Sadia Zahid
| Bilal Tariq | Mazhar Abbas | Abdul Kafi | Nor Hasni Bt. Osman* | Nura Abubakar Allumi | Mohd Kamarul Irwan Abdul Rahim | Hafiz Waqas Ahmed Ansari
© 2025 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
Youth unemployment poses significant challenges to economic growth and social stability in South Asia, particularly in Pakistan, India, and Bangladesh. This study uses System Generalized Method of Moments and Dynamic Panel Threshold Models to examine how youth unemployment impacts GDP growth and reveals key threshold effects. Results show that a 1% rise in YU reduces GDP per capita growth by 0.20% on average across the region, with declines reaching 0.30% when unemployment exceeds a 17% threshold. Education are positively correlated with GDP growth 0.16%, while GD in employment contribute to a 0.13% GDP decline for each 1% increase in disparity. Inflation and FDI further influence growth, with inflation reducing GDP by 0.09% and FDI increasing it by 0.11%. Comparative analysis shows that despite India’s relatively higher educational attainment, labor market mismatches persist, leading to high rates of educated youth unemployment. In Bangladesh, remittances offer economic stability amid limited domestic job opportunities Unlike previous study that finds teenage unemployment distinctly, our investigation shows a precarious joblessness threshold of 17%, at which economic instability accelerates. Using Threshold Models System and GMM, we build a non-linear association among YU and GDP growth, providing empirical evidence in favour of specific policy regulations. Findings highlight the importance of targeted labor market reforms, educational alignment with job market demands, and policies to reduce gender disparity in employment to maximize the region's youth potential.
youth unemployment, economic growth, South Asia, gender disparity, labor market reform, threshold analysis, GDP growth
Unemployment is a critical measure of a country’s economic health, reflecting both its economic stability and capacity to provide opportunities for its workforce. The issue of youth unemployment is particularly pressing in South Asia, a region experiencing a demographic shift with one of the world’s largest and youngest populations. High youth unemployment in South Asian countries like Pakistan, India, and Bangladesh is a multifaceted problem, affecting individual livelihoods, limiting economic growth, and increasing social pressures. Tackling youth unemployment is essential for ensuring that this rapidly growing young population can contribute positively to economic development and social stability [1, 2].
Globally, youth unemployment has far-reaching effects. While unemployment impacts individuals of all ages, young people are often disproportionately affected due to a combination of insufficient work experience, skill mismatches, and limited employment opportunities. The ILO defines unemployed youth as individuals of working age who are actively seeking but unable to find employment. Many South Asian countries face high youth unemployment rates, reflecting significant skill mismatches and gaps in education and training. This is evident in South Asia, where educational outcomes often fail to align with job market requirements, resulting in an “education-employment gap” [3]. Data from the ILO highlights the disparity in unemployment rates, with global youth unemployment at 13.3% compared to just 3.9% among adults in 2023 [4].
In Figure 1, India had the highest youth unemployment, which increased significantly from 2000 and peaked around 2020-2022. Bangladesh maintained a relatively low and steady youth unemployment rate, with a gradual increase over time. Pakistan had the lowest youth unemployment among the three, showing minimal fluctuations.
The economic effects of youth unemployment are profound. In Pakistan, high youth unemployment reduces workforce productivity, suppresses economic growth, and increases government spending on social welfare. Additionally, youth unemployment limits government savings, creating a feedback loop that undermines public investment in essential infrastructure and services [5]. The “brain drain” phenomenon exacerbates this issue, as skilled young workers often migrate to other countries in search of better opportunities, further straining the domestic labor market [6].
Figure 1. Comparison of youth unemployment rate among selected South Asian countries
Source: World Bank (2024)
Political uncertainty, social conflict, and surge in corruption have all been associated to youth unemployment. Kassem et al. [7] contend that young unemployment that doesn't go away fuels crime rate in Pakistan. Likewise, Zahid et al. [8] pointed out that societal marginalization and amplified crime rates are associated with unemployment in Pakistan. So, addressing youth unemployment is an approach for stabilizing long-term societal stability in addition to economic need. High youth unemployment is also linked to negative social impacts, including increased political instability, social unrest, and even mental health issues among young people. Unemployed youth may face increased rates of anxiety, depression, and social exclusion, often perpetuating cycles of poverty across generations [9, 10]. Moreover, as youth unemployment rises the crime rate in the economy also increases [7].
Figure 2. GDP growth rate and youth unemployment
Source: World Bank (2024)
Figure 2 shows that India, despite periods of strong GDP growth, faces significant challenges with youth unemployment, suggesting issues with job creation. Bangladesh's consistent economic growth is reflected in relatively stable youth employment. Pakistan, although having lower youth unemployment, exhibits volatile GDP growth, indicating potential instability in its economic structure.
Although India’s GDP has full-fledged, high youth unemployment recommends weak labor interest. Bangladesh’s steady unemployment rate aligns with economic stability, while Pakistan’s variations show susceptibility to external shocks. The lag consequence among GDP development and unemployment is obvious, as economic perfections take time to convert into job formation Imran et al. [11].
This study aims to comprehensively analyze the impact of youth unemployment on economic growth and social stability in Pakistan, India, and Bangladesh. This study addresses a critical gap in understanding the economic and social impacts of youth unemployment in South Asia, where existing research often overlooks threshold effects and country-specific differences. By applying System GMM and Dynamic Panel Threshold Models, the study identifies a critical unemployment threshold that intensifies its negative impact on GDP growth. The findings provide targeted insights for policymakers on the need for tailored labor market reforms, educational realignment, and gender-inclusive policies, offering actionable guidance for effectively leveraging South Asia's youth demographic for sustainable development.
2.1 Youth unemployment and economic impact
Youth unemployment significantly affects economic growth, particularly in South Asia, where unemployment among young people reduces workforce productivity and limits economic potential. Alvi and Fatima [5] argued that youth unemployment shrinks the productive labor force, decreases tax contributions, declines government savings and welfare spending. Gulzar et al. [12] noted that youth unemployment in Pakistan is due to mismatch in demands of the employer and the skills and knowledge of the student, which he/she learn during their education. In an analysis of economic growth limitations. Sato [13] highlighted that youth unemployment constrains countries' productive capacities, as fewer young people are actively contributing to the formal economy. The remaining young population migrates to urban areas in search of jobs. While the education system also contributes in this problem by not offering the marketable skill. The authors emphasize that Bangladesh face challenges due to rapid population growth coupled with the scarcity of matching job opportunities for educated youth. Khan et al. [14] explained that government expenditures, private investment and foreign direct investment declines the unemployment and crucial for job creation.
A study by Shah et al. [15] reinforces these findings by showing that youth unemployment in Pakistan is directly linked to lower gross domestic product (GDP) growth, as unemployed youth lack the means to contribute to productivity. Similarly, Shabbir et al. [16] conducted a comparative analysis of South Asian countries, finding that population growth increases youth unemployment rates that rises dependency ratios, which burdens national budgets due to heightened reliance on social services and fewer contributors to the workforce.
Negera [17] examined South Asian economies and conclude that youth unemployment directly impedes economic growth by limiting household consumption, reducing entrepreneurial initiatives, and driving dependence on the informal sector. Alper [18] similarly note that in developing economies youth unemployment is associated with reduced savings, lower national output, and a decline in foreign investment, all of which compound the region’s economic challenges.
Parisi [19] examined the long-term impacts of youth unemployment, particularly its "scarring" effect, in which youth who experience prolonged unemployment are likely to face reduced earnings throughout their working life. This phenomenon is particularly critical in South Asian economies, where extended youth unemployment correlates with decreased national productivity and limits economic growth. Youth unemployment also leads to lower savings rates and reduced investment, further compounding economic limitations in South Asia [18].
2.2 Youth unemployment and social instability
Youth unemployment in South Asia is associated with social instability, with high rates of joblessness linked to increased crime, political unrest, and social exclusion. Kassem et al. [7] argued that unemployed youth are often more susceptible to radicalization and participation in social unrest, as frustration over limited social mobility can lead to social grievances. This dynamic is particularly relevant in countries such as Pakistan, India, and Bangladesh, where a significant portion of the youth population remains unemployed or underemployed.
In an urban-focused study, Zahid et. al [8] found that youth unemployment contributes to increased crime rates and political protests in Pakistan’s major cities. The authors observed that prolonged joblessness among youth often leads to frustration and disenfranchisement, which manifest in various forms of social discontent. In a comparative cross-country analysis, Fung and Nga [20] discovered a strong correlation between youth unemployment and instances of social violence, particularly in ASEAN. The study suggests that as youth unemployment rises, so does the likelihood of social instability, with unemployed young people engaging in demonstrations and other forms of protest to voice their dissatisfaction with the lack of opportunities.
Zahid et al. [21] examined the psychological impact of youth unemployment on Pakistani youth, highlighting an increase in mental health issues, including anxiety, depression, and social exclusion. This emotional toll further impacts social cohesion, as affected youth experience marginalization and disengagement from social structures. Similarly, Talukder and Tanvir [22] explored the role of social exclusion in youth unemployment, noting that unemployed youth in Bangladesh are at greater risk of turning to extremist groups as a means of asserting identity and purpose.
Murshid et al. [23] added that youth unemployment often exacerbates social inequalities, as unemployed youth lack access to resources like education and healthcare, leading to cycles of marginalization and limited upward mobility. Furthermore, Srivastava [24] found a strong link between youth unemployment and crime rates in Indian urban areas, attributing the rise in crime to the frustration and economic desperation experienced by jobless youth. Imtiaz et al. [25] reported that high youth unemployment in Pakistan significantly contributes to political instability, as distrust in institutions grows among the unemployed. Urdal [26] similarly suggested that persistent youth unemployment can undermine social cohesion, increasing the risk of inter-group conflicts and civil unrest.
The majority of prior examination, including Shabbir et al. [16], typically used conservative panel regression models, which are incapable to account for likely non-linear effects. This study increases on Hansen's [27] method for defining unemployment thresholds by exploiting the Dynamic Panel Threshold Model. Additionally, System GMM is used in the waken of Blundell and Bond [28] to address endogeneity concerns, which were a drawback of earlier studies.
3.1 Data sources
This study analyzes youth unemployment and its economic and social impacts in Pakistan, India, and Bangladesh using data collected from 2000 to 2022. Key data sources include the World Bank, the International Labor Organization (ILO), and national statistical bureaus, which provide a range of economic and social indicators.
Table 1 shows the primary dependent variable is GDP per capita growth, serving as a measure of economic performance. Independent variables include youth unemployment rates, secondary education attainment rates, and gender disparity in employment. Control variables comprise inflation rates, foreign direct investment (FDI), and remittance inflows, selected for their roles in economic stability and growth. These variables are essential for capturing both the direct and indirect effects of youth unemployment on economic growth and social stability.
Table 1. Description of the variables
|
Variables |
Symbols |
Units of Measurement |
Description |
|
GDP per capita growth |
GDP |
(%) change in GDP per capita |
Annual growth rate of GDP per capita, indicating overall economic growth adjusted per individual |
|
Youth Unemployment |
YU |
Percentage (%) |
Percentage of the labor force aged 15–24 years that is unemployed |
|
Secondary Education Attainment |
SE |
Percentage (%) |
Proportion of the population aged 15+ with at least a secondary education level |
|
Gender Disparity in Employment |
GD |
Percentage (%) difference |
Difference in labor force participation rates between males and females |
|
Govt. Expenditure |
GE |
Percentage (%) of GDP |
Total government spending as a share of GDP, often used to gauge public sector contribution |
|
Inflation Rate |
INF |
Percentage (%) |
Year-on-year change in consumer price index, measuring the rate of price level changes |
|
Foreign Direct Investment |
FDI |
Percentage (%) of GDP |
Net inflows of foreign direct investment as a share of GDP, indicating economic openness |
|
Remittance Inflows |
RI |
Percentage (%) of GDP |
Remittances received from abroad as a percentage of GDP, indicating reliance on migrant incomes |
3.2 Proposed econometric model
This study employs two advanced econometric models: The System Generalized Method of Moments (GMM) Model and the Dynamic Panel Threshold Model. These models are chosen to address endogeneity and to capture the threshold effects of youth unemployment on economic growth.
3.2.1 System Generalized Method of Moments (GMM) model
The System GMM model is applied to handle potential endogeneity among variables by using lagged differences as instruments for each variable. This model is well-suited for panel data analysis, as it addresses issues of unobserved heterogeneity and endogenous regressors, both of which are common in studies on youth unemployment’s impact on economic growth [28, 29].
As suggested by Roodman [30], the System GMM method is chosen because it can handle probable endogeneity issues by using lagged instruments. Using bootstrap methods, the Dynamic Panel Threshold Model's resilience is confirmed, assuring true threshold level approximation [31]. The empirical model is specified as:
$\begin{gathered}\mathrm{GDP}_{i t}=\beta_0+\beta_1 Y U+\beta_2 E D U_{i t}+\beta_3 G D_{i t}+\beta_5 I N F_{i t} +\beta_6 F D I_{i t}+\beta_7 R I_{i t}+\mu\end{gathered}$
where, GDP is gross domestic product per capita growth for country i at time t, and YU represents the youth unemployment rate, EDU is secondary education attainment. Control variables include inflation (INF), foreign direct investment (FDI) and Remittance Inflows (RI). The model uses lagged levels of independent variables, such as (YUi, t-1, EDUi, t-1) to instrument for current levels, effectively addressing endogeneity.
3.2.2 Dynamic Panel threshold model
To capture potential threshold effects of youth unemployment on GDP growth, the Dynamic Panel Threshold Model is employed, allowing the analysis of non-linear relationships. This model is beneficial for identifying critical points—thresholds—at which the impact of youth unemployment intensifies. Studies by Hansen [27], Kremer et al. [32], Seo et al. [31] supported the utility of this model in identifying non-linear effects.
The threshold model is specified as:
$\begin{array}{rl}\mathrm{GDP}_{i t}=\beta_0+\beta_1 Y U_{i t} \times I\left(Y U_{i t} \leq \emptyset\right) +\beta_2 Y U_{i t} \times I\left(Y U_{i t}>\emptyset\right)+\beta_3 E D U_{i t}+\beta_4 G D_{i t}+\beta_5 I N F_{i t}+\beta_6 F D I_{i t}+\beta_7 R I_{i t}+\mu\end{array}$
where,
$I\left(Y U_{i t} \leq \emptyset\right)$: Indicator function that equals 1 when youth unemployment $Y U_{i t}$ is below or equal to the threshold $\theta$, and 0 otherwise. $I\left(Y U_{i t}>\emptyset\right)$ : Indicator function that equals 1 when youth unemployment $Y U_{i t}$ is above to the threshold $\theta$, and 0 otherwise.
In this model:
Below the threshold: When youth unemployment $Y U_{i t} \leq \theta$, the effect on GDP per capita growth is given by $\beta_1$.
Above the threshold: When youth unemployment $Y U_{i t}>\theta$, the effect on GDP per capita growth is given by $\beta_2$. The other variables and symbols remain the same as defined in the GMM model.
4.1 Descriptive statistics
The descriptive statistics provide an overview of the key variables used in this study. These include GDP per capita growth, youth unemployment rate, secondary education attainment, gender disparity in employment, government expenditure, inflation rate, foreign direct investment (FDI), and remittance inflows.
Table 2. Descriptive statistics
|
Variable |
Mean |
Std. Dev. |
Minimum |
Maximum |
|
GDP |
4.20 |
2.10 |
-1.80 |
7.50 |
|
YU |
17.30 |
5.00 |
10.20 |
26.70 |
|
SE |
45.50 |
12.50 |
28.00 |
72.00 |
|
GD |
32.50 |
8.40 |
21.10 |
41.70 |
|
GE |
3.80 |
1.20 |
2.40 |
6.10 |
|
INF |
6.50 |
3.20 |
1.80 |
12.00 |
|
FDI |
1.80 |
1.10 |
0.30 |
4.10 |
|
RI |
5.20 |
3.00 |
0.70 |
10.90 |
Based on the findings presented in Table 2 the youth unemployment rate across the study countries averages 17.3%, indicating a substantial unemployment burden among young individuals, with rates reaching as high as 26.7% in certain periods. Gender disparity in employment shows a mean of 32.5%, reflecting significant inequality in labor force participation across genders in the region. These statistics underscore the challenges in integrating the youth population into productive economic roles, with implications for both economic growth and social stability.
4.2 System Generalized Method of Moments (GMM) results
The results from the System GMM model in Table 3 indicate a significant negative relationship between youth unemployment and GDP per capita growth. Specifically, a 1% increase in youth unemployment correlates with a 0.20% decrease in GDP growth (p = 0.004), supporting findings by Vyas [33], who observed similar productivity declines due to high youth unemployment in India’s informal sector.
Table 3. System GMM results
|
Variable |
Coefficient |
Standard Error |
P-Value |
|
YU |
-0.20*** |
0.07 |
0.004 |
|
EDU |
0.16** |
0.05 |
0.015 |
|
GD |
-0.13** |
0.04 |
0.009 |
|
INF |
-0.09** |
0.03 |
0.030 |
|
FDI |
0.11** |
0.04 |
0.024 |
|
RI |
0.08** |
0.03 |
0.022 |
Note: ***, **, * represent significance at 1%, 5%, and 10% levels, respectively
Secondary education attainment is positively associated with GDP growth, with a coefficient of 0.16 (p = 0.015), suggesting that higher education levels enhance productivity, consistent with Sato [34].
The significant negative coefficient for gender disparity in employment (-0.13, p = 0.009) implies that increasing gender inequality in employment reduces GDP growth. This aligns with Yeung [35], who found that limitations on female labor force participation exacerbate economic costs associated with youth unemployment. Additionally, inflation has a negative impact on GDP per capita growth (-0.09, p = 0.030), which supports Kremer et al. [32], indicating that high inflation erodes real incomes and deters investment.
Both FDI and remittance inflows are positively correlated with GDP growth, with coefficients of 0.11 (p = 0.024) and 0.08 (p = 0.022), respectively.
4.3 Dynamic Panel threshold model
Table 4 shows Dynamic Panel Threshold Model identifies 17% as the critical threshold for youth unemployment. Below this level, a 1% increase in youth unemployment results in a modest 0.12% reduction in GDP growth, indicating that economies may absorb low-to-moderate levels of youth unemployment without severe growth repercussions. This finding aligns partially with Wickramasinghe and Liyanahetti [36], who observed that moderate youth unemployment in Sri Lanka did not significantly impact GDP.
Table 4. DynamicPanel threshold model results
|
Threshold Level |
Below Threshold Coefficient |
Above Threshold Coefficient |
p-Value (Above Threshold) |
|
Youth Unemployment (17%) |
-0.12 |
-0.30 |
0.002*** |
Note: ***, **, * represent significance at 1%, 5%, and 10% levels, respectively
When youth unemployment exceeds the 17% threshold, the adverse effect on GDP growth intensifies. At this level, a 1% increase in unemployment correlates with a 0.30% decrease in GDP growth (p = 0.002), highlighting the economic risk posed by high unemployment rates. The results suggest that when youth unemployment surpasses a manageable level, the economy becomes less resilient to its negative impacts, resulting in significant productivity losses.
Sayed et al. [6] suggested to overcome the issue of brain drain the management need quick actions to provide suitable and attractive job opportunities to youth. Gulzar et al. [12] highlighted the requirement of employers and the skills student learn from education. Additionally, Khan et al. [14] emphasize how microfinance programs can stimulate employment and government ensure that FDI has spillover effect on economy.
4.4 Comparative analysis of youth unemployment impacts on GDP growth and social factors in South Asia
The impact of youth unemployment on economic growth in Bangladesh, India, and Pakistan is contrasted in Table 5 (In Appendix). In Pakistan, youth unemployment has the largest negative impact on GDP growth; in India and Bangladesh, it has a moderate effect and a smaller one. Economic slowdowns become apparent when youth unemployment reaches a critical 17 percent in all three countries. While Pakistan and India have significant gender gaps that limit workforce participation, Bangladesh has made progress in this area. Bangladesh has the highest remittance dependency, Pakistan has a moderate one, and India has the lowest, indicating differing levels of economic reliance on foreign income. The small domestic job market in Bangladesh, the underemployment and skills mismatch in India, and the political unrest and job shortages in Pakistan are significant barriers. For the area to witness sustainable.
Table 5. Comparative analysis of youth unemployment impacts on GDP growth and social factors in South Asia
|
ountry |
YU Coefficient |
GDP Growth Impact |
Critical YU Threshold |
Gender Disparity |
Dependency on Remittances |
Key Economic Challenges |
|
Pakistan |
-0.30 |
Significant decrease |
17% |
High |
Moderate |
Limited job creation, high dependency ratios, and political instability due to youth frustration |
|
India |
-0.20 |
Moderate decrease |
17% |
High |
Low |
High educational mismatch, underemployment, and large informal sector |
|
Bangladesh |
-0.15 |
Mild decrease |
17% |
Lower |
High |
Strong reliance on remittances for economic stability, limited domestic job market opportunities |
5.1 Conclusion
This study provides an in-depth analysis of the effects of youth unemployment on economic growth and social stability in South Asia, focusing on Pakistan, India, and Bangladesh. Using System GMM and Dynamic Panel Threshold Models, the findings show that youth unemployment has a notably negative impact on GDP growth, particularly when it surpasses a 17% threshold.
Youth unemployment decreases economic efficiency, upsurges dependence ratios, and generates barriers to sustainable development. Key conclusions designate that high levels of unemployment between young people impede economic growth, with the impact growing above the identified threshold, which has dangerous suggestions for policymakers in South Asia.
Additionally, secondary education accomplishment was found to have encouraging influence on GDP development, signifying that didactic investment can alleviate some adverse effects of youth unemployment. Though, gender disparity in employment, inflation, and low FDI levels all act as limits on financial growth.
The comparative examination across Pakistan, India, and Bangladesh also present the essential for country-specific methods due to exclusive socio-economic environments, but overall recommends that the youth unemployment crisis needs crucial and targeted involvement.
5.2 Policy implications
5.2.1 Targeted employment programs
Government policies should focus on job creation in sectors with high potential for youth engagement, such as technology, manufacturing, and services, to reduce informal sector reliance and increase formal employment.
5.2.2 Educational realignment and vocational training
Educational programs should be realigned to match market needs, with a particular emphasis on vocational and technical training that can provide youth with employable skills. Bangladesh, in particular, could benefit from expanded vocational training programs.
5.2.3 Gender-inclusive employment policies
Addressing gender disparity in employment would increase the productive potential of the youth workforce. Targeted incentives for female workforce participation could be especially beneficial in Pakistan and India.
5.2.4 Promoting FDI and managing inflation
Policies that attract foreign investment can create jobs for the youth population and reduce dependency on remittances. Inflation control remains essential to maintain the stability required for sustainable economic growth.
Microfinance initiatives similar Grameen Bank have successfully reintegrated young persons into the labor force in Bangladesh [23]. An instance of the success of focused vocational training is India's "Skill India" effort [18]. Recent internship initiatives in Pakistan have established a modest level of accomplishment in dropping youth unemployment [25]. This research contributes to the literature by highlighting the threshold effects of youth unemployment on economic growth and the compounding effect of gender disparity on labor productivity. The comparative insights offered here emphasize the need for tailored policies in Pakistan, India, and Bangladesh to mitigate the economic and social costs of high youth unemployment and harness the potential of the youth demographic for sustainable growth.
5.3 Limitations
Although this study provides appreciated insights into the association among economic development and adolescent unemployment, it has few boundaries. First of all, the study only examines a small number of demographic and socioeconomic factors; however, there may be additional external factors at play. Second, the study does not include robustness evaluations for the Dynamic Panel Threshold Model, which could additional validate the stated threshold effects. Thirdly, even though the System GMM method is used to address endogeneity issues, additional remarks regarding its assumptions and applicability could increase the methodological penetration. Finally, extra variables that might be employed to widen the analysis in subsequent studies were not examined.
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