’s policymaking process on food security. The
design of a national food security policy is still in its early phases, with the policy being launched
for the first time in 2018. The study can be seen as a significant step forward in policy development
in many key areas. First, human capital accumulation is a prerequisite for ensuring food security in
the country. The allocation of resources to each head of human capital, that is, education, health,
and food, must be significantly raised to lower the percentage of the vulnerable population.
Concurrently, low household income and high levels of poverty are key obstacles to attaining these
resources. This may be addressed through equitable economic progress by assisting the poor in the
process of economic growth, improving their access to credit and productive means (land and
livestock holding), and supporting them with physical and market infrastructure.
The empowerment of women in terms of resource ownership and decision-making is the second
important area where significant actions are needed. Women might be specifically targeted for cash
transfers to enhance the food security status of families. Similar to this, there may be specific
opportunities for women to implement this long-term approach.
In addition to gender disparity, geographical and regional disparities may also be given careful
attention. Rural food insecurity can be reduced by increasing agricultural productivity and nonagricultural enterprises through the adoption of modern agricultural practices and by launching new
program initiatives for labor training in agricultural and non-agricultural businesses. Also in rural
areas, more investment, sanitation, and emphasis are needed in infrastructure, food distribution system, health, education, safe drinking water, and other basic facilities. Furthermore, off-farm job
alternatives in rural regions must be created to integrate surplus labor from the agriculture sector to
increase agricultural labor productivity and farm profitability.
The study is limited in many respects and provides the avenue for future research. Even while
FIES perfectly captures the core of food insecurity, it focuses mostly on the accessibility factor.
Future research can be carried out by taking into account the stability and utilization dimension of
food insecurity explicitly into account. In addition, the study may be expanded to compare the factors that affect food insecurity at the national and household levels.
Data availability statement
Data sources are clearly mentioned. However, data will also be shared on demand.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
ORCID iD
Saira Tufail
https://orcid.org/0000-0002-5871-1724
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Author biographies
Ayesha Perveen is an independent researcher and freelancer. She is also interested in graphic designing.
Saira Tufail, PhD, is an Assistant professor of Economics at Fatima Jinnah Women University. Her
research interests include household economics, Monetary ad Fiscal Policy analysis, and open economy
macroeconomics
Verda Salman, PhD, is an Assistant professor of Economics in the School of Social Sciences and Humanities
(S3H), National University of Sciences and Technology (NUST) Islamabad, Pakistan. Her research focuses
on experimental issues at individual & household levels, maternal and child health, human resource development, food insecurity and quantitative analysis of data.