In recent decades, the role of institutions has become a central topic of discussion among scholars and policy makers. This study used time-series data from Afghanistan between 1996 and 2024 to gain new insights into the impact of political instability (POI), corruption (COR) and government effectiveness (GEF) on economic growth. The results of Quantile-on-Quantile Regression and Wavelet Quantile regression reveal that POI, COR, and GEF have adverse and statistically significant effects on GDP growth across all quantiles and over long-term time periods. Event analysis through the interrupted time series technique shows that the key political events, including the Civil War (CW), the First Round of the Taliban Regime (FRTR), U.S.-NATO interventions (USN), the Second Round of Taliban Regime (SRTR), and Regime Changes (RCH), have had a negative impact on Afghanistan’s GDP growth. The immediate impact of the Soviet Union’s war is estimated to be positive. At the same time, Afghanistan’s GDP experienced negative growth during SUW, CW, FRTR, and RCH, while during USN and SRTR, the GDP growth was positive. Based on these findings, the paper discusses possible policy implications.

GDP Growth, Institutional Quality, Quantiles Analysis, ITS approach

In recent decades, economists, academics and policy makers have paid significant attention to the relationship between economic growth and institutions. North (1990) and many other researchers (e.g. De Almeida et al., 2024) emphasized the critical role of institutions in shaping national economic growth. To explain cross-country differences in economic development, North identified the underlying factors that contributed to both divergent and convergent economic outcomes. Institutions, on the one hand, can help create a favourable business environment if they reduce uncertainty in transaction costs and foster economic activity. They can also provide political stability, protect property rights, strengthen the rule of law, and rein in corruption (North, 1990; Acemoglu et al., 2001; Corradini, 2021; Heo & Hahm, 2015; Dirks & Schmidt, 2024). On the other hand, fragile institutions — those lacking transparency and accountability —undermine governance quality, fuel political instability, exacerbate inequality, and hinder long-term economic development (Acemoglu et al., 2014; Acemoglu et al., 2001; Brinks et al., 2019; North, 2003).

The concept of “institutions” encompasses several key elements, including the rule of law, control of corruption, government effectiveness, quality of regulation, voice and accountability, stability of political systems, protection of property rights, independence of the judiciary, economic freedom and the size of public sector. The study focuses on the impact of political instability, corruption and government effectiveness on economic growth in Afghanistan.

Over the past fifty years, Afghanistan has struggled to establish state and public institutions. This has been a difficult task due to deep socio-economic crises, poor governance, foreign interventions, civil war and persistent political instability (Beidollahkhani, 2025). Looking further back into the history of Afghanistan, the roots of institutional building can be traced back to the reign of Amunullah Khan, who was credited with securing the independence of Afghanistan from Britain (Chua, 2014). Amunullah was a modernist leader who sought to reform Afghani society and establish a strong state. In 1923 he introduced a constitution and made attempts to establish a robust judicial system within the framework of monarchy (Chua, 2014).

However, despite the relative political stability between 1933 and 1978, the country’s formal institutions were weak, heavily reliant on foreign aid, lacking the administrative capacity necessary to support broad-based economic development. Later political events, including the Soviet invasion and the ensuing civil war, the first and second Taliban regimes, and the U.S.-led intervention, further exacerbated institutional fragility (Alimi, 2025; Rubin, 1995). These events militarized governance, disrupted administrative structures, and contributed to repeated regime changes in 1979, 1992, 1996, 2001, and 2021.

In this war-torn country, discussions about the concept and role of institutions - particularly their ability to help the government provide public services, uphold the rule of law, protect national security and territorial integrity, and safeguard citizens’ rights and freedoms — resumed after the year 2001 (Hakimi, 2024). However, attempts to rebuild institutions and infrastructure failed to achieve sustainable outcomes even with US aid amounting to around $140bn (SIGAR, 2021). The influx of foreign aid, instead of promoting stability, unintentionally strengthened corrupt actors and their networks, thus intensifying conflict, insecurity, and political instability (Dyer, 2016; Shah, 2024; Meng et al., 2025). Furthermore, the combination of large amounts of foreign aid and weak governance created a breeding ground for systemic corruption and collusion between government officials, local elites, and warlords. This also led to the development of a culture of political kleptocracy (Transparency International, 2024; Jodi, 2021; Azizi, 2021; Meng et al., 2025). These interrelated dynamics eroded domestic institutional capacity, undermined the prospects for sustainable development, and ultimately led to the collapse of the republican regime on 15 August 2021.

Based on the information provided and our best understanding, this is the first empirical study of the relationship between institutional quality and economic growth in Afghanistan. Seeking to evaluate this relationship, we will use econometric techniques to answer the following research question:

RQ: How do political instability, corruption, and government (in)effectiveness influence economic performance in a country with a fragile institutional system, such as Afghanistan, in the short, medium, and long term?

By providing an answer to this RQ based on the empirical analysis, our study contributes to the existing literature in several ways. First, in line with most empirical studies, this paper provides evidence that political instability, corruption, and government (in)effectiveness have a negative impact on economic growth in the short, medium, and long term in Afghanistan. Second, our empirical findings show that political instability, corruption, and government effectiveness are not only economically relevant for developed and developing economies, but also have implications for the prosperity of Afghanistan and other less developed and war-torn nations. Third, our findings concerning the impact of corruption on economic growth support the theory of “sanding the wheels”, suggesting that corruption acts as a significant barrier to economic growth in Afghanistan. Finally, in addition to using advanced econometric approaches such as Quantile-on-Quantile Regression (QQR) and Wavelet Quantile Regression (WQR), which capture the effects of the explanatory variables’ quantiles on different quantiles of the dependent variable(s), this study also employs the Wavelet Quantile Correlation (WQC) technique for robustness verification and the Interrupted Time Series (ITS) method for event analysis. Collectively, these methodologies ensure the use of the most appropriate and rigorous econometric tools in order to effectively address the proposed research question (RQ).

The rest of the study is organized as follows: Section 2 reviews the related empirical studies, Section 3 provides information about the data and methodology, Section 4 focuses on the interpretation of the empirical findings, and Section 5 reports the conclusions and policy recommendations.

The fundamental drivers of economic growth have been the subject of intensive scholarly debate for a long time. Some scholars emphasise the pivotal role of human capital, which is the key engine for economic performance. This is supported by the work of Barro (2001), Hanushek and Woessmann (2008, 2012) who argue that human capital, especially the human capital of political elites and national business executives, shapes economic outcomes (Jones & Olken, 2005; Besley et al., 2011; Shi, 2024; Shi, 2025). At the same time, a prominent strand of literature posits that institutional quality remains the primary determinant of growth (Acemoglu et al., 2001; Rodrik et al., 2004).

Building upon the institutional perspective, recent empirical studies have increasingly sought to examine how institutional quality and stability influence economic performance across countries. For instance, Acemoglu et al. (2000, 2001, 2014), North (1990, 2003), and Matta et al. (2022) point out that political instability can lead to weak institutions, which, over time, have an unfavorable impact on economic performance. Similarly, studies by Jong-A-Pin (2009), Alesina et al. (1996), Alesina & Perotti (1996), Aisen & Veiga (2013), and Dirks & Schmidt (2024) have demonstrated that political instability has a detrimental impact on economic growth. Empirical studies conducted by Murad and Alshyab (2019), and Assfaw et al (2025), found that political instability impedes economic growth by reducing investment and leading to a loss of physical capital. This was observed in Jordan and Ethiopia respectively. Moreover, political instability is a major vulnerability that causes production disruptions in many developing countries. The precautionary principle motivates investors to reduce both domestic and foreign direct investment, (Asteriou & Price, 2001; Delgado et al., 2014; Gakpa, 2020). This ultimately deters consumption (Bahmani & Nayeri, 2020) and slows productivity (Alexandre et al., 2022; Paulo et al., 2022; Abdelkader, 2017).

Corruption is another significant factor affecting economic performance of a nation, which depends on the quality of institutions and governance. Its impact on economic growth is explained by two competing hypotheses. According to the “greasing the wheels” hypothesis, corruption occurs in economies suffering from weak governance and, by speeding up administrative procedures and overcoming bureaucratic bottlenecks at low cost, stimulates economic activities (Shabbir et al., 2016; Assfaw et al., 2025; Gründler, & Potrafke, 2019; Dokas et al., 2023). Empirical findings by Méndez & Sepúlveda (2006) and Paul (2010) support this theory, indicating that corruption exerts a positive impact on economic growth. Conversely, the “sanding the wheels” hypothesis maintains that corruption in economies with fragile institutional setups causes misallocation of resources, shifting public gains to private profits, thus creating socio-political uncertainty and disrupting economic progress (Méon & Sekkat, 2005; Dokas et al., 2023; Mauro, 1995; Aidt et al., 2008; Cieślik & Goczek, 2018). This hypothesis is supported by recent empirical studies, such as Gründler and Potrafke (2019), Nur-tegin and Jakee (2020), Sharma and Mitra (2019), Paulo et al. (2022), Dirks and Schmidt (2024), and Assfaw et al. (2025). In economies where institutions are not properly enforced, businesspeople, traders, politicians, and administrators often engage in corrupt practices (Acemoglu & Verdier, 2000). Corruption affects GDP by reducing the quality of human capital (Cieślik & Goczek, 2018) and encouraging inefficient allocation of government resources. Corrupt officials seek to maximize their rent-extracting potential (D’Agostino et al., 2016; Montinola & Jackman, 2001), which hinders investment and trade freedoms (Gründler & Potrafke, 2019), and limits funding for innovative activities (Xu & Yano, 2017; Dincer, 2019; Assfaw et al., 2025).

The third factor that poses a threat to the quality of governance and, hence, to economic performance, is institutional fragility. Fragile institutions often lack the ability to enforce laws, provide public services efficiently, and maintain accountability mechanisms. This undermines the overall capacity of the state (De Almeida et al., 2024). By contrast, a strong institutional framework, characterized by political stability, an effective legal system, and robust mechanisms for controlling corruption, contributes positively to economic growth (Asiamah et al., 2022; Mawardi et al., 2024; Correa & Esquivias, 2024). Consequently, good and effective governance plays a crucial role in economic development as it reduces expenses associated with transactions, enforces ownership rights, maintains administrative stability and facilitates various economic activities (Zhou & Feng, 2024; Pang et al., 2024). An effective government fosters trust among investors, promotes technological innovation, and enhances economic performance (Jia et al., 2021). It can facilitate business activities and create a favourable environment for investment and entrepreneurship by reducing administrative hurdles and optimizing public sector operations (Mafimisebi & Ogunsade, 2022). Furthermore, by designing optimal policies, effective governments target high-quality economic development (Kong et al., 2021; Li & Li, 2025). In contrast, weak governments face barriers to meeting public needs and achieving high-quality development. (Zhao et al., 2022; Li & Li, 2025).

The literature emphasizes that weak institutions and corruption hinder economic performance and prevent wider development outcomes. Ear (2016) points out that corruption in Cambodia remains deeply entrenched, with anti-corruption initiatives largely ineffective due to insufficient political will and the persistence of institutional loopholes that undermine enforcement efforts. Similarly, Oyebanji and Omale (2025) regard financial and economic corruption among Nigerian public officials as a systemic reality driven by greed, lack of accountability, political patronage, external influence, and social pressures. They argue that meaningful reform is impossible without addressing the abuse of constitutional immunities by political elites. Complementing these insights, Adelakun et al. (2025) demonstrate that stronger institutional quality can mitigate the migration-induced effects of instability and insecurity, highlighting the importance of governance reform in reducing brain drain in fragile environments. Likewise, Egbetokun et al. (2019) show that institutional quality significantly influences the relationship between economic growth and environmental degradation in Nigeria. This implies that governance effectiveness has a critical mediating effect on sustainable development.

Collectively, these studies confirm that institutional fragility — manifested through corruption, weak governance, and political instability — remains a central barrier to stability and growth in fragile countries. However, despite extensive research into similar contexts, the non-linear effects of political instability, corruption, and government (in)effectiveness on Afghanistan’s GDP growth remain empirically unexplored, which underscores the significance of the present study.

This section provides detailed information about the econometric model, the dataset, and the steps of the novel empirical technique used in this study. It also explains the justification for this approach.

3.1. Econometric Models and Data

To examine the impacts of political instability (POI), corruption (COR), and government effectiveness (GEF), following Dirks & Schmidt (2024), Alexandre et al. (2022), Dokas et al. (2023), Li & Li (2025), and Assfaw et al. (2025), we employ the following econometric model:

GDP_t = f (POI_t , COR_t , GEF_t) (1)

where GDP_t is the gross domestic product, POI_t denotes political (in)stability, COR_t shows control of corruption, and GEF_t stands for government effectiveness.

To empirically estimate model (1), this study utilizes a time series dataset for Afghanistan covering the period from 1996 to 2024 (see Panel A of Table 1). The selection of this time frame is primarily driven by data availability, particularly for the institutional quality indicators that serve as the key independent variables in this study. Moreover, during this period Afghanistan underwent multiple governance regimes, most of which were brought to power by force, violence or prolonged military conflicts - often as a result of direct or indirect intervention by major regional or global powers. These dynamics not only hindered the country’s economic development but also intensified political instability, caused widespread corruption, and undermined government effectiveness.

Table 1.

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Variables, measurements, and data sources.

Variables	Acronyms	Definition and measurement
Panel A: Data related to GDP, POI, COR, and GEF will be used for conducting baseline analysis using QQR and WQR techniques, and robustness checks analysis using the WQC technique.
Economic Growth	GDP	Gross domestic product (constant, US$, 2015)
Political (In)stability Index	POI	Political instability measures perceptions of the likelihood of overthrow by unconstitutional means or violence and terrorism.
Control of corruption	COR	Control of the corruption measures the degree to which the government uses its power for personal gain, encompassing both small-scale and large-scale corruption, as well as the “capture” of the state by elites and private interests.
Government Effectiveness Index	GEF	Government Effectiveness: Standard Error
Panel B: Data related to GDP, SUW, CW, FRTR, USN, and SRTR will be used for conducting event analysis using the ITS technique.
Economic Growth	GDP	Gross domestic product (constant, US$, 2015)
Soviet Union War	SUW	The dummy variable takes the value 1 for the war period (1979-1989) and otherwise 0.
Civil War, including the Taliban First Round Regime	CW	The dummy variable takes the value 1 for the war period (1989-2001) and otherwise 0.
First Round Taliban Regime	FRTR	The dummy variable takes the value 1 for the war period (1996-2001) and otherwise 0.
United States and NATO Military Presence	USN	The dummy variable takes the value 1 for the war period (2001-2021) and otherwise 0.
Second Round Taliban Regime	SRTR	The dummy variable takes the value 1 for the war period (2021-2024) and otherwise 0.

Note: QQR, WQR, WQC, and ITS stand for Quantile-on-Quantile Regression, Wavelet Quantile Regression, Wavelet Quantile Correlation, and Interrupted Time Series econometric techniques, respectively. Additionally, this study used the Random Forest technique to handle missing values in the dataset. The Random Forest model is an appropriate method for imputing missing values due to its robustness in capturing complex relationships, mitigating overfitting, and improving predictive accuracy.

To capture the pre-event economic growth trajectory, the immediate impact of the event, and the subsequent growth trend, this study extends the GDP data from 1996–2024 to 1975–2024 and incorporates several dummy variables (see Panel B of Table 1). The use of GDP data over this extended period is justified because it covers the major political events that led to a decline in institutional quality and hindered Afghanistan’s economic development.

This study uses GDP as the primary dependent variable, while the key independent variables include POI, COR, and GEF. The dataset is sourced from the World Development Indicators (WDI) and World Governance Indicators (WGI) databases. Table 1 provides detailed information about the variables’ names, definitions, and measurements.

Fig. 1 illustrates the annual trends of the targeted variables. The natural log of total GDP trend reveals two critical points: one in 2001, marked by the U.S. invasion of Afghanistan, and the other in 2021, when the Islamic Republic of Afghanistan collapsed and the Taliban took control. Additionally, GDP demonstrates positive growth between these two inflection points and continues to exhibit an upward trajectory following the 2021 regime change. In contrast, political POI, COR, and the GEF have shown a consistent decline over the past three decades (1996-2024).

Fig. 1. 

Annual trends of LnGDP, POI, COR, and GEF from 1996 to 2024.

3.2. Empirical Estimation Strategies

To empirically examine the impact of POI, COR, and GEF on GDP growth, we employ Quantile-on-Quantile Regression (QQR) and Wavelet Quantile Regression (WQR) to estimate the baseline results. For robustness checks aimed at validating the reliability of the QQR and WQR findings, we use Wavelet Quantile Correlation (WQC), and, finally, we apply the Interrupted Time Series (ITS) method to examine the impact of major political events on growth in Afghanistan.

3.2.1. QQR and WQR Techniques

This study examines the non-linear effects of explanatory variables on the dependent variable using Quantile-on-Quantile Regression (QQR) and Wavelet Quantile Regression (WQR). Sim and Zhou (2015) proposed the QQR methodology to capture the non-linear association between the quantile of a dependent variable and the quantile of an independent variable(s). Assuming the X is the primary independent variable and Y is the main dependent variable, the single-variate-based QQR mathematical form for the connection between the quantiles of Y and X can be structured as follows:

(2)

where θ and Φ show the quantiles (0.05–0.95) of the Y and X, and ${ϵ}_t^{\theta }$ stands for error term with θ — quantiles. Additionally, part (*) of Eq. (2) represents the dependence structure between dependent and independent variables through their respective distributions. Moreover, following Alola et al. (2023) methodology, and assuming Y as the dependent variable and X₁, X₂, ... X_n as independent variables, the multivariate-based QQR mathematical model can be designed as follows:

$\begin{array}{c}{Y}_t={\beta }_0(\theta ,{\Phi }_1,{\Phi }_2,\dots ,{\Phi }_n)+{\beta }_1(\theta ,{\Phi }_1)(X_{1t}-X_1^{{\Phi }_1})+\\ +{\beta }_2(\theta ,{\Phi }_2)(X_{2t}-X_2^{{\Phi }_2})+{\beta }_n(\theta ,{\Phi }_n)(X_{nt}-X_n^{{\Phi }_n})+{\alpha }^{\theta }{Y}_{t-1}+{ϵ}_t^{\theta }\end{array}$ (3)

where Φ₁, Φ₂, …, Φ_n quantify the quantiles of X₁, X₂, ... X_n, respectively, and the θ shows the quantile of Y. In this paper, the QQR implementation procedure follows the studies by Alola et al. (2023) and Sim and Zhou (2015).

The WQR technique, proposed by Adebayo and Özkan (2024), can capture the impact of X on Y across time scales and quantiles, addressing the drawbacks of Quantile Regression (QR). Following the empirical studies, including Adebayo and Özkan (2024), Liu et al. (2024), and Kumar and Padakandla (2022), this study decomposes the time series data of the response variable (Y_t), and explanatory variable (X_t) utilizing the MODWT¹ (maximal overlapping discrete wavelet transform). Let X[i] be a signal with a length of T, where T = 2^J For an integer J. Then, consider h₁[i] as the low-pass filter and g₁[i] as the high-pass filter, both defining the orthogonal wavelet. At the initial step, X[i] undergoes convolution with h₁[i] to yield the approximation coefficients denoted as α₁[i] of length N, and with g₁[i] to yield the detail coefficients denoted as d₁[i] of length N. These procedures can be defined as follows:

${\alpha }_1\left[i\right]=h_1\left[i{]}^{\ast }s\right[i]=\sum _k{h}_1[i-k\left]s\right[k]$ (4)

$d_1\left[i\right]=g_1\left[i\right]\ast s\left[i\right]=\sum _k{g}_1[i-k]s\left[k\right]$ (5)

Following the same steps, we employ a similar method to filter α₁[i], using modified filters h₂[i] and g₂[i] obtained from the dyadic up-sampling of g₁[i] and h₁[i]. This recursive process is carried out iteratively. For values of J spanning from 1 to j₀ – 1, where J₀ ≤ J, we can calculate the coefficients of the approximate and detailed as follows:

${\alpha }_{j+1}\left[i\right]=h_{j+1}\left[i\right]\ast {\alpha }_j\left[i\right]=\sum _k{h}_{j+1}[i-k]{\alpha }_j\left[k\right]$ (6)

$d_{j+1}\left[i\right]=h_{j+1}\left[i{]}^{\ast }{\alpha }_j\right[i]=\sum _k{g}_{j+1}[i-k\left]{\alpha }_j\right[k]$ (7)

In this context, h_{j + 1}[i] = U(h_j[i]) and g_{j + 1}[i] = U(g_j[i]), where the up-sampling operation is illustrated by operator U. This operation involves inserting a zero value between each consecutive pair of time series.

Following the application of a J-level decomposition to Y_t and X_t, and the subsequent acquisition of the detail coefficients, we proceed to implement QR on the pair of WQR details, d_j(Y) and d_j(X), for all levels of J. Consequently, we derive the WQR outcomes for each level of J. Finally, the WQR technique for the dependent variable Y and the independent variable X at a specific decomposition level J, and for a given quantile q, can be rewritten as follows:

${\varphi }_{\left(q\right)}=d_j\left[Y\right]\mid d_j\left[X\right])=B_{0\left(q\right)}+B_{1\left(q\right)}{d}_j\left[X\right]$ (8)

3.2.2. WQC Technique

To verify the reliability of the baseline results, we employ the WQC technique. The WQC technique proposed by Kumar and Padakandla (2022) is based on the quantile correlation method by Li et al. (2015). To calculate the quantile-wise correlation between two time series variables, Li et al. (2015) suggests that Q_τ,X = τth quantile of the independent variable, while Q_τ,Y = τth quantile of the dependent variable, with the following mathematical expression:

${\varphi }_{\left(q\right)}=d_j\left[Y\right]\mid d_j\left[X\right])=B_{0\left(q\right)}+B_{1\left(q\right)}{d}_j\left[X\right]$ (9)

where qcov_t(Y_t ,X_t) presents the quantile correlation between the two series.

To extend the described method, following Kumar and Padakandla’s (2022), this study decomposed the modeled series, such as Y_t and X_t, using the MODWT technique at Jth level, and obtained the WQC for each J level based on the following mathematical equation.

${\mathrm{qcov}}_t(d_j\left[Y_t\right],d_j\left[X_t\right])=\frac{q{\mathrm{cov}}_t(d_j\left[Y_t\right],d_j\left[X_t\right])}{\sqrt{\mathrm{var}\left({\phi }_{\tau }(d_j\left[Y_t\right]-Q_{\tau ,dj}\left[Y_t\right])\right)\mathrm{var}\left(d_j\left[X_t\right]\right)}}$ (10)

Eq. (10) shows the WQC between the modeled series, such as Y and X. This method can handle potential outliers in the data and produce consistent results (Kumar & Padakandla, 2022). Furthermore, WQC captures plausible asymmetries by providing results for different quantiles and presents a comprehensive view of the dynamic relationship over the whole sampled period (Kumar & Padakandla, 2022).

Fig. 2 outlines the sequential empirical analysis framework, from data collection through descriptive analysis, diagnostic tests (Unit Root, BDS), baseline regressions (QOR, WQR), robustness checks (WQC), and event analysis via Interrupted Time Series (ITS). This structured approach ensures a comprehensive and methodologically coherent investigation.

Fig. 2. 

Empirical analysis steps.

In this paper, we have examined the impact of Political Instability (POI), Corruption (COR), and Government Effectiveness (GEF) on Afghanistan’s GDP performance. Using time series data from 1996 to 2024 and implementing Quantile-on-Quantile Regression (QQR) and Wavelet Quantile Regression (WQR) models, we found that POI, COR, and GEF had an adverse effect on GDP growth across all quantiles, in the long term. The results of the QQR and WQR techniques were confirmed by the Wavelet Quantile Correlation (WQC) model, indicating that POI, COR, and GEF are negatively correlated with GDP across all quantiles in the short, medium, and long term. Moreover, the event analysis revealed that, during the Soviet Union War (SUW), Civil War (CW), First Round Taliban Regime (FRTR), and Regime Changes (RCH), Afghanistan’s GDP growth had declined. During the U.S.-NATO military presence and the Second Round of the Taliban Regime (SRTR), Afghanistan’s GDP experienced positive growth.

Based on these empirical findings, the following policy recommendations can be discussed: Afghanistan must prioritize institutional reform to address the persistent adverse effects of political instability, corruption, and weak governance on GDP growth. Strengthening the rule of law through independent anti-corruption agencies, enhancing judicial transparency, and promoting merit-based recruitment in the public sector can significantly improve government effectiveness. These reforms would not only restore public trust but also attract foreign investment and foster a more stable economic environment, as evidenced by the consistent negative impact of POI, COR, and GEF across all GDP quantiles.

In light of the significant economic disruptions caused by past conflicts and regime changes, Afghanistan needs to adopt policies that are resilient to conflict. This includes designing reconstruction plans that prioritize infrastructure development, job creation, and regional equality, especially in post-conflict zones. Establishing a sovereign stabilization fund and aligning development initiatives with peacebuilding efforts can help to shield the economy from future political shocks. Such strategies are essential for breaking the cyclical relationship between conflict and economic decline, as highlighted by the results of the events analysis.

Finally, the observed positive growth of GDP during the Second Round Taliban Regime (SRTR) suggests that even under constrained political conditions, economic stabilization is possible. Policymakers and international stakeholders should consider pragmatic engagement with de facto authorities to ensure continuity in essential services and macroeconomic management. Encouraging decentralized governance and linking international aid to measurable improvements in transparency and service delivery can foster short-term stability and promote long-term development. This approach strikes a balance between the need for economic recovery and the imperative of promoting accountable governance.