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Research Article
Analyzing the Composite Effect of Corruption and Socio-Economic Variables on Food Insecurity in Pakistan: A Comprehensive Study
expand article infoSaira Habib, Hasnain Didar
‡ Comsats University Islamabad, Islamabad, Pakistan
Open Access

Abstract

Food insecurity affects 842 million people, or 12% of the global population, with Asia and Africa accounting for over 92% of the undernourished population. Notably, South Asia comprises 35% of this figure. In countries like Pakistan, factors such as corruption, foreign investment, remittances, education, population growth, gross domestic product (GDP), and unemployment exacerbate food insecurity. This study, based on data from the World Development Indicators (WDI) and Transparency International spanning 1995 to 2021, employs an Autoregressive Distributed Lag (ARDL) Model to examine both short- and long-term relationships between corruption and socio-economic variables. Our findings reveal that corruption, population growth, and unemployment significantly increase food insecurity. Specifically, corruption negatively impacts production and quality management, while high unemployment discourages production, and population growth strains available resources. Conversely, GDP growth is found to significantly reduce food insecurity, suggesting that economic growth can help address this issue. Additionally, education, remittances, and foreign direct investment pose potential threats to food security. For Pakistan’s long-term economic development, reducing food insecurity is essential, underscoring the need for political stability. Effective oversight of corruption, and strategic management of GDP, inflation, and remittances are vital to stabilizing food security.

Keywords

food insecurity, corruption, GDP, population growth, unemployment

JEL codes: D74, Q19, Q11

Introduction

Food insecurity is a critical global issue with far-reaching implications for individual well-being and development. The Food and Agriculture Organization (FAO) of the United Nations defines food insecurity as “the lack of regular access to safe and nutritious food, which hampers normal growth, development, and the ability to lead active and healthy lives.” This issue affects approximately 842 million people globally, or around 12% of the world’s population. Developing countries, particularly in Asia and Africa, bear the greatest burden, accounting for over 92% of the undernourished population. South Asia alone is home to 294.7 million food-insecure individuals, comprising 35% of the global total of undernourished people1.

Food insecurity manifests at various levels – global, regional, national, and local, as well as within households. Its determinants vary across these levels, making it a complex, multidimensional phenomenon influenced by environmental, political, economic, and social factors. Despite an overall increase in global food production, factors such as natural disasters, economic crises, political instability, rural poverty, and corruption continue to undermine food security. Pakistan, like many other countries, faces these challenges, further exacerbated by events such as the war on terror, military operations affecting residential areas, and frequent natural disasters, including floods that destroy capital and crops and recent earthquakes. Pakistan ranks 93rd out of 104 countries on the Global Hunger Index, indicating an “extremely alarming” status. Although Pakistan’s score improved from 43.6 to 33.9 between 1990 and 2015, the issue remains persistent.

Addressing food insecurity is a crucial aspect of the United Nations’ 17 Sustainable Development Goals (SDGs), aimed at eradicating poverty and hunger by 2030. Achieving this goal requires a focus on increasing agricultural production and improving the income of smallholder farmers through optimized land use and other productive resources. In rural areas, food insecurity is closely tied to agriculture, which serves as a primary source of both nutrition and income for households. Consequently, the agricultural sector plays a pivotal role in ensuring food security and is considered the economic backbone of many nations. Scholars such as Srinita (2015) and Baiphethi and Jacobs (2009) have emphasized the importance of the agricultural sector in achieving food security objectives. Addressing the multifaceted nature of food insecurity requires collaborative efforts at international, regional, national, and local levels, with coordinated actions across environmental, political, economic, and social spheres.

Despite significant progress in poverty reduction in recent years, food insecurity remains a pervasive issue in Pakistan. As an agricultural country, Pakistan has struggled to ensure food security for its population. South Asia, including Pakistan and India, is recognized as one of the world’s most food-insecure regions, with 500 million people suffering from hunger (Asghar and Muhammad 2013).

To address these challenges, the government of Pakistan has introduced various modern and scientific agricultural methods and techniques. The agricultural sector is vital to the country’s economy, accounting for approximately 20.88% of GDP and employing 43.5% of the labor force. It is also closely interconnected with other sectors, contributing significantly to Pakistan’s overall socio-economic development. According to a report by the Government of Pakistan (GOP 2018), per capita income in U.S. dollars increased from К1,531 to К1,629; nevertheless, food insecurity remains widespread, with nearly 60% of Pakistan’s population experiencing malnutrition (World Hunger 2018).

Pakistan is one of seven nations, along with China, Bangladesh, the Democratic Republic of the Congo, Indonesia, India, and Ethiopia, that together account for nearly two-thirds of the world’s undernourished population (FAO 2019; SBP 2019). It ranks 77th globally for malnutrition. Among South Asian countries, Pakistan has the highest rates of stunting and significant rates of wasting, as reported by UNICEF (2019) and WHO (2019). The country faces severe malnutrition in children under five, with elevated levels of stunting, wasting, underweight, and overweight cases, especially in comparison to other developing nations. For low-income countries, agricultural growth and poverty reduction are critical to combating food insecurity and malnutrition. However, over the past decade, Pakistan’s agricultural sector has been heavily impacted by climate change, leading to increased food prices. Data from 1980–2010 indicate that inflation, driven by rising production costs, disproportionately affects the poor. Sharp price hikes have impacted staple foods like rice, meat, and wheat. According to the Pakistan Bureau of Statistics (2015), the annual inflation rate fell to 8.4% in November 2020, a decrease attributed to lower food and healthcare costs.

Like other factors in Pakistan, corruption is a pervasive and destructive force that undermines economic growth and development. Corruption is particularly pressing due to its negative impacts across various sectors, including agriculture and food security. It often leads to inefficient resource allocation and diverts resources away from public goods, such as food security programs. Corruption levels are measured through the Corruption Perceptions Index (CPI), a rating system that scores each country based on perceived corruption in government, with scores ranging from zero (high corruption) to 100 (low corruption). Transparency International, an organization dedicated to combating public-sector corruption, publishes the CPI annually.

Corruption directly impacts food availability and affordability, especially for vulnerable populations. In macroeconomic environments, food security fundamentally requires integrity in public administration and a rejection of corruption, much like economic development (Escobar et al. 2009). Countries with deeply corrupt political and legal systems experience the worst levels of food security. A lack of transparency and accountability in political power severely undermines national food security (FAO 2005).

Literature Review

Food security plays a pivotal role in household well-being, particularly as societies evolve. Initially viewed primarily from a supply-side perspective, the concept of food security expanded in the 1980s to include demand-side challenges, focusing on individual and household access to food. Research indicates that food security is multidimensional, influenced by factors such as civil conflict, climate change, and natural disasters. This study evaluates the impact of social, political, and economic variables on national food insecurity.

Hameed et al. (2020) identified key socio-economic contributors to food insecurity in Pakistan, finding that 47.7% of households faced food insecurity based on PSLM 2014–15 data. Major factors included low income, limited education, and unemployment. Abdullah et al. (2022) observed that larger households tended to experience lower food security, while households with higher income and education levels were more secure. Similarly, Rasheed et al. (2022) found that income, education, and employment were significant determinants of food security in Pakistan. Ishaq et al. (2018) highlighted regional disparities, emphasizing the importance of addressing underlying causes, such as poverty and limited access to education.

Muhammad and Sidique (2019) identified household income, education, and agricultural involvement as key determinants of food security in Nigeria. Rose and Adil (2021) found that income, education, and market access significantly influenced food security in Punjab, Pakistan. Kibria et al. (2023) emphasized poverty as a primary driver of food insecurity in Peshawar. Afridi et al. (2021) noted widespread food insecurity in Pakistan, particularly in rural areas, while Munawar et al. (2021) highlighted social capital, resource access, and education as resilience factors against food insecurity. Asghar and Muhammad (2013) pointed to poverty, limited education, and restricted credit access as major contributors to Pakistan’s food insecurity. Nazli and Hamid (1999) underscored gender disparities as critical factors affecting food allocation and security. Finally, Asif (2013) highlighted the impact of climate change on Pakistan’s food security due to decreased irrigation availability.

Research on corruption suggests that certain societies are predatory and lack principled leaders committed to controlling corrupt officials (Fjeldstad and Isaksen 2008). In such cases, the principal-agent approach to analyzing corruption and implementing anti-corruption legislation may be less effective. Political scientists have found that only a few countries possess universalistic social systems characterized by fair administration and low levels of corruption (Bardhan 1997). In contrast, countries with particularistic power structures, where individuals are treated inequitably, tend to experience systemic corruption (Johnston 2005). In such contexts, some political scientists advocate for collective action theory over principal-agent theory to address entrenched corruption. In collective action settings, free-riding can drive primary corruption: individuals who doubt that others will make sacrifices to combat corruption are more likely to rely on those who do, despite the associated risks and costs. In highly corrupt societies, individuals may respond to institutional changes aimed at reducing corruption by (a) fully cooperating by neither engaging in corruption nor tolerating it, (b) partially cooperating by participating in corruption but reporting others’ misconduct, (c) privately cooperating by avoiding corrupt acts while refraining from reporting, or (d) fully deflecting by engaging in corruption without reporting it (Johnston 2014).

Corruption negatively affects the availability, access, utilization, and stability of food security by diverting resources away from agricultural investment, leading to inadequate infrastructure and lower productivity (Mehlum et al. 2006). It increases food prices through hoarding and price-fixing, further limiting access for disadvantaged populations (Transparency International 2019). Corruption in land administration contributes to farmers losing their land, exacerbating food insecurity among these individuals (Deininger and Feder 2001). Additionally, corruption undermines food safety regulations, resulting in low-quality food products that jeopardize public health (FAO 2018).

The present study reveals that corruption exacerbates food insecurity by undermining governance, distorting the equitable distribution of resources, contributing to high rural poverty rates, and slowing the implementation of necessary infrastructure projects (Kubik 2023; Khurshid and Abid 2024). Despite the growing body of literature on food insecurity in Pakistan, there remains a significant research gap in understanding the relationship between macro-level socio-political factors, such as corruption, and economic variables (including unemployment, inflation, infrastructure, population growth, economic development, education, and foreign remittances) that contribute to food insecurity. While several studies have examined food insecurity at the national level, few have focused on the specific socio-political factors that drive food insecurity in Pakistan. Although some research has explored the impact of corruption on economic growth, poverty, and social development, investigations into the direct effects of corruption on food insecurity are scarce. Furthermore, there is a lack of studies analyzing the relationship between corruption and various social and economic factors contributing to food insecurity in the country. Thus, a research gap exists in exploring the role of corruption as a driver of food insecurity at the national level and in understanding how it interacts with other socio-economic factors to exacerbate this issue. Such research could provide valuable insights into the complex relationship between corruption, food insecurity, and socio-economic development in Pakistan, informing evidence-based policy interventions to address these challenges.

Brinkman and Hendrix (2011) examined the relationship between food insecurity and violent conflict, discussing how factors such as poverty, environmental degradation, and political instability exacerbate both food insecurity and conflict. They emphasized the importance of addressing these root causes to achieve lasting peace and development. Hellegers (2022) assessed the risks associated with global food security stemming from trade dependence on Russia and Ukraine, highlighting the threats posed by political instability and climate change. The author stressed the necessity of developing a diverse and resilient food system. Önder (2021) linked corruption to food security, demonstrating through data from 75 countries that corruption negatively impacts food security. The study underscores the importance of addressing corruption to improve global food security.

Mukhtar and Abdullahi (2020) explored the factors influencing food insecurity in Nigeria, identifying political instability, corruption, and poverty as primary contributors. Malik (2011) investigated the relationship between food insecurity, landlessness, and violent conflict in Pakistan, suggesting that addressing poverty, land access, and food security could mitigate the risks of violent conflict. Kousar et al. (2021) studied the correlations between food security, population growth, urbanization, water availability, and government stability, concluding that stable governments are essential for combating food insecurity and water scarcity.

Molotoks et al. (2021) forecasted global food security trends up to 2050, taking into account climate change, population shifts, and land use. They emphasized the necessity of a holistic approach to ensure food security. Aziz et al. (2021) explored the relationship between women’s empowerment in agriculture and household food insecurity in Pakistan, finding that empowered women contribute to reduced food insecurity, particularly in low-income households. Akbar et al. (2020) investigated factors leading to food insecurity in Pakistani households, revealing that households headed by women, the elderly, or disabled individuals are more food insecure. Additionally, larger households with lower incomes experience greater levels of insecurity. Suleiman et al. (2022) studied the effects of food inflation on daily wage earners in Pakistan, demonstrating that food inflation negatively impacts their livelihoods.

Mora-Rivera and Gameren (2021) analyzed the influence of remittances on food security in Mexico, finding that remittances have a positive effect on food security, especially in households with specific characteristics. Similarly, Moniruzzaman (2022) examined the effect of remittances on food security in Bangladesh, concluding that remittances improve living standards and enhance food security for recipient households. Hossain et al. (2020) evaluated the implications of land degradation for food security, advocating for sustainable land management practices to ensure future food security. Farooq et al. (2019) highlighted the critical link between sustainable agriculture and food security, asserting that sustainable agricultural practices are essential for both environmental sustainability and global food security.

M. Akbar et al. (2020) explored the effects of parental employment and education on food security in Pakistan, emphasizing the critical role of female education and employment in enhancing household food security. Dhahri and Omri (2020) assessed the importance of global capital flows in achieving the Sustainable Development Goals (SDGs), revealing that foreign direct investment (FDI) and foreign aid significantly reduce poverty and hunger in developing countries.

While numerous studies address food insecurity in Pakistan, few have analyzed the relationship between corruption and the economic factors that contribute to food insecurity. The impact of corruption, in conjunction with other socio-economic factors, remains underexplored in the Pakistani context. This gap presents a valuable research opportunity to investigate corruption as a significant contributor to food insecurity in the country.

Methodology

This study employs the Autoregressive Distributed Lag (ARDL) bounds testing approach to analyze the relationship between food insecurity and various descriptive variables. The ARDL method effectively captures data processes, differentiates between dependent and explanatory variables, and often outperforms the Johansen and Juselius cointegration technique, particularly in small sample sizes.

Our primary goal is to understand the impact of socio-political and economic factors on food insecurity in Pakistan. Given the limitations of available data, we selected ARDL bounds testing, a method introduced by Pesaran et al. (2001). This approach enables testing for level relationships between a dependent variable and regressors, regardless of their stationarity. It utilizes conventional F and t-statistics to assess the significance of lagged levels in a univariate equilibrium correction mechanism. Due to its effectiveness in identifying variable relationships, the ARDL method has gained popularity in climate-agriculture studies across various regions, including Ghana (Asumadu-Sarkodie and Owusu 2016), Pakistan (Arshed and Abduqayumov 2016), and Europe (Acaravci and Ozturk 2010).

Data and Variables

Secondary data was obtained from the World Development Indicators (WDI) and the Corruption Perception Index (CPI) for the period from 1995 to 2021, rather than using primary data. Consequently, this paper examines food insecurity from a macroeconomic standpoint, utilizing variables gathered at a macro level rather than focusing on a specific region or state. In this study, food insecurity serves as the dependent variable, for which the Food Production Index is used as a proxy. The independent variables include foreign direct investment, current GDP, personal remittances, government expenditure on education, inflation, unemployment, population growth, and corruption. The selection of these independent variables was based on their close relationship with the dependent variable.

The prevalence of undernourishment, expressed as a percentage of the population, is employed as a proxy for food insecurity. This metric reflects the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels required to maintain a normal, active, and healthy life. For instance, a prevalence of undernourishment below 2.5% would be indicated by a data point of 2.5.

Econometric Model

The Autoregressive Distributed Lag (ARDL) model is an econometric tool utilized to analyze both short-term and long-term relationships between economic variables. By integrating autoregressive and distributed lag components, the ARDL model accommodates a flexible lag structure. A thorough understanding of these components and their interpretations facilitates an effective analysis of dynamic economic variables.

FIN = β0 + β1FDIt + β2GDPt + β3REMt + β4EDUt +

+ β5INFt + β6UNEt + β7POPt + β8CRPt + εt, (1)

where t – Time series; LFIN – Food Insecurity; β – Coefficient; LFDI – Foreign Direct Investment; LGDP – Gross Domestic Product; REM – Personal Remittances; EDU – Government Expenditure on Education; UNE – Unemployment; POP – Population Growth; CRP – Corruption; α – Constant; ε – Error Term; β1, β2 and β3 are the coefficient of independent Variables.

(2)

ARDL Co-integration

(3)

Where in the above equation

  • The prevalence of undernourishment (% of population) is used as a proxy for food insecurity. This metric represents the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels required to maintain a normal, active, and healthy life.
  • In period t the independent variables include Foreign Direct Investment (FDI), Gross Domestic Product (GDP), remittances, education expenditure, inflation, unemployment, population growth, and corruption. FDI, GDP, and remittances are expressed in nominal terms, and their logarithmic values have been used for analysis. The remaining variables – inflation, unemployment, and population growth – are presented in percentage form.
  • λ, η are the short-run coefficients associated with their respective lagged variables.
  • ε represents the error term, which captures the unexplained variation in the dependent variable.

Results

Augmented Dickey-Fuller (ADF)

The ADF test, presented in Table 1, is commonly used in time series analysis to assess stationarity. This test is an enhanced version of the Dickey-Fuller test, particularly suitable for larger and more complex sets of time series models. All variables exhibit stationarity at the 1% significance level when considering the first difference, and no variables show non-stationarity at this level. At the level of analysis, only education (EDU) demonstrates stationarity at the 10% significance level, while all other variables display non-stationarity.

Table 1.

Augmented Dickey-Fuller Test (Augmented Dickey-Fuller)

Variables At Level At First Difference Integration
t-Statistic Prob. t-Statistic Prob.
Variables 1.76 0.99 -7.003*** 0.00 I(1)
LFIN -0.56 0.86 -4.36*** 0.002 I(1)
CRP -1.49 0.53 -4.24*** 0.003 I(1)
EDU -2.88* 0.06 -5.96*** 0.00 I(0)
LFDI -2.49 0.13 -5.97*** 0.00 I(1)
LGDP -1.43 0.55 -10.28*** 0.00 I(1)
LLAND -1.22 0.65 -4.46*** 0.003 I(1)
POP -1.45 0.55 -4.74*** 0.001 I(1)
REM -0.27 0.92 -4.15*** 0.005 I(1)

Bounds Test

The bounds test presented in Table 2 utilizes F-statistics and t-statistics at various levels to examine the relationships between the variables. The null hypothesis posits that no level of relationship exists among the variables. Table 4 displays an F-statistic value of 3.91. To interpret these results, it is essential to compare this value against the critical values at different significance levels. The F-statistic of 3.9084 exceeds both the upper and lower bound critical values at the 10%, 5%, and 1% significance levels. Consequently, we reject the null hypothesis at these levels, indicating the presence of a significant relationship. Based on the test statistics and critical values, we conclude that a significant relationship exists, thereby disproving the null hypothesis, which asserts that no relationship exists at any level.

Table 2.

Bounds Test (Null Hypothesis: No levels relationship)

Test Statistic Value Signif. I(0) I(1)
F-statistic 3.91 10% 1.85 2.85
K 8 5% 2.11 3.15
2.50% 2.33 3.42
1% 2.62 3.77
Table 3.

ARDL Results

Variables Value Variables Value
R-squared 0.99 Mean dependent var 81.89
Adjusted R-squared 0.97 S.D. dependent var 22.15
S.E. of regression 4.028 Akaike info criterion 5.9
Sum squared resid 162.21 Schwarz criterion 6.67
Log likelihood -60.69 Hannan-Quinn criter. 6.13
F-statistic 49.76 Durbin-Watson stat 2.12
Prob (F-statistic) 0
Table 4.

Short Run Results

Variable Coefficient t-Statistic Prob.
C -54.595 -3.10 0.01
FINS(-1)* -0.796 -3.90 0.00
CORRPERCENT(-1) 7.094 3.19 0.01
EDUPERCENT(-1) 1.487 3.29 0.01
FDIPERCENT** 1.013 3.93 0.00
GDPG** -0.211 -3.00 0.02
POPPERCENT(-1) 10.699 3.26 0.01
REMPERCENT(-1) 1.646 3.35 0.01
UNMPERCENT** 0.230 3.32 0.01
D(FINS(-1)) 0.466 2.43 0.04
D(CORRPERCENT) 2.024 1.96 0.09
D(EDUPERCENT) 0.734 3.08 0.02
D(POPPERCENT) 4.404 2.68 0.03
D(REMPERCENT) 1.112 3.32 0.01
CointEq(-1)* -0.796 -13.0027 0

ARDL Method

In our analysis, Table 3 shows that the R-squared value is 0.890149, indicating a strong fit for our model. This value suggests that the independent variables are effectively predicting the dependent variable. In multiple regression analysis, we typically refer to the adjusted R-squared, which accounts for the number of predictors in the model and can sometimes yield a negative value due to the degrees of freedom. The range for adjusted R-squared is from -1 to 1, with higher values being more desirable. It is worth noting that the adjusted R-squared is generally considered more reliable than the traditional R-squared, as it provides a more accurate measure of model fit when multiple variables are involved. Furthermore, the Durbin-Watson statistic, which measures autocorrelation in the residuals, has a recommended range of 1.7 to 2.3. Our model falls within this range with a value of 2.11, indicating no significant autocorrelation issues.

Short-run Results

Table 4 presents the short-run estimates, highlighting the immediate effects of various factors on food insecurity, such as inflation rate, unemployment, corruption, GDP, population growth, foreign direct investment, education expenditure, and remittances. This table includes detailed information on the coefficients, standard errors, t-statistics, and p-values for each variable.

Long-run Results

Table 5 presents the long-run outcomes of the estimations, illustrating the effects of various factors such as inflation rate, unemployment, corruption, GDP, population growth, foreign direct investments, and remittances on food insecurity. This table includes the coefficients, t-statistics, and p-values for each variable, derived from the long-run ARDL (Autoregressive Distributed Lag) model. Now, we will analyze each component:

Corruption significantly increases food insecurity over time. Specifically, a one-unit increase in the Corruption Perception Index (CPI) is associated with an increase of 8.917 units in food insecurity. As corruption – manifested through bribery, fraud, and illegal dealings – pervades the agricultural sector, it discourages farmers from producing adequate quantities and quality of food. This leads to a decrease in the variety and nutritional value available to consumers, thereby exacerbating food insecurity and making individuals more vulnerable to hunger.

Educational expenditures, as a percentage of GDP, have a positive effect on food insecurity. Specifically, a one-unit increase in educational expenditures correlates with an increase of 1.869 units in food insecurity. Given the p-value of zero and a high t-statistic of 12.819, we can conclude that this relationship is statistically significant. Investing in higher education supports economic shifts from agriculture to industrial and service industries which causes a decrease in farmers and domestically produced food. If these changes are not accompanied by new investments in agricultural education, innovation and rural infrastructure, it could harm food security in the longer term. This observation is in contrast to M. Akbar et al. (2020), which emphasizes women’s education and employment on improving household food security, their study focused on the microeconomic (household) level while our assessment between macroeconomic trends. Women’s education might bolster household food security, but the impacts can vary on a national level, especially with changes in economic structure. Furthermore, education is acknowledged as a contributing factor to economic and agricultural development in a related study by the FAO Committee on Food Security (2024)2. The important difference is in what you do with that education: without investing in agricultural education, rural economies cannot flourish to the benefit of everyone, resulting in food insecurity even with economic growth overall.

Similarly, foreign direct investment (LFDI) also positively affects food insecurity. A one-unit increase in LFDI results in an increase of 1.273 units in food insecurity, as indicated by the coefficient. In contrast, GDP (LGDP) has a significant negative impact on food insecurity. For every one-unit increase in LGDP, food insecurity decreases by 0.266 units, supported by a t-statistic of 13.525 and a p-value of 0, confirming statistical significance. As GDP rises, it typically drives growth across all sectors, including agriculture, which enhances the quality, quantity, and variety of food items available.

A one-unit increase in population (POP) results in a 13.447-unit increase in food insecurity, indicating a strong, statistically significant, and positive relationship. This relationship highlights a critical issue: as the population grows at a faster rate than food production and availability, more individuals face food insecurity. In contrast, the relationship between remittances (REM) and food insecurity is somewhat different. While the relationship is statistically significant and positive, indicating that food insecurity increases with rising remittances, this finding may initially seem counterintuitive. As affordability improves due to remittances, households may have greater access to food, including higher-quality options. However, it is essential to recognize that increased affordability alone does not guarantee food consumption; availability remains a crucial factor. If food production is compromised or if there are issues with quality control, households may not be able to access the required and nutritious food. This situation underscores the detrimental role of corruption, as it discourages farmers from meeting production requirements and allows bribery to impede food quality during processing.

Food security is significantly and positively correlated with unemployment (UNE). Specifically, a one-unit increase in unemployment leads to a 0.289-unit increase in food insecurity. This relationship underscores the critical role of unemployment as a precursor to economic downturns, which can exacerbate food insecurity. As unemployment rises, households may experience reduced income and purchasing power, making it more difficult to access sufficient and nutritious food. In the regression model, the intercept, represented by the constant term (C), is -68.619. This negative coefficient indicates that, when all independent variables are set to zero, food insecurity reaches a baseline level of -68.619, suggesting a potentially severe level of insecurity without the influence of the independent variables. Over the long term, food insecurity is significantly affected by each of the model’s independent variables: Corruption (CRP), Educational Expenditures (EDU), Foreign Direct Investment (LFDI), Gross Domestic Product (LGDP), Population Growth (POP), Remittances (REM), and Unemployment (UNE). While most of these variables show a beneficial impact on food security, LGDP has an adverse effect. The significance levels, with p-values at or near zero, provide robust evidence that these variables significantly influence food insecurity.

Table 5.

Long Run Results

Variables Coefficient t-Statistic Prob.
CRP 8.917 8.830 0
EDU 1.869 12.819 0
LFDI 1.273 13.525 0
LGDP -0.266 -6.776 0.0001
POP 13.447 14.311 0
REM 2.068 12.133 0
UNE 0.289 7.869 0
C -68.619 -9.546 0

Diagnostic Test

According to Bhatti et al. (2006), the CUSUM (Cumulative Sum) test allows for the assessment of the stability of coefficient values for the variables within a regression model. This test uses cumulative data to determine whether the regression coefficients exhibit consistent variation or fluctuate unpredictably. Figures 1 and 2 illustrate the CUSUM and CUSUM of squares graphs. At a 5% significance level, the stability of the model is confirmed if the plot remains within the designated boundaries. In this case, the blue line representing the CUSUM remains between the two red threshold lines, indicating that the parameters of the model are stable. Furthermore, both the CUSUM and CUSUM of squares falling below the 0.05% threshold limits signify structural stability of the model. This further confirms the overall quality of fit, reinforcing the reliability of the regression analysis conducted.

Figure 1.

CUSUM

Figure 2.

CUSUM of Squares

Discussion and Conclusion

This research paper investigates the impact of various macro-level variables on food insecurity in Pakistan, a pressing issue with significant implications for the nation’s social and economic development. Utilizing the Autoregressive Distributed Lag (ARDL) approach – an established and robust econometric methodology – the study analyzes how several independent variables influence food insecurity. These variables include foreign direct investment (FDI), gross domestic product (GDP), remittances (REM), education spending (EDU), inflation (INF), unemployment (UNE), population growth (POP), and corruption (CRP). By distinguishing between short-term and long-term effects, the study provides valuable insights into the complex relationships among these macroeconomic factors and food insecurity in Pakistan.

The findings particularly highlight the roles of population growth and foreign direct investment in exacerbating food insecurity. Notably, the results indicate that population growth positively correlates with food insecurity, underscoring the challenges posed by rapid demographic expansion. As the second most populous nation in South Asia, Pakistan is on track to become the fourth most populous country globally, which presents significant challenges in ensuring adequate food availability for its growing population. This finding emphasizes the urgent need for policies focused on job creation, income generation, and equitable resource distribution to effectively harness the potential demographic dividend associated with population growth.

Moreover, foreign direct investment (FDI) is found to have a positive correlation with food insecurity, indicating that an increase in FDI may lead to a decline in the agricultural and food sectors while fostering growth in industrial and technical sectors. This finding aligns with previous research that highlights the benefits of FDI for host countries, particularly in terms of job creation, technology transfer, knowledge sharing, and overall economic growth. However, it also underscores the necessity for policies that ensure FDI reaches the most vulnerable segments of the population to effectively address food insecurity.

Another significant finding is that gross domestic product (GDP) exerts a notable negative impact on food insecurity. This confirms the conventional belief that economic growth tends to reduce hunger, highlighting the importance of exploring the complexities of economic development and its relationship with food security. While economic growth is often viewed as a key driver of poverty alleviation and improvements in living standards, this study emphasizes the need for nuanced understanding of how this relationship operates.

The study also reveals adverse effects of corruption, inflation, unemployment, and education spending on food insecurity in Pakistan. Specifically, corruption has a significant positive impact on food insecurity, as it infiltrates the food supply chain, disrupts resource allocation, distorts market mechanisms, and raises transaction costs. These factors ultimately lead to decreased agricultural productivity, limited access to resources for farmers, and disrupted distribution channels. This underscores the critical importance of addressing corruption as a fundamental component of any strategy aimed at alleviating food insecurity in Pakistan.

Corruption is widespread within Pakistan’s governmental and economic systems, manifesting in more insidious forms such as bribery. Citizens frequently resort to paying bribes for basic commodities, highlighting systemic issues within the country. Although organizations like the Truth-Seeking Global Organization play a crucial role in exposing corruption, powerful individuals in Pakistan often evade accountability, perpetuating injustice and hindering progress. To effectively combat corruption, it is essential to prioritize information transparency, enhance jurisdiction, and hold public officials accountable. However, genuine political will is necessary for meaningful action, which has been lacking despite previous commitments and efforts. Establishing a comprehensive framework for addressing corruption, particularly concerning foreign banks, could be achieved by aligning domestic laws with international treaties such as the United Nations Convention Against Corruption (UNCAC).

Additionally, the study underscores the positive relationship between unemployment and food insecurity. The loss of employment and stable income significantly undermines individuals’ ability to afford a sufficient and nutritious diet. This often results in reduced food quality, limited choices, and inadequate access to essential nutrients, especially among the unemployed and their families. Prolonged unemployment exacerbates food insecurity, increasing vulnerability among affected populations.

Furthermore, the study reveals an unexpected positive relationship between education spending and food insecurity. The calculation results indicate that spending on education, as a percentage of GDP, has a negative impact on food security. This is due to the fact that the education of the population contributes to technological progress and structural shifts towards industrial sectors, as a result of which the agricultural sector is stagnating.

In the case of Pakistan, government spending on education primarily contributes to a relatively skilled labor force that tends to prefer employment in the manufacturing and service sectors over agriculture. Agriculture in Pakistan is often perceived as a low-skilled profession, making it less attractive to educated individuals. Additionally, most of the education budget is allocated to traditional degree programs rather than research-oriented education, limiting innovation in agricultural practices.

Our findings contrast with those of M. Akbar et al. (2020), who studied the impact of employment and parental education on household food security using micro-level data from a small primary survey. Given the differences in scope and methodology, it is understandable that our results diverge.

Furthermore, while the 52nd FAO Committee on Food Security (2024) highlighted the positive impact of education on GDP and the agricultural sector in general, this trend does not necessarily hold in Pakistan. Empirical data from the Bureau of Statistics, Pakistan shows a decline in agricultural production and its share in GDP over time, indicating a different dynamic at play.

To effectively address food insecurity in Pakistan, comprehensive strategies and interventions are essential. This requires coordinated efforts from the government, civil society, and international organizations to tackle the root causes of food insecurity, including structural challenges, socio-economic disparities, and governance issues. Investing in agricultural infrastructure, research, and technology is particularly crucial for enhancing productivity and ensuring food availability.

Additionally, addressing food insecurity necessitates targeted policies aimed at improving access to education, healthcare, and employment opportunities – especially in rural areas where a significant portion of the population relies on agriculture. A multi-faceted approach is essential to empower individuals and communities, breaking the cycle of poverty and food insecurity while promoting sustainable and inclusive development.

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Information about the authors

Saira Habib – assistant professor at Department of Economics, Comsats University Islamabad, Islamabad, 45550, Pakistan. Email: saira.habib@comsats.edu.pk

Hasnain Didar – student, Department of Economics, Comsats University Islamabad, Islamabad, 45550, Pakistan. Email: hasnain.shah.9674@gmail.com

1 FAO (2013).
2 The 52nd CFS Plenary (October 2024, Rome) adopted its Final Report, focusing on food security, urban food systems, and the 2024–2027 work programme.
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