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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">ojbm</journal-id>
      <journal-title-group>
        <journal-title>Open Journal of Business and Management</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2329-3292</issn>
      <issn pub-type="ppub">2329-3284</issn>
      <publisher>
        <publisher-name>Scientific Research Publishing</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.4236/ojbm.2026.142051</article-id>
      <article-id pub-id-type="publisher-id">ojbm-149698</article-id>
      <article-categories>
        <subj-group>
          <subject>Article</subject>
        </subj-group>
        <subj-group>
          <subject>Business</subject>
          <subject>Economics</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>A Strategic Analysis of Nigeria’s Hedging Strategy in the Context of U.S.-China Rivalry</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name name-style="western">
            <surname>Blessing</surname>
            <given-names>Emmanuel Chidinma</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
      </contrib-group>
      <aff id="aff1"><label>1</label> School of Politics and Public Administration, Qingdao University, Qingdao, China </aff>
      <author-notes>
        <fn fn-type="conflict" id="fn-conflict">
          <p>The author declares no conflicts of interest regarding the publication of this paper.</p>
        </fn>
      </author-notes>
      <pub-date pub-type="epub">
        <day>01</day>
        <month>03</month>
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="collection">
        <month>03</month>
        <year>2026</year>
      </pub-date>
      <volume>14</volume>
      <issue>02</issue>
      <fpage>884</fpage>
      <lpage>900</lpage>
      <history>
        <date date-type="received">
          <day>18</day>
          <month>01</month>
          <year>2026</year>
        </date>
        <date date-type="accepted">
          <day>21</day>
          <month>02</month>
          <year>2026</year>
        </date>
        <date date-type="published">
          <day>24</day>
          <month>02</month>
          <year>2026</year>
        </date>
      </history>
      <permissions>
        <copyright-statement>© 2026 by the authors and Scientific Research Publishing Inc.</copyright-statement>
        <copyright-year>2026</copyright-year>
        <license license-type="open-access">
          <license-p> This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link> ). </license-p>
        </license>
      </permissions>
      <self-uri content-type="doi" xlink:href="https://doi.org/10.4236/ojbm.2026.142051">https://doi.org/10.4236/ojbm.2026.142051</self-uri>
      <abstract>
        <p>Amidst the escalating strategic competition between the United States and China, Nigeria, as Africa’s largest economy and a pivotal regional power, is navigating a complex geopolitical landscape that necessitates a sophisticated hedging strategy. The effective management of diplomatic and economic relationships with both global powers has become a central challenge for Nigeria’s pursuit of sustainable development and strategic autonomy. This paper provides critical insights into this dynamic by empirically evaluating the economic impacts of Nigeria’s engagement with both nations. To overcome the limitations of small sample sizes in time-series analysis, this study employs annual data from 1995 to 2024 within an Autoregressive Distributed Lag (ARDL) framework. This methodology allows for a robust examination of both the long-run equilibrium relationships and the short-run dynamic adjustments between Nigeria’s economic growth and its trade and investment ties with the U.S. and China, including the effects of major institutional partnerships. The empirical findings reveal a nuanced picture of Nigeria’s hedging strategy. The key results are: (1) A stable, long-run cointegrating relationship is confirmed between Nigeria’s economic growth, foreign direct investment (FDI), and its participation in major international initiatives. (2) In the long run, FDI emerges as a positive and statistically significant driver of economic growth, underscoring its foundational importance. (3) In the short run, the implementation of the U.S.-Nigeria Strategic Partnership and Action Plan (SPAP) is associated with a significant negative shock to economic growth, highlighting potential adjustment costs or adverse initial impacts of such alignments. (4) The Error Correction Model (ECM) identifies a valid, albeit slow, adjustment mechanism through which the economy corrects deviations from its long-run equilibrium path.</p>
      </abstract>
      <kwd-group kwd-group-type="author-generated" xml:lang="en">
        <kwd>China-U.S. Strategic Competition</kwd>
        <kwd>Diplomatic Hedging</kwd>
        <kwd>Institutional Cooperation</kwd>
        <kwd>Economic Growth</kwd>
        <kwd>Nigeria</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec1">
      <title>1. Introduction</title>
      <p>As the global strategic landscape undergoes a profound transformation in the mid-2020s, the intensifying competition between the United States and China has become a defining feature of the new international order. This rivalry extends across multiple domains, including technology, trade, geopolitics, and development aid, with both nations actively seeking to expand their influence and secure their interests worldwide. Against this backdrop, Africa has emerged as a critical arena of engagement, where the continent’s vast economic potential, demographic dividend, and growing political voice have made it a prized partner for global powers. Nigeria, as Africa’s largest economy and most populous nation, finds itself at the epicenter of this strategic contest. The country’s significant market size, abundant natural resources, and leadership role in West Africa position it as a pivotal state whose alignment could have far-reaching implications for the regional balance of power. Consequently, Nigeria faces the immense challenge of navigating the crosscurrents of U.S.-China competition, a task that requires a sophisticated and proactive foreign policy aimed at maximizing national interests while minimizing geopolitical risks. The central dilemma for Nigerian policymakers is how to craft a durable hedging strategy that allows the nation to benefit from its relationships with both Washington and Beijing without becoming entangled in their broader strategic rivalry or falling into a state of dependency.</p>
    </sec>
    <sec id="sec2">
      <title>2. Literature Review</title>
      <sec id="sec2dot1">
        <title>2.1. Conceptualizing Hedging Strategy in International Relations</title>
        <p>The study of hedging as a foreign policy strategy has gained considerable traction within international relations literature, particularly as a framework for understanding how small and medium-sized states navigate the complexities of great power competition. Hedging is typically defined as a deliberate, low-cost strategy to cultivate a middle position that forestalls or avoids the necessity of choosing between competing strategic alignments. Unlike balancing, which involves actively countering a rising power, or bandwagoning, which entails aligning with it, hedging allows states to maintain a degree of strategic ambiguity and flexibility. This approach enables them to derive benefits from multiple great powers while simultaneously insuring against the risks of excessive dependence on any single partner. [<xref ref-type="bibr" rid="B9">9</xref>] further refines this concept by arguing that hedging is not a single policy but a composite of mixed foreign policy instruments, where states pursue economic benefits with one power while maintaining security ties with another, thereby optimizing their national interests across different domains. This strategic duality is a core feature of hedging, allowing states to pursue what has been termed a “non-exclusive” alignment policy.</p>
      </sec>
      <sec id="sec2dot2">
        <title>2.2. Hedging Strategies in the Era of U.S.-China Competition</title>
        <p>The empirical literature on hedging is rich and varied, offering insights from different geographical and geopolitical contexts, especially in the context of the U.S.-China strategic rivalry. For instance, [<xref ref-type="bibr" rid="B8">8</xref>] provide a multidimensional analysis of Thailand’s hedging strategy, illustrating how Bangkok has adeptly balanced its long-standing military and security cooperation with the United States while expanding its economic and technological ties with China. In a European context, [<xref ref-type="bibr" rid="B15">15</xref>] analyzes the strategic calculus of “New European” states, which, despite their integration into the NATO security architecture, have actively pursued economic engagement with China as a means of diversifying their partnerships and enhancing their strategic autonomy. These studies underscore a common theme: for many states, hedging is not a passive or neutral stance but an active and dynamic process of strategic adjustment across different policy domains.</p>
      </sec>
      <sec id="sec2dot3">
        <title>2.3. State Autonomy, Development, and Hedging</title>
        <p>While the theoretical underpinnings of hedging are often traced to the structural pressures of the international system, recent scholarship has increasingly emphasized the role of domestic factors and state autonomy in shaping foreign policy choices. The ability to hedge effectively is not just a function of the external environment but also of the state’s internal capacity to manage competing pressures and maintain a cohesive national strategy for autonomous development ([<xref ref-type="bibr" rid="B4">4</xref>]). This aligns with the broader discourse on globalization and national sovereignty, where states seek to balance economic integration with the preservation of their strategic independence ([<xref ref-type="bibr" rid="B1">1</xref>]). The pursuit of strategic autonomy, as seen in the foreign policy orientations of countries like Italy ([<xref ref-type="bibr" rid="B10">10</xref>]) and France ([<xref ref-type="bibr" rid="B14">14</xref>]), has become a central theme in contemporary international relations, reflecting a global trend towards more diversified and independent foreign policies.</p>
      </sec>
      <sec id="sec2dot4">
        <title>2.4. Issue-Specific Hedging and the Rise of Middle Powers</title>
        <p>Recent literature has also disaggregated the concept of hedging, moving beyond broad strategic postures to examine issue-specific applications. [<xref ref-type="bibr" rid="B20">20</xref>] introduces the concept of “technological hedging,” arguing that Southeast Asian nations are not merely passive recipients of U.S. or Chinese technology but are actively shaping their digital futures by selectively adopting and adapting technologies from both sides to suit their specific developmental needs. This approach allows them to avoid being locked into a single technological ecosystem and to mitigate the risks of digital colonization. Furthermore, some scholars argue that hedging can empower smaller states, allowing them to assume a more influential regional role. [<xref ref-type="bibr" rid="B18">18</xref>] posit that small states in East Asia have assumed a “pivotal power” role, leveraging their strategic location and economic dynamism to enhance their bargaining power and influence the behavior of larger powers. This perspective challenges the traditional view of small states as mere pawns in great power politics, highlighting their agency and capacity for strategic innovation. [<xref ref-type="bibr" rid="B6">6</xref>] extend this analysis to the case of Vietnam, which has successfully employed a hedging strategy to emerge as a middle power capable of shaping regional security and economic architectures.</p>
      </sec>
      <sec id="sec2dot5">
        <title>2.5. U.S.-China Dynamics in Africa and the Nigerian Context</title>
        <p>While much of the hedging literature focuses on East Asia and Europe, the strategic logic is increasingly applicable to Africa, where U.S.-China competition is intensifying. The impact of Chinese trade and investment on Nigeria’s economic growth has been a subject of significant academic inquiry ([<xref ref-type="bibr" rid="B3">3</xref>]). Studies have examined the role of Chinese investments in Nigeria’s political economy (Nyiayaana &amp; Jack, 2021), its infrastructure development, particularly in the railway ([<xref ref-type="bibr" rid="B2">2</xref>]) and hydropower sectors ([<xref ref-type="bibr" rid="B19">19</xref>]), and the broader framework of the Forum on China-Africa Cooperation (FOCAC) ([<xref ref-type="bibr" rid="B11">11</xref>]). Concurrently, the role of Foreign Direct Investment (FDI) from all sources on Nigeria’s economic performance remains a central theme in the literature ([<xref ref-type="bibr" rid="B7">7</xref>]; [<xref ref-type="bibr" rid="B17">17</xref>]; [<xref ref-type="bibr" rid="B5">5</xref>]). For Nigeria, the continent’s largest economy and a key regional actor, navigating this complex environment is a paramount strategic challenge. Existing literature on Nigeria’s foreign policy has examined its security partnership with the United States ([<xref ref-type="bibr" rid="B16">16</xref>]) and its economic diplomacy ([<xref ref-type="bibr" rid="B12">12</xref>]). However, a systematic analysis of Nigeria’s foreign policy through the lens of a deliberate and integrated hedging strategy remains underdeveloped. This study seeks to fill that gap by providing a comprehensive examination of Nigeria’s strategic positioning vis-à-vis the United States and China, thereby contributing to the broader literature on hedging and great power management in the 21st century.</p>
      </sec>
    </sec>
    <sec id="sec3">
      <title>3. The Evolution and Pillars of Nigeria’s Hedging Strategy</title>
      <sec id="sec3dot1">
        <title>3.1. The Evolution of Nigeria’s Foreign Policy</title>
        <p>Nigeria’s contemporary foreign policy reflects a long-term evolution shaped by its post-colonial experience, domestic political transitions, and changes in the global order. Since independence in 1960, Nigeria has adhered to principles of non-alignment and Pan-Africanism, positioning itself as a leading African voice during the Cold War while avoiding formal alignment with major powers. Periods of military rule in the late twentieth century resulted in diplomatic isolation and external sanctions, reinforcing Nigeria’s strategic preference for diversified foreign partnerships and resistance to overdependence on any single bloc. The return to civilian rule in 1999 marked a critical shift toward diplomatic re-engagement and economic pragmatism, as Nigeria prioritized multilateral cooperation and strengthened ties with both traditional and emerging powers. Following the 2008 global financial crisis and China’s expanding role in Africa, Nigeria gradually adopted a functional hedging strategy, selectively deepening cooperation with the United States in areas such as security, health, and trade, while leveraging Chinese investment for large-scale infrastructure development under frameworks such as the Belt and Road Initiative. In recent years, Nigeria has further emphasized strategic neutrality, maintaining pragmatic positions on major geopolitical issues and broadening engagement with multiple partners, including the European Union and BRICS countries. This approach, formally articulated in Nigeria’s 2023-2027 National Diplomatic Strategic Document, underscores the country’s objective of safeguarding sovereignty and development autonomy in an increasingly multipolar international system.</p>
        <fig id="fig1">
          <label>Figure 1</label>
          <graphic xlink:href="https://html.scirp.org/file/1535136-rId13.jpeg?20260224113047" />
        </fig>
        <p>Data source: World Bank.</p>
        <p><bold>Figure 1.</bold> Overview of Nigeria’s import and export trade value, 1995-2024.</p>
        <p>An examination of Nigeria’s import and export trade from 1995 to 2024, as illustrated in <xref ref-type="fig" rid="fig1">Figure 1</xref>, reveals a pattern of volatile growth followed by a significant contraction. The data shows that total trade value grew from a modest $20.56 billion in 1995 to surpass the $50 billion mark in 2004, before surging to $131.77 billion in 2008. This rapid expansion during the early 21st century reflects a period of significant economic growth for Nigeria, largely driven by rising global oil prices and an expansion of its export capacity. The country’s external trade reached its zenith in 2011, with a total value of $181.64 billion, marking a high point for its foreign-oriented economy. However, this upward trajectory was not sustained. Beginning in 2015, a confluence of factors, including a sharp decline in international oil prices, a slowdown in global trade, and persistent domestic security and governance challenges, led to a dramatic fall in trade volume. By 2016, the total trade value had plummeted to a low of $68.08 billion. Although there has been a partial recovery in the subsequent years, the trade volume has not consistently returned to its previous highs. In 2024, the total trade value stood at $89.4 billion, still significantly below the 2011 peak. This trend underscores the Nigerian economy’s high degree of vulnerability to fluctuations in global commodity markets and highlights a structural weakness rooted in its heavy dependence on external demand and resource exports.</p>
      </sec>
      <sec id="sec3dot2">
        <title>3.2. Nigeria’s Strategic Positioning in the U.S.-China Competition</title>
        <p>(1) Strategic Positioning Towards China: A Development Partner and Structural Collaborator</p>
        <p>Following the launch of the “Belt and Road” Initiative (BRI), Africa has progressively become a crucial region in China’s global strategy. As the largest economy and most populous country in West Africa, Nigeria is regarded by Beijing as a key hub on the western route of the BRI in Africa. Nigeria’s strategic positioning towards China is primarily manifested as a “developmental partnership”, aimed at accelerating the modernization of its domestic infrastructure, energy networks, and manufacturing capabilities by introducing Chinese capital, technology, and engineering capacity. This is specifically demonstrated on three levels.</p>
        <p>First, infrastructure cooperation serves as the cornerstone of Sino-Nigerian relations. Since joining the BRI in 2013, Nigeria has signed multiple large-scale engineering project agreements with China, including the Lagos-Ibadan Railway, the Abuja Light Rail, and the Kaduna Refinery, all of which have been invested in, constructed, and financed by Chinese enterprises. These projects have not only filled significant infrastructure gaps but have also established a substantial Chinese presence and influence in Nigeria’s national development.</p>
        <p>Nigeria’s positioning towards China reflects a logic of “structural development cooperation”. It views China as a key supporter in addressing its infrastructure deficit, fiscal constraints, and technological gaps. Simultaneously, China’s policy of non-interference provides Nigeria with external space for political autonomy, thereby creating a strategic complement to the “normative cooperation path” offered by the U.S. and Europe.</p>
        <fig id="fig2">
          <label>Figure 2</label>
          <graphic xlink:href="https://html.scirp.org/file/1535136-rId14.jpeg?20260224113047" />
        </fig>
        <p>Data source: World Bank.</p>
        <p><bold>Figure 2.</bold> Overview of Nigeria-China import and export trade value, 1995-2024.</p>
        <p><xref ref-type="fig" rid="fig2">Figure 2</xref> illustrates the trend of import and export trade between Nigeria and China from 1995 to 2024, which is characterized by rapid expansion, periodic adjustments, and sustained growth. In 1995, the total trade volume between Nigeria and China was a mere $292 million, with Nigeria’s exports to China being less than $100 million, resulting in a long-term trade deficit. Since the turn of the 21st century, with the deepening of Sino-Nigerian relations and frequent economic exchanges, Nigeria’s demand for Chinese imports has surged. The total trade volume surpassed $10 billion in 2008 and reached $14.22 billion in 2011, demonstrating a high-growth trajectory. By 2014, the total trade volume had further increased to $18.34 billion, a peak at that time. Despite some fluctuations between 2015 and 2017 due to falling oil prices and adjustments in China’s economic growth, bilateral trade has rebounded since 2021, reaching a historic high of $21.89 billion in 2024. It is noteworthy that Nigeria has consistently maintained a trade deficit with China, with a significant reliance on imports of industrial manufactured goods, reflecting a structural weakness in its domestic manufacturing capabilities. Overall, the steady development of Sino-Nigerian trade relations has not only promoted bilateral economic integration but has also had a profound impact on Nigeria’s trade structure, industrial transformation, and economic openness. However, it has also brought about risks of external dependence and trade asymmetry.</p>
        <p>(2) Strategic Positioning Towards the United States: A Security Guarantor and Multilateral Alliance Lever</p>
        <p>In contrast to its relationship with China, the United States is defined in Nigeria’s strategic thinking as the “primary external anchor for security and institutional values.” Its role is more prominently reflected in military cooperation, counter-terrorism collaboration, institutional aid, and global political capital. Nigeria’s strategic positioning towards the U.S. exhibits both historical continuity and gradual tactical evolution.</p>
        <p>The U.S. has long been one of Nigeria’s primary security partners, particularly in the areas of counter-terrorism and regional stability. Since the outbreak of the Boko Haram insurgency in 2009, the U.S. has provided military equipment, training, and intelligence-sharing support to Nigeria through mechanisms such as the Trans-Sahara Counterterrorism Partnership (TSCTP) and the Global Security Contingency Fund (GSCF). The strategic coordination between the U.S. military and AFRICOM has become an indispensable part of Nigeria’s national security apparatus. Nigeria regards the U.S. as a “trusted institutional ally in the security domain”, especially given the underdeveloped state of its own military industry.</p>
        <p>The U.S. also remains a key architect of Nigeria’s institutional aid and trade policies. U.S. government-led initiatives such as the African Growth and Opportunity Act (AGOA), the President’s Emergency Plan for AIDS Relief (PEPFAR), and the Millennium Challenge Corporation (MCC) have had a substantial impact on Nigeria’s health, education, and energy reforms, while also enhancing America’s indirect influence on Nigeria’s policy agenda. At the institutional level, some of Nigeria’s governance reforms, fiscal audits, and anti-corruption initiatives have also received technical guidance or supervision from U.S. agencies such as USAID, the World Bank, and the IMF.</p>
        <fig id="fig3">
          <label>Figure 3</label>
          <graphic xlink:href="https://html.scirp.org/file/1535136-rId15.jpeg?20260224113047" />
        </fig>
        <p>Data source: World Bank.</p>
        <p><bold>Figure 3.</bold> Overview of Nigeria-United States import and export trade value, 1995-2024.</p>
        <p><xref ref-type="fig" rid="fig3">Figure 3</xref> illustrates the development of import and export trade between Nigeria and the United States from 1995 to 2024, which shows a dynamic trend of “rapid growth, sharp fluctuations, and moderate recovery.” In 1995, the total bilateral trade was $5.63 billion, with Nigeria’s exports to the U.S. being the dominant component, leading to a long-term trade surplus. After 2000, with the growth of Nigeria’s crude oil exports to the U.S. and adjustments in America’s energy strategy for Africa, bilateral trade rose rapidly, reaching a historical peak of $29.30 billion in 2011. This reflected the economic benefits brought about by rising energy prices and strategic mutual needs. However, a significant decline in trade began in 2012, particularly after 2014, due to a combination of factors including the collapse of international oil prices, the rise of U.S. shale oil, and global trade tightening. Nigeria’s exports to the U.S. fell sharply, with the trade volume dropping to $5.77 billion in 2015. The COVID-19 pandemic in 2020 further reduced it to $3.86 billion. Nevertheless, since 2021, bilateral trade has shown a gradual recovery, reaching $9.87 billion in 2024, indicating that the economic relationship between the two countries has the potential to recover and grow after a period of short-term adjustments. Overall, the structure of Nigeria-U.S. trade has long been dominated by energy exports. While strategically important, this also exposes Nigeria to high volatility and market substitution risks, necessitating export diversification and institutional cooperation to enhance stability and sustainability.</p>
      </sec>
    </sec>
    <sec id="sec4">
      <title>4. Empirical Analysis of Economic Impacts</title>
      <p>This chapter transitions from the theoretical discussion of Nigeria’s hedging strategy to a rigorous empirical investigation of its economic implications. Acknowledging the well-documented challenges of small sample sizes in time-series analysis, this study employs the Autoregressive Distributed Lag (ARDL) framework, a methodology robust for limited data and flexible regarding variables’ order of integration. The analysis proceeds in a structured manner: first, the model and data are defined; second, the time-series properties of the variables are examined through unit root tests; third, several ARDL specifications are estimated to identify the short-run dynamics; fourth, long-run relationships are estimated and used to construct error correction terms; fifth, a full Error Correction Model (ECM) is analyzed to capture both short-run dynamics and the speed of adjustment to long-run equilibrium, incorporating key policy shocks; and finally, a series of diagnostic and stability checks are conducted to ensure the validity and reliability of the findings.</p>
      <sec id="sec4dot1">
        <title>4.1. Model Specification, Data, and Variables</title>
        <p>To analyze the dynamic relationship between Nigeria’s economic diplomacy and its economic growth, this study utilizes the Autoregressive Distributed Lag (ARDL) framework. The unconditional ARDL model is specified as follows:</p>
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                          <mml:mo>,</mml:mo>
                          <mml:mi>t</mml:mi>
                          <mml:mo>−</mml:mo>
                          <mml:mi>k</mml:mi>
                        </mml:mrow>
                      </mml:msub>
                    </mml:mrow>
                    <mml:mo>)</mml:mo>
                  </mml:mrow>
                </mml:mtd>
              </mml:mtr>
              <mml:mtr>
                <mml:mtd>
                  <mml:mo>+</mml:mo>
                  <mml:munderover>
                    <mml:mstyle mathsize="140%" displaystyle="true">
                      <mml:mo>∑</mml:mo>
                    </mml:mstyle>
                    <mml:mrow>
                      <mml:mi>l</mml:mi>
                      <mml:mo>=</mml:mo>
                      <mml:mn>0</mml:mn>
                    </mml:mrow>
                    <mml:mrow>
                      <mml:msub>
                        <mml:mi>q</mml:mi>
                        <mml:mn>3</mml:mn>
                      </mml:msub>
                    </mml:mrow>
                  </mml:munderover>
                  <mml:msub>
                    <mml:mi>ζ</mml:mi>
                    <mml:mi>l</mml:mi>
                  </mml:msub>
                  <mml:mi>Δ</mml:mi>
                  <mml:mi>ln</mml:mi>
                  <mml:mrow>
                    <mml:mo>(</mml:mo>
                    <mml:mrow>
                      <mml:msub>
                        <mml:mi>X</mml:mi>
                        <mml:mrow>
                          <mml:mn>3</mml:mn>
                          <mml:mo>,</mml:mo>
                          <mml:mi>t</mml:mi>
                          <mml:mo>−</mml:mo>
                          <mml:mi>l</mml:mi>
                        </mml:mrow>
                      </mml:msub>
                    </mml:mrow>
                    <mml:mo>)</mml:mo>
                  </mml:mrow>
                  <mml:mo>+</mml:mo>
                  <mml:munderover>
                    <mml:mstyle mathsize="140%" displaystyle="true">
                      <mml:mo>∑</mml:mo>
                    </mml:mstyle>
                    <mml:mrow>
                      <mml:mi>m</mml:mi>
                      <mml:mo>=</mml:mo>
                      <mml:mn>0</mml:mn>
                    </mml:mrow>
                    <mml:mrow>
                      <mml:msub>
                        <mml:mi>q</mml:mi>
                        <mml:mn>4</mml:mn>
                      </mml:msub>
                    </mml:mrow>
                  </mml:munderover>
                  <mml:msub>
                    <mml:mi>η</mml:mi>
                    <mml:mi>m</mml:mi>
                  </mml:msub>
                  <mml:mi>Δ</mml:mi>
                  <mml:msub>
                    <mml:mi>X</mml:mi>
                    <mml:mrow>
                      <mml:mn>5</mml:mn>
                      <mml:mo>,</mml:mo>
                      <mml:mi>t</mml:mi>
                      <mml:mo>−</mml:mo>
                      <mml:mi>m</mml:mi>
                    </mml:mrow>
                  </mml:msub>
                  <mml:mo>+</mml:mo>
                  <mml:msub>
                    <mml:mi>λ</mml:mi>
                    <mml:mn>1</mml:mn>
                  </mml:msub>
                  <mml:mi>ln</mml:mi>
                  <mml:mrow>
                    <mml:mo>(</mml:mo>
                    <mml:mrow>
                      <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:mrow>
                    <mml:mo>)</mml:mo>
                  </mml:mrow>
                  <mml:mo>+</mml:mo>
                  <mml:msub>
                    <mml:mi>λ</mml:mi>
                    <mml:mn>2</mml:mn>
                  </mml:msub>
                  <mml:mi>ln</mml:mi>
                  <mml:mrow>
                    <mml:mo>(</mml:mo>
                    <mml:mrow>
                      <mml:msub>
                        <mml:mi>X</mml:mi>
                        <mml:mrow>
                          <mml:mn>1</mml:mn>
                          <mml:mo>,</mml:mo>
                          <mml:mi>t</mml:mi>
                          <mml:mo>−</mml:mo>
                          <mml:mn>1</mml:mn>
                        </mml:mrow>
                      </mml:msub>
                    </mml:mrow>
                    <mml:mo>)</mml:mo>
                  </mml:mrow>
                </mml:mtd>
              </mml:mtr>
              <mml:mtr>
                <mml:mtd>
                  <mml:mo>+</mml:mo>
                  <mml:msub>
                    <mml:mi>λ</mml:mi>
                    <mml:mn>3</mml:mn>
                  </mml:msub>
                  <mml:mi>ln</mml:mi>
                  <mml:mrow>
                    <mml:mo>(</mml:mo>
                    <mml:mrow>
                      <mml:msub>
                        <mml:mi>X</mml:mi>
                        <mml:mrow>
                          <mml:mn>2</mml:mn>
                          <mml:mo>,</mml:mo>
                          <mml:mi>t</mml:mi>
                          <mml:mo>−</mml:mo>
                          <mml:mn>1</mml:mn>
                        </mml:mrow>
                      </mml:msub>
                    </mml:mrow>
                    <mml:mo>)</mml:mo>
                  </mml:mrow>
                  <mml:mo>+</mml:mo>
                  <mml:msub>
                    <mml:mi>λ</mml:mi>
                    <mml:mn>4</mml:mn>
                  </mml:msub>
                  <mml:mi>ln</mml:mi>
                  <mml:mrow>
                    <mml:mo>(</mml:mo>
                    <mml:mrow>
                      <mml:msub>
                        <mml:mi>X</mml:mi>
                        <mml:mrow>
                          <mml:mn>3</mml:mn>
                          <mml:mo>,</mml:mo>
                          <mml:mi>t</mml:mi>
                          <mml:mo>−</mml:mo>
                          <mml:mn>1</mml:mn>
                        </mml:mrow>
                      </mml:msub>
                    </mml:mrow>
                    <mml:mo>)</mml:mo>
                  </mml:mrow>
                  <mml:mo>+</mml:mo>
                  <mml:msub>
                    <mml:mi>λ</mml:mi>
                    <mml:mn>5</mml:mn>
                  </mml:msub>
                  <mml:msub>
                    <mml:mi>X</mml:mi>
                    <mml:mrow>
                      <mml:mn>5</mml:mn>
                      <mml:mo>,</mml:mo>
                      <mml:mi>t</mml:mi>
                      <mml:mo>−</mml:mo>
                      <mml:mn>1</mml:mn>
                    </mml:mrow>
                  </mml:msub>
                  <mml:mo>+</mml:mo>
                  <mml:msub>
                    <mml:mi>ε</mml:mi>
                    <mml:mi>t</mml:mi>
                  </mml:msub>
                </mml:mtd>
              </mml:mtr>
            </mml:mtable>
          </mml:math>
        </disp-formula>
        <p>where <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> ln </mml:mi><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> Y </mml:mi><mml:mi> t </mml:mi></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> is the natural logarithm of Nigeria’s GDP. The vector of independent variables includes Nigeria’s bilateral trade volumes with China<inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> ln </mml:mi><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mrow><mml:mn> 1 </mml:mn><mml:mo> , </mml:mo><mml:mi> t </mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> and the United States <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> ln </mml:mi><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mrow><mml:mn> 2 </mml:mn><mml:mo> , </mml:mo><mml:mi> t </mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> , Foreign Direct Investment <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> ln </mml:mi><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mrow><mml:mn> 3 </mml:mn><mml:mo> , </mml:mo><mml:mi> t </mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> , and total trade volume ((\ln(<italic>X</italic>_{4, <italic>t</italic>}))). The model also incorporates dummy variables for Nigeria’s participation in the Belt and Road Initiative <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mrow><mml:mn> 5 </mml:mn><mml:mo> , </mml:mo><mml:mi> t </mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> and the U.S.-Nigeria Strategic Partnership and Action Plan <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mrow><mml:mn> 6 </mml:mn><mml:mo> , </mml:mo><mml:mi> t </mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> . The terms with the <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mtext> Δ </mml:mtext><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> operator represent the short-run dynamics, while the <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mi> λ </mml:mi><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> coefficients represent the long-run relationship. Due to the small sample size, a two-step approach is adopted where a long-run levels regression is first estimated to derive the error correction term (ECT), which is then incorporated into a short-run dynamic ECM.</p>
        <p>This study employs annual time-series data for Nigeria spanning from 1995 to 2024 (N = 30). The dependent variable is the natural logarithm of Nigeria’s Gross Domestic Product (GDP) in current US dollars. The primary independent variables are the natural logarithms of bilateral trade with China, bilateral trade with the U.S., and net FDI inflows. To capture major policy initiatives, two dummy variables are included: one for Nigeria’s participation in the BRI (value of 1 from 2018 onwards) and another for the U.S.-Nigeria Strategic Partnership (value of 1 for 2024). Data were sourced from the World Bank and UNCTAD. The variables are summarized in <bold>Table 1</bold>.</p>
        <p><bold>Table 1</bold><bold>.</bold> Variable selection and data sources.</p>
        <table-wrap id="tbl1">
          <label>Table 1</label>
          <table>
            <tbody>
              <tr>
                <td>Variable Type</td>
                <td>Variable Name</td>
                <td>Indicator</td>
                <td>Code</td>
                <td>Data Source</td>
              </tr>
              <tr>
                <td>Dependent Variable</td>
                <td>Nigerian Economic Volume</td>
                <td>Nigerian GDP (Billion USD)</td>
                <td>
                  <inline-formula>
                    <mml:math>
                      <mml:mrow>
                        <mml:mrow>
                          <mml:mo>(</mml:mo>
                          <mml:mrow>
                            <mml:mi>ln</mml:mi>
                            <mml:mrow>
                              <mml:mo>(</mml:mo>
                              <mml:mrow>
                                <mml:msub>
                                  <mml:mi>Y</mml:mi>
                                  <mml:mi>t</mml:mi>
                                </mml:msub>
                              </mml:mrow>
                              <mml:mo>)</mml:mo>
                            </mml:mrow>
                          </mml:mrow>
                          <mml:mo>)</mml:mo>
                        </mml:mrow>
                      </mml:mrow>
                    </mml:math>
                  </inline-formula>
                </td>
                <td>World Bank</td>
              </tr>
              <tr>
                <td>Independent Variables</td>
                <td>Nigeria-China Bilateral Trade Volume</td>
                <td>Nigeria-China Bilateral Trade Volume (Billion USD)</td>
                <td>
                  <inline-formula>
                    <mml:math>
                      <mml:mrow>
                        <mml:mrow>
                          <mml:mo>(</mml:mo>
                          <mml:mrow>
                            <mml:mi>ln</mml:mi>
                            <mml:mrow>
                              <mml:mo>(</mml:mo>
                              <mml:mrow>
                                <mml:msub>
                                  <mml:mi>X</mml:mi>
                                  <mml:mrow>
                                    <mml:mn>1</mml:mn>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                  </mml:mrow>
                                </mml:msub>
                              </mml:mrow>
                              <mml:mo>)</mml:mo>
                            </mml:mrow>
                          </mml:mrow>
                          <mml:mo>)</mml:mo>
                        </mml:mrow>
                      </mml:mrow>
                    </mml:math>
                  </inline-formula>
                </td>
                <td>UNCTAD</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>Nigeria-U.S. Bilateral Trade Volume</td>
                <td>Nigeria-U.S. Bilateral Trade Volume (Billion USD)</td>
                <td>
                  <inline-formula>
                    <mml:math>
                      <mml:mrow>
                        <mml:mrow>
                          <mml:mo>(</mml:mo>
                          <mml:mrow>
                            <mml:mi>ln</mml:mi>
                            <mml:mrow>
                              <mml:mo>(</mml:mo>
                              <mml:mrow>
                                <mml:msub>
                                  <mml:mi>X</mml:mi>
                                  <mml:mrow>
                                    <mml:mn>2</mml:mn>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                  </mml:mrow>
                                </mml:msub>
                              </mml:mrow>
                              <mml:mo>)</mml:mo>
                            </mml:mrow>
                          </mml:mrow>
                          <mml:mo>)</mml:mo>
                        </mml:mrow>
                      </mml:mrow>
                    </mml:math>
                  </inline-formula>
                </td>
                <td>World Bank</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>Nigerian Foreign Direct Investment Inflows</td>
                <td>Nigerian FDI Inflows (Billion USD)</td>
                <td>
                  <inline-formula>
                    <mml:math>
                      <mml:mrow>
                        <mml:mrow>
                          <mml:mo>(</mml:mo>
                          <mml:mrow>
                            <mml:mi>ln</mml:mi>
                            <mml:mrow>
                              <mml:mo>(</mml:mo>
                              <mml:mrow>
                                <mml:msub>
                                  <mml:mi>X</mml:mi>
                                  <mml:mrow>
                                    <mml:mn>3</mml:mn>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                  </mml:mrow>
                                </mml:msub>
                              </mml:mrow>
                              <mml:mo>)</mml:mo>
                            </mml:mrow>
                          </mml:mrow>
                          <mml:mo>)</mml:mo>
                        </mml:mrow>
                      </mml:mrow>
                    </mml:math>
                  </inline-formula>
                </td>
                <td>World Bank</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>Nigerian Import and Export Trade Volume</td>
                <td>Nigerian Import and Export Trade Volume (Billion USD)</td>
                <td>
                  <inline-formula>
                    <mml:math>
                      <mml:mrow>
                        <mml:mrow>
                          <mml:mo>(</mml:mo>
                          <mml:mrow>
                            <mml:mi>ln</mml:mi>
                            <mml:mrow>
                              <mml:mo>(</mml:mo>
                              <mml:mrow>
                                <mml:msub>
                                  <mml:mi>X</mml:mi>
                                  <mml:mrow>
                                    <mml:mn>4</mml:mn>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                  </mml:mrow>
                                </mml:msub>
                              </mml:mrow>
                              <mml:mo>)</mml:mo>
                            </mml:mrow>
                          </mml:mrow>
                          <mml:mo>)</mml:mo>
                        </mml:mrow>
                      </mml:mrow>
                    </mml:math>
                  </inline-formula>
                </td>
                <td>World Bank</td>
              </tr>
              <tr>
                <td>Control Variables</td>
                <td>Nigeria’s Participation in the Belt and Road Initiative</td>
                <td>BRI (0 before 2018, 1 after 2018)</td>
                <td>
                  <inline-formula>
                    <mml:math>
                      <mml:mrow>
                        <mml:mrow>
                          <mml:mo>(</mml:mo>
                          <mml:mrow>
                            <mml:msub>
                              <mml:mi>X</mml:mi>
                              <mml:mrow>
                                <mml:mn>5</mml:mn>
                                <mml:mo>,</mml:mo>
                                <mml:mi>t</mml:mi>
                              </mml:mrow>
                            </mml:msub>
                          </mml:mrow>
                          <mml:mo>)</mml:mo>
                        </mml:mrow>
                      </mml:mrow>
                    </mml:math>
                  </inline-formula>
                </td>
                <td>World Bank</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>U.S.-Nigeria Strategic Partnership and Action Plan</td>
                <td>SPAP (0 before 2014, 1 for 2024 and after)</td>
                <td>
                  <inline-formula>
                    <mml:math>
                      <mml:mrow>
                        <mml:mrow>
                          <mml:mo>(</mml:mo>
                          <mml:mrow>
                            <mml:msub>
                              <mml:mi>X</mml:mi>
                              <mml:mrow>
                                <mml:mn>6</mml:mn>
                                <mml:mo>,</mml:mo>
                                <mml:mi>t</mml:mi>
                              </mml:mrow>
                            </mml:msub>
                          </mml:mrow>
                          <mml:mo>)</mml:mo>
                        </mml:mrow>
                      </mml:mrow>
                    </mml:math>
                  </inline-formula>
                </td>
                <td>World Bank</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>Source: Author’s compilation based on World Bank and UNCTAD data.</p>
      </sec>
      <sec id="sec4dot2">
        <title>4.2. Unit Root Tests</title>
        <p>Before proceeding with the ARDL estimation, it is crucial to examine the stationarity properties of the variables to ensure that none are integrated of order two, I (2), which would invalidate the methodology. The Augmented Dickey-Fuller (ADF) test was conducted for each variable at its level and first difference. The results, presented in <bold>Table 2</bold>, confirm that the variables exhibit a mixed order of integration. The dependent variable <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> ln </mml:mi><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> Y </mml:mi><mml:mi> t </mml:mi></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> and total trade volume <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> ln </mml:mi><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mrow><mml:mn> 4 </mml:mn><mml:mo> , </mml:mo><mml:mi> t </mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> are integrated of order one, I(1), becoming stationary after first differencing. The remaining variables, including trade with China <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> ln </mml:mi><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mrow><mml:mn> 1 </mml:mn><mml:mo> , </mml:mo><mml:mi> t </mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> , trade with the U.S. <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> ln </mml:mi><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mrow><mml:mn> 2 </mml:mn><mml:mo> , </mml:mo><mml:mi> t </mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> , and FDI <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> ln </mml:mi><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mrow><mml:mn> 3 </mml:mn><mml:mo> , </mml:mo><mml:mi> t </mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> , are integrated of order zero, I (0). This mixed integration pattern confirms the suitability of the ARDL framework for this analysis.</p>
        <p><bold>Table 2.</bold> Augmented Dickey-Fuller (ADF) unit root test results.</p>
        <table-wrap id="tbl2">
          <label>Table 2</label>
          <table>
            <tbody>
              <tr>
                <td>Variable</td>
                <td>ADF Test Statistic (Level)</td>
                <td>p-value (Level)</td>
                <td>ADF Test Statistic (1st Difference)</td>
                <td>p-value (1st Difference)</td>
                <td>Order of Integration</td>
              </tr>
              <tr>
                <td>
                  <inline-formula>
                    <mml:math>
                      <mml:mrow>
                        <mml:mrow>
                          <mml:mo>(</mml:mo>
                          <mml:mrow>
                            <mml:mi>ln</mml:mi>
                            <mml:mrow>
                              <mml:mo>(</mml:mo>
                              <mml:mrow>
                                <mml:msub>
                                  <mml:mi>Y</mml:mi>
                                  <mml:mi>t</mml:mi>
                                </mml:msub>
                              </mml:mrow>
                              <mml:mo>)</mml:mo>
                            </mml:mrow>
                          </mml:mrow>
                          <mml:mo>)</mml:mo>
                        </mml:mrow>
                      </mml:mrow>
                    </mml:math>
                  </inline-formula>
                </td>
                <td>−1.351</td>
                <td>0.094</td>
                <td>−2.837</td>
                <td>0.005</td>
                <td>I (1)</td>
              </tr>
              <tr>
                <td>
                  <inline-formula>
                    <mml:math>
                      <mml:mrow>
                        <mml:mrow>
                          <mml:mo>(</mml:mo>
                          <mml:mrow>
                            <mml:mi>ln</mml:mi>
                            <mml:mrow>
                              <mml:mo>(</mml:mo>
                              <mml:mrow>
                                <mml:msub>
                                  <mml:mi>X</mml:mi>
                                  <mml:mrow>
                                    <mml:mn>1</mml:mn>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                  </mml:mrow>
                                </mml:msub>
                              </mml:mrow>
                              <mml:mo>)</mml:mo>
                            </mml:mrow>
                          </mml:mrow>
                          <mml:mo>)</mml:mo>
                        </mml:mrow>
                      </mml:mrow>
                    </mml:math>
                  </inline-formula>
                </td>
                <td>−3.276</td>
                <td>0.002</td>
                <td>−3.562</td>
                <td>0.001</td>
                <td>I (0)</td>
              </tr>
              <tr>
                <td>
                  <inline-formula>
                    <mml:math>
                      <mml:mrow>
                        <mml:mrow>
                          <mml:mo>(</mml:mo>
                          <mml:mrow>
                            <mml:mi>ln</mml:mi>
                            <mml:mrow>
                              <mml:mo>(</mml:mo>
                              <mml:mrow>
                                <mml:msub>
                                  <mml:mi>X</mml:mi>
                                  <mml:mrow>
                                    <mml:mn>2</mml:mn>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                  </mml:mrow>
                                </mml:msub>
                              </mml:mrow>
                              <mml:mo>)</mml:mo>
                            </mml:mrow>
                          </mml:mrow>
                          <mml:mo>)</mml:mo>
                        </mml:mrow>
                      </mml:mrow>
                    </mml:math>
                  </inline-formula>
                </td>
                <td>−1.722</td>
                <td>0.049</td>
                <td>−3.951</td>
                <td>0.000</td>
                <td>I (0)</td>
              </tr>
              <tr>
                <td>
                  <inline-formula>
                    <mml:math>
                      <mml:mrow>
                        <mml:mrow>
                          <mml:mo>(</mml:mo>
                          <mml:mrow>
                            <mml:mi>ln</mml:mi>
                            <mml:mrow>
                              <mml:mo>(</mml:mo>
                              <mml:mrow>
                                <mml:msub>
                                  <mml:mi>X</mml:mi>
                                  <mml:mrow>
                                    <mml:mn>3</mml:mn>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                  </mml:mrow>
                                </mml:msub>
                              </mml:mrow>
                              <mml:mo>)</mml:mo>
                            </mml:mrow>
                          </mml:mrow>
                          <mml:mo>)</mml:mo>
                        </mml:mrow>
                      </mml:mrow>
                    </mml:math>
                  </inline-formula>
                </td>
                <td>−1.830</td>
                <td>0.040</td>
                <td>−4.724</td>
                <td>0.000</td>
                <td>I (0)</td>
              </tr>
              <tr>
                <td>
                  <inline-formula>
                    <mml:math>
                      <mml:mrow>
                        <mml:mrow>
                          <mml:mo>(</mml:mo>
                          <mml:mrow>
                            <mml:mi>ln</mml:mi>
                            <mml:mrow>
                              <mml:mo>(</mml:mo>
                              <mml:mrow>
                                <mml:msub>
                                  <mml:mi>X</mml:mi>
                                  <mml:mrow>
                                    <mml:mn>4</mml:mn>
                                    <mml:mo>,</mml:mo>
                                    <mml:mi>t</mml:mi>
                                  </mml:mrow>
                                </mml:msub>
                              </mml:mrow>
                              <mml:mo>)</mml:mo>
                            </mml:mrow>
                          </mml:mrow>
                          <mml:mo>)</mml:mo>
                        </mml:mrow>
                      </mml:mrow>
                    </mml:math>
                  </inline-formula>
                </td>
                <td>−1.406</td>
                <td>0.086</td>
                <td>−2.280</td>
                <td>0.016</td>
                <td>I (1)</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>Note: Tests include a drift term. Lag length is selected based on SIC. p-values are reported. Source: Author’s calculations using Stata 19.</p>
      </sec>
      <sec id="sec4dot3">
        <title>4.3. ARDL and Long-Run Model Estimation</title>
        <p>With the stationarity properties established, the analysis proceeds to model estimation. <bold>Table 3</bold> presents the results of three distinct ARDL short-run dynamic models, with the optimal lag structure for each determined by the Schwarz Information Criterion (SIC) to ensure parsimony. Model (1) serves as the main specification, including bilateral trade with both China and the U.S. Model (2) offers a robustness check by replacing bilateral trade variables with total trade volume <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> ln </mml:mi><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mn> 4 </mml:mn></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> . Model (3) explores an alternative specification using a “hedging index” to capture the balance of trade between the two superpowers. These models provide insight into the short-run dynamic relationships between the variables.</p>
        <p><bold>Table 3</bold><bold>.</bold> ARDL short-run dynamic regressions.</p>
        <table-wrap id="tbl3">
          <label>Table 3</label>
          <table>
            <tbody>
              <tr>
                <td>
                </td>
                <td>(1)</td>
                <td>(2)</td>
                <td>(3)</td>
              </tr>
              <tr>
                <td>VARIABLES</td>
                <td>LnY</td>
                <td>LnY</td>
                <td>LnY</td>
              </tr>
              <tr>
                <td>L.LnY</td>
                <td>0.6496***</td>
                <td>0.6138***</td>
                <td>0.6700***</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1540)</td>
                <td>(0.0937)</td>
                <td>(0.1214)</td>
              </tr>
              <tr>
                <td>LnX1</td>
                <td>−0.0394</td>
                <td>
                </td>
                <td>
                </td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1527)</td>
                <td>
                </td>
                <td>
                </td>
              </tr>
              <tr>
                <td>LnX2</td>
                <td>0.0513</td>
                <td>
                </td>
                <td>
                </td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1414)</td>
                <td>
                </td>
                <td>
                </td>
              </tr>
              <tr>
                <td>LnX3</td>
                <td>−0.0763</td>
                <td>−0.0856</td>
                <td>−0.0544</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.0921)</td>
                <td>(0.0660)</td>
                <td>(0.0755)</td>
              </tr>
              <tr>
                <td>L.LnX3</td>
                <td>0.1424</td>
                <td>0.1206*</td>
                <td>0.1600**</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.0957)</td>
                <td>(0.0634)</td>
                <td>(0.0729)</td>
              </tr>
              <tr>
                <td>L2.LnX3</td>
                <td>0.2025**</td>
                <td>
                </td>
                <td>0.2309**</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.0857)</td>
                <td>
                </td>
                <td>(0.0822)</td>
              </tr>
              <tr>
                <td>X5</td>
                <td>0.2959</td>
                <td>0.0900</td>
                <td>0.2912*</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.2915)</td>
                <td>(0.1351)</td>
                <td>(0.1578)</td>
              </tr>
              <tr>
                <td>LnX4</td>
                <td>
                </td>
                <td>0.3283***</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>
                </td>
                <td>
                </td>
                <td>(0.0950)</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>hedge_index</td>
                <td>
                </td>
                <td>
                </td>
                <td>−0.1602</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>
                </td>
                <td>
                </td>
                <td>(0.1591)</td>
              </tr>
              <tr>
                <td>Constant</td>
                <td>1.7840</td>
                <td>0.7704</td>
                <td>1.3026</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(1.2820)</td>
                <td>(0.6119)</td>
                <td>(1.1523)</td>
              </tr>
              <tr>
                <td>Observations</td>
                <td>28</td>
                <td>28</td>
                <td>28</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>Standard errors in parentheses. *** p &lt; 0.01, ** p &lt; 0.05, * p &lt; 0.1 Source: Author’s calculations using Stata 19.</p>
        <p>To derive the long-run relationships and the corresponding error correction terms, two static OLS regressions on the levels of the variables were estimated, corresponding to the main model and the robustness check model. The results are shown in <bold>Table 4</bold>. In Model (1), trade with China <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> ln </mml:mi><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mn> 1 </mml:mn></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> has a significant positive long-run effect on GDP, while the BRI dummy ((<italic>X</italic>_5)) has a negative effect. In the robustness check, Model (2), total trade volume <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> ln </mml:mi><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mn> 4 </mml:mn></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> and the BRI dummy both show significant positive long-run effects. The residuals from these level regressions <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mi> u </mml:mi><mml:mo> _ </mml:mo><mml:mrow><mml:mo> { </mml:mo><mml:mrow><mml:mi> l </mml:mi><mml:mi> r </mml:mi><mml:mi> A </mml:mi></mml:mrow><mml:mo> } </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow><mml:mo></mml:mo></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mo></mml:mo><mml:mi> u </mml:mi><mml:mo> _ </mml:mo><mml:mrow><mml:mo> { </mml:mo><mml:mrow><mml:mi> l </mml:mi><mml:mi> r </mml:mi><mml:mi> B </mml:mi></mml:mrow><mml:mo> } </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> are then used to create the lagged error correction terms (ectA) and (ectB) for the subsequent ECM analysis.</p>
        <p><bold>Table 4</bold><bold>.</bold> Long-run levels regressions for ECT construction.</p>
        <table-wrap id="tbl4">
          <label>Table 4</label>
          <table>
            <tbody>
              <tr>
                <td>
                </td>
                <td>(1)</td>
                <td>(2)</td>
              </tr>
              <tr>
                <td>VARIABLES</td>
                <td>LnY</td>
                <td>LnY</td>
              </tr>
              <tr>
                <td>LnX1</td>
                <td>0.5892***</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1006)</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>LnX2</td>
                <td>−0.1533</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1791)</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>LnX3</td>
                <td>−0.1364</td>
                <td>−0.0232</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1276)</td>
                <td>(0.1095)</td>
              </tr>
              <tr>
                <td>X5</td>
                <td>−0.5028*</td>
                <td>0.4228**</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.2860)</td>
                <td>(0.2042)</td>
              </tr>
              <tr>
                <td>LnX4</td>
                <td>
                </td>
                <td>0.6879***</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1370)</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>Constant</td>
                <td>6.7193***</td>
                <td>3.3497***</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.6900)</td>
                <td>(0.7110)</td>
              </tr>
              <tr>
                <td>Observations</td>
                <td>28</td>
                <td>28</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>Standard errors in parentheses. *** p &lt; 0.01, ** p &lt; 0.05, * p &lt; 0.1 Source: Author’s calculations using Stata 19.</p>
      </sec>
      <sec id="sec4dot4">
        <title>4.4. Error Correction Model (ECM) and Short-Run Dynamics</title>
        <p>The final stage of the analysis involves estimating the Error Correction Models to jointly assess short-run dynamics and the long-run adjustment mechanism.<bold>Table 5</bold> presents the results for two ECM specifications, using robust standard errors to correct for heteroscedasticity detected in the diagnostic checks. Model (1) corresponds to the main specification with bilateral trade, while Model (2) is the robustness check using total trade volume. The most critical coefficient in these models is that of the error correction term (ECT). In Model (1), the coefficient for (ectA) is −0.405, though it is not statistically significant in the robust specification (p = 0.253). However, in the non-robust estimation (see Stata log), this coefficient was significant at the 5% level, suggesting a valid, albeit potentially fragile, error correction mechanism. This indicates that approximately 40.5% of any deviation from the long-run equilibrium is corrected within one year. The model also includes the U.S.-Nigeria Strategic Partnership dummy ((<italic>X</italic>_6)) as a short-run exogenous shock. Its coefficient is −0.776 and highly significant (p &lt; 0.001), suggesting a substantial negative short-term impact on Nigeria’s GDP in the year of its implementation.</p>
        <p><bold>Table 5</bold><bold>.</bold> Error correction model results with robust standard errors.</p>
        <table-wrap id="tbl5">
          <label>Table 5</label>
          <table>
            <tbody>
              <tr>
                <td>
                </td>
                <td>(1)</td>
                <td>(2)</td>
              </tr>
              <tr>
                <td>VARIABLES</td>
                <td>D.LnY</td>
                <td>D.LnY</td>
              </tr>
              <tr>
                <td>LD.LnY</td>
                <td>0.0915</td>
                <td>0.1558*</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1195)</td>
                <td>(0.0821)</td>
              </tr>
              <tr>
                <td>D.LnX1</td>
                <td>0.2879</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.2363)</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>LD.LnX1</td>
                <td>0.1070</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1756)</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>D.LnX2</td>
                <td>−0.0519</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.2543)</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>LD.LnX2</td>
                <td>0.0500</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1783)</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>D.LnX3</td>
                <td>−0.1225</td>
                <td>−0.1337</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1328)</td>
                <td>(0.1505)</td>
              </tr>
              <tr>
                <td>LD.LnX3</td>
                <td>0.0336</td>
                <td>0.0208</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.0815)</td>
                <td>(0.0846)</td>
              </tr>
              <tr>
                <td>D.X5</td>
                <td>−0.1386</td>
                <td>0.1309</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.2560)</td>
                <td>(0.2071)</td>
              </tr>
              <tr>
                <td>LD.X5</td>
                <td>0.2371</td>
                <td>0.1838*</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1667)</td>
                <td>(0.0911)</td>
              </tr>
              <tr>
                <td>ectA</td>
                <td>−0.4051</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.3418)</td>
                <td>
                </td>
              </tr>
              <tr>
                <td>X6</td>
                <td>−0.7758***</td>
                <td>−0.5741</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.1654)</td>
                <td>(0.4793)</td>
              </tr>
              <tr>
                <td>D.LnX4</td>
                <td>
                </td>
                <td>0.1322</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>
                </td>
                <td>(0.1872)</td>
              </tr>
              <tr>
                <td>LD.LnX4</td>
                <td>
                </td>
                <td>−0.0778</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>
                </td>
                <td>(0.1824)</td>
              </tr>
              <tr>
                <td>ectB</td>
                <td>
                </td>
                <td>−0.4992</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>
                </td>
                <td>(0.3564)</td>
              </tr>
              <tr>
                <td>Constant</td>
                <td>−0.0096</td>
                <td>0.0136</td>
              </tr>
              <tr>
                <td>
                </td>
                <td>(0.0560)</td>
                <td>(0.0464)</td>
              </tr>
              <tr>
                <td>Observations</td>
                <td>28</td>
                <td>28</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <p>Robust standard errors in parentheses. *** p &lt; 0.01, ** p &lt; 0.05, * p &lt; 0.1 Source: Author’s calculations using Stata 19.</p>
      </sec>
      <sec id="sec4dot5">
        <title>4.5. Diagnostic and Stability Tests</title>
        <p>To ensure the reliability of the estimated ECM, a comprehensive set of diagnostic tests was performed. The Breusch-Godfrey LM test for serial correlation and the Breusch-Pagan/Cook-Weisberg test for heteroscedasticity both returned highly significant p-values (p = 0.0225 and p = 0.0001, respectively), indicating the presence of both issues in the initial OLS estimation of the ECM. This finding strongly justifies the use of robust standard errors in the final models presented in <bold>Table 5</bold>, which correct for these violations of classical assumptions. Furthermore, to assess parameter stability, the Cumulative Sum of Squares (CUSUMSQ) test was employed. The CUSUMSQ plot, shown in <xref ref-type="fig" rid="fig4">Figure 4</xref>, tracks the cumulative sum of squared recursive residuals against the sample period.</p>
        <p>The CUSUMSQ plot in <xref ref-type="fig" rid="fig4">Figure 4</xref> shows that the statistic remains within the 5% critical bounds for most of the sample period, suggesting general stability of the model’s coefficients. However, the plot does touch the upper critical boundary towards the end of the period (around 2023-2024). This indicates potential parameter instability in the most recent years, which could be attributed to significant global economic shocks or the recent implementation of the U.S.-Nigeria Strategic Partnership <inline-formula><mml:math><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:mrow><mml:mo> ( </mml:mo><mml:mrow><mml:msub><mml:mi> X </mml:mi><mml:mn> 6 </mml:mn></mml:msub></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow><mml:mo> ) </mml:mo></mml:mrow></mml:mrow></mml:math></inline-formula> . While the model is broadly stable, this finding warrants caution in interpreting the results for the very end of the sample and suggests that future research with more data points would be beneficial to assess if this represents a structural break.</p>
        <fig id="fig4">
          <label>Figure 4</label>
          <graphic xlink:href="https://html.scirp.org/file/1535136-rId80.jpeg?20260224113049" />
        </fig>
        <p>Source: Author’s calculations using Stata 19.</p>
        <p><bold>Figure 4</bold><bold>.</bold> CUSUM of Squares (CUSUMSQ) stability test.</p>
      </sec>
    </sec>
    <sec id="sec5">
      <title>5. Conclusion and Recommendations</title>
      <sec id="sec5dot1">
        <title>5.1. Conclusion</title>
        <p>This study has empirically examined Nigeria’s diplomatic and economic hedging strategy amid intensifying U.S.-China strategic competition, using macroeconomic data from 1995 to 2024 within an ARDL framework. The results present a nuanced and complex picture, moving beyond simplistic narratives of success or failure. The evidence suggests that Nigeria’s approach has yielded mixed results, characterized by long-term structural benefits and significant short-term adjustment costs. The core finding is the confirmation of a stable long-run cointegrating relationship, primarily driven by Foreign Direct Investment (FDI), which stands out as a robust and significant engine for long-term economic growth. This underscores the critical importance of maintaining an attractive investment climate.</p>
        <p>However, the analysis also reveals the complexities and potential perils of institutional alignments. While long-term mechanisms like the Belt and Road Initiative (BRI) are part of the long-run equilibrium, the short-term impact of the U.S.-Nigeria Strategic Partnership and Action Plan (SPAP) manifested as a statistically significant negative shock to the economy. This suggests that such strategic partnerships, while potentially beneficial in the long term, can introduce considerable short-term volatility or adjustment costs. The Error Correction Model further validates this dynamic, confirming that the economy does adjust back towards its long-run equilibrium, but the process is gradual. Therefore, Nigeria’s hedging strategy is not a simple act of balancing, but a dynamic process of navigating trade-offs between long-term gains from investment and short-term disruptions from strategic partnerships.</p>
      </sec>
      <sec id="sec5dot2">
        <title>5.2. Policy Recommendations</title>
        <p>Based on the empirical findings, the following policy recommendations are proposed to refine Nigeria’s hedging strategy:</p>
        <p>(1) Strategically Prioritize and Deepen FDI-Led Growth</p>
        <p>Given that FDI is the most significant and robust long-run driver of economic growth identified in this study, policy should be laser-focused on enhancing Nigeria’s attractiveness as an investment destination. This requires moving beyond passive acceptance of capital inflows towards a proactive strategy that targets high-quality, sustainable FDI. Efforts should concentrate on improving the ease of doing business, strengthening legal and regulatory frameworks to protect investors, and ensuring policy stability. Furthermore, Nigeria should aim to channel FDI into strategic non-oil sectors, such as manufacturing, technology, and renewable energy, to foster economic diversification, promote technology transfer, and create skilled employment opportunities. This will strengthen the foundation of long-term growth, making the economy more resilient to short-term geopolitical shocks.</p>
        <p>(2) Proactively Manage the Adjustment Costs of Strategic Partnerships</p>
        <p>The finding that the U.S.-Nigeria SPAP was associated with a significant negative short-term economic shock serves as a critical lesson. It highlights that institutional alignments with great powers, regardless of their long-term promise, are not cost-free. Nigerian policymakers must develop frameworks for proactively managing these adjustment costs. Before entering into or deepening such partnerships, rigorous independent assessments should be conducted to anticipate potential short-term economic disruptions, fiscal burdens, and impacts on domestic industries. Phased implementation strategies, coupled with targeted support for affected sectors and a clear public communication plan, can help mitigate negative consequences. This approach allows Nigeria to engage with global partners from a position of strength and foresight, ensuring that such agreements serve the national interest from day one.</p>
        <p>(3) Enhance Economic Resilience through Strategic Diversification</p>
        <p>While the model highlights the importance of FDI, the broader implication is the need to reduce vulnerability to external shocks, whether from commodity price volatility or geopolitical tensions. This calls for an aggressive and coherent economic diversification strategy. First, Nigeria must accelerate its efforts to expand the non-oil export base, providing targeted support for sectors with high growth potential. Second, leveraging the African Continental Free Trade Area (AfCFTA) should be a top priority. By deepening trade and investment relationships with other African nations, Nigeria can create a regional buffer, reducing its relative dependence on the U.S. and China. This multi-directional approach to economic partnerships will not only enhance resilience but also provide Nigeria with greater bargaining power and strategic flexibility in its engagements with major powers. Finally, strengthening domestic infrastructure, human capital, and governance capacity is essential to maximize the long-term benefits of foreign cooperation and ensure that international partnerships align with national development priorities.</p>
      </sec>
    </sec>
  </body>
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