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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">115</journal-id>
      <journal-id journal-id-type="index">urn:lsid:arphahub.com:pub:32e1b97d-7003-598d-92e7-0ceb44416cc9</journal-id>
      <journal-title-group>
        <journal-title xml:lang="en">BRICS Journal of Economics</journal-title>
        <abbrev-journal-title xml:lang="en">brics-econ</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">2712-7702</issn>
      <issn pub-type="epub">2712-7508</issn>
      <publisher>
        <publisher-name>BRICS Journal of Economics</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.3897/brics-econ.7.e170868</article-id>
      <article-id pub-id-type="publisher-id">170868</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group subj-group-type="scientific_subject">
          <subject>(A1) General Economics</subject>
          <subject>(E6) Macroeconomic Policy</subject>
          <subject> Macroeconomic Aspects of Public Finance</subject>
          <subject> and General Outlook</subject>
          <subject>(O1) Economic Development</subject>
          <subject>(O4) Economic Growth and Aggregate Productivity</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Quantile Evidence on Institutional Quality and Economic Growth in a Fragile State: The Case of Afghanistan</article-title>
      </title-group>
      <contrib-group content-type="authors">
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Jingjing</surname>
            <given-names>Yang</given-names>
          </name>
          <xref ref-type="aff" rid="A1">1</xref>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Mowahed</surname>
            <given-names>Shah Mir</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0009-0009-5464-6476</uri>
          <xref ref-type="aff" rid="A1">1</xref>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Reha</surname>
            <given-names>Mariam</given-names>
          </name>
          <email xlink:type="simple">rahamariam111@gmail.com</email>
          <uri content-type="orcid">https://orcid.org/0009-0000-9991-0861</uri>
          <xref ref-type="aff" rid="A2">2</xref>
        </contrib>
      </contrib-group>
      <aff id="A1">
        <label>1</label>
        <addr-line content-type="verbatim">School of Economics and Trade, Hunan University (China)</addr-line>
        <institution>School of Economics and Trade, Hunan University</institution>
        <addr-line content-type="city">Changsha</addr-line>
        <country>China</country>
        <uri content-type="ror">https://ror.org/05htk5m33</uri>
      </aff>
      <aff id="A2">
        <label>2</label>
        <addr-line content-type="verbatim">General Directorate of the Institute of Legislative Affairs and Academic-Legal Research, Ministry of Justice (Afghanistan)</addr-line>
        <institution>General Directorate of the Institute of Legislative Affairs and Academic-Legal Research, Ministry of Justice</institution>
        <addr-line content-type="city">Kabul</addr-line>
        <country>Afghanistan</country>
      </aff>
      <author-notes>
        <fn fn-type="corresp">
          <p>Corresponding author: Mariam Reha (rahamariam111@gmail.com)</p>
        </fn>
        <fn fn-type="edited-by">
          <p>Academic editor: Sharko E.</p>
        </fn>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>06</day>
        <month>03</month>
        <year>2026</year>
      </pub-date>
      <volume>7</volume>
      <issue>1</issue>
      <fpage>49</fpage>
      <lpage>84</lpage>
      <uri content-type="arpha" xlink:href="http://openbiodiv.net/7057DEB6-9888-5D94-80FB-EB2CD02BA48A">7057DEB6-9888-5D94-80FB-EB2CD02BA48A</uri>
      <history>
        <date date-type="received">
          <day>02</day>
          <month>09</month>
          <year>2025</year>
        </date>
        <date date-type="accepted">
          <day>11</day>
          <month>11</month>
          <year>2025</year>
        </date>
      </history>
      <permissions>
        <copyright-statement>Yang Jingjing, Shah Mir Mowahed, Mariam Reha</copyright-statement>
        <license license-type="creative-commons-attribution" xlink:href="http://creativecommons.org/licenses/by/4.0/" xlink:type="simple">
          <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
        </license>
      </permissions>
      <abstract>
        <label>Abs tract</label>
        <p>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 (<abbrev xlink:title="political instability">POI</abbrev>), corruption (<abbrev xlink:title="corruption">COR</abbrev>) and government effectiveness (<abbrev xlink:title="government effectiveness">GEF</abbrev>) on economic growth. The results of Quantile-on-Quantile Regression and Wavelet Quantile regression reveal that <abbrev xlink:title="political instability">POI</abbrev>, <abbrev xlink:title="corruption">COR</abbrev>, and <abbrev xlink:title="government effectiveness">GEF</abbrev> have adverse and statistically significant effects on <abbrev xlink:title="Gross domestic product">GDP</abbrev> 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 (<abbrev xlink:title="Civil War">CW</abbrev>), the First Round of the Taliban Regime (<abbrev xlink:title="First Round of the Taliban Regime">FRTR</abbrev>), U.S.-NATO interventions (<abbrev xlink:title="U.S.-NATO interventions">USN</abbrev>), the Second Round of Taliban Regime (<abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev>), and Regime Changes (<abbrev xlink:title="Regime Changes">RCH</abbrev>), have had a negative impact on Afghanistan’s <abbrev xlink:title="Gross domestic product">GDP</abbrev> growth. The immediate impact of the Soviet Union’s war is estimated to be positive. At the same time, Afghanistan’s <abbrev xlink:title="Gross domestic product">GDP</abbrev> experienced negative growth during <abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev>, <abbrev xlink:title="Civil War">CW</abbrev>, <abbrev xlink:title="First Round of the Taliban Regime">FRTR</abbrev>, and <abbrev xlink:title="Regime Changes">RCH</abbrev>, while during <abbrev xlink:title="U.S.-NATO interventions">USN</abbrev> and <abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev>, the <abbrev xlink:title="Gross domestic product">GDP</abbrev> growth was positive. Based on these findings, the paper discusses possible policy implications.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>GDP Growth</kwd>
        <kwd>Institutional Quality</kwd>
        <kwd>Quantiles Analysis</kwd>
        <kwd>ITS approach</kwd>
      </kwd-group>
      <custom-meta-group>
        <custom-meta>
          <meta-name>JEL</meta-name>
          <meta-value>O43, C22, P48, D74</meta-value>
        </custom-meta>
      </custom-meta-group>
    </article-meta>
    <notes>
      <sec sec-type="Citation" id="sec1">
        <title>Citation</title>
        <p>Jingjing, Y., Mowahed, S. M., &amp; Reha, M. (2026). Quantile Evidence on Institutional Quality and Economic Growth in a Fragile State: The Case of Afghanistan. <italic>BRICS Journal of Economics, 7</italic>(1), 1–36. <ext-link xlink:type="simple" ext-link-type="doi" xlink:href="10.3897/brics-econ.7.e170868">https://doi.org/10.3897/brics-econ.7.e170868</ext-link></p>
      </sec>
    </notes>
  </front>
  <body>
    <sec sec-type="1. Introduction" id="sec2">
      <title>1. Introduction</title>
      <p>In recent decades, economists, academics and policy makers have paid significant attention to the relationship between economic growth and institutions. <xref ref-type="bibr" rid="B84">North (1990)</xref> and many other researchers (e.g. <xref ref-type="bibr" rid="B37">De Almeida et al., 2024</xref>) 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 (<xref ref-type="bibr" rid="B84">North, 1990</xref>; <xref ref-type="bibr" rid="B3">Acemoglu et al., 2001</xref>; <xref ref-type="bibr" rid="B32">Corradini, 2021</xref>; <xref ref-type="bibr" rid="B55">Heo &amp; Hahm, 2015</xref>; <xref ref-type="bibr" rid="B40">Dirks &amp; Schmidt, 2024</xref>). 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 (<xref ref-type="bibr" rid="B2">Acemoglu et al., 2014</xref>; <xref ref-type="bibr" rid="B3">Acemoglu et al., 2001</xref>; <xref ref-type="bibr" rid="B25">Brinks et al., 2019</xref>; <xref ref-type="bibr" rid="B85">North, 2003</xref>).</p>
      <p>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.</p>
      <p>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 (<xref ref-type="bibr" rid="B22">Beidollahkhani, 2025</xref>). 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 (<xref ref-type="bibr" rid="B29">Chua, 2014</xref>). 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 (<xref ref-type="bibr" rid="B29">Chua, 2014</xref>).</p>
      <p>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 (<xref ref-type="bibr" rid="B12">Alimi, 2025</xref>; <xref ref-type="bibr" rid="B97">Rubin, 1995</xref>). These events militarized governance, disrupted administrative structures, and contributed to repeated regime changes in 1979, 1992, 1996, 2001, and 2021.</p>
      <p>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 (<xref ref-type="bibr" rid="B50">Hakimi, 2024</xref>). However, attempts to rebuild institutions and infrastructure failed to achieve sustainable outcomes even with US aid amounting to around $140bn (<xref ref-type="bibr" rid="B103">SIGAR, 2021</xref>). The influx of foreign aid, instead of promoting stability, unintentionally strengthened corrupt actors and their networks, thus intensifying conflict, insecurity, and political instability (<xref ref-type="bibr" rid="B42">Dyer, 2016</xref>; <xref ref-type="bibr" rid="B99">Shah, 2024</xref>; <xref ref-type="bibr" rid="B78">Meng et al., 2025</xref>). 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 (<xref ref-type="bibr" rid="B105">Transparency International, 2024</xref>; <xref ref-type="bibr" rid="B58">Jodi, 2021</xref>; <xref ref-type="bibr" rid="B19">Azizi, 2021</xref>; <xref ref-type="bibr" rid="B78">Meng et al., 2025</xref>). 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.</p>
      <p>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:</p>
      <p><bold><italic><abbrev xlink:title="research question">RQ</abbrev></italic></bold>:   <italic>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</italic>?</p>
      <p>By providing an answer to this <abbrev xlink:title="research question">RQ</abbrev> 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 “<italic>sanding the wheels</italic>”, 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 (<abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev>) and Wavelet Quantile Regression (<abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev>), 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 (<abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev>) technique for robustness verification and the Interrupted Time Series (<abbrev xlink:title="Interrupted Time Series">ITS</abbrev>) 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 (<abbrev xlink:title="research question">RQ</abbrev>).</p>
      <p>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.</p>
    </sec>
    <sec sec-type="2. Literature Review" id="sec3">
      <title>2. Literature Review</title>
      <p>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 <xref ref-type="bibr" rid="B21">Barro (2001)</xref>, Hanushek and Woessmann (<xref ref-type="bibr" rid="B52">2008</xref>, <xref ref-type="bibr" rid="B53">2012</xref>) who argue that human capital, especially the human capital of political elites and national business executives, shapes economic outcomes (<xref ref-type="bibr" rid="B59">Jones &amp; Olken, 2005</xref>; <xref ref-type="bibr" rid="B23">Besley et al., 2011</xref>; <xref ref-type="bibr" rid="B101">Shi, 2024</xref>; <xref ref-type="bibr" rid="B102">Shi, 2025</xref>). At the same time, a prominent strand of literature posits that institutional quality remains the primary determinant of growth (<xref ref-type="bibr" rid="B3">Acemoglu et al., 2001</xref>; <xref ref-type="bibr" rid="B96">Rodrik et al., 2004</xref>).</p>
      <p>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 <xref ref-type="bibr" rid="B74">Matta et al. (2022)</xref> point out that political instability can lead to weak institutions, which, over time, have an unfavorable impact on economic performance. Similarly, studies by <xref ref-type="bibr" rid="B60">Jong-A-Pin (2009)</xref>, <xref ref-type="bibr" rid="B9">Alesina et al. (1996)</xref>, <xref ref-type="bibr" rid="B10">Alesina &amp; Perotti (1996)</xref>, <xref ref-type="bibr" rid="B8">Aisen &amp; Veiga (2013)</xref>, and <xref ref-type="bibr" rid="B40">Dirks &amp; Schmidt (2024)</xref> have demonstrated that political instability has a detrimental impact on economic growth. Empirical studies conducted by <xref ref-type="bibr" rid="B82">Murad and Alshyab (2019)</xref>, and <xref ref-type="bibr" rid="B15">Assfaw et al (2025)</xref>, 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, (<xref ref-type="bibr" rid="B16">Asteriou &amp; Price, 2001</xref>; <xref ref-type="bibr" rid="B38">Delgado et al., 2014</xref>; <xref ref-type="bibr" rid="B45">Gakpa, 2020</xref>). This ultimately deters consumption (<xref ref-type="bibr" rid="B20">Bahmani &amp; Nayeri, 2020</xref>) and slows productivity (<xref ref-type="bibr" rid="B11">Alexandre et al., 2022</xref>; <xref ref-type="bibr" rid="B92">Paulo et al., 2022</xref>; <xref ref-type="bibr" rid="B1">Abdelkader, 2017</xref>).</p>
      <p>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 “<italic>greasing the wheels</italic>” 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 (<xref ref-type="bibr" rid="B98">Shabbir et al., 2016</xref>; <xref ref-type="bibr" rid="B15">Assfaw et al., 2025</xref>; <xref ref-type="bibr" rid="B49">Gründler, &amp; Potrafke, 2019</xref>; <xref ref-type="bibr" rid="B41">Dokas et al., 2023</xref>). Empirical findings by <xref ref-type="bibr" rid="B77">Méndez &amp; Sepúlveda (2006)</xref> and <xref ref-type="bibr" rid="B91">Paul (2010)</xref> support this theory, indicating that corruption exerts a positive impact on economic growth. Conversely, the “<italic>sanding the wheels</italic>” 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 &amp; Sekkat, 2005; <xref ref-type="bibr" rid="B41">Dokas et al., 2023</xref>; <xref ref-type="bibr" rid="B75">Mauro, 1995</xref>; <xref ref-type="bibr" rid="B7">Aidt et al., 2008</xref>; <xref ref-type="bibr" rid="B31">Cieślik &amp; Goczek, 2018</xref>). This hypothesis is supported by recent empirical studies, such as <xref ref-type="bibr" rid="B49">Gründler and Potrafke (2019)</xref>, <xref ref-type="bibr" rid="B86">Nur-tegin and Jakee (2020)</xref>, <xref ref-type="bibr" rid="B100">Sharma and Mitra (2019)</xref>, <xref ref-type="bibr" rid="B92">Paulo et al. (2022)</xref>, <xref ref-type="bibr" rid="B40">Dirks and Schmidt (2024)</xref>, and <xref ref-type="bibr" rid="B15">Assfaw et al. (2025)</xref>. In economies where institutions are not properly enforced, businesspeople, traders, politicians, and administrators often engage in corrupt practices (<xref ref-type="bibr" rid="B4">Acemoglu &amp; Verdier, 2000</xref>). Corruption affects <abbrev xlink:title="Gross domestic product">GDP</abbrev> by reducing the quality of human capital (<xref ref-type="bibr" rid="B31">Cieślik &amp; Goczek, 2018</xref>) and encouraging inefficient allocation of government resources. Corrupt officials seek to maximize their rent-extracting potential (<xref ref-type="bibr" rid="B34">D’Agostino et al., 2016</xref>; <xref ref-type="bibr" rid="B81">Montinola &amp; Jackman, 2001</xref>), which hinders investment and trade freedoms (<xref ref-type="bibr" rid="B49">Gründler &amp; Potrafke, 2019</xref>), and limits funding for innovative activities (<xref ref-type="bibr" rid="B106">Xu &amp; Yano, 2017</xref>; <xref ref-type="bibr" rid="B39">Dincer, 2019</xref>; <xref ref-type="bibr" rid="B15">Assfaw et al., 2025</xref>).</p>
      <p>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 (<xref ref-type="bibr" rid="B37">De Almeida et al., 2024</xref>). 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 (<xref ref-type="bibr" rid="B14">Asiamah et al., 2022</xref>; <xref ref-type="bibr" rid="B76">Mawardi et al., 2024</xref>; <xref ref-type="bibr" rid="B33">Correa &amp; Esquivias, 2024</xref>). 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 (<xref ref-type="bibr" rid="B109">Zhou &amp; Feng, 2024</xref>; <xref ref-type="bibr" rid="B90">Pang et al., 2024</xref>). An effective government fosters trust among investors, promotes technological innovation, and enhances economic performance (<xref ref-type="bibr" rid="B56">Jia et al., 2021</xref>). It can facilitate business activities and create a favourable environment for investment and entrepreneurship by reducing administrative hurdles and optimizing public sector operations (<xref ref-type="bibr" rid="B71">Mafimisebi &amp; Ogunsade, 2022</xref>). Furthermore, by designing optimal policies, effective governments target high-quality economic development (<xref ref-type="bibr" rid="B65">Kong et al., 2021</xref>; <xref ref-type="bibr" rid="B69">Li &amp; Li, 2025</xref>). In contrast, weak governments face barriers to meeting public needs and achieving high-quality development. (<xref ref-type="bibr" rid="B108">Zhao et al., 2022</xref>; <xref ref-type="bibr" rid="B69">Li &amp; Li, 2025</xref>).</p>
      <p>The literature emphasizes that weak institutions and corruption hinder economic performance and prevent wider development outcomes. <xref ref-type="bibr" rid="B43">Ear (2016)</xref> 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, <xref ref-type="bibr" rid="B89">Oyebanji and Omale (2025)</xref> 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, <xref ref-type="bibr" rid="B6">Adelakun et al. (2025)</xref> 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, <xref ref-type="bibr" rid="B44">Egbetokun et al. (2019)</xref> 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.</p>
      <p>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 <abbrev xlink:title="Gross domestic product">GDP</abbrev> growth remain empirically unexplored, which underscores the significance of the present study.</p>
    </sec>
    <sec sec-type="3. Data and Methodology" id="sec4">
      <title>3. Data and Methodology</title>
      <p>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.</p>
      <sec sec-type="3.1. Econometric Models and Data" id="sec5">
        <title>3.1. Econometric Models and Data</title>
        <p>To examine the impacts of political instability (<abbrev xlink:title="political instability">POI</abbrev>), corruption (<abbrev xlink:title="corruption">COR</abbrev>), and government effectiveness (<abbrev xlink:title="government effectiveness">GEF</abbrev>), following Dirks &amp; Schmidt (2024), <xref ref-type="bibr" rid="B11">Alexandre et al. (2022)</xref>, <xref ref-type="bibr" rid="B41">Dokas et al. (2023)</xref>, <xref ref-type="bibr" rid="B69">Li &amp; Li (2025)</xref>, and <xref ref-type="bibr" rid="B15">Assfaw et al. (2025)</xref>, we employ the following econometric model:</p>
        <p><italic><abbrev xlink:title="Gross domestic product">GDP</abbrev><sub>t</sub></italic> = <italic>f</italic> (<italic><abbrev xlink:title="political instability">POI</abbrev><sub>t</sub></italic> , <italic><abbrev xlink:title="corruption">COR</abbrev><sub>t</sub></italic> , <italic><abbrev xlink:title="government effectiveness">GEF</abbrev><sub>t</sub></italic>) (1)</p>
        <p>where <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev><sub>t</sub></italic> is the gross domestic product, <italic><abbrev xlink:title="political instability">POI</abbrev><sub>t</sub></italic> denotes political (in)stability, <italic><abbrev xlink:title="corruption">COR</abbrev><sub>t</sub></italic> shows control of corruption, and <italic><abbrev xlink:title="government effectiveness">GEF</abbrev><sub>t</sub></italic> stands for government effectiveness.</p>
        <p>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 <xref ref-type="table" rid="T1">1</xref>). 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.</p>
        <table-wrap id="T1" position="float" orientation="portrait">
          <label>Table 1.</label>
          <caption>
            <p>Variables, measurements, and data sources.</p>
          </caption>
          <table>
            <tbody>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>Variables</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Acronyms</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Definition and measurement</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="3"><bold>Panel A: Data related to <abbrev xlink:title="Gross domestic product">GDP</abbrev>, <abbrev xlink:title="political instability">POI</abbrev>, <abbrev xlink:title="corruption">COR</abbrev>, and <abbrev xlink:title="government effectiveness">GEF</abbrev> will be used for conducting baseline analysis using <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> and <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> techniques, and robustness checks analysis using the <abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev> technique</bold>.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Economic Growth</td>
                <td rowspan="1" colspan="1">
                  <abbrev xlink:title="Gross domestic product">GDP</abbrev>
                </td>
                <td rowspan="1" colspan="1">Gross domestic product (constant, US$, 2015)</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Political (In)stability Index</td>
                <td rowspan="1" colspan="1">
                  <abbrev xlink:title="political instability">POI</abbrev>
                </td>
                <td rowspan="1" colspan="1">Political instability measures perceptions of the likelihood of overthrow by unconstitutional means or violence and terrorism.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Control of corruption</td>
                <td rowspan="1" colspan="1">
                  <abbrev xlink:title="corruption">COR</abbrev>
                </td>
                <td rowspan="1" colspan="1">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.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Government Effectiveness Index</td>
                <td rowspan="1" colspan="1">
                  <abbrev xlink:title="government effectiveness">GEF</abbrev>
                </td>
                <td rowspan="1" colspan="1">Government Effectiveness: Standard Error</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="3"><bold>Panel B: Data related to <abbrev xlink:title="Gross domestic product">GDP</abbrev>, <abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev>, <abbrev xlink:title="Civil War">CW</abbrev>, <abbrev xlink:title="First Round of the Taliban Regime">FRTR</abbrev>, <abbrev xlink:title="U.S.-NATO interventions">USN</abbrev>, and <abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev> will be used for conducting event analysis using the <abbrev xlink:title="Interrupted Time Series">ITS</abbrev> technique</bold>.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Economic Growth</td>
                <td rowspan="1" colspan="1">
                  <abbrev xlink:title="Gross domestic product">GDP</abbrev>
                </td>
                <td rowspan="1" colspan="1">Gross domestic product (constant, US$, 2015)</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Soviet Union War</td>
                <td rowspan="1" colspan="1">
                  <abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev>
                </td>
                <td rowspan="1" colspan="1">The dummy variable takes the value 1 for the war period (1979-1989) and otherwise 0.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Civil War, including the Taliban First Round Regime</td>
                <td rowspan="1" colspan="1">
                  <abbrev xlink:title="Civil War">CW</abbrev>
                </td>
                <td rowspan="1" colspan="1">The dummy variable takes the value 1 for the war period (1989-2001) and otherwise 0.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">First Round Taliban Regime</td>
                <td rowspan="1" colspan="1">
                  <abbrev xlink:title="First Round of the Taliban Regime">FRTR</abbrev>
                </td>
                <td rowspan="1" colspan="1">The dummy variable takes the value 1 for the war period (1996-2001) and otherwise 0.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">United States and NATO Military Presence</td>
                <td rowspan="1" colspan="1">
                  <abbrev xlink:title="U.S.-NATO interventions">USN</abbrev>
                </td>
                <td rowspan="1" colspan="1">The dummy variable takes the value 1 for the war period (2001-2021) and otherwise 0.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Second Round Taliban Regime</td>
                <td rowspan="1" colspan="1">
                  <abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev>
                </td>
                <td rowspan="1" colspan="1">The dummy variable takes the value 1 for the war period (2021-2024) and otherwise 0.</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn>
              <p><italic>Note</italic>: <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev>, <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev>, <abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev>, and <abbrev xlink:title="Interrupted Time Series">ITS</abbrev> 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.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <p>To capture the pre-event economic growth trajectory, the immediate impact of the event, and the subsequent growth trend, this study extends the <abbrev xlink:title="Gross domestic product">GDP</abbrev> data from 1996–2024 to 1975–2024 and incorporates several dummy variables (see Panel B of Table <xref ref-type="table" rid="T1">1</xref>). The use of <abbrev xlink:title="Gross domestic product">GDP</abbrev> 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.</p>
        <p>This study uses <abbrev xlink:title="Gross domestic product">GDP</abbrev> as the primary dependent variable, while the key independent variables include <abbrev xlink:title="political instability">POI</abbrev>, <abbrev xlink:title="corruption">COR</abbrev>, and <abbrev xlink:title="government effectiveness">GEF</abbrev>. The dataset is sourced from the World Development Indicators (<abbrev xlink:title="World Development Indicators">WDI</abbrev>) and World Governance Indicators (<abbrev xlink:title="World Governance Indicators">WGI</abbrev>) databases. Table <xref ref-type="table" rid="T1">1</xref> provides detailed information about the variables’ names, definitions, and measurements.</p>
        <p><italic>Fig.</italic><xref ref-type="fig" rid="F1">1</xref> illustrates the annual trends of the targeted variables. The natural log of total <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> 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, <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> demonstrates positive growth between these two inflection points and continues to exhibit an upward trajectory following the 2021 regime change. In contrast, political <italic><abbrev xlink:title="political instability">POI</abbrev></italic>, <italic><abbrev xlink:title="corruption">COR</abbrev></italic>, and the <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> have shown a consistent decline over the past three decades (1996-2024).</p>
        <fig id="F1">
          <object-id content-type="doi">10.3897/brics-econ.7.e170868.fig1</object-id>
          <object-id content-type="arpha">3D4098CB-9B44-5D65-88F1-259D5EFB07EF</object-id>
          <label>Fig. 1.</label>
          <caption>
            <p>Annual trends of <italic>LnGDP</italic>, <italic><abbrev xlink:title="political instability">POI</abbrev></italic>, <italic><abbrev xlink:title="corruption">COR</abbrev></italic>, and <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> from 1996 to 2024.</p>
          </caption>
          <graphic xlink:href="brics-econ-07-049-g001.jpg" id="oo_1556082.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1556082</uri>
          </graphic>
        </fig>
      </sec>
      <sec sec-type="3.2. Empirical Estimation Strategies" id="sec6">
        <title>3.2. Empirical Estimation Strategies</title>
        <p>To empirically examine the impact of <italic><abbrev xlink:title="political instability">POI</abbrev></italic>, <italic><abbrev xlink:title="corruption">COR</abbrev></italic>, and <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth, we employ Quantile-on-Quantile Regression (<abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev>) and Wavelet Quantile Regression (<abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev>) to estimate the baseline results. For robustness checks aimed at validating the reliability of the <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> and <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> findings, we use Wavelet Quantile Correlation (<abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev>), and, finally, we apply the Interrupted Time Series (<abbrev xlink:title="Interrupted Time Series">ITS</abbrev>) method to examine the impact of major political events on growth in Afghanistan.</p>
        <sec sec-type="3.2.1. QQR and WQR Techniques" id="sec7">
          <title>3.2.1. QQR and WQR Techniques</title>
          <p>This study examines the non-linear effects of explanatory variables on the dependent variable using Quantile-on-Quantile Regression (<abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev>) and Wavelet Quantile Regression (<abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev>). <xref ref-type="bibr" rid="B104">Sim and Zhou (2015)</xref> proposed the <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> methodology to capture the non-linear association between the quantile of a dependent variable and the quantile of an independent variable(s). Assuming the <italic>X</italic> is the primary independent variable and <italic>Y</italic> is the main dependent variable, the single-variate-based <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> mathematical form for the connection between the quantiles of <italic>Y</italic> and <italic>X</italic> can be structured as follows:</p>
          <p><inline-graphic xlink:href="brics-econ-07-049-i001.jpg" xlink:type="simple" id="oo_1556111.jpg"/>	(2)</p>
          <p>where θ and Φ show the quantiles (0.05–0.95) of the <italic>Y</italic> and <italic>X</italic>, and <mml:math id="M1"><mml:msubsup><mml:mi>ϵ</mml:mi><mml:mi>t</mml:mi><mml:mi>θ</mml:mi></mml:msubsup></mml:math> 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 <xref ref-type="bibr" rid="B13">Alola et al. (2023)</xref> methodology, and assuming <italic>Y</italic> as the dependent variable and <italic>X</italic><sub>1</sub>, <italic>X</italic><sub>2</sub>, ... <italic>X<sub>n</sub></italic> as independent variables, the multivariate-based <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> mathematical model can be designed as follows:</p>
          <p><mml:math id="M2"><mml:mtable displaystyle="true" columnspacing="1em" rowspacing="3pt"><mml:mtr><mml:mtd><mml:msub><mml:mi>Y</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>θ</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>Φ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>Φ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mi>Φ</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>θ</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>Φ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msubsup><mml:mi>X</mml:mi><mml:mn>1</mml:mn><mml:mrow><mml:msub><mml:mi>Φ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msubsup><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mo>+</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>θ</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>Φ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msubsup><mml:mi>X</mml:mi><mml:mn>2</mml:mn><mml:mrow><mml:msub><mml:mi>Φ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:msubsup><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>θ</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>Φ</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msubsup><mml:mi>X</mml:mi><mml:mi>n</mml:mi><mml:mrow><mml:msub><mml:mi>Φ</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mrow></mml:msubsup><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mi>α</mml:mi><mml:mi>θ</mml:mi></mml:msup><mml:msub><mml:mi>Y</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msubsup><mml:mi>ϵ</mml:mi><mml:mi>t</mml:mi><mml:mi>θ</mml:mi></mml:msubsup></mml:mtd></mml:mtr></mml:mtable></mml:math>	(3)</p>
          <p>where Φ<sub>1</sub>, Φ<sub>2</sub>, …, Φ<italic><sub>n</sub></italic> quantify the quantiles of <italic>X</italic><sub>1</sub>, <italic>X</italic><sub>2</sub>, ... <italic>X<sub>n</sub></italic>, respectively, and the θ shows the quantile of <italic>Y.</italic> In this paper, the <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> implementation procedure follows the studies by <xref ref-type="bibr" rid="B13">Alola et al. (2023)</xref> and <xref ref-type="bibr" rid="B104">Sim and Zhou (2015)</xref>.</p>
          <p>The <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> technique, proposed by <xref ref-type="bibr" rid="B5">Adebayo and Özkan (2024)</xref>, can capture the impact of <italic>X</italic> on <italic>Y</italic> across time scales and quantiles, addressing the drawbacks of Quantile Regression (<abbrev xlink:title="Quantile Regression">QR</abbrev>). Following the empirical studies, including <xref ref-type="bibr" rid="B5">Adebayo and Özkan (2024)</xref>, <xref ref-type="bibr" rid="B70">Liu et al. (2024)</xref>, and <xref ref-type="bibr" rid="B66">Kumar and Padakandla (2022)</xref>, this study decomposes the time series data of the response variable (<italic>Y<sub>t</sub></italic>), and explanatory variable (<italic>X<sub>t</sub></italic>) utilizing the MODWT<sup><xref ref-type="fn" rid="en1">1</xref></sup> (maximal overlapping discrete wavelet transform). Let <italic>X</italic>[<italic>i</italic>] be a signal with a length of <italic>T</italic>, where <italic>T</italic> = 2<sup><italic>J</italic></sup> For an integer <italic>J.</italic> Then, consider <italic>h</italic><sub>1</sub>[<italic>i</italic>] as the low-pass filter and <italic>g</italic><sub>1</sub>[<italic>i</italic>] as the high-pass filter, both defining the orthogonal wavelet. At the initial step, <italic>X</italic>[<italic>i</italic>] undergoes convolution with <italic>h</italic><sub>1</sub>[<italic>i</italic>] to yield the approximation coefficients denoted as α<sub>1</sub>[<italic>i</italic>] of length <italic>N</italic>, and with <italic>g</italic><sub>1</sub>[<italic>i</italic>] to yield the detail coefficients denoted as <italic>d</italic><sub>1</sub>[<italic>i</italic>] of length <italic>N.</italic> These procedures can be defined as follows:</p>
          <p><mml:math id="M3"><mml:msub><mml:mi>α</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>]</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi>h</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:msup><mml:mo>]</mml:mo><mml:mo>∗</mml:mo></mml:msup><mml:mi>s</mml:mi><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>]</mml:mo><mml:mo>=</mml:mo><mml:munder><mml:mo>∑</mml:mo><mml:mi>k</mml:mi></mml:munder><mml:msub><mml:mi>h</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>−</mml:mo><mml:mi>k</mml:mi><mml:mo>]</mml:mo><mml:mi>s</mml:mi><mml:mo>[</mml:mo><mml:mi>k</mml:mi><mml:mo>]</mml:mo></mml:math>	(4)</p>
          <p><mml:math id="M4"><mml:msub><mml:mi>d</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>]</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi>g</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>]</mml:mo><mml:mo>∗</mml:mo><mml:mi>s</mml:mi><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>]</mml:mo><mml:mo>=</mml:mo><mml:munder><mml:mo>∑</mml:mo><mml:mi>k</mml:mi></mml:munder><mml:msub><mml:mi>g</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>−</mml:mo><mml:mi>k</mml:mi><mml:mo>]</mml:mo><mml:mi>s</mml:mi><mml:mo>[</mml:mo><mml:mi>k</mml:mi><mml:mo>]</mml:mo></mml:math>	(5)</p>
          <p>Following the same steps, we employ a similar method to filter α<sub>1</sub>[<italic>i</italic>], using modified filters <italic>h</italic><sub>2</sub>[<italic>i</italic>] and <italic>g</italic><sub>2</sub>[<italic>i</italic>] obtained from the dyadic up-sampling of <italic>g</italic><sub>1</sub>[<italic>i</italic>] and <italic>h</italic><sub>1</sub>[<italic>i</italic>]. This recursive process is carried out iteratively. For values of <italic>J</italic> spanning from 1 to <italic>j</italic><sub>0</sub> – 1, where <italic>J</italic><sub>0</sub> ≤ <italic>J</italic>, we can calculate the coefficients of the approximate and detailed as follows:</p>
          <p><mml:math id="M5"><mml:msub><mml:mi>α</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>]</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi>h</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>]</mml:mo><mml:mo>∗</mml:mo><mml:msub><mml:mi>α</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>]</mml:mo><mml:mo>=</mml:mo><mml:munder><mml:mo>∑</mml:mo><mml:mi>k</mml:mi></mml:munder><mml:msub><mml:mi>h</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>−</mml:mo><mml:mi>k</mml:mi><mml:mo>]</mml:mo><mml:msub><mml:mi>α</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi>k</mml:mi><mml:mo>]</mml:mo></mml:math>	(6)</p>
          <p><mml:math id="M6"><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>]</mml:mo><mml:mo>=</mml:mo><mml:msub><mml:mi>h</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:msup><mml:mo>]</mml:mo><mml:mo>∗</mml:mo></mml:msup><mml:msub><mml:mi>α</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>]</mml:mo><mml:mo>=</mml:mo><mml:munder><mml:mo>∑</mml:mo><mml:mi>k</mml:mi></mml:munder><mml:msub><mml:mi>g</mml:mi><mml:mrow><mml:mi>j</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>−</mml:mo><mml:mi>k</mml:mi><mml:mo>]</mml:mo><mml:msub><mml:mi>α</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi>k</mml:mi><mml:mo>]</mml:mo></mml:math>	(7)</p>
          <p>In this context, <italic>h<sub>j</sub></italic><sub>+ 1</sub>[<italic>i</italic>] = <italic>U</italic>(<italic>h<sub>j</sub></italic>[<italic>i</italic>]) and <italic>g<sub>j</sub></italic><sub>+ 1</sub>[<italic>i</italic>] = <italic>U</italic>(<italic>g<sub>j</sub></italic>[<italic>i</italic>]), where the up-sampling operation is illustrated by operator <italic>U.</italic> This operation involves inserting a zero value between each consecutive pair of time series.</p>
          <p>Following the application of a J-level decomposition to <italic>Y<sub>t</sub></italic> and <italic>X<sub>t</sub></italic>, and the subsequent acquisition of the detail coefficients, we proceed to implement <abbrev xlink:title="Quantile Regression">QR</abbrev> on the pair of <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> details, <italic>d<sub>j</sub></italic>(<italic>Y</italic>) and <italic>d<sub>j</sub></italic>(<italic>X</italic>), for all levels of J. Consequently, we derive the <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> outcomes for each level of J. Finally, the <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> technique for the dependent variable <italic>Y</italic> and the independent variable <italic>X</italic> at a specific decomposition level <italic>J</italic>, and for a given quantile <italic>q</italic>, can be rewritten as follows:</p>
          <p><mml:math id="M7"><mml:mrow><mml:mo fence="true" stretchy="true" symmetric="true"/><mml:msub><mml:mi>ϕ</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>q</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi>Y</mml:mi><mml:mo>]</mml:mo><mml:mo>∣</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi>X</mml:mi><mml:mo>]</mml:mo><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>B</mml:mi><mml:mrow><mml:mn>0</mml:mn><mml:mo>(</mml:mo><mml:mi>q</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>B</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>(</mml:mo><mml:mi>q</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:msub><mml:msub><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi>X</mml:mi><mml:mo>]</mml:mo></mml:math>	(8)</p>
        </sec>
        <sec sec-type="3.2.2. WQC Technique" id="sec8">
          <title>3.2.2. WQC Technique</title>
          <p>To verify the reliability of the baseline results, we employ the <abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev> technique. The <abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev> technique proposed by <xref ref-type="bibr" rid="B66">Kumar and Padakandla (2022)</xref> is based on the quantile correlation method by <xref ref-type="bibr" rid="B67">Li et al. (2015)</xref>. To calculate the quantile-wise correlation between two time series variables, <xref ref-type="bibr" rid="B67">Li et al. (2015)</xref> suggests that <italic>Q</italic><sub>τ,<italic>X</italic></sub> = τ<italic>th</italic> quantile of the independent variable, while <italic>Q</italic><sub>τ,<italic>Y</italic></sub> = τ<italic>th</italic> quantile of the dependent variable, with the following mathematical expression:</p>
          <p><mml:math id="M8"><mml:mrow><mml:mo fence="true" stretchy="true" symmetric="true"/><mml:msub><mml:mi>ϕ</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>q</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi>Y</mml:mi><mml:mo>]</mml:mo><mml:mo>∣</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi>X</mml:mi><mml:mo>]</mml:mo><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>B</mml:mi><mml:mrow><mml:mn>0</mml:mn><mml:mo>(</mml:mo><mml:mi>q</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>B</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>(</mml:mo><mml:mi>q</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:msub><mml:msub><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>[</mml:mo><mml:mi>X</mml:mi><mml:mo>]</mml:mo></mml:math>	(9)</p>
          <p>where <italic>qcov<sub>t</sub></italic>(<italic>Y<sub>t</sub></italic> ,<italic>X<sub>t</sub></italic>) presents the quantile correlation between the two series.</p>
          <p>To extend the described method, following Kumar and Padakandla’s (2022), this study decomposed the modeled series, such as <italic>Y<sub>t</sub></italic> and <italic>X<sub>t</sub></italic>, using the MODWT technique at <italic>Jth</italic> level, and obtained the <abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev> for each <italic>J</italic> level based on the following mathematical equation.</p>
          <p><mml:math id="M9"><mml:msub><mml:mi>qcov</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>⁡</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:msub><mml:mi>Y</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>]</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>]</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>q</mml:mi><mml:msub><mml:mi>cov</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>⁡</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:msub><mml:mi>Y</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>]</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>]</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:msqrt><mml:mi>var</mml:mi><mml:mo>⁡</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>φ</mml:mi><mml:mi>τ</mml:mi></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:msub><mml:mi>Y</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>]</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:msub><mml:mi>Q</mml:mi><mml:mrow><mml:mi>τ</mml:mi><mml:mo>,</mml:mo><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:msub><mml:mi>Y</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>]</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mi>var</mml:mi><mml:mo>⁡</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mi>t</mml:mi></mml:msub><mml:mo>]</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:msqrt></mml:mfrac></mml:math>	(10)</p>
          <p>Eq. (10) shows the <abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev> between the modeled series, such as <italic>Y</italic> and <italic>X.</italic> This method can handle potential outliers in the data and produce consistent results (<xref ref-type="bibr" rid="B66">Kumar &amp; Padakandla, 2022</xref>). Furthermore, <abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev> captures plausible asymmetries by providing results for different quantiles and presents a comprehensive view of the dynamic relationship over the whole sampled period (Kumar &amp; Padakandla, 2022).</p>
          <p>Fig. <xref ref-type="fig" rid="F2">2</xref> outlines the sequential empirical analysis framework, from data collection through descriptive analysis, diagnostic tests (Unit Root, BDS), baseline regressions (QOR, <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev>), robustness checks (<abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev>), and event analysis via Interrupted Time Series (<abbrev xlink:title="Interrupted Time Series">ITS</abbrev>). This structured approach ensures a comprehensive and methodologically coherent investigation.</p>
          <fig id="F2">
            <object-id content-type="doi">10.3897/brics-econ.7.e170868.fig2</object-id>
            <object-id content-type="arpha">DEBE25DF-4AE6-5355-B93E-3CA39F811D22</object-id>
            <label>Fig. 2.</label>
            <caption>
              <p>Empirical analysis steps.</p>
            </caption>
            <graphic xlink:href="brics-econ-07-049-g002.jpg" id="oo_1556083.jpg">
              <uri content-type="original_file">https://binary.pensoft.net/fig/1556083</uri>
            </graphic>
          </fig>
        </sec>
      </sec>
    </sec>
    <sec sec-type="4. Empirical Results and Discussions" id="sec9">
      <title>4. Empirical Results and Discussions</title>
      <sec sec-type="4.1. Preliminary Time Series Analysis" id="sec10">
        <title>4.1. Preliminary Time Series Analysis</title>
        <p>Before conducting the primary analysis, we performed several preliminary calculations and tests. These included descriptive statistics, correlation analysis, unit root tests for quantiles and the nonlinear BDS test. The purpose of these tests was to capture the characteristics of the variables used in our study.</p>
        <p>Table <xref ref-type="table" rid="T2">2</xref> presents descriptive statistics for the variables, including mean and standard deviation, minimum, maximum values, skewness and kurtosis. It also includes the results of Jarque-Berra normality test. Based on these outcomes, <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> exhibits the highest mean and standard deviation, whereas <italic><abbrev xlink:title="corruption">COR</abbrev></italic> and <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> record the lowest mean and standard deviation, respectively, reflecting the variability within the dataset. Table <xref ref-type="table" rid="T2">2</xref> presents descriptive statistics for the variables, including mean, standard deviation, minimum, maximum values, skewness and kurtosis. It also includes the results of Jarque-Berra normality test. Moreover, the Jarque-Bera test and the associated probabilities show that all the variables significantly follow a normal distribution. <italic>Fig.</italic><xref ref-type="fig" rid="F3">3</xref> shows the correlation coefficients between <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> and all explanatory variables. As shown, <italic><abbrev xlink:title="political instability">POI</abbrev></italic> has the strongest negative correlation with <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic>, followed by <italic><abbrev xlink:title="corruption">COR</abbrev></italic> and <italic><abbrev xlink:title="government effectiveness">GEF</abbrev>.</italic></p>
        <fig id="F3">
          <object-id content-type="doi">10.3897/brics-econ.7.e170868.fig3</object-id>
          <object-id content-type="arpha">B3DD29E6-2EFA-527F-A686-333B91FDA6FC</object-id>
          <label>Fig. 3.</label>
          <caption>
            <p>Correlation coefficients heatmap between <abbrev xlink:title="Gross domestic product">GDP</abbrev>, <abbrev xlink:title="political instability">POI</abbrev>, <abbrev xlink:title="corruption">COR</abbrev>, and <abbrev xlink:title="government effectiveness">GEF</abbrev></p>
          </caption>
          <graphic xlink:href="brics-econ-07-049-g003.jpg" id="oo_1556084.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1556084</uri>
          </graphic>
        </fig>
        <table-wrap id="T2" position="float" orientation="portrait">
          <label>Table 2.</label>
          <caption>
            <p>Descriptive statistics results</p>
          </caption>
          <table>
            <tbody>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>Variables</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>
                    <abbrev xlink:title="Gross domestic product">GDP</abbrev>
                  </bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>
                    <abbrev xlink:title="political instability">POI</abbrev>
                  </bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>
                    <abbrev xlink:title="corruption">COR</abbrev>
                  </bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>
                    <abbrev xlink:title="government effectiveness">GEF</abbrev>
                  </bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Mean</td>
                <td rowspan="1" colspan="1">23.229</td>
                <td rowspan="1" colspan="1">0.303</td>
                <td rowspan="1" colspan="1">0.222</td>
                <td rowspan="1" colspan="1">0.260</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Std. dev.</td>
                <td rowspan="1" colspan="1">0.446</td>
                <td rowspan="1" colspan="1">0.085</td>
                <td rowspan="1" colspan="1">0.069</td>
                <td rowspan="1" colspan="1">0.034</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Min</td>
                <td rowspan="1" colspan="1">22.449</td>
                <td rowspan="1" colspan="1">0.208</td>
                <td rowspan="1" colspan="1">0.154</td>
                <td rowspan="1" colspan="1">0.187</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Max</td>
                <td rowspan="1" colspan="1">23.773</td>
                <td rowspan="1" colspan="1">0.474</td>
                <td rowspan="1" colspan="1">0.352</td>
                <td rowspan="1" colspan="1">0.331</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Skewness</td>
                <td rowspan="1" colspan="1">-0.301</td>
                <td rowspan="1" colspan="1">0.776</td>
                <td rowspan="1" colspan="1">0.822</td>
                <td rowspan="1" colspan="1">0.167</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Kurtosis</td>
                <td rowspan="1" colspan="1">-1.497</td>
                <td rowspan="1" colspan="1">-0.868</td>
                <td rowspan="1" colspan="1">-0.908</td>
                <td rowspan="1" colspan="1">-0.725</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Jarque-Bera</td>
                <td rowspan="1" colspan="1">3.150</td>
                <td rowspan="1" colspan="1">3.827</td>
                <td rowspan="1" colspan="1">4.270</td>
                <td rowspan="1" colspan="1">0.772</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">p-values</td>
                <td rowspan="1" colspan="1">0.206</td>
                <td rowspan="1" colspan="1">0.147</td>
                <td rowspan="1" colspan="1">0.118</td>
                <td rowspan="1" colspan="1">0.679</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Obs.</td>
                <td rowspan="1" colspan="1">29</td>
                <td rowspan="1" colspan="1">29</td>
                <td rowspan="1" colspan="1">29</td>
                <td rowspan="1" colspan="1">29</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p><italic>Fig.</italic><xref ref-type="fig" rid="F4">4</xref> presents the quantile unit root test based on the Quantile Augmented-Dickey Filler (QADF) method, respectively, for each variable. Conducting these tests is a crucial step before applying <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> and <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> techniques. This study employs the QADF unit root test proposed by <xref ref-type="bibr" rid="B5">Adebayo and Özkan (2024)</xref>. Unlike traditional unit root tests, such as ADF, which assume constant distributional properties across the entire range, the QADF test accounts for potential distributional differences across various quantiles of the distribution. Based on the results shown in <italic>Figs.</italic><xref ref-type="fig" rid="F4">4</xref>, all variables exhibit non-stationarity at the level. After taking the first difference of the variables, <abbrev xlink:title="Gross domestic product">GDP</abbrev> and <abbrev xlink:title="government effectiveness">GEF</abbrev> exhibit stationarity across all quantiles, while <abbrev xlink:title="political instability">POI</abbrev> and <abbrev xlink:title="corruption">COR</abbrev> show stationarity between 0.35-0.40 and 0.15-0.8 quantiles, respectively.</p>
        <fig id="F4">
          <object-id content-type="doi">10.3897/brics-econ.7.e170868.fig4</object-id>
          <object-id content-type="arpha">2FAEE60C-1783-555A-BDC1-AD366C77D1E0</object-id>
          <label>Fig. 4.</label>
          <caption>
            <p>QADF unit root test</p>
          </caption>
          <graphic xlink:href="brics-econ-07-049-g004.jpg" id="oo_1556085.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1556085</uri>
          </graphic>
        </fig>
        <p>Finally, we performed the BDS test introduced by <xref ref-type="bibr" rid="B26">Brock et al. (1996)</xref><sup><xref ref-type="fn" rid="en2">2</xref></sup> to examine the non-linear characteristics of the response and explanatory variables. Table <xref ref-type="table" rid="T3">3</xref> presents the BDS non-linearity test results, which indicate that the relationships between <abbrev xlink:title="Gross domestic product">GDP</abbrev> and <abbrev xlink:title="political instability">POI</abbrev>, <abbrev xlink:title="Gross domestic product">GDP</abbrev> and <abbrev xlink:title="corruption">COR</abbrev>, and <abbrev xlink:title="Gross domestic product">GDP</abbrev> and <abbrev xlink:title="government effectiveness">GEF</abbrev> exhibit non-linear behavior, as evidenced by their corresponding p-values being significant at the 1% significance level. These results confirm the suitability of applying nonlinear techniques, such as <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> and <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev>.</p>
        <table-wrap id="T3" position="float" orientation="portrait">
          <label>Table 3.</label>
          <caption>
            <p>BDS test results</p>
          </caption>
          <table>
            <tbody>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>Dimensions</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold><italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> = <italic>f</italic>(<italic><abbrev xlink:title="political instability">POI</abbrev></italic>)</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold><italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> = <italic>f</italic>(<italic><abbrev xlink:title="corruption">COR</abbrev></italic>)</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold><italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> = <italic>f</italic>(<italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic>)</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Dimensions-II</td>
                <td rowspan="1" colspan="1">23.188*** (0.000)</td>
                <td rowspan="1" colspan="1">27.585*** (0.000)</td>
                <td rowspan="1" colspan="1">10.073*** (0.000)</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Dimensions-III</td>
                <td rowspan="1" colspan="1">32.399*** (0.000)</td>
                <td rowspan="1" colspan="1">38.338*** (0.000)</td>
                <td rowspan="1" colspan="1">14.355*** (0.000)</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Dimensions-IV</td>
                <td rowspan="1" colspan="1">93.995*** (0.000)</td>
                <td rowspan="1" colspan="1">120.606*** (0.000)</td>
                <td rowspan="1" colspan="1">10.179*** (0.000)</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Dimensions-V</td>
                <td rowspan="1" colspan="1">120.188*** (0.000)</td>
                <td rowspan="1" colspan="1">152.834*** (0.000)</td>
                <td rowspan="1" colspan="1">10.415*** (0.000)</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Dimensions-VI</td>
                <td rowspan="1" colspan="1">25.235*** (0.000)</td>
                <td rowspan="1" colspan="1">38.943*** (0.000)</td>
                <td rowspan="1" colspan="1">7.859*** (0.000)</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Dimensions-VII</td>
                <td rowspan="1" colspan="1">27.811*** (0.000)</td>
                <td rowspan="1" colspan="1">44.714*** (0.000)</td>
                <td rowspan="1" colspan="1">7.497*** (0.000)</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn>
              <p><italic>Notes</italic>: This table presents the outcomes of the BDS test for the functions, including <abbrev xlink:title="Gross domestic product">GDP</abbrev>-<abbrev xlink:title="political instability">POI</abbrev>, <abbrev xlink:title="Gross domestic product">GDP</abbrev>-<abbrev xlink:title="corruption">COR</abbrev>, and <abbrev xlink:title="Gross domestic product">GDP</abbrev>-<abbrev xlink:title="government effectiveness">GEF</abbrev>, in Afghanistan from 1975 to 2024. *** signifies 1% statistical significance level.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
      </sec>
      <sec sec-type="4.2. Baseline Results" id="sec11">
        <title>4.2. Baseline Results</title>
        <sec sec-type="4.2.1. QQR Results" id="sec12">
          <title>4.2.1. QQR Results</title>
          <p>To evaluate the non-linear impacts of <italic><abbrev xlink:title="political instability">POI</abbrev></italic>, <italic><abbrev xlink:title="corruption">COR</abbrev></italic>, and <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> in the context of Afghanistan, we used the <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> technique and the results of this method are shown in <italic>Fig.</italic><xref ref-type="fig" rid="F5">5</xref>. <italic>Fig.</italic><xref ref-type="fig" rid="F5">5(a)</xref> illustrates the effect of <italic><abbrev xlink:title="political instability">POI</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic>, indicating that within the lower and middle quantiles (ranging from 0.2 to 0.60), <abbrev xlink:title="political instability">POI</abbrev> has a significant negative impact on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev>.</italic> In the higher quantiles (ranging from 0.8 to 0.9), the effect was also negative, but weak. This relationship suggests that <italic><abbrev xlink:title="political instability">POI</abbrev></italic> consistently hinders <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth across all quantiles in Afghanistan. These results are consistent with the theories used and can provide practical explanations of the political economy of Afghanistan from different perspectives.</p>
          <fig id="F5">
            <object-id content-type="doi">10.3897/brics-econ.7.e170868.fig5</object-id>
            <object-id content-type="arpha">0269CD74-7CBB-5014-8F5E-E082A4EC68B6</object-id>
            <label>Fig. 5.</label>
            <caption>
              <p>(a). Impact of <abbrev xlink:title="political instability">POI</abbrev> on <abbrev xlink:title="Gross domestic product">GDP</abbrev>, (b). Impact of <abbrev xlink:title="corruption">COR</abbrev> on <abbrev xlink:title="Gross domestic product">GDP</abbrev>, (c). Impact of <abbrev xlink:title="government effectiveness">GEF</abbrev> on <abbrev xlink:title="Gross domestic product">GDP</abbrev>, and (d). Impact of POP</p>
            </caption>
            <graphic xlink:href="brics-econ-07-049-g005.jpg" id="oo_1556086.jpg">
              <uri content-type="original_file">https://binary.pensoft.net/fig/1556086</uri>
            </graphic>
          </fig>
          <p>In lower and middle <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> quantiles, where the economy is already weak, <italic><abbrev xlink:title="political instability">POI</abbrev></italic> exacerbates vulnerabilities due to fragile institutions, weak role of low-income groups, and inconsistent policy implementation. This result presents a current image of political economy in Afghanistan, with its fragile institutions and government deficits over the past three decades. Frequent regime changes and contested governance reduce investor confidence, disrupt aid flows and weaken the delivery of government services, collectively hindering the positive dynamics of <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth (<xref ref-type="bibr" rid="B57">Jingjing et al., 2025</xref>). Moreover, persistent internal conflicts, insurgencies, and external interventions have created a highly risky environment where productive sectors are struggling to operate efficiently. Besides, <italic><abbrev xlink:title="political instability">POI</abbrev></italic> often triggers security breakdowns, which interrupt trade, limit mobility and damage infrastructure, particularly in the sectors with medium to low growth rates. Political instability and inefficient institutions significantly weaken the state’s capacity to absorb resources. This leads to misallocation of resources and economic decline. It is not surprising that Afghanistan heavily relies on international aid for its economic development.</p>
          <p>Additionally, <italic><abbrev xlink:title="political instability">POI</abbrev></italic> frequently leads to policy reversals and disrupted long-run development strategies, which prevents the accumulation of human capital, limits technological innovation, and hinders macroeconomic planning and public-private collaboration. Political unrest inhibits foreign direct investment and prevents the local capital holders from externalizing assets. As a result, the base for industrial growth and job creation is shrinking. This is especially true for Afghanistan, whose economy relies heavily on external aid, trading routes, and geopolitical dynamics. These findings are supported by empirical literature, including <xref ref-type="bibr" rid="B8">Aisen &amp; Veiga (2013)</xref>, Murad et al. (2019), <xref ref-type="bibr" rid="B1">Abdelkader (2017)</xref>, <xref ref-type="bibr" rid="B87">Okafor (2015)</xref>, <xref ref-type="bibr" rid="B40">Dirks &amp; Schmidt (2024)</xref>, and <xref ref-type="bibr" rid="B15">Assfaw et al. (2025)</xref>.</p>
          <p>Moreover, <italic>Fig.</italic><xref ref-type="fig" rid="F5">5(b)</xref> shows that <italic><abbrev xlink:title="corruption">COR</abbrev></italic> adversely influences Afganistan’s <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth across all quantiles. The effects are observed at the three levels: low, medium, and high. Within the lower quantile (ranging from 0.2 to 0.4), the negative impact on performance is most significant. In the middle and upper quantiles (from 0.5 to 0.8), the effects are still negative, but they decrease in magnitude over time. These findings are both theoretically and practically sound.</p>
          <p>The persistent negative impact of <italic><abbrev xlink:title="corruption">COR</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> supports the “<italic>sanding the wheels</italic>” hypothesis, suggesting that <italic><abbrev xlink:title="corruption">COR</abbrev></italic> impedes rather than facilitates growth by increasing transaction costs, lowering institutional efficiency, and degrading public trust. Furthermore, the gradual move toward zero from the medium to the higher quantiles may reflect the growth resilience of the system in the face of <italic><abbrev xlink:title="corruption">COR</abbrev></italic>, but still shows that <italic><abbrev xlink:title="corruption">COR</abbrev></italic> acts as a structural ceiling on Afghanistan’s economic performance.</p>
          <p>Afghanistan’s public sector is characterized by weak legal enforcement and limited bureaucratic capacity, where <italic><abbrev xlink:title="corruption">COR</abbrev></italic> deters <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth by undermining trust in government and discouraging both domestic and foreign investment. Additionally, <italic><abbrev xlink:title="corruption">COR</abbrev></italic> often redirects government spending from productive sectors, such as infrastructure and education, toward low-return patronage networks (<xref ref-type="bibr" rid="B75">Mauro, 1955</xref>), specifically in lower <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> quantiles, where investment is already insufficient. Moreover, <italic><abbrev xlink:title="corruption">COR</abbrev></italic> raises the cost of doing business through informal fees, regulatory uncertainty, and favoritism, which collectively restricts entrepreneurship, diminishes innovation, and erodes the competitive environment (<xref ref-type="bibr" rid="B83">Murphy et al., 2008</xref>) even in relatively higher <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> quantiles. Finally, <italic><abbrev xlink:title="corruption">COR</abbrev></italic> frequently results in unpredictable policy changes, making investors and firms reluctant to commit to long-term investments (<xref ref-type="bibr" rid="B88">Olofsgård &amp; Zahran, 2008</xref>). These findings are in line with empirical studies by <xref ref-type="bibr" rid="B61">Kaufmann et al. (1999)</xref>, <xref ref-type="bibr" rid="B95">Rock and Bonnett (2004)</xref>, <xref ref-type="bibr" rid="B63">Knack and Keefer (1995)</xref>, <xref ref-type="bibr" rid="B68">Li and Xu (2000)</xref>, Méon &amp; Sekkat, (2005), <xref ref-type="bibr" rid="B80">Mo, (2001)</xref>, and <xref ref-type="bibr" rid="B15">Assfaw et al. (2025)</xref>, but contradict the study by <xref ref-type="bibr" rid="B91">Paul (2010)</xref> who concludes that <abbrev xlink:title="corruption">COR</abbrev> is positively associated with <abbrev xlink:title="Gross domestic product">GDP</abbrev> growth in Bangladesh.</p>
          <p><italic>Fig.</italic><xref ref-type="fig" rid="F5">5(c)</xref> reveals a counterintuitive, non-linear relationship between <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> and <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth in Afghanistan. Contrary to conventional theory, medium-to-high levels of <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> (quantiles 0.30–0.90) have a significant negative influence on growth quantiles. Conversely, in the lowest quantiles of the <abbrev xlink:title="government effectiveness">GEF</abbrev> (0.1–0.2), the effect changes from a weakly negative to a positive one. This paradox is theoretically consistent in the context of Afghanistan, where institutions are characterized by a deep-seated fragility. It suggests that in such a setting, efforts to enhance formal governance — particularly when they are perceived as incomplete or externally imposed — may create friction, raise transaction costs, or disrupt established (if informal) economic networks without providing the corresponding benefits of stability and predictability, thereby stifling growth.</p>
          <p>Theoretically, the non-linear relationship between <abbrev xlink:title="government effectiveness">GEF</abbrev> and <abbrev xlink:title="Gross domestic product">GDP</abbrev> growth in Afghanistan can be explained through the lens of institutional transition theory and governance traps. The most significant negative impact on <abbrev xlink:title="Gross domestic product">GDP</abbrev> is from the middle and high quantile of <abbrev xlink:title="government effectiveness">GEF</abbrev>. At the extremes of the distribution, there are weaker adverse effects. This pattern suggests that partial or fragmented improvements to governance may initially destabilize informal economic arrangements, without delivering the necessary institutional coherence to support sustained economic growth (<xref ref-type="bibr" rid="B18">Auerbach &amp; Azariadis, 2015</xref>). Such dynamics are consistent with the concept of the governance trap, in which countries with weak institutional capacities find it difficult to translate their governance reforms into economic benefits (<xref ref-type="bibr" rid="B2">Acemoglu et al., 2014</xref>; <xref ref-type="bibr" rid="B84">North, 1990</xref>).</p>
          <p>In the case of Afghanistan, medium quantiles may represent transition phases, where reforms are often driven externally, disrupting rent-seeking networks and informal governance structures. At the same time, the reforms lack the capacity or legitimacy to replace these structures with functional alternatives. The resulting institutional incoherence can suppress growth by increasing uncertainty, overregulation, and policy inconsistency (<xref ref-type="bibr" rid="B75">Mauro, 1995</xref>; <xref ref-type="bibr" rid="B96">Rodrik et al., 2004</xref>; <xref ref-type="bibr" rid="B28">Chowdhury et al., 2018</xref>). These findings align with empirical studies showing that growth reversals are more likely to occur in countries undergoing a shallow institutional transition, where governance reforms are not supported by credible enforcement mechanisms or inclusive political settlements (<xref ref-type="bibr" rid="B64">Knez &amp; Lokar, 2024</xref>). Afghanistan’s experience shows that it is important to sequentially implement reforms and establish institutional credibility before expecting governance improvement to yield consistent economic benefits. Empirical studies by <xref ref-type="bibr" rid="B62">Kinyondo et al. (2021)</xref>, <xref ref-type="bibr" rid="B107">Yapatake et al. (2022)</xref>, <xref ref-type="bibr" rid="B27">Chhabra et al. (2023)</xref>, and <xref ref-type="bibr" rid="B17">Atemnkeng et al. (2024)</xref> support the idea that good and effective governance contributes to economic growth and development.</p>
          <p>The numerical values of <italic>Figs.</italic><xref ref-type="fig" rid="F5">5(a)</xref>, <xref ref-type="fig" rid="F5">5(b)</xref> and <xref ref-type="fig" rid="F5">5(c)</xref> are presented in Tables <xref ref-type="table" rid="T5">A1</xref>, <xref ref-type="table" rid="T6">A2</xref> and <xref ref-type="table" rid="T7">A3</xref> of the Appendix, respectively.</p>
        </sec>
        <sec sec-type="4.2.2. WQR Results" id="sec13">
          <title>4.2.2. WQR Results</title>
          <p>Building on the confirmed non-linear impacts of <italic><abbrev xlink:title="political instability">POI</abbrev></italic>, <italic><abbrev xlink:title="corruption">COR</abbrev></italic>, and <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth established in the previous section, this paper employs the Wavelet Quantile Regression (<abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev>) methodology to examine the associations between these explanatory variables and <abbrev xlink:title="Gross domestic product">GDP</abbrev> growth in the short, medium, and long term. In contrast to conventional quantile regression (<abbrev xlink:title="Quantile Regression">QR</abbrev>), which is effective at handling non-linear relationships, wavelet quantile regression (<abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev>) offers enhanced flexibility by analyzing the impact of explanatory variables over various time horizons and quantiles. This allows for the capture of a more diverse range of non-linear patterns. The results of the <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> analysis are presented in <italic>Fig.</italic><xref ref-type="fig" rid="F6">6</xref>, using a heatmap visualization where colors progress from dark purple (representing adverse effects) to light green (representing positive effects), in ascending order.</p>
          <fig id="F6">
            <object-id content-type="doi">10.3897/brics-econ.7.e170868.fig6</object-id>
            <object-id content-type="arpha">719F69BC-6392-5DFA-B631-44314EB6C664</object-id>
            <label>Fig. 6.</label>
            <caption>
              <p>(a). Impact of <italic><abbrev xlink:title="political instability">POI</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic>, (b). Impact of <italic><abbrev xlink:title="corruption">COR</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic>, and (c). Impact of <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic></p>
            </caption>
            <graphic xlink:href="brics-econ-07-049-g006.jpg" id="oo_1556087.jpg">
              <uri content-type="original_file">https://binary.pensoft.net/fig/1556087</uri>
            </graphic>
          </fig>
          <p><italic>Fig.</italic><xref ref-type="fig" rid="F6">6(a)</xref> illustrates the impact of <italic><abbrev xlink:title="political instability">POI</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth in Afghanistan, indicating that in the short and medium term, the effects of <italic><abbrev xlink:title="political instability">POI</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> are adverse but weak. In the long term, however, <italic><abbrev xlink:title="political instability">POI</abbrev></italic> exerts a more substantial adverse effect. Specifically, the long-term estimated slope coefficients show that <italic><abbrev xlink:title="political instability">POI</abbrev></italic> severely impacts <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> in all the quantiles (0.05–0.95). In contrast, the short-term coefficients indicate that <italic><abbrev xlink:title="political instability">POI</abbrev></italic>’s effect on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth is weak and hovers around zero across the same quantile range.</p>
          <p>Similarly, <italic>Fig.</italic><xref ref-type="fig" rid="F6">6(b)</xref> illustrates the impact of <italic><abbrev xlink:title="corruption">COR</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic>, demonstrating that corruption harms <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth in Afghanistan in the long term across all quantiles. Notably, within the 0.45–0.75 quantiles of <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic>, the medium-term impact of <italic><abbrev xlink:title="corruption">COR</abbrev></italic> remains negative and strong, while its short-term impact is negative but close to zero. Overall, the adverse long-term effect of <italic><abbrev xlink:title="corruption">COR</abbrev></italic> is more severe than its short- and medium-term impacts across all quantiles of <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev>.</italic></p>
          <p>Finally, <italic>Fig.</italic><xref ref-type="fig" rid="F6">6(c)</xref> displays the relationship between <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> and <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth in Afghanistan. The results indicate that the long-term effects of <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> quantiles (ranging from 0.05 to 0.80) are adverse and strong, while the short- and medium-term effects across all quantiles are adverse but close to zero. The findings from the <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> technique provide theoretical and practical justification for the discussions based on the <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> methodology presented in the previous section.</p>
          <p>The numerical values of <italic>Figs.</italic><xref ref-type="fig" rid="F6">6(a)</xref>, <xref ref-type="fig" rid="F6">6(b)</xref> and <xref ref-type="fig" rid="F6">6(c)</xref> are presented in Table <xref ref-type="table" rid="T8">A4</xref> of the Appendix.</p>
        </sec>
      </sec>
      <sec sec-type="4.3. Robustness Checks using WQC Method" id="sec14">
        <title>4.3. Robustness Checks using WQC Method</title>
        <p>To verify the reliability of the findings obtained through the Quantile-on-Quantile Regression (<abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev>) and Wavelet Quantile Regression (<abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev>) methodologies, we use the Wavelet Quantile Correlation (<abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev>) technique. The results concerning the quantile correlation between the response variable (<italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic>) and the explanatory variables (<italic><abbrev xlink:title="political instability">POI</abbrev></italic>, <italic><abbrev xlink:title="corruption">COR</abbrev></italic>, and <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic>) are presented in <italic>Fig.</italic><xref ref-type="fig" rid="F7">7</xref>. Based on the estimated correlation coefficients between <abbrev xlink:title="political instability">POI</abbrev> and <abbrev xlink:title="Gross domestic product">GDP</abbrev> shown in <italic>Fig.</italic><xref ref-type="fig" rid="F7">7(a)</xref>, we conclude that the relationship between <italic><abbrev xlink:title="political instability">POI</abbrev></italic> and <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> is strongly negative in the long and medium term, notably in quantile 0.50; in the short term it remains negative but weak. This result confirms the primary findings regarding the impact of <italic><abbrev xlink:title="political instability">POI</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic>, as determined by the <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> and <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> techniques.</p>
        <fig id="F7">
          <object-id content-type="doi">10.3897/brics-econ.7.e170868.fig7</object-id>
          <object-id content-type="arpha">A8BC179C-E1E3-5116-B71E-B7FD8F49FE40</object-id>
          <label>Fig. 7.</label>
          <caption>
            <p>(a). <abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev> between <italic><abbrev xlink:title="political instability">POI</abbrev></italic> and <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic>, (b). <abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev> between <italic><abbrev xlink:title="corruption">COR</abbrev></italic> and <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic>, and (c). <italic><abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev></italic> between <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> and <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev>.</italic></p>
          </caption>
          <graphic xlink:href="brics-econ-07-049-g007.jpg" id="oo_1556088.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1556088</uri>
          </graphic>
        </fig>
        <p><italic>Fig.</italic><xref ref-type="fig" rid="F7">7(b)</xref> presents the correlation coefficients between <italic><abbrev xlink:title="corruption">COR</abbrev></italic> and <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic>, indicating that the medium- and long-term relationship between <italic><abbrev xlink:title="corruption">COR</abbrev></italic> and <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> is negative and strong. In the short term, the correlations across quantiles ranging from 0.05 to 0.95 remain negative but weak. However, in the quantiles of 0.40 and 0.50, <italic><abbrev xlink:title="corruption">COR</abbrev></italic> exhibits the strongest negative correlation with <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth in Afghanistan, in the medium and long term, respectively. This result confirms the robustness of the findings regarding the relationship between <italic><abbrev xlink:title="corruption">COR</abbrev></italic> and <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth obtained using the <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> and <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> techniques.</p>
        <p>Finally, <italic>Fig.</italic><xref ref-type="fig" rid="F7">7(c)</xref> illustrates the correlation coefficients between <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> and <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth across all quantiles. It reveals that there is a consistently negative relationship between them across all time horizons and at all quantile levels. This is particularly true for quantiles 0.4 and 0.5 in the medium-term. These outcomes further validate the main findings on the impact of <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth obtained using the <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> and <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> techniques.</p>
      </sec>
      <sec sec-type="4.4. Event Analysis using Interrupted Time Series (ITS) Model" id="sec15">
        <title>4.4. Event Analysis using Interrupted Time Series (ITS) Model</title>
        <p>To further enhance the analysis in this paper, we have examined the impact of several significant political events on Afghanistan’s <abbrev xlink:title="Gross domestic product">GDP</abbrev> growth by using data from Panel B of Table <xref ref-type="table" rid="T1">1</xref> and applying the Interrupted Time Series (<abbrev xlink:title="Interrupted Time Series">ITS</abbrev>) method. These events include the Soviet Union War in Afghanistan (<italic><abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev></italic>), the Civil War (<italic><abbrev xlink:title="Civil War">CW</abbrev></italic>), the First-Round Taliban Regime (<italic><abbrev xlink:title="First Round of the Taliban Regime">FRTR</abbrev></italic>), the U.S. and NATO Military Presence in Afghanistan (<italic><abbrev xlink:title="U.S.-NATO interventions">USN</abbrev></italic>), Regime Changes (<italic><abbrev xlink:title="Regime Changes">RCH</abbrev></italic>), and the Second-Round Taliban Regime (<italic><abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev></italic>). The mathematical formulation of these events can be expressed through the following <abbrev xlink:title="Interrupted Time Series">ITS</abbrev> models:</p>
        <p><mml:math id="M10"><mml:msub><mml:mtext> LnGDP </mml:mtext><mml:mi>t</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>ϑ</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>ϑ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:msub><mml:mtext> Time </mml:mtext><mml:mi>t</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>ϑ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mtext> Intervention </mml:mtext><mml:mi>t</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>ϑ</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mtext> Time </mml:mtext><mml:mi>t</mml:mi></mml:msub><mml:mo>∗</mml:mo><mml:msub><mml:mtext> Intervention </mml:mtext><mml:mi>t</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>ε</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:math>	(11)</p>
        <p>where <italic>LnGDP</italic> stands for the natural log of total <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic>, <italic>ϑ</italic><sub>0</sub> is the baseline intercept, <italic>ϑ</italic><sub>1</sub> is the baseline trend (before intervention), <italic>ϑ</italic><sub>2</sub> is the level change (immediate effect) right after the intervention, and <italic>ϑ</italic><sub>3</sub> slope change (trend effect) during the intervention.</p>
        <p>Table <xref ref-type="table" rid="T4">4</xref> presents the estimated effects of major political events — including the Soviet Union War (<abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev>), Civil War (<italic><abbrev xlink:title="Civil War">CW</abbrev></italic>), First-Round Taliban Regime (<italic><abbrev xlink:title="First Round of the Taliban Regime">FRTR</abbrev></italic>), U.S. and NATO military presence (<italic><abbrev xlink:title="U.S.-NATO interventions">USN</abbrev></italic>), Second-Round Taliban Regime (<italic><abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev></italic>), and Regime Changes (<italic><abbrev xlink:title="Regime Changes">RCH</abbrev></italic>) — on Afghanistan’s <abbrev xlink:title="Gross domestic product">GDP</abbrev> performance. The results across all panels confirm a positive trend in <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth prior to each event, whereas <abbrev xlink:title="Gross domestic product">GDP</abbrev> growth during these events is generally estimated to be negative, except for the <italic><abbrev xlink:title="U.S.-NATO interventions">USN</abbrev></italic> period. Furthermore, the immediate impacts of events such as the <italic><abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev></italic> and <italic><abbrev xlink:title="Regime Changes">RCH</abbrev></italic> are found to be positive, while those of the <italic><abbrev xlink:title="Civil War">CW</abbrev></italic>, <italic><abbrev xlink:title="First Round of the Taliban Regime">FRTR</abbrev></italic>, <italic><abbrev xlink:title="U.S.-NATO interventions">USN</abbrev></italic>, and <italic><abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev></italic> exhibit adverse immediate effects on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth.</p>
        <table-wrap id="T4" position="float" orientation="portrait">
          <label>Table 4.</label>
          <caption>
            <p>Event analysis results using the ITS approach.</p>
          </caption>
          <table>
            <tbody>
              <tr>
                <th rowspan="1" colspan="1">
                  <bold>Variables</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>Coefficient</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>t-statistic</bold>
                </th>
                <th rowspan="1" colspan="1">
                  <bold>P &gt;|t|</bold>
                </th>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>Variables</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Coefficient</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>t-statistic</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>P &gt;|t|</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="4"><bold>Panel A: The Impact of the Soviet Union-Afghan War on Afghanistan’s GDP</bold>.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Pre-<abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev>-G</td>
                <td rowspan="1" colspan="1">0.027***</td>
                <td rowspan="1" colspan="1">9.922</td>
                <td rowspan="1" colspan="1">0.000</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">IE-<abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev></td>
                <td rowspan="1" colspan="1">0.305*</td>
                <td rowspan="1" colspan="1">1.788</td>
                <td rowspan="1" colspan="1">0.080</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">GD-<abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev></td>
                <td rowspan="1" colspan="1">-0.026</td>
                <td rowspan="1" colspan="1">-1.187</td>
                <td rowspan="1" colspan="1">0.241</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Adj-R<sup>2</sup></td>
                <td rowspan="1" colspan="1">0.713</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">F-Statistics</td>
                <td rowspan="1" colspan="1">41.480</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Prob.</td>
                <td rowspan="1" colspan="1">0.000</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Obs.</td>
                <td rowspan="1" colspan="1">50</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="4"><bold>Panel B: The impact of the Civil War on Afghanistan’s GDP</bold>.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Pre-<abbrev xlink:title="Civil War">CW</abbrev>-G</td>
                <td rowspan="1" colspan="1">0.023***</td>
                <td rowspan="1" colspan="1">13.456</td>
                <td rowspan="1" colspan="1">0.000</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">IE-<abbrev xlink:title="Civil War">CW</abbrev></td>
                <td rowspan="1" colspan="1">-0.075</td>
                <td rowspan="1" colspan="1">-0.499</td>
                <td rowspan="1" colspan="1">0.499</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">GD-<abbrev xlink:title="Civil War">CW</abbrev></td>
                <td rowspan="1" colspan="1">-0.036***</td>
                <td rowspan="1" colspan="1">-2.727</td>
                <td rowspan="1" colspan="1">0.009</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Adj-R<sup>2</sup></td>
                <td rowspan="1" colspan="1">0.835</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">F-Statistics</td>
                <td rowspan="1" colspan="1">83.620</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Prob.</td>
                <td rowspan="1" colspan="1">0.000</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Obs.</td>
                <td rowspan="1" colspan="1">50</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="4"><bold>Panel C: The Impact of the First Round of the Taliban Regime on Afghanistan’s GDP</bold>.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Pre-<abbrev xlink:title="First Round Taliban Regime">FRTR</abbrev>-G</td>
                <td rowspan="1" colspan="1">0.024***</td>
                <td rowspan="1" colspan="1">11.829</td>
                <td rowspan="1" colspan="1">0.000</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">IE-<abbrev xlink:title="First Round Taliban Regime">FRTR</abbrev></td>
                <td rowspan="1" colspan="1">-0.175</td>
                <td rowspan="1" colspan="1">-0.772</td>
                <td rowspan="1" colspan="1">0.444</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">GD-<abbrev xlink:title="First Round Taliban Regime">FRTR</abbrev></td>
                <td rowspan="1" colspan="1">-0.055</td>
                <td rowspan="1" colspan="1">-0.825</td>
                <td rowspan="1" colspan="1">0.414</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Adj-R<sup>2</sup></td>
                <td rowspan="1" colspan="1">0.756</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">F-Statistics</td>
                <td rowspan="1" colspan="1">51.540</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Prob.</td>
                <td rowspan="1" colspan="1">0.000</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Obs.</td>
                <td rowspan="1" colspan="1">50</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="4"><bold>Panel D: The Impact of <abbrev xlink:title="USA and NATO Military Presence in Afghanistan">USN</abbrev> military presence on Afghanistan’s GDP</bold>.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Pre-<abbrev xlink:title="USA and NATO Military Presence in Afghanistan">USN</abbrev>-G</td>
                <td rowspan="1" colspan="1">0.016**</td>
                <td rowspan="1" colspan="1">7.032</td>
                <td rowspan="1" colspan="1">0.000</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">IE-<abbrev xlink:title="USA and NATO Military Presence in Afghanistan">USN</abbrev></td>
                <td rowspan="1" colspan="1">-0.270***</td>
                <td rowspan="1" colspan="1">-3.342</td>
                <td rowspan="1" colspan="1">0.000</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">GD-<abbrev xlink:title="USA and NATO Military Presence in Afghanistan">USN</abbrev></td>
                <td rowspan="1" colspan="1">0.046***</td>
                <td rowspan="1" colspan="1">7.406</td>
                <td rowspan="1" colspan="1">0.000</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Adj-R<sup>2</sup></td>
                <td rowspan="1" colspan="1">0.863</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">F-Statistics</td>
                <td rowspan="1" colspan="1">104.100</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Prob.</td>
                <td rowspan="1" colspan="1">0.000</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Obs.</td>
                <td rowspan="1" colspan="1">50</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="4"><bold>Panel E: The Impact of the Second Round of the Taliban Regime on Afghanistan’s GDP</bold>.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Pre-<abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev>-G</td>
                <td rowspan="1" colspan="1">0.025***</td>
                <td rowspan="1" colspan="1">16.541</td>
                <td rowspan="1" colspan="1">0.000</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">IE-<abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev></td>
                <td rowspan="1" colspan="1">-0.114***</td>
                <td rowspan="1" colspan="1">-2.891</td>
                <td rowspan="1" colspan="1">0.706</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">GD-<abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev></td>
                <td rowspan="1" colspan="1">0.017</td>
                <td rowspan="1" colspan="1">-0.293</td>
                <td rowspan="1" colspan="1">0.872</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Adj-R<sup>2</sup></td>
                <td rowspan="1" colspan="1">0.693</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">F-Statistics</td>
                <td rowspan="1" colspan="1">37.830</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Prob.</td>
                <td rowspan="1" colspan="1">0.000</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Obs.</td>
                <td rowspan="1" colspan="1">50</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="4"><bold>Panel F: The Impact of <abbrev xlink:title="Regime Change">RCH</abbrev> on Afghanistan’s GDP</bold>.</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Pre-<abbrev xlink:title="Regime Change">RCH</abbrev>-G</td>
                <td rowspan="1" colspan="1">0.026***</td>
                <td rowspan="1" colspan="1">10.993</td>
                <td rowspan="1" colspan="1">0.000</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">IE-<abbrev xlink:title="Regime Change">RCH</abbrev></td>
                <td rowspan="1" colspan="1">0.314</td>
                <td rowspan="1" colspan="1">1.364</td>
                <td rowspan="1" colspan="1">0.179</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">GD-<abbrev xlink:title="Regime Change">RCH</abbrev></td>
                <td rowspan="1" colspan="1">-0.116**</td>
                <td rowspan="1" colspan="1">-2.010</td>
                <td rowspan="1" colspan="1">0.050</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Adj-R<sup>2</sup></td>
                <td rowspan="1" colspan="1">0.722</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">F-Statistics</td>
                <td rowspan="1" colspan="1">43.360</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Prob.</td>
                <td rowspan="1" colspan="1">0.000</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">Obs.</td>
                <td rowspan="1" colspan="1">50</td>
                <td rowspan="1" colspan="1"/>
                <td rowspan="1" colspan="1"/>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn>
              <p><italic>Notes</italic>: This table provides empirical evidence on the impact of major political events on Afghanistan’s GDP growth from 1975 to 2024. Soviet Union War in Afghanistan (<abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev>) is a dummy variable that takes the value 1 from 1979 to 1989, and 0 otherwise. Civil War (<abbrev xlink:title="Civil War">CW</abbrev>) is a dummy variable that takes the value 1 from 1989 to 2001, and 0 otherwise. The First Round Taliban Regime (<abbrev xlink:title="First Round Taliban Regime">FRTR</abbrev>) is a dummy variable that takes the value 1 from 1996 to 2000 and 0 otherwise. USA and NATO Military Presence in Afghanistan (USP) is a dummy variable that takes the value 1 from 2001 to 2021, and 0 otherwise. The Second Round of Taliban Regime (<abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev>) is a dummy variable that takes the value 1 from 2021 to 2024 and 0 otherwise. Finally, Regime Change (<abbrev xlink:title="Regime Change">RCH</abbrev>) is a dummy variable that takes the value 1 for years in which Afghanistan experienced a regime change, and 0 otherwise.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <p>The <italic><abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev></italic>, which lasted from 1979 to 1989, is considered a pivotal political episode in Afghanistan’s economic history (see, for details, <xref ref-type="bibr" rid="B24">Bloch, 2021</xref>). This period was marked by intense military conflict, the destruction of economic infrastructure, and profound human suffering, including approximately 1.2 million deaths, 2.0 million internally displaced persons, 5.9 million refugees to Pakistan and Iran (<xref ref-type="bibr" rid="B46">Ghaussy, 1989</xref>), and nearly 1.5 million individuals disabled (<xref ref-type="bibr" rid="B72">Maley, 2002</xref>). Additionally, the prolonged conflict weakened the Soviet Union’s political and economic standing, contributing to its eventual collapse in 1991 (Reuveny et al., 1999).</p>
        <p>Similarly, the <italic><abbrev xlink:title="Civil War">CW</abbrev></italic> constrained investment opportunities, hindered entrepreneurial activity, disrupted supply chains, destroyed infrastructure, triggered brain drain, and led to capital flight, all of which contributed to the decline in <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> performance in this war-torn country. Empirical studies, including Giustozzi (2008, 2009), D’Souza and Jolliffe (2012a, 2012b), and <xref ref-type="bibr" rid="B30">Ciarli et al. (2010)</xref>, support the findings of the event analysis for Afghanistan.</p>
        <p>The adverse effects observed during both periods of Taliban rule (<italic><abbrev xlink:title="First Round of the Taliban Regime">FRTR</abbrev></italic> and <italic><abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev></italic>) are plausible, as these periods were characterized by violent conflict through which the Taliban consolidated power by seizing provinces via armed confrontations with rival groups. Their regime inherited a country devastated by war, subject to international sanctions, and marked by the collapse of formal institutions, as well as severe restrictions on trade, education, and women’s participation during the first round (<xref ref-type="bibr" rid="B54">Haqpal, 2025</xref>). During the second round, however, the Taliban gained control over all provinces primarily through negotiations and political settlements. In the <italic><abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev></italic> period, major cities, including the capital, remained largely intact, and the regime inherited administrative and physical infrastructure from the preceding Islamic Republic. These conditions enabled the Taliban to exercise greater control over key economic levers, such as the exchange rate, inflation, taxation, trade, and the management of mineral and natural resources, thereby facilitating a degree of relative economic recovery (see, for details, <xref ref-type="bibr" rid="B51">Hamoon et al., 2025</xref>).</p>
        <p>Between 1975 and 2024, Afghanistan experienced multiple regime changes, nearly all of which occurred through assassinations or coups d’état, with the notable exception of the transition from President Burhanuddin Rabbani to President Hamid Karzai, which was achieved through the Bonn political agreement (see <xref ref-type="bibr" rid="B73">Mark and Ramsha, 2011</xref>, for details).</p>
        <p>To determine whether the observed effects of each event on <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth were influenced by unobserved factors or random noise, a placebo test was conducted. As shown in <italic>Fig.</italic><xref ref-type="fig" rid="F8">8</xref>, the estimated coefficients follow an approximately normal distribution centered around zero, indicating no statistically significant relationship between <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth and randomly assigned intervention points. The actual estimated <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> growth during the event period (denoted by the vertical blue line) derived from the Interrupted Time Series (<abbrev xlink:title="Interrupted Time Series">ITS</abbrev>) model lies well outside this distribution of placebo effects. This finding provides strong evidence that the observed positive or negative impact is not the result of random chance but reflects a genuine causal relationship.</p>
        <fig id="F8">
          <object-id content-type="doi">10.3897/brics-econ.7.e170868.fig8</object-id>
          <object-id content-type="arpha">3D2D3D81-B621-5CEF-BDB4-B1E3721F9632</object-id>
          <label>Fig. 8.</label>
          <caption>
            <p>Placebo test for the event analysis, including <italic><abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev></italic>, <italic><abbrev xlink:title="Civil War">CW</abbrev></italic>, <italic><abbrev xlink:title="First Round of the Taliban Regime">FRTR</abbrev></italic>, <italic><abbrev xlink:title="U.S.-NATO interventions">USN</abbrev></italic>, <italic><abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev></italic>, and <italic><abbrev xlink:title="Regime Changes">RCH</abbrev></italic>, respectively. The red curve represents density, the green dot plot represents P-values, and the blue vertical lines indicate the <abbrev xlink:title="Gross domestic product">GDP</abbrev> growth rate during the event</p>
          </caption>
          <graphic xlink:href="brics-econ-07-049-g008.jpg" id="oo_1556089.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1556089</uri>
          </graphic>
        </fig>
        <p>Finally, <italic>Fig.</italic><xref ref-type="fig" rid="F9">9</xref> illustrates the event analysis by depicting the trends of observed <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> (blue line), predicted <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> with the event (red-dashed line), counterfactual <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> without the event (green-dashed line), the impact area (pink-shaded region), and the event’s start date (vertical black-dashed line).</p>
        <fig id="F9">
          <object-id content-type="doi">10.3897/brics-econ.7.e170868.fig9</object-id>
          <object-id content-type="arpha">3BA1A6EC-0FCE-544A-9633-8549C6C77A67</object-id>
          <label>Fig. 9.</label>
          <caption>
            <p><abbrev xlink:title="Interrupted Time Series">ITS</abbrev> plot for the event analysis. (a). <abbrev xlink:title="Interrupted Time Series">ITS</abbrev> plot for <italic><abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev></italic>, (b). <abbrev xlink:title="Interrupted Time Series">ITS</abbrev> plot for <italic><abbrev xlink:title="Civil War">CW</abbrev></italic>, (c). <abbrev xlink:title="Interrupted Time Series">ITS</abbrev> plot for <italic><abbrev xlink:title="First Round of the Taliban Regime">FRTR</abbrev></italic>, (d). <abbrev xlink:title="Interrupted Time Series">ITS</abbrev> plot for <italic><abbrev xlink:title="U.S.-NATO interventions">USN</abbrev></italic>, (e) <abbrev xlink:title="Interrupted Time Series">ITS</abbrev> plot for <italic><abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev></italic>, and (f) <abbrev xlink:title="Interrupted Time Series">ITS</abbrev> plot for <italic><abbrev xlink:title="Regime Changes">RCH</abbrev></italic></p>
          </caption>
          <graphic xlink:href="brics-econ-07-049-g009.jpg" id="oo_1556090.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1556090</uri>
          </graphic>
        </fig>
      </sec>
    </sec>
    <sec sec-type="5. Conclusion and Policy Recommendations" id="sec16">
      <title>5. Conclusion and Policy Recommendations</title>
      <p>In this paper, we have examined the impact of Political Instability (<italic><abbrev xlink:title="political instability">POI</abbrev></italic>), Corruption (<italic><abbrev xlink:title="corruption">COR</abbrev></italic>), and Government Effectiveness (<italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic>) on Afghanistan’s <abbrev xlink:title="Gross domestic product">GDP</abbrev> performance. Using time series data from 1996 to 2024 and implementing Quantile-on-Quantile Regression (<abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev>) and Wavelet Quantile Regression (<abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev>) models, we found that <italic><abbrev xlink:title="political instability">POI</abbrev></italic>, <italic><abbrev xlink:title="corruption">COR</abbrev></italic>, and <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> had an adverse effect on <abbrev xlink:title="Gross domestic product">GDP</abbrev> growth across all quantiles, in the long term. The results of the <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev> and <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev> techniques were confirmed by the Wavelet Quantile Correlation (<abbrev xlink:title="Wavelet Quantile Correlation">WQC</abbrev>) model, indicating that <italic><abbrev xlink:title="political instability">POI</abbrev></italic>, <italic><abbrev xlink:title="corruption">COR</abbrev></italic>, and <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> are negatively correlated with <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> across all quantiles in the short, medium, and long term. Moreover, the event analysis revealed that, during the Soviet Union War (<italic><abbrev xlink:title="Soviet Union War in Afghanistan">SUW</abbrev></italic>), Civil War (<italic><abbrev xlink:title="Civil War">CW</abbrev></italic>), First Round Taliban Regime (<italic><abbrev xlink:title="First Round of the Taliban Regime">FRTR</abbrev></italic>), and Regime Changes (<italic><abbrev xlink:title="Regime Changes">RCH</abbrev></italic>), Afghanistan’s <abbrev xlink:title="Gross domestic product">GDP</abbrev> growth had declined. During the U.S.-NATO military presence and the Second Round of the Taliban Regime (<italic><abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev></italic>), Afghanistan’s <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> experienced positive growth.</p>
      <p>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 <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> 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 <italic><abbrev xlink:title="political instability">POI</abbrev></italic>, <italic><abbrev xlink:title="corruption">COR</abbrev></italic>, and <italic><abbrev xlink:title="government effectiveness">GEF</abbrev></italic> across all <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> quantiles.</p>
      <p>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.</p>
      <p>Finally, the observed positive growth of <italic><abbrev xlink:title="Gross domestic product">GDP</abbrev></italic> during the Second Round Taliban Regime (<italic><abbrev xlink:title="Second Round of Taliban Regime">SRTR</abbrev></italic>) 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.</p>
    </sec>
  </body>
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    <app-group>
      <app id="app1">
        <title>Appendix</title>
        <table-wrap id="T5" position="float" orientation="portrait">
          <label>Table A1.</label>
          <caption>
            <p>Impact of <abbrev xlink:title="political instability">POI</abbrev> on <abbrev xlink:title="Gross domestic product">GDP</abbrev> using <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev></p>
          </caption>
          <table>
            <tbody>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold><abbrev xlink:title="Gross domestic product">GDP</abbrev>-Quantiles</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.10</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.20</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.30</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.40</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.50</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.60</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.70</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.80</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.90</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.10</td>
                <td rowspan="1" colspan="1">-0.798</td>
                <td rowspan="1" colspan="1">-0.942</td>
                <td rowspan="1" colspan="1">-0.942</td>
                <td rowspan="1" colspan="1">-0.942</td>
                <td rowspan="1" colspan="1">-0.942</td>
                <td rowspan="1" colspan="1">-0.968</td>
                <td rowspan="1" colspan="1">-0.968</td>
                <td rowspan="1" colspan="1">0.322</td>
                <td rowspan="1" colspan="1">0.322</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.20</td>
                <td rowspan="1" colspan="1">-0.811</td>
                <td rowspan="1" colspan="1">-0.855</td>
                <td rowspan="1" colspan="1">-0.855</td>
                <td rowspan="1" colspan="1">-0.855</td>
                <td rowspan="1" colspan="1">-1.016</td>
                <td rowspan="1" colspan="1">-0.947</td>
                <td rowspan="1" colspan="1">-0.947</td>
                <td rowspan="1" colspan="1">-0.168</td>
                <td rowspan="1" colspan="1">-0.013</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.30</td>
                <td rowspan="1" colspan="1">-0.620</td>
                <td rowspan="1" colspan="1">-0.620</td>
                <td rowspan="1" colspan="1">-0.620</td>
                <td rowspan="1" colspan="1">-0.652</td>
                <td rowspan="1" colspan="1">-0.924</td>
                <td rowspan="1" colspan="1">-0.924</td>
                <td rowspan="1" colspan="1">-0.874</td>
                <td rowspan="1" colspan="1">-0.160</td>
                <td rowspan="1" colspan="1">-0.160</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.40</td>
                <td rowspan="1" colspan="1">-0.620</td>
                <td rowspan="1" colspan="1">-0.620</td>
                <td rowspan="1" colspan="1">-0.674</td>
                <td rowspan="1" colspan="1">-0.674</td>
                <td rowspan="1" colspan="1">-0.727</td>
                <td rowspan="1" colspan="1">-0.727</td>
                <td rowspan="1" colspan="1">-0.727</td>
                <td rowspan="1" colspan="1">-0.160</td>
                <td rowspan="1" colspan="1">-0.160</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.50</td>
                <td rowspan="1" colspan="1">-0.452</td>
                <td rowspan="1" colspan="1">-0.526</td>
                <td rowspan="1" colspan="1">-0.686</td>
                <td rowspan="1" colspan="1">-0.686</td>
                <td rowspan="1" colspan="1">-0.754</td>
                <td rowspan="1" colspan="1">-0.754</td>
                <td rowspan="1" colspan="1">-0.754</td>
                <td rowspan="1" colspan="1">-0.157</td>
                <td rowspan="1" colspan="1">0.074</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.60</td>
                <td rowspan="1" colspan="1">-0.523</td>
                <td rowspan="1" colspan="1">-0.592</td>
                <td rowspan="1" colspan="1">-0.598</td>
                <td rowspan="1" colspan="1">-0.598</td>
                <td rowspan="1" colspan="1">-0.721</td>
                <td rowspan="1" colspan="1">-0.721</td>
                <td rowspan="1" colspan="1">-0.721</td>
                <td rowspan="1" colspan="1">-0.113</td>
                <td rowspan="1" colspan="1">0.045</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.70</td>
                <td rowspan="1" colspan="1">-0.548</td>
                <td rowspan="1" colspan="1">-0.550</td>
                <td rowspan="1" colspan="1">-0.550</td>
                <td rowspan="1" colspan="1">-0.550</td>
                <td rowspan="1" colspan="1">-0.662</td>
                <td rowspan="1" colspan="1">-0.855</td>
                <td rowspan="1" colspan="1">-0.855</td>
                <td rowspan="1" colspan="1">-0.113</td>
                <td rowspan="1" colspan="1">0.043</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.80</td>
                <td rowspan="1" colspan="1">-0.480</td>
                <td rowspan="1" colspan="1">-0.492</td>
                <td rowspan="1" colspan="1">-0.492</td>
                <td rowspan="1" colspan="1">-0.492</td>
                <td rowspan="1" colspan="1">-0.820</td>
                <td rowspan="1" colspan="1">-0.820</td>
                <td rowspan="1" colspan="1">-0.820</td>
                <td rowspan="1" colspan="1">-0.098</td>
                <td rowspan="1" colspan="1">0.013</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.90</td>
                <td rowspan="1" colspan="1">-0.719</td>
                <td rowspan="1" colspan="1">-0.719</td>
                <td rowspan="1" colspan="1">-0.719</td>
                <td rowspan="1" colspan="1">-0.719</td>
                <td rowspan="1" colspan="1">-0.729</td>
                <td rowspan="1" colspan="1">-0.719</td>
                <td rowspan="1" colspan="1">-0.719</td>
                <td rowspan="1" colspan="1">-0.064</td>
                <td rowspan="1" colspan="1">-0.064</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <table-wrap id="T6" position="float" orientation="portrait">
          <label>Table A2.</label>
          <caption>
            <p>Impact of <abbrev xlink:title="corruption">COR</abbrev> on <abbrev xlink:title="Gross domestic product">GDP</abbrev> using <abbrev xlink:title="Quantile-on-Quantile Regression">QQR</abbrev></p>
          </caption>
          <table>
            <tbody>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold><abbrev xlink:title="Gross domestic product">GDP</abbrev>-Quantiles</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.10</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.20</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.30</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.40</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.50</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.60</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.70</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.80</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.90</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.10</td>
                <td rowspan="1" colspan="1">-1.577</td>
                <td rowspan="1" colspan="1">-1.577</td>
                <td rowspan="1" colspan="1">-1.577</td>
                <td rowspan="1" colspan="1">-1.602</td>
                <td rowspan="1" colspan="1">-1.602</td>
                <td rowspan="1" colspan="1">-1.602</td>
                <td rowspan="1" colspan="1">-1.602</td>
                <td rowspan="1" colspan="1">0.405</td>
                <td rowspan="1" colspan="1">0.405</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.20</td>
                <td rowspan="1" colspan="1">-1.523</td>
                <td rowspan="1" colspan="1">-1.523</td>
                <td rowspan="1" colspan="1">-1.523</td>
                <td rowspan="1" colspan="1">-1.606</td>
                <td rowspan="1" colspan="1">-1.606</td>
                <td rowspan="1" colspan="1">-1.606</td>
                <td rowspan="1" colspan="1">-1.452</td>
                <td rowspan="1" colspan="1">-0.294</td>
                <td rowspan="1" colspan="1">-0.188</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.30</td>
                <td rowspan="1" colspan="1">-1.525</td>
                <td rowspan="1" colspan="1">-1.525</td>
                <td rowspan="1" colspan="1">-1.525</td>
                <td rowspan="1" colspan="1">-1.525</td>
                <td rowspan="1" colspan="1">-1.525</td>
                <td rowspan="1" colspan="1">-1.525</td>
                <td rowspan="1" colspan="1">-0.716</td>
                <td rowspan="1" colspan="1">-0.229</td>
                <td rowspan="1" colspan="1">-0.229</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.40</td>
                <td rowspan="1" colspan="1">-1.141</td>
                <td rowspan="1" colspan="1">-1.141</td>
                <td rowspan="1" colspan="1">-1.141</td>
                <td rowspan="1" colspan="1">-0.872</td>
                <td rowspan="1" colspan="1">-0.872</td>
                <td rowspan="1" colspan="1">-0.848</td>
                <td rowspan="1" colspan="1">-0.841</td>
                <td rowspan="1" colspan="1">-0.229</td>
                <td rowspan="1" colspan="1">-0.229</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.50</td>
                <td rowspan="1" colspan="1">-0.860</td>
                <td rowspan="1" colspan="1">-0.860</td>
                <td rowspan="1" colspan="1">-0.860</td>
                <td rowspan="1" colspan="1">-0.860</td>
                <td rowspan="1" colspan="1">-0.860</td>
                <td rowspan="1" colspan="1">-0.860</td>
                <td rowspan="1" colspan="1">-0.806</td>
                <td rowspan="1" colspan="1">-0.214</td>
                <td rowspan="1" colspan="1">-0.214</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.60</td>
                <td rowspan="1" colspan="1">-0.910</td>
                <td rowspan="1" colspan="1">-0.910</td>
                <td rowspan="1" colspan="1">-0.910</td>
                <td rowspan="1" colspan="1">-0.910</td>
                <td rowspan="1" colspan="1">-0.910</td>
                <td rowspan="1" colspan="1">-0.858</td>
                <td rowspan="1" colspan="1">-0.823</td>
                <td rowspan="1" colspan="1">-0.179</td>
                <td rowspan="1" colspan="1">-0.018</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.70</td>
                <td rowspan="1" colspan="1">-0.787</td>
                <td rowspan="1" colspan="1">-0.787</td>
                <td rowspan="1" colspan="1">-0.835</td>
                <td rowspan="1" colspan="1">-0.878</td>
                <td rowspan="1" colspan="1">-0.878</td>
                <td rowspan="1" colspan="1">-0.878</td>
                <td rowspan="1" colspan="1">-0.837</td>
                <td rowspan="1" colspan="1">-0.179</td>
                <td rowspan="1" colspan="1">0.027</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.80</td>
                <td rowspan="1" colspan="1">-0.784</td>
                <td rowspan="1" colspan="1">-0.784</td>
                <td rowspan="1" colspan="1">-0.784</td>
                <td rowspan="1" colspan="1">-0.806</td>
                <td rowspan="1" colspan="1">-0.806</td>
                <td rowspan="1" colspan="1">-0.812</td>
                <td rowspan="1" colspan="1">-0.867</td>
                <td rowspan="1" colspan="1">-0.113</td>
                <td rowspan="1" colspan="1">-0.113</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.90</td>
                <td rowspan="1" colspan="1">-0.676</td>
                <td rowspan="1" colspan="1">-0.791</td>
                <td rowspan="1" colspan="1">-0.791</td>
                <td rowspan="1" colspan="1">-0.791</td>
                <td rowspan="1" colspan="1">-0.791</td>
                <td rowspan="1" colspan="1">-0.791</td>
                <td rowspan="1" colspan="1">-0.791</td>
                <td rowspan="1" colspan="1">-0.102</td>
                <td rowspan="1" colspan="1">-0.102</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <table-wrap id="T7" position="float" orientation="portrait">
          <label>Table A3.</label>
          <caption>
            <p>Impact of GEF on GDP using QQR</p>
          </caption>
          <table>
            <tbody>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>GDP-Quantiles</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.10</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.20</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.30</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.40</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.50</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.60</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.70</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.80</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.90</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.10</td>
                <td rowspan="1" colspan="1">-0.404</td>
                <td rowspan="1" colspan="1">-0.525</td>
                <td rowspan="1" colspan="1">-0.525</td>
                <td rowspan="1" colspan="1">-0.525</td>
                <td rowspan="1" colspan="1">-0.525</td>
                <td rowspan="1" colspan="1">-0.178</td>
                <td rowspan="1" colspan="1">-0.305</td>
                <td rowspan="1" colspan="1">-0.921</td>
                <td rowspan="1" colspan="1">-0.921</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.20</td>
                <td rowspan="1" colspan="1">0.065</td>
                <td rowspan="1" colspan="1">-1.450</td>
                <td rowspan="1" colspan="1">-1.512</td>
                <td rowspan="1" colspan="1">-1.619</td>
                <td rowspan="1" colspan="1">-1.619</td>
                <td rowspan="1" colspan="1">-0.404</td>
                <td rowspan="1" colspan="1">-0.108</td>
                <td rowspan="1" colspan="1">-0.284</td>
                <td rowspan="1" colspan="1">-0.284</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.30</td>
                <td rowspan="1" colspan="1">1.495</td>
                <td rowspan="1" colspan="1">-1.615</td>
                <td rowspan="1" colspan="1">-1.615</td>
                <td rowspan="1" colspan="1">-1.615</td>
                <td rowspan="1" colspan="1">-1.615</td>
                <td rowspan="1" colspan="1">-1.131</td>
                <td rowspan="1" colspan="1">-0.696</td>
                <td rowspan="1" colspan="1">-0.174</td>
                <td rowspan="1" colspan="1">-0.481</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.40</td>
                <td rowspan="1" colspan="1">1.653</td>
                <td rowspan="1" colspan="1">-0.930</td>
                <td rowspan="1" colspan="1">-1.033</td>
                <td rowspan="1" colspan="1">-1.033</td>
                <td rowspan="1" colspan="1">-1.084</td>
                <td rowspan="1" colspan="1">-1.102</td>
                <td rowspan="1" colspan="1">-1.007</td>
                <td rowspan="1" colspan="1">-0.603</td>
                <td rowspan="1" colspan="1">-0.603</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.50</td>
                <td rowspan="1" colspan="1">0.566</td>
                <td rowspan="1" colspan="1">-0.013</td>
                <td rowspan="1" colspan="1">0.081</td>
                <td rowspan="1" colspan="1">-0.222</td>
                <td rowspan="1" colspan="1">-0.286</td>
                <td rowspan="1" colspan="1">-1.313</td>
                <td rowspan="1" colspan="1">-1.420</td>
                <td rowspan="1" colspan="1">-1.420</td>
                <td rowspan="1" colspan="1">-0.728</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.60</td>
                <td rowspan="1" colspan="1">0.215</td>
                <td rowspan="1" colspan="1">0.193</td>
                <td rowspan="1" colspan="1">0.193</td>
                <td rowspan="1" colspan="1">-0.086</td>
                <td rowspan="1" colspan="1">-0.086</td>
                <td rowspan="1" colspan="1">-0.800</td>
                <td rowspan="1" colspan="1">-0.800</td>
                <td rowspan="1" colspan="1">-0.938</td>
                <td rowspan="1" colspan="1">-1.224</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.70</td>
                <td rowspan="1" colspan="1">0.425</td>
                <td rowspan="1" colspan="1">0.423</td>
                <td rowspan="1" colspan="1">0.215</td>
                <td rowspan="1" colspan="1">0.200</td>
                <td rowspan="1" colspan="1">-0.220</td>
                <td rowspan="1" colspan="1">-0.532</td>
                <td rowspan="1" colspan="1">-0.532</td>
                <td rowspan="1" colspan="1">-1.224</td>
                <td rowspan="1" colspan="1">-1.657</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.80</td>
                <td rowspan="1" colspan="1">0.565</td>
                <td rowspan="1" colspan="1">0.565</td>
                <td rowspan="1" colspan="1">0.300</td>
                <td rowspan="1" colspan="1">0.300</td>
                <td rowspan="1" colspan="1">0.117</td>
                <td rowspan="1" colspan="1">-0.563</td>
                <td rowspan="1" colspan="1">-0.563</td>
                <td rowspan="1" colspan="1">-1.657</td>
                <td rowspan="1" colspan="1">-1.657</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">τ = 0.90</td>
                <td rowspan="1" colspan="1">0.588</td>
                <td rowspan="1" colspan="1">0.588</td>
                <td rowspan="1" colspan="1">0.588</td>
                <td rowspan="1" colspan="1">0.487</td>
                <td rowspan="1" colspan="1">0.487</td>
                <td rowspan="1" colspan="1">-0.072</td>
                <td rowspan="1" colspan="1">-1.108</td>
                <td rowspan="1" colspan="1">-1.108</td>
                <td rowspan="1" colspan="1">-1.108</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <table-wrap id="T8" position="float" orientation="portrait">
          <label>Table A4.</label>
          <caption>
            <p>Impact of <abbrev xlink:title="political instability">POI</abbrev>, <abbrev xlink:title="corruption">COR</abbrev>, and <abbrev xlink:title="government effectiveness">GEF</abbrev> on <abbrev xlink:title="Gross domestic product">GDP</abbrev> using <abbrev xlink:title="Wavelet Quantile Regression">WQR</abbrev></p>
          </caption>
          <table>
            <tbody>
              <tr>
                <td rowspan="2" colspan="1">
                  <bold>Quantiles</bold>
                </td>
                <td rowspan="1" colspan="3">
                  <bold>Impact of <abbrev xlink:title="political instability">POI</abbrev> on <abbrev xlink:title="Gross domestic product">GDP</abbrev></bold>
                </td>
                <td rowspan="1" colspan="3">
                  <bold>Impact of <abbrev xlink:title="corruption">COR</abbrev> on <abbrev xlink:title="Gross domestic product">GDP</abbrev></bold>
                </td>
                <td rowspan="1" colspan="3">
                  <bold>Impact of <abbrev xlink:title="government effectiveness">GEF</abbrev> on <abbrev xlink:title="Gross domestic product">GDP</abbrev></bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>Short</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Medium</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Long</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Short</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Medium</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Long</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Short</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Medium</bold>
                </td>
                <td rowspan="1" colspan="1">
                  <bold>Long</bold>
                </td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.05</bold>
                </td>
                <td rowspan="1" colspan="1">0.000</td>
                <td rowspan="1" colspan="1">-0.078</td>
                <td rowspan="1" colspan="1">-0.488</td>
                <td rowspan="1" colspan="1">0.029</td>
                <td rowspan="1" colspan="1">-0.092</td>
                <td rowspan="1" colspan="1">-0.484</td>
                <td rowspan="1" colspan="1">-0.067</td>
                <td rowspan="1" colspan="1">-0.051</td>
                <td rowspan="1" colspan="1">-0.343</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.10</bold>
                </td>
                <td rowspan="1" colspan="1">0.015</td>
                <td rowspan="1" colspan="1">-0.055</td>
                <td rowspan="1" colspan="1">-0.489</td>
                <td rowspan="1" colspan="1">0.016</td>
                <td rowspan="1" colspan="1">-0.063</td>
                <td rowspan="1" colspan="1">-0.484</td>
                <td rowspan="1" colspan="1">-0.067</td>
                <td rowspan="1" colspan="1">-0.022</td>
                <td rowspan="1" colspan="1">-0.339</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.15</bold>
                </td>
                <td rowspan="1" colspan="1">0.010</td>
                <td rowspan="1" colspan="1">-0.062</td>
                <td rowspan="1" colspan="1">-0.483</td>
                <td rowspan="1" colspan="1">0.011</td>
                <td rowspan="1" colspan="1">-0.069</td>
                <td rowspan="1" colspan="1">-0.475</td>
                <td rowspan="1" colspan="1">-0.023</td>
                <td rowspan="1" colspan="1">-0.013</td>
                <td rowspan="1" colspan="1">-0.328</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.20</bold>
                </td>
                <td rowspan="1" colspan="1">0.010</td>
                <td rowspan="1" colspan="1">-0.032</td>
                <td rowspan="1" colspan="1">-0.494</td>
                <td rowspan="1" colspan="1">0.006</td>
                <td rowspan="1" colspan="1">-0.037</td>
                <td rowspan="1" colspan="1">-0.494</td>
                <td rowspan="1" colspan="1">0.004</td>
                <td rowspan="1" colspan="1">-0.070</td>
                <td rowspan="1" colspan="1">-0.328</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.25</bold>
                </td>
                <td rowspan="1" colspan="1">-0.035</td>
                <td rowspan="1" colspan="1">-0.030</td>
                <td rowspan="1" colspan="1">-0.483</td>
                <td rowspan="1" colspan="1">-0.025</td>
                <td rowspan="1" colspan="1">-0.032</td>
                <td rowspan="1" colspan="1">-0.483</td>
                <td rowspan="1" colspan="1">0.025</td>
                <td rowspan="1" colspan="1">-0.075</td>
                <td rowspan="1" colspan="1">-0.324</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.30</bold>
                </td>
                <td rowspan="1" colspan="1">0.078</td>
                <td rowspan="1" colspan="1">0.066</td>
                <td rowspan="1" colspan="1">-0.516</td>
                <td rowspan="1" colspan="1">-0.006</td>
                <td rowspan="1" colspan="1">0.075</td>
                <td rowspan="1" colspan="1">-0.506</td>
                <td rowspan="1" colspan="1">-0.004</td>
                <td rowspan="1" colspan="1">0.077</td>
                <td rowspan="1" colspan="1">-0.339</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.35</bold>
                </td>
                <td rowspan="1" colspan="1">-0.060</td>
                <td rowspan="1" colspan="1">0.074</td>
                <td rowspan="1" colspan="1">-0.521</td>
                <td rowspan="1" colspan="1">0.082</td>
                <td rowspan="1" colspan="1">0.070</td>
                <td rowspan="1" colspan="1">-0.516</td>
                <td rowspan="1" colspan="1">0.025</td>
                <td rowspan="1" colspan="1">0.051</td>
                <td rowspan="1" colspan="1">-0.350</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.40</bold>
                </td>
                <td rowspan="1" colspan="1">0.214</td>
                <td rowspan="1" colspan="1">0.078</td>
                <td rowspan="1" colspan="1">-0.527</td>
                <td rowspan="1" colspan="1">-0.046</td>
                <td rowspan="1" colspan="1">0.063</td>
                <td rowspan="1" colspan="1">-0.521</td>
                <td rowspan="1" colspan="1">-0.015</td>
                <td rowspan="1" colspan="1">0.061</td>
                <td rowspan="1" colspan="1">-0.363</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.45</bold>
                </td>
                <td rowspan="1" colspan="1">-0.284</td>
                <td rowspan="1" colspan="1">-0.116</td>
                <td rowspan="1" colspan="1">-0.555</td>
                <td rowspan="1" colspan="1">0.257</td>
                <td rowspan="1" colspan="1">-0.084</td>
                <td rowspan="1" colspan="1">-0.548</td>
                <td rowspan="1" colspan="1">0.115</td>
                <td rowspan="1" colspan="1">-0.080</td>
                <td rowspan="1" colspan="1">-0.343</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.50</bold>
                </td>
                <td rowspan="1" colspan="1">0.009</td>
                <td rowspan="1" colspan="1">-0.150</td>
                <td rowspan="1" colspan="1">-0.561</td>
                <td rowspan="1" colspan="1">0.002</td>
                <td rowspan="1" colspan="1">-0.120</td>
                <td rowspan="1" colspan="1">-0.554</td>
                <td rowspan="1" colspan="1">0.024</td>
                <td rowspan="1" colspan="1">-0.112</td>
                <td rowspan="1" colspan="1">-0.403</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.55</bold>
                </td>
                <td rowspan="1" colspan="1">0.006</td>
                <td rowspan="1" colspan="1">-0.196</td>
                <td rowspan="1" colspan="1">-0.563</td>
                <td rowspan="1" colspan="1">0.029</td>
                <td rowspan="1" colspan="1">-0.073</td>
                <td rowspan="1" colspan="1">-0.552</td>
                <td rowspan="1" colspan="1">0.006</td>
                <td rowspan="1" colspan="1">-0.046</td>
                <td rowspan="1" colspan="1">-0.385</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.60</bold>
                </td>
                <td rowspan="1" colspan="1">-0.023</td>
                <td rowspan="1" colspan="1">0.063</td>
                <td rowspan="1" colspan="1">-0.474</td>
                <td rowspan="1" colspan="1">0.020</td>
                <td rowspan="1" colspan="1">-0.230</td>
                <td rowspan="1" colspan="1">-0.473</td>
                <td rowspan="1" colspan="1">-0.029</td>
                <td rowspan="1" colspan="1">-0.027</td>
                <td rowspan="1" colspan="1">-0.431</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.65</bold>
                </td>
                <td rowspan="1" colspan="1">-0.062</td>
                <td rowspan="1" colspan="1">0.072</td>
                <td rowspan="1" colspan="1">-0.474</td>
                <td rowspan="1" colspan="1">-0.109</td>
                <td rowspan="1" colspan="1">-0.155</td>
                <td rowspan="1" colspan="1">-0.475</td>
                <td rowspan="1" colspan="1">-0.052</td>
                <td rowspan="1" colspan="1">-0.078</td>
                <td rowspan="1" colspan="1">-0.468</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.70</bold>
                </td>
                <td rowspan="1" colspan="1">0.025</td>
                <td rowspan="1" colspan="1">0.105</td>
                <td rowspan="1" colspan="1">-0.445</td>
                <td rowspan="1" colspan="1">-0.138</td>
                <td rowspan="1" colspan="1">-0.212</td>
                <td rowspan="1" colspan="1">-0.445</td>
                <td rowspan="1" colspan="1">-0.066</td>
                <td rowspan="1" colspan="1">-0.109</td>
                <td rowspan="1" colspan="1">-0.230</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.75</bold>
                </td>
                <td rowspan="1" colspan="1">-0.007</td>
                <td rowspan="1" colspan="1">0.013</td>
                <td rowspan="1" colspan="1">-0.453</td>
                <td rowspan="1" colspan="1">-0.146</td>
                <td rowspan="1" colspan="1">-0.212</td>
                <td rowspan="1" colspan="1">-0.456</td>
                <td rowspan="1" colspan="1">0.006</td>
                <td rowspan="1" colspan="1">0.014</td>
                <td rowspan="1" colspan="1">-0.333</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.80</bold>
                </td>
                <td rowspan="1" colspan="1">0.018</td>
                <td rowspan="1" colspan="1">-0.028</td>
                <td rowspan="1" colspan="1">-0.463</td>
                <td rowspan="1" colspan="1">-0.019</td>
                <td rowspan="1" colspan="1">0.028</td>
                <td rowspan="1" colspan="1">-0.468</td>
                <td rowspan="1" colspan="1">-0.026</td>
                <td rowspan="1" colspan="1">0.024</td>
                <td rowspan="1" colspan="1">-0.260</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.85</bold>
                </td>
                <td rowspan="1" colspan="1">0.027</td>
                <td rowspan="1" colspan="1">0.072</td>
                <td rowspan="1" colspan="1">-0.469</td>
                <td rowspan="1" colspan="1">-0.081</td>
                <td rowspan="1" colspan="1">-0.063</td>
                <td rowspan="1" colspan="1">-0.473</td>
                <td rowspan="1" colspan="1">-0.026</td>
                <td rowspan="1" colspan="1">-0.054</td>
                <td rowspan="1" colspan="1">0.053</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.90</bold>
                </td>
                <td rowspan="1" colspan="1">-0.011</td>
                <td rowspan="1" colspan="1">0.036</td>
                <td rowspan="1" colspan="1">-0.434</td>
                <td rowspan="1" colspan="1">-0.021</td>
                <td rowspan="1" colspan="1">-0.072</td>
                <td rowspan="1" colspan="1">-0.440</td>
                <td rowspan="1" colspan="1">0.036</td>
                <td rowspan="1" colspan="1">0.029</td>
                <td rowspan="1" colspan="1">0.059</td>
              </tr>
              <tr>
                <td rowspan="1" colspan="1">
                  <bold>τ = 0.95</bold>
                </td>
                <td rowspan="1" colspan="1">0.018</td>
                <td rowspan="1" colspan="1">0.042</td>
                <td rowspan="1" colspan="1">-0.437</td>
                <td rowspan="1" colspan="1">0.000</td>
                <td rowspan="1" colspan="1">-0.053</td>
                <td rowspan="1" colspan="1">-0.442</td>
                <td rowspan="1" colspan="1">0.000</td>
                <td rowspan="1" colspan="1">0.029</td>
                <td rowspan="1" colspan="1">0.064</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
      </app>
    </app-group>
    <fn-group>
      <fn id="en1">
        <p>MODWT (maximal overlapping discrete wavelet transform) introduced by Percival and Walden (2000).</p>
      </fn>
      <fn id="en2">
        <p>BDS test refers to Brock-Dechert-Scheinkman (1996) independence test.</p>
      </fn>
    </fn-group>
  </back>
</article>
