This study aims to examine the mediating role of political stability in the relationship between economic growth and poverty reduction in the MENA region. While several countries in the region have experienced economic expansion, persistent poverty remains a challenge, often attributed to political instability and weak governance structures. The paper also investigates whether long-term political dominance, despite ensuring stability, may inadvertently hinder inclusive development.
Using annual panel data from 13 low- and middle-income Middle East and North Africa (MENA) countries over the period 1996–2022, the study employs the system generalized method of moments (GMM) to estimate the dynamic effects of political stability and absence of violence on the poverty headcount ratio. The analysis also includes a squared term to test for potential non-linear effects.
The empirical results suggest that political stability significantly contributes to poverty reduction across the sample. However, the positive and significant coefficient on the squared term indicates a non-linear relationship – suggesting that prolonged political dominance by a single party or coalition may dampen competition, institutional responsiveness and inclusive policymaking, thereby limiting poverty alleviation efforts.
While this study provides robust evidence on the mediating role of political stability in the poverty–growth nexus in the MENA region, several limitations should be acknowledged. Data constraints, reliance on macro-level indicators and the use of aggregate governance measures may limit the granularity and causal depth of the findings. Additionally, the analysis does not account for subnational variations, regime types or the impact of external shocks such as climate and geopolitical instability. Future research could address these gaps by employing micro-level data, exploring institutional heterogeneity and adopting advanced causal inference methods.
While existing literature often highlights the roles of financial development and economic growth in poverty reduction, this study provides novel insights into how political stability – particularly in fragile or transitioning states – acts as a critical governance factor. The findings underscore the importance of balanced and accountable political systems in enhancing the poverty-reducing impact of growth within the MENA region.
1. Introduction
Historically, development economics placed limited emphasis on political dynamics, viewing poverty largely as an economic issue (Abdejumobi, 2006). However, the emergence of New Institutional Economics (NIE) brought renewed focus on the role of institutions in promoting economic growth and reducing poverty (Alamoudi, 2024). Political stability—often defined as the absence of widespread violence, civil unrest, and institutional breakdown (Sottilotta, 2013)—also reflects effective governance, characterized by the proper functioning of state apparatus and public institutions. A country is considered politically stable when it experiences minimal internal conflict and maintains the security of both people and property. Nevertheless, some studies caution that excessive political stability may lead to complacency and stagnation, which can stifle innovation and competition (Ogbuabor et al., 2019).
Mainstream literature largely posits a one-way relationship from political stability to economic growth. In this view, political stability strengthens governance by promoting accountability, transparency, the rule of law, and more efficient use of public resources (Alamoudi, 2025). It also extends the policy horizon of decision-makers, facilitating long-term macroeconomic planning and ensuring consistency in policy implementation (Singha and Singh, 2022).
This study focuses on political stability—rather than broader governance indicators—because political dynamics are especially critical in the Middle East and North Africa (MENA) region, where conflict, regime volatility, and institutional fragility significantly shape economic outcomes. Political stability directly influences state capacity, public investment, and policy continuity, all of which are essential for translating economic growth into poverty reduction (World Bank, 2022). While broader governance indicators remain relevant, in the MENA context, political stability often acts as a gateway to other forms of institutional effectiveness.
Instability—manifested through conflict, weak government legitimacy, or abrupt regime changes—can disrupt economic activity, reduce investor confidence, and hinder public service delivery (Aisen and Veiga, 2013). These effects are often more immediate and pronounced than those caused by more subtle governance failures. Theoretical frameworks support the notion that strong political stability, including the rule of law and decentralization, positively influences poverty alleviation (Fagbemi and Fajingbesi, 2022; Aloui, 2019). Aloui (2019) argues that violent conflict often emerges when social groups are unable to meet their basic needs, a notion echoed in the frustration–aggression theory, which links unmet expectations to social unrest and low political stability. The United Nations (2023) supports this view, noting that poverty, inequality, and underdevelopment are key drivers of armed conflict and terrorism.
According to the “rooted-in-poverty” hypothesis, persistent violence often stems from unresolved socioeconomic grievances (Gemechu, 2023). Therefore, improving political stability is critical to reducing poverty and fostering development, especially in conflict-prone regions like Sub-Saharan Africa and MENA (Fagbemi and Fajingbesi, 2022). Economic development theory has long emphasized the relationship between growth and poverty, beginning with the foundational work of Kuznets (1955), and Okun (1975). Since 2000, economic growth has been widely recognized as a powerful tool for poverty reduction. This study revisits the growth–poverty nexus by exploring the mediating role of political stability in the MENA region.
Poverty is multidimensional and often defined as a severe lack of resources necessary for maintaining physical and mental well-being (Baratz and Grigsby, 1971). Political stability can influence poverty through various channels, including the promotion of investment, governance efficiency, and social cohesion (Aracil et al., 2022). Strong institutions, often underpinned by political stability, enforce fair regulations, protect property rights, and reduce corruption—thereby boosting investor confidence and fostering inclusive economic growth (Saidi et al., 2023). In contrast, political instability undermines institutional effectiveness and contributes to rising poverty rates, as observed in countries such as Somalia, Yemen, and Libya.
Furthermore, consistent policy environments fostered by political stability support economic growth by minimizing uncertainty and ensuring the security of assets (Uddin et al., 2017). Stable societies are also more likely to experience social cohesion and reduced conflict—conditions necessary for sustainable development. Conversely, instability often leads to social unrest, forced displacement, and asset loss, all of which exacerbate poverty (Radu, 2015).
Despite some progress, global poverty remains a pressing concern. Over 900 million people still live in extreme poverty, with the most vulnerable populations found in Sub-Saharan Africa and the low-income countries of the MENA region (United Nations Development Program, 2023). According to the World Bank (2003), the MENA region experienced the largest increase in extreme poverty between 2010 and 2022, with rates rising from 12.3% to 18.1% (based on the $3.65/day poverty line). In contrast, Sub-Saharan Africa saw a 10% increase, and other regions experienced only marginal changes.
Limited access to education and healthcare remains a defining feature of poverty in the MENA region. While some resource-rich MENA countries have invested heavily in education, others struggle due to political instability. In 2023, the region’s average public spending on education was 3.8% of GDP—below the global average of 4.5% (World Bank, 2003). Health outcomes also vary widely. According to Mate et al. (2017), MENA countries can be categorized into three groups: low-income (e.g. Yemen, Djibouti), middle-income (e.g. Egypt, Jordan, and Morocco), and high-income countries (e.g. those in the GCC). Each group faces distinct challenges and progress levels in health infrastructure and outcomes (Katoue et al., 2022).
Over the years, many MENA countries have experienced political revolutions, armed conflict, and prolonged instability—conditions that reinforce poverty and limit institutional development. The relationship is bidirectional: poverty can fuel conflict, and conflict can entrench poverty. Strengthening political stability is thus essential not only for national welfare but also for regional and global peace and prosperity. Policies promoting inclusive governance, social rights, and employment opportunities are critical to breaking this cycle.
This paper investigates the main drivers of political stability and explores how macroeconomic factors, including GDP growth, inflation, education, and governance, contribute to poverty reduction in the MENA region. While existing research has illuminated various aspects of the growth–poverty relationship (e.g. Dollar and Kraay, 2002), and the link between political stability and economic growth (Aisen and Veiga, 2013), fewer studies have integrated these dimensions to explore their interdependence. This study contributes to that gap by analyzing the mediating role of political stability in the poverty–growth nexus, offering insights for both theory and policy in the context of fragile and transitional states.
2. Literature review
2.1 Theoretical framework
The relationship between poverty and economic growth has been a central focus in development. Traditional neoclassical theory suggests a negative relationship, where higher economic growth reduces poverty through job creation, income generation, and capital investment (Okun, 1975). This logic assumes efficient markets and equitable distribution, aligning with the trickle-down theory.
However, empirical evidence increasingly challenges this assumption. The pro-poor growth perspective argues that growth only significantly reduces poverty when the benefits directly reach the poor (Ravallion and Chen, 2003). In this view, the elasticity of poverty to growth is shaped by policy frameworks, structural inequalities, and distribution patterns. This view finds further support in the Kuznets Curve Hypothesis (Kuznets, 1955), which theorizes that inequality initially rises with development, potentially worsening poverty in the short term before improving in mature economies, assuming inclusive institutions are in place.
Moreover, poverty itself may affect growth indirectly through its influence on political and social stability, access to public services, and investment climate—suggesting a more complex, bidirectional relationship.
The study of poverty is informed by a wide array of theories, broadly categorized into political, structural, and behavioral approaches (Ochi et al., 2023). Among these, political theories of poverty emphasize that poverty is not merely a consequence of market failures or individual shortcomings, but a political outcome shaped by power dynamics, institutional choices, and group-based decision-making (Brady et al., 2016). These theories argue that the distribution of resources and opportunities is determined by political institutions and elite preferences, which can either perpetuate or alleviate poverty.
Structural theories, by contrast, focus on how economic systems and labor markets systematically marginalize certain populations, while behavioral theories examine how individual decisions—shaped by incentives, social norms, and expectations—contribute to poverty outcomes. These frameworks are not mutually exclusive but rather interlinked in complex ways.
Within the political economy literature, governance and political stability are frequently cited as central determinants of economic performance. According to Aisen and Veiga (2013), political instability disrupts economic growth through heightened uncertainty, policy volatility, and declining rule of law, all of which discourage productive investment and innovation. This perspective aligns with the institutionalist view (North, 1990), which posits that stable political environments facilitate better governance, long-term policy continuity, and investor confidence.
Low political stability affects economic growth through various channels, including the labor market, resource allocation, efficiency, public and private investment, and economic productivity (Alesina et al., 1996). These disruptions weaken the foundations needed for sustainable development. The theoretical model developed by Cukierman et al. (1992) also highlights how low political stability leads to policy inefficiency and overreliance on inflationary financing mechanisms. In politically polarized or weak governments, resistance to public spending demands is minimal, often resulting in high inflation, thereby further eroding the economic conditions needed to escape poverty.
Political stability is not only crucial for sustaining economic growth but also plays a direct role in poverty alleviation. Good governance—marked by accountability, transparency, control of corruption, and institutional capacity—enables the effective delivery of services such as water, electricity, and healthcare (Kaufmann et al., 2010; Singha and Singh, 2022). These services are foundational to reduce extreme poverty in developing countries (Tebaldi and Mohan, 2010; Hasan et al., 2007).
Conversely, weak governance and political instability signal systemic government failure, especially in contexts where basic needs remain unmet (Ochi et al., 2023). When political systems are unstable, governments often become more susceptible to elite capture and corruption, diverting resources away from poverty reduction efforts. Kimenyi (2006) notes that such environments perpetuate clientelism and policy inefficiencies, disproportionately harming the poor. The poor are particularly vulnerable to political instability due to their reliance on public services and limited capacity to cope with inflation, unemployment, and institutional failures (World Bank, 2022). Therefore, governance and political stability are not only complementary but essential preconditions for equitable and sustainable poverty reduction.
Given its simultaneous impact on both poverty and economic growth, political stability can be conceptualized as a mediating variable—transmitting the effects of poverty onto economic performance. This conceptualization is supported by two interconnected theoretical pathways:
Poverty → Political Stability: High levels of poverty can erode social cohesion and political legitimacy, leading to unrest, protests, and weakened institutions. Gurr (1970) theorized that relative deprivation and unmet expectations fuel political violence and instability in societies with significant socioeconomic disparities.
Political Stability → Economic Growth: Stable political systems create a favorable climate for long-term investment, private sector development, and coherent macroeconomic policies (Acemoglu et al., 2005).
Thus, political stability mediates the poverty-growth nexus by shaping the institutional and policy environment through which growth is either enabled or constrained. This framework resonates with the developmental state theory, where strong, autonomous, and stable states—like those in East Asia—successfully aligned state capacity with inclusive economic policies (Evans, 1995).
The literature reviewed suggests that poverty, growth, and political stability are deeply interrelated. Political stability functions not only as an outcome of poverty but also as a driver of growth and governance quality. While traditional models have treated political stability as external control, emerging theories advocate for its central role as a mediator—linking poverty with economic performance and determining the effectiveness of development efforts.
Despite growing interest in this topic, few empirical studies explicitly investigate how poverty-induced political instability may undermine economic growth, or how improving political conditions might amplify growth’s impact on poverty reduction. Addressing this gap could provide new insights into the design of development policies, particularly in fragile and low-income states.
2.2 Empirical studies
A growing body of empirical research emphasizes the critical role of governance quality and political stability in shaping poverty and economic growth outcomes across diverse contexts. For instance, Singha and Singh (2022) found that the Indian state of Sikkim, noted for its high levels of political stability and effective governance, exhibits significantly lower poverty rates, higher economic growth, and improved well-being compared to national averages. This suggests that political stability and good governance are not only developmental outcomes but also key enablers of inclusive economic transformation.
In line with this, Aloui (2019) emphasized that both bilateral and multilateral donors increasingly view good governance as central to achieving sustainable development. Effective governance is seen as both a means to stimulate growth and a mechanism to reduce poverty by improving the allocation and delivery of public services.
The COVID-19 pandemic further illuminated the vulnerabilities associated with weak governance. Studies by Ferrannini et al. (2021) and Hossein and Ronaghi (2021) demonstrated that countries with lower governance indicators—particularly low-income nations with high mortality rates—faced disproportionately severe economic and health-related shocks. These findings reinforce the view that good governance can strengthen societal resilience, protect vulnerable populations, and mitigate the adverse effects of crises on poverty.
Nunan et al. (2021) offered a complementary perspective by analyzing the role of governance in mediating the relationship between ecosystem services and poverty alleviation. Their study found that the poverty-reducing potential of ecological benefits is significantly enhanced in contexts where inclusive and locally governed systems are present, highlighting the need for adequate institutional incentives.
Institutional governance has also been shown to moderate the impact of financial inclusion on poverty. Aracil et al. (2022), analyzing data from 75 developing countries between 2014 and 2017, found that stronger governance structures amplify the poverty-reducing effects of financial inclusion. This aligns with broader findings that institutional quality can condition the effectiveness of economic interventions.
Similarly, Workneh (2020), in a study of 34 Sub-Saharan African countries, found a strong positive correlation between effective governance and poverty reduction. Notably, the study revealed that the combination of gender inequality and weak governance exacerbates poverty, suggesting that governance improvements must also be inclusive and intersectional to be effective.
In the Vietnamese context, Nguyen et al. (2019) examined how public administration and governance quality impact poverty, income levels, and inequality. Their findings indicated a nonlinear but positive relationship between governance and poverty reduction, with more effective governance associated with more equitable income distribution.
More recently, Ochi et al. (2023) explored non-linear threshold effects of governance quality on extreme poverty across 57 South Asian and Sub-Saharan African countries between 2010 and 2019. The study revealed a statistically significant non-linear relationship, suggesting that governance improvements beyond certain thresholds yield more substantial reductions in poverty rates. The authors argue that governance quality is not only important for stimulating growth but also for translating that growth into tangible poverty reduction.
Additional empirical support is provided by Jamil et al. (2022), who studied 29 countries between 2004 and 2016 and found that strong, accountable governments are consistently associated with lower poverty levels. This finding is echoed in the work of Wang et al. (2024), who assessed the relationship between governance, energy poverty, and income inequality in 43 Sub-Saharan African countries over the period 1990–2017. Their results affirmed that effective governance frameworks are critical for reducing poverty and narrowing income disparities.
Further evidence from China—highlighted by Aracil et al. (2022)—shows that the country’s model of poverty reduction has been strengthened through a combination of livelihood assistance, life quality enhancement, and grassroots mobilization. These outcomes are facilitated by a governance model that emphasizes stability, capacity, and community-level implementation, offering global insights into the governance–poverty linkage.
While most empirical studies point toward a positive relationship between political stability and poverty reduction, Smith (1997) offered a more nuanced view. He argued that long-term political dominance by a single party or coalition, though often associated with stability, may breed complacency, reduce accountability, and eventually erode governance effectiveness. As a result, economic growth in such contexts may not be equitably distributed, and the benefits of stability may plateau or even reverse.
Based on the empirical literature discussed, this study proposes two testable hypotheses relevant to the MENA region:
Political stability significantly reduces the extent of poverty in the MENA region by stimulating more equitable growth in income distribution.
There is a non-linear relationship between political stability and poverty rate in the MENA region.
These hypotheses reflect the evolving understanding that political stability alone may not be uniformly beneficial; rather, the quality, accountability, and institutional inclusiveness of governance determine how effectively stability translates into poverty alleviation. Accordingly, this study investigates the mediating role of political stability in the poverty–growth nexus, focusing on MENA countries where political transitions, governance reforms, and socioeconomic disparities remain central development concerns.
Despite extensive research on poverty, growth, and governance, there is a notable gap in understanding the mediating role of political stability in the poverty–growth nexus, particularly within the MENA region, where political and economic dynamics are distinct and underexplored. Existing studies often examine these variables in isolation, overlooking non-linear relationships and the interactive effects between poverty, stability, and economic outcomes. Additionally, governance is frequently treated as a monolith, with insufficient attention to its multidimensional nature (e.g. accountability, rule of law, public service delivery). Few studies employ holistic analytical models that capture the complexity of these interrelationships. This study addresses these limitations by investigating how political stability mediates the poverty–growth relationship, incorporating both linear and non-linear dynamics, and focusing on region-specific insights relevant to development in politically volatile settings.
3. Data and methodology
3.1 Empirical model
Studies that examined the nexus between political stability and poverty have employed the quantitative analysis and qualitative assessments ordinary (Singha and Singh, 2022) least squares-fixed effect (Aloui, 2019), meta method, effective factor codes (Hossein and Ronaghi, 2021). However, these methods have not been able to overcome the difficulties of endogeneity and heteroskedasticity, and they typically do not yield strong and dependable results for panel data methods (Workneh, 2020). In order to address these issues without compromising the validity of our conclusions, we applied Arellano and Bover’s (1995) S-GMM system estimator, a robust panel data technique, to investigate the influence of governance and institutions on the relationship between political stability and poverty in 13 MENA countries from 1996 to 2022. Equation (1) represents the dynamic panel regression specifies the empirical model of the study:
In equation no. (1), Pov i,t is the dependent variable representing the outcome variable along with its lagged on the other side, Pov i, t−1, are the headcount ratio at $1.90 per day as a percentage of country i’s population at time t. The vector of control variables, X, is based on previously published research. That is, the inflation rate, trade as a percentage of GDP, and per capita forum income (log), natural resources as a percentage of GDP, and the annual population growth rate, are the control variables. The interest variable in this study is PS, which represents political stability, while μ represents the residual term of country i at time t. The study further examines the second hypothesis of a non-linear relationship between poverty and political stability, and it specifies equation (2) as follows:
Where (PS)2 is the square of overall political stability, which is used to measure the non-linear impact of stability in the MENA region. Following (Emara and Mohieldin, 2020), the dynamic panel data model is applied in this study using the GMM estimator to avoid the correlation issue between variables. To overcome the bias issues caused by differences in the GMM methodology, a dynamic system GMM that builds a system of Equations (1) And (2) at the level and first-difference equations of (3) and (4), is used in this study (Arellano and Bover, 1995). Appropriately lagged variables are then applied as instruments for both the levels and first-difference equations.
Using properly lags variables as IV from both the levels and the first difference equations, the system GMM constructs a system of level and first difference equations. The usefulness of applying the SGMM is that it applies an additional linear population of orthogonality conditions to guarantee the absence of correlation between poverty rate and the set of explanatories with the fixed effects of MENA region (Anderson and Hsiao, 1981; Ahn and Schmidt, 1995; Arellano and Bover, 1995).
3.2 Data
The global development indicators from the World Bank database are used to create a panel of country observations that make up the dataset. Thirteen (13) MENA countries are included in the dataset for the years 1996–2022. Table A1 provides a list of the countries included in the sample. These countries are classified as low-income, lower-middle-income, and upper-middle-income, according to the World Bank. The poverty headcount ratio, expressed as a percentage of the population at $1.90 per day, is used as the dependent variable in the model. The perception of the possibility of political instability and/or politically motivated violence, including terrorism, is measured by the Political Stability and Absence of Violence/Terrorism Index. Estimates provide the country’s aggregate indicator score in units of standard normal distribution, i.e. approximately between −2.5 and 2.5. Table A2 in the Appendix contains a list of the variables that were used in this study to explain and determine poverty based on the previous literature (Emara and Mohieldin, 2020). These variables are population growth, inflation rate, real GDP per capita growth rate, trade as a proportion of GDP, and financial inclusion indicators covering access to deposit accounts and ATM availability per 100,000 adults.
The correlations and summary statistics for the variables in our analysis are shown in Table 1. It’s important to highlight a few noteworthy and intriguing facts. For instance, Iraq, which has seen turmoil and war over the years, has the lowest political stability [1] score, at −2.5. Overall, the MENA low- and middle-income nations have an average rate of −0.07 in the sample of this study from 1996 to 2022. It suggests the unstable circumstances conditions in these countries which in turn might affect its development sustainability in the long run. The correlation matrix indicates a positive relationship between the poverty variable and several variables, namely the inflation rate and population growth, while GDP per capita (log), political stability, trade openness, human capital (education) and the financial indicator appear to have a negative relation with the poverty rate variable.
Summary Statistics and correlation between variables
| Variables | Mean | S.D. | Min | Max |
|---|---|---|---|---|
| Panel A: Summary statistics | ||||
| POV | 0.161 | 0.796 | 0 | 7.9 |
| PS | −0.07 | 1.08 | −2.50 | 2.13 |
| PG | 2.620 | 2.257 | −4.53 | 17.51 |
| IR | 13.28997 | 39.54152 | −3.846 | 387.3109 |
| GDP (log) | 8.906 | 1.22 | 6.45 | 11.15 |
| TO | 95.75 | 82.44 | 0.104 | 347.99 |
| EDU | ||||
| ATD | 106.26 | 261.64 | 0 | 1245.9 |
| AAT | 90.21 | 195.21 | 0 | 956.25 |
| B. Matrix of Explanatory Variables | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Panel B: Correlation matrix | |||||||||
| POV | 1 | ||||||||
| PS | −0.29* | 1 | |||||||
| PG | 0.196* | 0.143* | 1 | ||||||
| IR | 0.274 | −0.05* | 0.059 | 1 | |||||
| GDP (log) | −0.460 | −0.517 | 0.017* | 0.617* | 1 | ||||
| TO | −0.19* | 0.143* | −0.16 | 0.01 | 0.240 | 1 | |||
| EDU | −0.510 | 0.631 | 0.341* | 0.206* | 0.501* | 0.107 | 1 | ||
| ATD | −0.13* | 0.052* | 0.055* | 0.361 | 0.121* | 0.148 | 0.018* | 1 | |
| AAT | 0.014 | 0.048 | 0.0621 | 0.015* | 0.24* | 0.191* | 0.025 | 0.031 | 1 |
Indicates significant at 5%
In Table 2, all values of the variance inflation factor (VIF) are below 2.00; and the tolerance values of all independent variables are above 0.10, calculated by the author. The values in this table and the validity of the model are also supported by the results of previous research, as shown by the findings of Javid et al. (2023) and Dospinescu and Dospinescu (2020).
Multicollinearity analysis, Variance Inflation Factors (VIF)
| Variable | Variance inflation factors | (Variance inflation factors)2 | Tolerance | R2 |
|---|---|---|---|---|
| POV | 1.26 | 1.13 | 0.813 | 0.197 |
| PS | 1.02 | 1.01 | 0.981 | 0.018 |
| PG | 1.45 | 1.02 | 0.408 | 0.592 |
| IR | 1.91 | 1.38 | 0.52 | 0.47 |
| GDP (log) | 1.12 | 1.04 | 0.471 | 0.528 |
| TO | 1.50 | 1.23 | 0.665 | 0.334 |
| EDU | 1.44 | 1.19 | 0.76 | 0.24 |
| ATD | 1.50 | 1.23 | 0.66 | 0.33 |
| AAT | 1.14 | 1.0 | 0.49 | 0.51 |
4. Empirical results and discussion
4.1 Preliminary results
Table 3 presents the results from the baseline dynamic panel regression estimated using the system GMM estimator. The lagged poverty variable is statistically significant across all specifications, suggesting a strong degree of poverty persistence in the MENA region. Specifically, in column 1, the coefficient of 0.0093 implies that a 1% increase in poverty in the previous year results in a 0.93% increase in current poverty, ceteris paribus. This confirms the autoregressive nature of poverty and aligns with prior findings that poverty tends to be persistent in low- and middle-income countries due to structural constraints and limited mobility (Ferreira et al., 2011).
GMM poverty estimate results during political stability
| Variable | (1) | (2) | (3) | (4) | (5) | (6) |
|---|---|---|---|---|---|---|
| Pov -1 | 0.934 (0.012)*** | 0.925 (0.014)*** | 0.934 (0.013)*** | 0.932 (0.014)*** | 0.932 (0.014)*** | |
| PS | −1.150 (0.046)*** | −1.550 (0.046)*** | −1.120 (0.806) | −1.140 (0.060)*** | −1.483 (0.030)*** | |
| PS2 | 1.005 (0.189)* | |||||
| PG | 0.059 (0.021)*** | 0.086 (0.011)*** | 0.083 (0.007)*** | |||
| IR | 0.108 (0.025)** | 0.061 (0.012)** | 0.251 (0.061)*** | |||
| GDP (log) | −0.081 (0.026)*** | −0.078 (0.024)*** | −0.0820 (0.025)*** | |||
| TO | −0.026 (0.003)*** | −0.045 (0.003)*** | ||||
| EDU | −0.381 (0.012)*** | −0.380 (0.012)*** | ||||
| ATD | −0.591 (0.117)*** | |||||
| AAT | −1.228 (0.025)*** | |||||
| Observations | 211 | 211 | 211 | 211 | 210 | 210 |
| No. of Countries | 13 | 13 | 13 | 13 | 13 | 13 |
| Wald-test | 1452.01 (0.000) | 1524.19 (0.000) | 1526.11 (0.000) | 1450.05 (0.000) | 1489.15 (0.000) | 1420.35 (0.000) |
| Hansen test (p-value) | 0.281 | 0.277 | 0.280 | 0.290 | 0.311 | 0.301 |
| AR test AR(1) (p-value) | (0.012) | (0.012) | (0.011) | (0.011) | (0.075) | (0.04) |
| AR test AR(2) (p-value) | (0.291) | (0.302) | (0.302) | (0.294) | (0.431) | (0.549) |
Note(s): 1. The results of GMM estimations of how political stability and growth affect poverty is shown in the table. The time frame spans from 1996 to 2022; 2. Standard errors are in brackets; 3. Significance is indicated at the 1, 5, and 10% levels, respectively, by ***, **, and *; 4. The Hansen test is used to check whether estimations of the GMM dynamic model over-identify constraints. The Arellano-A bond test is used in the AB test AR (1) and AR (2), where the average auto-covariance in residuals of rank 1 and order 2, respectively, is 0 (H0: no autocorrelation); p-values are indicated in brackets
The Arellano-Bond AR(2) test yields a p-value of 0.29, indicating no second-order serial correlation, and the Hansen test confirms instrument validity. These diagnostics support the robustness of the GMM estimates and reinforce the suitability of the dynamic panel model for this analysis.
Column 2 introduces political stability and absence of violence into the model. The coefficient is negative and statistically significant at the 1% level, supporting the first hypothesis (H1) that increased political stability contributes to poverty reduction. This finding is consistent with the theoretical argument that stable political environments enhance state capacity, policy implementation, and service delivery (North, 1990), and with empirical evidence from Singha and Singh (2022), who found that politically stable regions in India experience lower poverty rates and better development outcomes.
However, the squared term of political stability is positive and significant at the 10% level, suggesting a non-linear relationship. This supports the second hypothesis (H2) that while moderate improvements in political stability reduce poverty, excessively long-term or rigid political arrangements—such as those seen under dominant-party regimes—can eventually hinder inclusive development. This interpretation resonates with Smith (1997), who argues that prolonged political dominance can reduce accountability, breed complacency, and ultimately limit the redistributive impact of economic growth.
Column 4 includes GDP per capita (log) and population growth. As expected, GDP per capita is negatively associated with poverty and significant at the 1% level, reaffirming the growth-poverty reduction linkage documented by Ravallion and Chen (2003) and Dollar and Kraay (2002). The population growth variable is positively associated with poverty, consistent with the view that rapidly growing populations place pressure on infrastructure, health systems, and employment opportunities, particularly in the MENA region where youth bulges and labor market rigidities are pronounced (World Bank, 2022).
Inflation is added in column 4 and is positively associated with poverty, supporting prior work by Easterly and Fischer (2001), who argue that inflation disproportionately harms the poor by eroding real wages and reducing access to essential goods. The negative and significant coefficients on globalization (trade openness) echo the findings of Ajide and Dada (2024) and Berdiev and Saunoris (2017), who observe that increased trade integration can create employment opportunities and lower consumer prices, thereby improving household welfare.
Education and human capital also emerge as critical poverty-reducing factors. The negative and statistically significant coefficient associated with education aligns with Ferguson et al. (2007) and Hofmarcher (2021), who highlight that better educational attainment increases productivity and labor market inclusion, both of which are essential for poverty reduction in transitioning economies.
4.2 Financial access and inclusive development
Columns 5 and 6 examine the effect of financial access indicators on poverty. The number of ATMs per 100,000 adults (ATD) and the number of commercial bank depositors per 1,000 adults (AAT) serve as proxies for financial inclusion. Both variables exhibit negative and statistically significant coefficients, indicating that improved access to financial services is associated with lower poverty levels.
This result is supported by empirical findings from Aracil et al. (2022), who argue that financial inclusion plays a crucial role in empowering the poor, smoothing consumption, and facilitating investment in health, education, and small enterprises. It also supports the broader argument by Demirgüç-Kunt and Klapper (2013), who find that inclusive financial systems can help reduce poverty and inequality by expanding access to credit, savings, and insurance products.
4.3 Governance quality and extended analysis
In the extended analysis, the study includes corruption control as a measure of governance quality, as per the Worldwide Governance Indicators framework (Kaufmann et al., 2010). The coefficient on corruption control is negative and statistically significant at the 1% level, suggesting that better corruption control significantly reduces poverty, as shown in Table 4.
GMM estimate results of poverty control of corruption
| Variable | (1) | (2) | (3) | (4) | (5) | (6) |
|---|---|---|---|---|---|---|
| Pov -1 | 0.934 (0.012)*** | 0.945 (0.014)*** | 0.910 (0.012)*** | 0.930 (0.013)*** | 0.912 (0.014)*** | 0.932 (0.014)*** |
| COC | −1.028 (0.004)*** | −1.028 (0.004)*** | −1.059 (0.021)*** | −1.028 (0.004)*** | −1.059 (0.021)*** | |
| COC2 | 1.028 (0.897) | |||||
| PG | 0.059 (0.021)*** | 0.086 (0.011)*** | −0.032 (0.007)*** | |||
| IR | 0.115 (0.906) | 0.061 (0.012)** | 0.251 (0.061)*** | |||
| GDP (log) | −0.084 (0.025)*** | 0.079 (0.029)*** | −0.081 (0.022)*** | |||
| TO | −0.026 (0.003)*** | −045 (0.003)*** | ||||
| EDU | −0.401 (0.012)*** | |||||
| ATD | −0.951 (0.552)** | |||||
| AAT | −1.141 (0.021)*** | |||||
| Observations | 211 | 211 | 211 | 211 | 210 | 210 |
| No. of Countries | 13 | 13 | 13 | 13 | 13 | 13 |
| Wald-test | 1620.01 (0.000) | 1650.08 (0.000) | 1612.08 (0.000) | 1662.01 (0.000) | 1620.01 (0.000) | 1650.08 (0.000) |
| Hansen test (p-value) | 0.243 | 0.297 | 0.210 | 0.301 | 0.243 | 0.297 |
| AR test AR(1) (p-value) | (0.015) | (0.01) | (0.01) | (0.085) | (0.015) | (0.01) |
| AR test AR(2) (p-value) | (0.305) | (0.351) | (0.301) | (0.411) | (0.305) | (0.351) |
Note(s): 1. The results of GMM estimations of how control of corruption and growth affect poverty is shown in the table. The time frame spans from 1996 to 2022; 2. Standard errors are in brackets; 3. Significance is indicated at the 1, 5, and 10% levels, respectively, by ***, **, and *; 4. The Hansen test is used to check whether estimations of the GMM dynamic model over-identify constraints. The Arellano-A bond test is used in the AB test AR (1) and AR (2), where the average auto-covariance in residuals of rank 1 and order 2, respectively, is 0 (H0: no autocorrelation); p-values are indicated in brackets
This supports the theoretical assertion that governance quality conditions the effectiveness of public spending and social programs (Tebaldi and Mohan, 2010; Hasan et al., 2007). The result is also consistent with Nguyen et al. (2019), who found that improved public administration and accountability enhance income equality and poverty outcomes. Although the squared term for corruption control is statistically insignificant, its inclusion reflects the possibility of diminishing returns to governance reform, as noted in Ochi et al. (2023), who find non-linear effects in governance-poverty dynamics.
4.4 Summary and policy relevance
Together, the results reinforce the mediating role of political stability in the poverty-growth nexus, showing that political stability enhances the transmission of growth into poverty reduction. The evidence supports a nuanced, non-linear perspective: while political stability is generally beneficial, the structure and quality of governance matter. Stability that promotes accountability, public investment, and institutional responsiveness can amplify growth’s impact on poverty. In contrast, stability that reduces competition or fosters elite capture may blunt these effects.
From a policy standpoint, the findings underscore the need for inclusive, accountable, and adaptive governance frameworks in the MENA region. Reforms should focus not only on maintaining political order but also on strengthening institutions that can deliver equitable development outcomes.
5. Conclusion
This study investigates the mediating role of political stability in the relationship between poverty and economic growth across 13 MENA countries from 1996 to 2022, employing a dynamic panel estimation using the System GMM approach. While multiple governance indicators were included for robustness analysis, the central focus remains on political stability and its direct and non-linear impact on poverty. The findings reveal that a one-unit increase in the political stability index significantly reduces the poverty headcount ratio by approximately 1.15%, confirming the critical role of political stability in alleviating poverty in the MENA region. However, the squared term of political stability is positive and statistically significant at the 10% level, indicating a non-linear relationship. This suggests that beyond a certain threshold, prolonged political dominance by a single party or coalition may become counterproductive leading to inefficient, populist policies aimed at electoral retention, which in turn create economic distortions, discourage investment, and exacerbate poverty (Acemoglu et al., 2005).
Furthermore, robust results underscore the importance of economic growth and human capital development. GDP per capita is negatively and significantly associated with poverty, reaffirming the growth–poverty reduction link. Education emerges as a particularly effective poverty reduction mechanism, echoing Hofmarcher (2021), who found that each additional year of schooling significantly lowers the risk of poverty and social exclusion. Similarly, trade plays a pivotal role by stimulating economic activity and improving market access, especially for the poor. As supported by Frankel and Romer (1999), trade-induced growth can have substantial indirect poverty-reducing effects by creating employment and improving productivity, provided the poor can access these opportunities.
In conclusion, while governance quality broadly matters, political stability specifically stands out as a critical mediating factor in the poverty–growth nexus. The results emphasize the need for balanced, inclusive, and stable political systems that foster long-term development while avoiding the pitfalls of entrenched political dominance.
6. Limitations and directions for future research
While this study provides robust evidence on the mediating role of political stability in the poverty–growth nexus in the MENA region, several limitations should be acknowledged. Data constraints, reliance on macro-level indicators, and the use of aggregate governance measures may limit the granularity and causal depth of the findings. Additionally, the analysis does not account for subnational variations, regime types, or the impact of external shocks such as climate and geopolitical instability. Future research could address these gaps by employing micro-level data, exploring institutional heterogeneity, and adopting advanced causal inference methods. Such efforts would enhance understanding of how political stability and governance quality shape inclusive development in diverse contexts.
Appendix
List of MENA countries included in the sample
| 1 | Syria |
| 2 | Yemen |
| 3 | Algeria |
| 4 | Djibouti |
| 5 | Egypt |
| 6 | Morocco |
| 7 | Tunisia |
| 8 | Iran |
| 9 | Lebanon |
| 10 | Libya |
| 11 | Iraq |
| 12 | Jordan |
| 13 | Sudan |
Definitions of economic variables
| Variable name | Definition | Data source | Abbreviation |
|---|---|---|---|
| Poverty | Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population). Increase in poverty gap at $1.90 ($ 2011 PPP) of poverty line, due to out-of-pocket health care expenditure, as a percentage of $1.90 poverty line | WDI | Pov |
| Stability in Politics | Estimates of political stability and the absence of terrorism or violence range from roughly −2.5 to 2.5 | WDI | PS |
| Population Growth | The annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship | WDI | PG |
| Inflation Rate | Inflation, measured by the consumer price index, reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services, which may be fixed or modified at specified intervals, such as yearly. The Laspeyres formula is generally used | WDI | IR |
| GDP per capita | GDP per capita is gross domestic product divided by mid-year population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without deducting the depreciation of manufactured assets or the depletion and degradation of natural resources. Data are in constant 2015 U.S. dollars | WDI | GDP (log) |
| Trade Openness | Trade is the sum of exports and imports of goods and services, measured as a percentage of gross domestic product | TO | |
| Education | Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education beginning at the primary level and aims to lay the foundations for lifelong learning and human development by offering more subject—or skill-oriented learning delivered by more specialized teachers | EDU | |
| Automated teller machines (ATMs) (per 100,000 adults) | Automated teller machines (ATMs) are computerized telecommunications devices that enable financial institution clients to carry out financial transactions in a public place | WDI | ATD |
| Bank accounts per 1,000 adults | Commercial bank depositors are the reported number of deposit account holders at commercial banks and other resident banks functioning as commercial banks that are resident non-financial corporations (public and private) and households. For many countries, the data cover the total number of deposit accounts due to the lack of information on account holders. The major types of deposits are checking accounts, savings accounts, and term deposits | WDI | AAT |
Note(s): World Development Indicators
Note
Perceptions of the possibility of political instability and/or politically motivated violence, including terrorism, are measured by the Political Stability and Absence of Violence/Terrorism index. The estimate provides the nation’s score on the overall indication in standard normal distribution units, or roughly between −2.5 and 2.5, Kaufmann et al. (2010). (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130).
