The Role of Financial Development in Economic Complexity: An Analysis of Asymmetry and Nonlinearity Perspectives

Abstract

This study enhances the knowledge base by providing an empirical inquiry into the asymmetric sensitivity of economic complexity (ECI) to changes in financial development (FD), using data from 30 African countries for the period of 1995–2023. To deliver robust estimates in the face of econometric pitfalls, this study employs estimators such as Hatemi-J data decomposition procedures, robust standard-error regression of Driscoll and Kraay, Feasible Generalised Least Squares, Lewbel’s IV-Two-Stage Least Squares, and Quantile regression via moments. The findings from the linear model indicate that FD enhances ECI upgrades in Africa. The findings provide robust evidence of asymmetric structures in ECI’s sensitivity to changes in FD. It highlights that both positive and negative change components (financial sector expansionary and contractionary policies, respectively) in the FD significantly contribute to ECI upgrades. These findings reveal the obscure aspects of how FD change components contribute differently to ECI upgrades in African countries. These findings highlight that expansionary financial sector policies aid the development of knowledge-based productivity, technology diffusion, and manufacturing capabilities, enabling the production of a chain of high-tech, high-quality, and globally competitive products for export. On the other hand, contractionary financial sector policies in African countries spur cumulative reductions in the channelling of financial resources and other technical support to ECI-impeding initiatives, thereby making more resources available to fund ECI-enhancing initiatives that aid the manufacturing of quality, competitive products for exports. This study draws and outlines relevant policy implications of the findings.

1. Introduction

The competitiveness of a country’s export product chain in the international market depends mainly on the extent of knowledge-based expertise, the efficiency of its knowledge economy, and the technological capabilities inherent in its manufacturing complement and productive structure—its economic complexity, henceforth referred to as ECI (C. O. Olaniyi & Odhiambo, 2023; L. K. Chu, 2020). This assertion has prompted developmental experts to standardise ECI as a basis for classifying countries worldwide into developed, emerging, developing, and underdeveloped based on their ECI rankings and performance (Njangang & Nvuh-Njoya, 2023; Yu & Qayyum, 2023; Hidalgo & Hausmann, 2009). Hence, countries, scholars, and policymakers continue to make intensified efforts to research into the fundamental determinants of their ECI. These research exploits are due to the need to transition from primitive production techniques to sophisticated ones that enhance countries’ capacity to manufacture a chain of knowledge-intensive, high-tech products for competitive exports. Due to the heavy capital requirements and substantial investment in ECI-related initiatives needed to transition from primitive technology to a sophisticated productive structure, existing studies have identified financial development (FD, hereafter) as a crucial determinant of ECI. An efficient and well-performing financial system plays multiple roles in promoting ECI-related initiatives. Within the context of endogenous growth theory, the role of the financial sector in bridging the gap between surplus and deficit units through mobilising and channelling financial resources to high-returning ECI initiatives, such as technology and innovation, research and development, human capital development, high-tech infrastructure, manufacturing skill acquisition, and other initiatives, is critical to enhancing the knowledge element and skill intensity in the productive structure to produce quality products for export.

FD plays a key role in reducing the cost of sourcing and borrowing funds to finance and incentivise the ECI-related initiatives (Njangang & Nvuh-Njoya, 2023; F. M. Ajide et al., 2023; Nguyen & Su, 2021b; Fan et al., 2015; Levine, 1997; King & Levine, 1993), diminishing the likelihood of exploitation of information gap between lenders and borrowers in financial transactions (L. K. Chu, 2020; Ang, 2008), easing the bottlenecks and bureaucracies in securing credit facilities for innovation and technological advancement (Arooj & Sajid, 2022), spreading risks associated with finance, innovation, and entrepreneurship (Ahmad et al., 2024; Greenwood & Jovanovic, 1990), and providing technical supports and expertise to spur entrepreneurial inclinations and innovative activities (C. O. Olaniyi & Adedokun, 2022). The ability of financial intermediaries to facilitate ECI expansion depends on the financial sector playing these critical roles efficiently. Given the critical role of FD in ECI, empirical studies continue to emerge on its effects, yet the results remain mixed and inconclusive. Most studies find a positive role for FD (F. M. Ajide et al., 2025; K. B. Ajide, 2025; Özbek & Şahİn, 2025; Soumtang Bime et al., 2024; Zechlin, 2025; Emeka, 2025; Kamdem & Toukam, 2025; C. O. Olaniyi & Odhiambo, 2023, 2025a; Low et al., 2024; Ndoya et al., 2024; Kamguia et al., 2023; Nguyen & Su, 2021b; L. K. Chu, 2020), while a few report either an adverse effect (Maxwele, 2025; C. O. Olaniyi & Odhiambo, 2025b) or an insignificant role (Izadi et al., 2025; Osinubi et al., 2024). Aside from these conflicting findings, existing research assumes that FD has symmetric, linear effects on ECI. These assumptions are inconsistent with the fundamental features of asymmetries and nonlinearities in the data distributions of FD and ECI (C. O. Olaniyi & Odhiambo, 2025b; C. O. Olaniyi et al., 2023). Also, recent developments in empirical research and econometrics refute the baseline assumptions of symmetry and linearity in the socioeconomic dynamics of financial data (Shin et al., 2014; Hatemi-J, 2012), such as FD and ECI; they do not explain real-world events. Hence, this study contributes to the global discussion by examining whether asymmetric and nonlinear structures matter for ECI’s sensitivity to changes in FD. Our critical review and thorough evaluation of existing studies (see

Table 1. The review of studies on the roles of financial development in economic complexity.

Study	Data Span	Country	Techniques	Findings
Özbek and Şahİn (2025)	1996–2021	12 Eastern European countries	SGMM, MG, PMG, DOLS, FMOLS	FD↑ECI
Ndoya et al. (2024)	1995–2018	92 developing countries	Fixed effect estimator	FD↑↓ECI
L. K. Chu (2020)	1968–2015	94 countries	SGMM	FD↑ECI
Nguyen et al. (2020)	1995–2017	52 countries	PMG_ARDL estimator, Robust pooled OLS, and PCSE	FD↑↓ECI
Ha (2023)	2011–2019	26 European economies	PCSE, FGLS, PARDL, Dynamic fixed effect	FD↑ECI
Shoufu et al. (2023)	2000–2016	194 Chinese cities	Two-step GMM and OLS	FD↑ECI
Aslam et al. (2022)	2000–2020	33 BRI participating countries	System GMM	FD↑↓≠ECI
Nguyen and Su (2021b)	2002–2017	86 countries	SGMM, FGLS, Fixed effect, Random effect, pooled OLS and robust OLS	FD↑ECI
Nguyen and Su (2021a)	1980–2014	128 countries	SGMM, FGLS, Fixed effect, Random effect, pooled OLS and robust OLS	FD↑ECI
Avom et al. (2022)	1995–2017	108 countries	SGMM, fixed and random effect estimators	FD↑ECI
Kamguia et al. (2022)	1990–2017	78 countries	SGMM, Instrumental Variable-2SLS	FD↑ECI
Kamguia et al. (2023)	2003–2016	45 African countries	SGMM and quantile regression	FD↑ECI
Njangang and Nvuh-Njoya (2023)	1982–2019	116 countries	The Sequential Linear Panel Dynamic Model	FD↑ECI
Njangang et al. (2021)	1983–2017	24 African countries	Driscoll–Kraay and SGMM	FD↑ECI
Arooj and Sajid (2022)	1990–2019	Pakistan	ARDL	FD↑ECI
Karasoy (2022)	1970–2017	Turkey	Fourier–Granger Causality Test	FD↑ECI
Atasoy (2021)	1995–2017	61 countries	SGMM and pooled OLS	FD↑ECI
Avom and Ndoya (2024)	1995–2018	118 countries	SGMM	FD↑ECI
F. M. Ajide and Osinubi (2024)	1996–2017	21 countries	PADRL and quantile regression	FD↑ECI
Ketu et al. (2022)	2003–2016	27 African countries	OLS, SGMM, and Quantile regression	FD↑ECI
Ndoya and Bakouan (2023)	1995–2018	29 African countries	OLS and SGMM	FD↑ECI
K. B. Ajide (2025)	1995–2018	32 African countries	Pooled OLS, fixed effect, random effect, and SGMM	FD↑ECI
C. O. Olaniyi and Odhiambo (2023)	1995–2020	29 African countries	SGMM, Driscoll–Kraay, and quantile regression	FD↑ECI
Low et al. (2024)	1998–2017	107 countries	SGMM	FD↑ECI
F. M. Ajide et al. (2025)	2003–2022	34 African countries	TSLS, Driscoll–Kraay, and quantile regression	FD↑ECI
Kamdem and Toukam (2025)	1995–2017	28 Sub-Saharan African countries	SGMM	FD↑ECI
Emeka (2025)	2010–2020	45 African countries	Lewbel 2TLS and SGMM	FD↑ECI
Izadi et al. (2025)	2005–2021	10 MENA countries	FMOLS	FD≠ECI
Zechlin (2025)	1996–2020	24 European Union countries	Fixed effect estimator	FD↑ECI
Maxwele (2025)	1995–2022	South Africa	FMOLS	FD↓ECI
Soumtang Bime et al. (2024)	1995–2020	28 Sub-Saharan African countries	Instrumental Variables Two-Stage Least Squares	FD↑ECI
C. O. Olaniyi and Odhiambo (2025b)	1984–2021	Nigeria	Nonlinear ARDL and FMOLS	FD↓ECI
C. O. Olaniyi and Odhiambo (2025a)	1995–2021	20 resource-rich African countries	Driscoll–Kraay, FMOLS, DCCE, and quantile regression	FD↑ECI
K. B. Ajide (2022)	1995–2018	32 African countries	Pooled OLS, fixed effect, random effect, and SGMM	FD↑ECI
Kamguia et al. (2024)	1998–2017	67 countries	OLS, Lewbel 2SLS	FD↑ECI
Osinubi et al. (2024)	1984–2019	Nigeria	Nonlinear ARDL and FMOLS	FD≠ECI
Imam et al. (2025)	1995–2019	71 developing economies	SGMM and dynamic panel threshold	FD↑ECI

FD: Financial development; ECI: economic complexity; FMOLS: fully modified least squares; OLS: ordinary least squares; TSLS: two-stage least squares; ARDL: autoregressive distributed lag; FGLS: feasible generalised least squares; PCSE: panel-corrected standard error; DCCE: dynamic common correlated effect; DOLS: dynamic ordinary least squares; MG: mean group; PMG: pooled mean group. ↑ denotes a significant positive effect; ↓ represents a significant negative effect; ≠ implies an insignificant effect; MENA: Middle East and North Africa; BRI: Belt and Road Initiative.

) reveal that no study has examined the asymmetric effects of FD on ECI, despite robust explanations for potential asymmetric features in ECI’s responsiveness to changes in FD. This approach has an edge over the existing conventional symmetric methods in the following ways.

First, the extent of asymmetric information in financial markets and institutions appears to be more robust than in other markets (Chen et al., 2020; Capasso, 2004; Stiglitz & Weiss, 1981; Leland & Pyle, 1977) because economic agents, lenders, borrowers, and investors have unequal access to information, and the likelihood of exploitation of the information gap is high. Potential borrowers may exaggerate the qualities of their projects to secure credit facilities. Detecting the actual borrower with sincere projects in these circumstances for the lender becomes costly, if not impossible. This situation may lead to adverse selection and, subsequently, moral hazard. This problem may lead to the wrong choice of borrowers for funds and result in channelling credit facilities into ECI-impeding initiatives that stunt knowledge-driven productive capacity and technological innovation to produce globally competitive exports. Thus, accounting for asymmetric phenomena is critical to examining how ECI responds to shifts in FD. Higher FD in the presence of robust asymmetric information and weak institutions may not translate to better ECI. It implies that ignoring asymmetric structures in ECI’s sensitivity to changes in FD may bias estimates and lead to incorrect policy recommendations. Also, Hatemi-J (2012) affirms that asymmetric informational phenomena in financial markets rationalise the asymmetric approach in the analysis of financial and macroeconomic variables, particularly FD and ECI.

Secondly, previous research uses symmetric methods that assume ECI is linearly and monotonically sensitive to changes in FD. This assumption is impractical because FD may exhibit a nonlinear, threshold effect on ECI (Imam et al., 2025). Thus, this study relaxes these implausible assumptions by using an asymmetric analysis. Thirdly, the asymmetric method reveals the hidden effect of FD on ECI by decomposing FD data into positive (cumulative increases in FD) and negative (cumulative decreases in FD) change components. This process breaks the limits of symmetric approaches, as it allows for differential effects of cumulative increases in FD on ECI, distinct from those of cumulative declines in FD. These processes provide deeper insights and more comprehensive analyses of FD’s impact on ECI, which prior studies have neglected. Fourth, this approach has broader policy implications and allows for more flexible policy dimensions that align with real-world realities. It provides pragmatic policy insights by allowing examination of how ECI responds to the financial sector’s expansionary policy, distinctively from its sensitivity to this sector’s contractionary policy. This policy standpoint helps test the impact of FD on ECI, accounting for real-world asymmetries and nonlinearity, thereby addressing issues surrounding ECI upgrades that are missing from existing research. Fifth, activities of financial systems are often shrouded in complex bureaucracies and involve many potential hidden manipulative moves that are not obvious to the public and regulatory institutions on the surface. Thus, an advanced asymmetric approach can unravel obscure dimensions and provide deeper insights and policy instruments regarding the sensitivity of ECI to changes in FD. Sixth, this approach provides more information and insights than symmetric methods because it allows for testing the differential impacts of the positive and negative components of FD on ECI. Hence, it provides more insightful highlights and practical policy options for utilising FD as a macroeconomic tool to drive ECI upgrades.

In light of these highlighted hints, this study adds novelty to existing research by providing an empirical inquiry into the asymmetric effect of FD on ECI, using a dataset of African countries. The choice of Africa’s economies is strategic and aligns with the study’s arguments and objectives for the following reasons: First, the extent of asymmetric information exploitation in Africa’s financial markets tends to be greater than in other continents due to a weak institutional framework guiding the financial system. The coexistence of high asymmetry in information exploitation and weak institutions in Africa may foster opportunistic behaviour and deepen rent-seeking, impairing financial intermediation and the channelisation of financial resources to productive activities, innovations, and ECI-related initiatives. Secondly, existing research documents that imperfect information and its exploitation are more severe in economies at the early stage of industrial development (Bardhan, 2000). This situation explains the case of African countries. Thus, it is more appropriate to account for asymmetries in ECI’s sensitivity to shifts in FD. Thirdly, there are mismatches and puzzles in the relationship between borrowers and lenders in Africa’s financial institutions. The World Bank’s study reveals that households and firms see finance as a significant constraint, while banks complain about an inadequate number of creditworthy borrowers (Demetriades & Fielding, 2012). This imbroglio and mismatch justify the existence of robust asymmetric information and may impede the effective channelisation of credit facilities to ECI-related initiatives in Africa. It suggests the need to examine the asymmetric effect of FD on ECI.

Fourthly, the existing research provides abundant evidence that Africa has one of the least developed financial systems in the world (C. O. Olaniyi et al., 2025; C. O. Olaniyi & Oladeji, 2021; IMF, 2016; Kuada, 2016). A few other studies also highlight the backwardness of Africa’s financial systems compared to the pace of FDs in other continents (Asante et al., 2023; Aluko & Ibrahim, 2020; Mlachila et al., 2016). These facts indicate that most African countries still operate within underdeveloped financial systems (Muoneke et al., 2023; An et al., 2021), which may have impaired financial intermediaries’ ability to mobilise and allocate resources to initiatives that can enhance ECI in Africa. The average performance (1995–2020) of broad-based financial development statistics from the IMF indicates that African countries perform below average, with a score of 0.13 on a scale of 0–1. The average across 29 sampled countries is 0.48, while 55% of all African countries score below the continent’s average (C. O. Olaniyi & Odhiambo, 2023). A study by Andrianaivo and Yartey (2010) confirms that Africa’s financial depth indicators are the lowest in the world. The state of underdevelopment and inefficiencies in Africa’s financial systems may constitute a significant drag, starving and weakening the knowledge-based productivity and sophistication of manufacturing capabilities, thereby hindering the production of an array of high-tech and complex products for export.

The existing research abounds with evidence that Africa’s productive structures and manufacturing capabilities suffer inherent deficiencies, as evidenced by weak ECI. The following highlights support the evidence of weak ECI ranking and performance in Africa: (a) Most African countries, on average, are on the negative side of the global ranking of ECI (K. B. Ajide, 2022, 2025; F. M. Ajide & Dada, 2024; Mesagan & Vo, 2024; Ketu et al., 2022). (b) Africa is the least-ranked continent in the ECI worldwide. Earlier studies have highlighted that 75% of the countries at the bottom of the global ranking of ECI are from Africa (Ogbuabor et al., 2023; Nguea et al., 2022; Olasehinde-Williams & Oshodi, 2021). This implies that Africa’s exports lack the potency to compete in the international market, as many are raw materials and products from the extractive industry with little or no sophistication. (c) A critical evaluation of ECI data from 2015 to 2023 from the Observatory of Economic Complexity reveals that none of the African countries appears among the top 50 most sophisticated economies worldwide. It suggests the extent of backwardness in knowledge intensity and technological advancement in their productive capacities, hindering the production of a chain of high-quality exports.

Unimpressive performance and ranking of African countries in ECI require further empirical scrutiny. In addition, the issues raised and discussed regarding how FD contributes to ECI in Africa are profound, given that asymmetries and nonlinearities may influence the relationship that existing studies have neglected. This study re-examines the relationship between FD and ECI in light of fundamental realities of asymmetric and nonlinear features in the socioeconomic dynamics of real-world events.

In summary, this study adds to the existing knowledge base in four distinctive ways: First, it examines how asymmetric structures and nonlinear features shape ECI’s sensitivity to changes in FD. Second, it tests the differential impacts of the financial sector’s expansionary and contractionary policies on ECI. Third, this study innovatively integrates potential asymmetries and nonlinearity into panel-data estimators that account for heterogeneity and cross-sectional dependence. This process helps explain the intricate relationship between FD and ECI, using data from African countries. Fourth, this study offers evidence-based policy recommendations for leveraging FD to enhance ECI, grounded in asymmetry and nonlinearity.

Following this introductory section, we structure the remaining sections of this study as follows: Section 2 reviews the theoretical and empirical literature, while Section 3 addresses data description, sources, and methodological procedures. Empirical analyses and the discussion of findings are the primary focus of Section 4. Section 5 presents the summary, conclusion and the study’s policy suggestions. The last section identifies the study’s limitations and offers suggestions for future research.

2. Literature Review

2.1. Theoretical Insights

This study contextualises its theoretical framework in two major theories. The finance theory of innovation explains the symmetric effect of FD on ECI, while its asymmetric effect stems from the theory of markets under imperfect information. The finance theory of innovation draws its foundation from the supply-leading hypothesis, credited to Patrick (1966) and Schumpeter (1911), and which evolved as an offshoot of endogenous growth theory. This theory posits that an efficient and well-functioning financial system mobilises and channels financial resources to support innovation, knowledge productivity, technological advancement, and other initiatives that enhance an economy’s capacity to manufacture a chain of sophisticated and competitive products for export. FD provides financing and other technical support to build a country’s investment in research and development, entrepreneurial inclinations, technology, innovation, human capital development, high-tech infrastructure, and other initiatives that are essential to stimulating upgrades and improvements in key components of ECI.

Moreover, the theory of imperfect information in markets (Bardhan, 2000) is a perfect fit for explaining the link between FD and ECI. Financial markets and institutions are shrouded in robust evidence of information asymmetry, with a high likelihood of its exploitation by stakeholders. These entrenched issues often come with problems of adverse selection and moral hazard. Lack of symmetrical access to information between borrowers and lenders may lead to the misallocation of credit facilities to unproductive initiatives, thereby impeding the ECI-enhancing benefits of FD. ECI-related initiatives may suffer, as further FD may not spur ECI expansion due to the high propensity to exploit information asymmetry, given its associated problems of adverse selection and moral hazard. Borrowers may divert credit facilities obtained from financial systems to ECI-inhibiting initiatives. The theory is particularly relevant in the case of African countries, as exploitation of asymmetric information is high in countries at an early stage of industrial development (Bardhan, 2000). Thus, the supposition of symmetry and linearity may be overly simplistic to capture the complex relationship between FD and ECI as posited in existing research.

2.2. Empirical Evidence

Studies continue to emerge on the critical role of financial development (FD) in spurring economic complexity (ECI), yet their findings are mixed and conflicting. Table 1 provides robust information and explanations regarding the scope, years covered, methodologies, and findings of the previous study on the role of FD in ECI. The predominant strand of the literature indicates that FD positively stimulates ECI. Only a few establish an adverse impact of FD on ECI, while the less predominant ones find an insignificant role. There are very few studies that report mixed findings on the adverse and beneficial roles of FD in ECI. Our critical reviews, examinations, and evaluations of these studies highlight some key points. These exercises show the strengths and deficiencies of the existing knowledge space. This study highlights that all previous research on the sensitivity of ECI to changes in FD has premised its studies and analyses on the assumptions of symmetry and linearity. First, the assumption of monotonic, linear, and symmetric responses does not align with real-world evidence indicating asymmetric structures and nonlinearities in the socioeconomic dynamics underlying the distribution of FD and ECI data. Secondly, the conventional symmetric approaches captured in the existing research neglect the asymmetric informational phenomenon in financial markets. Neglecting this aspect may bias estimates and lead to incorrect policy recommendations due to information gaps between borrowers and lenders, which create adverse selection and moral hazard that may undermine the efficient allocation of financial resources to ECI-related initiatives. Hence, symmetric arguments and analytical approaches may be inadequate to explain ECI’s complex responsiveness to shifts in FD.

Thirdly, symmetric approaches overlook real-world-driven theoretical applications, as they neglect the asymmetric information phenomenon in financial transactions and data. The prevailing theoretical arguments and analytical methods in existing research wave aside the issues of asymmetry and information gap. Fourthly, asymmetric perspectives provide more flexible and insightful estimates and policy recommendations. The approach decomposes the FD data into positive (expansionary policy) and negative (contractionary policy) change components. This process allows for the differential impacts of the financial sector’s policies on ECI. Hence, it provides more comprehensive estimates and insights that explain deep issues in the dynamics of FD and ECI. This study has an edge over existing studies that focus on the prevailing symmetric and linear impacts of FD on ECI.

3. Methodological Procedures, Data Sources and Descriptions Related to Issues

3.1. Data Descriptions and Sources

This study uses data from thirty African countries spanning 1995 to 2023. Data availability determines the scope and number of countries included. It excludes countries with insufficient data. Meanwhile, the sample size is representative and captures all sub-regions of Africa.

Table 2. Descriptions of variables, symbols, and data sources.

Variables	Symbols	Descriptions	Sources
Economic complexity index	eci	Economic complexity index	MIT Media Lab’s Observatory of Economic Complexity
Financial development index	fd	Financial development index	IMF
Total natural resource rents	nrr	Total natural resources rents (% of GDP)	WDI
Corruption control (0–6)	inst	Corruption control	ICRG
GDP per capita	rgdpp	GDP per capita (constant 2015 US$)	WDI
Foreign direct investment inflow	fdi	Foreign direct investment, net inflows (% of GDP)	WDI
Foreign aid	fa	Net ODA received (% of GNI)	WDI

IMF: International Monetary Fund; ICRG: International Country Risk Guide; WDI: World Development Indicator; MIT: Massachusetts Institute of Technology. The financial development adopted is the broad-based indicator that contains the access, depth, and efficiency of financial markets and institutions from the IMF financial statistics database. This study takes natural logarithms of rgdpp to deal with outliers and heteroscedasticity in the data spreads. Corruption control (0–6) is used as a measure of institutional quality.

provides information on data descriptions and sources. This study obtains data from the World Bank’s World Development Indicators (WDI), the International Country Risk Guide (ICRG), the International Monetary Fund (financial statistics database), and the Observatory of Economic Complexity, collated by the MIT Media Lab (see Table 2 for detailed information). This study utilises a comprehensive indicator of financial development sourced from the International Monetary Fund (IMF) financial statistics database. This index is preferred to others because it captures all dimensions of an economy’s overall financial system performance, including both financial markets and institutions. Consequently, it is robust and thorough. The index reflects the efficiency, depth, and accessibility of financial markets and institutions in allocating resources to productive and innovative initiatives. Also, the Observatory of Economic Complexity, an arm of the Massachusetts Institute of Technology (MIT) Media Lab, provides a robust and improved index of economic complexity (https://oec.world/en/rankings/eci/hs6/hs96) (accessed on 12 December 2025). This dataset uses information from international trade transactions, reflecting the quality of exports and the sophisticated knowledge and technological advancement that underpin them. The index transcends the ordinary level of knowledge intensity and extends beyond growth-related data to capture the core development focuses. Hence, this study relies on this dataset for the economic complexity index data of African countries. This study utilises corruption control as a metric for assessing the role of institutional quality, aligning with existing research (C. O. Olaniyi & Odhiambo, 2023, 2025a, 2025b). This measurement is particularly crucial for African countries, as many of these economies perform below average. Effective corruption control is essential to the formulation and implementation of policies to promote the Economic Complexity Index (ECI).

3.2. Modelling Procedures

This study follows the modelling strategies of previous studies (Nguyen et al., 2020; Avom et al., 2022; Nguyen & Su, 2021b; C. O. Olaniyi & Odhiambo, 2023, 2025a, 2025b; K. B. Ajide, 2022; Kamguia et al., 2022; and others), with augmentations and modifications to address the study’s novelty. We specify the study’s model as follows:

$${eci}_{it}={\beta }_0+ {\beta }_1{fd}_{it}+{\beta }_2{rgdpp}_{it}+{\beta }_3{inst}_{it}+{\beta }_4{fdi}_{it}+{\beta }_5{nrr}_{it}+{\beta }_6{fa}_{it}+{\epsilon }_{it}$$

(1)

where $eci, fd, rgdpp, inst, fdi, nrr, \mathrm{a}\mathrm{n}\mathrm{d}fa$ are economic complexity, financial development, real GDP per capita, institutional quality (proxied by corruption control), foreign direct investment, natural resource rents, and foreign aid, respectively. $i \mathrm{a}\mathrm{n}\mathrm{d}t$ are cross-sectional units and the time dimension, respectively. The parameters to explain the contribution of each variable to the dependent variable, eci, are ${\beta }_1, \dots , {\beta }_6$. The stochastic error is $\epsilon$ and ${\beta }_0$ is the shift parameter. Following the theoretical expositions in previous studies that examines the determinants of ECI, this study chooses control variables as follows: real GDP per capita (Njangang & Nvuh-Njoya, 2023; Nguyen & Su, 2021a; Avom & Ndoya, 2024), institutional quality (C. O. Olaniyi & Odhiambo, 2023, 2025a; Mini et al., 2025; F. M. Ajide et al., 2025), natural resource rents (K. L. Chu, 2023; Njangang & Nvuh-Njoya, 2023; Avom & Ndoya, 2024; Ketu et al., 2022), foreign direct investment (Nguyen & Su, 2021b; Neagu et al., 2022; C. O. Olaniyi & Odhiambo, 2025a), foreign aid (Arpaci-Ayhan, 2023; Ogbuabor et al., 2023; C. O. Olaniyi & Odhiambo, 2025a). These control variables are consistent with the factors identified in existing studies as influencing ECI and may also influence the asymmetric sensitivity of ECI to changes in FD.

The expressions in Equation (1) only explain the symmetric effect of FD on ECI. To capture the study’s idea of the asymmetric effect, we decompose the FD data into positive and negative change components. The data decomposition procedures follow the influential works of Hatemi-J (2014) and Granger and Yoon (2002) and are as follows:

$${fd}_{it}= {fd}_{i0}+{fd}_{it}^{+}+{fd}_{it}^{-}$$

(2)

where ${fd}^{+}$ and ${fd}^{-}$ represent partial cumulation sums of the positive and negative components of $fd$, respectively. ${fd}_{it}^{+}$ represents the cumulative expansionary policy of the financial sector and ${fd}_{it}^{-}$ denotes the cumulative contractionary policy of the financial sector in terms of depth, access, and efficiency of financial markets and institutions.

$${fd}_{it}^{+}={\displaystyle \sum _{j=1}^t}∆{fd}_{i,j}^{+}= {\displaystyle \sum _{j=1}^t}\max \left(∆{fd}_{i,j}, 0\right),$$

(3)

$${fd}_{it}^{-}={\displaystyle \sum _{j=1}^t}∆{fd}_{i,j}^{-}= {\displaystyle \sum _{j=1}^t}\min \left(∆{fd}_{i,j}, 0\right)$$

(4)

To account for nonlinearity and asymmetry in the sensitivity of ECI to changes in FD $({fd}^{+}, {fd}^{-})$, the constructed components, ${fd}_{it}^{+}$ and ${fd}_{it}^{-}$, replace ${fd}_{it}$. Hence, we respecify Equation (1) as follows:

$${eci}_{it}={\beta }_0+ {\theta }_1{fd}_{it}^{+}+{\theta }_2{fd}_{it}^{-}+{\beta }_2{rgdpp}_{it}+{\beta }_3{inst}_{it}+{\beta }_4{fdi}_{it}+{\beta }_5{nrr}_{it}+{\beta }_6{fa}_{it}+{\epsilon }_{it}.$$

(5)

The interpretations of asymmetric effects of FD on ECI depend on the statistical properties, magnitudes, and signs of the coefficients of ${\theta }_1$ and ${\theta }_2$ in Equation (5). Following previous studies (C. Olaniyi, 2019; Shin et al., 2014; Hatemi-J, 2012), this study uses Wald diagnostic tests to test whether the two parameters differ significantly. An asymmetric effect subsists if they are significantly different; otherwise, a symmetric effect prevails. This study expresses the Wald test null and alternative hypotheses as follows:

$$H_0: {\theta }_1={\theta }_2=0$$

(6)

$$H_0: {\theta }_1={\theta }_2\ne 0.$$

(7)

This study augments the model with control variables in line with extant studies. The existing works guide the choice of these variables, which examine the effect of FD on ECI (Nguyen et al., 2020; Avom et al., 2022; Nguyen & Su, 2021b; C. O. Olaniyi & Odhiambo, 2023, 2025a, 2025b; K. B. Ajide, 2022; Kamguia et al., 2022; and others).

3.3. Estimation Strategies

To account for cross-sectional dependence and spatial correlation, which are prevalent in panel data analysis (Acheampong et al., 2021), this study uses a robust standard-error regression developed by Driscoll and Kraay (1998). Also, to address endogeneity, omitted-variable bias, simultaneity bias, and measurement error, this study employs the instrumental variable estimator, namely, Lewbel’s two-stage least squares (2SLS) (Lewbel, 2012). This approach is widely used in the extant literature to address endogeneity and potential reversal causality (F. M. Ajide et al., 2025). This method is the most appropriate when external instruments are unavailable or weak. Thus, we use the Hansen J-Statistic to determine instrument validity. To deliver comprehensive estimates, we also use a feasible generalised least squares (FGLS) estimator to address heteroscedasticity, autocorrelation, and heterogeneity (Dimnwobi et al., 2026; Hoechle, 2007). This approach is important because African countries differ in the development of their financial markets and institutions, as well as in their levels of economic complexity. Hence, heterogeneity is a key issue of concern. In addition to the mean-based estimators highlighted above, this study uses method-of-moments quantile regression to account for conditional and distributional heterogeneity and to assess the flexibility with which FD and other control variables explain ECI in Africa. We present the method as follows:

$$Q_{{eci}_{it}}\left({\tau }_j/{fd}_{it}, X_{it}\right)={\beta }_1\left({\tau }_j\right){fd}_{it}+X^{\prime }\delta \left({\tau }_i\right)+{\varnothing }_i\left({\tau }_j\right)+U_t\left({\tau }_j\right), {\tau }_j\in \left(0,1\right).$$

(8)

$fd\left({\tau }_j/fd,X_{it}\right)$ is the ${\tau }_j$ conditional distribution effect on eci, while a vector of explanatory variables is denoted by $X_{it}$. The scale and location variant estimates are the normal basis for the estimated coefficients. The time-specific effects and unobserved cross-sectional identities are $U_t$ and ${\varnothing }_i$, respectively. The method-of-moment quantile regression captures nonlinearity in the effect of financial development on economic complexity by accounting for distributional and heterogeneous effects across quantiles. The approach generates deeper and more convincing information that addresses outliers, heterogeneity, and non-normality in panel data (C. O. Olaniyi & Odhiambo, 2023). It is equally superior to the fixed-effect variant of quantile regression developed by Koenker and Bassett (1978), because it produces non-crossing estimates in quantile regression analysis (C. O. Olaniyi & Odhiambo, 2025a). It also allows for scale and location effects in quantile regression.

4. Preliminary Analyses, Primary Findings, and Discussions

4.1. Preliminary Test and Data Properties

Table 3. Descriptive statistics.

	eci	fd	lrgdpp	inst	fdi	nrr	fa
Mean	−0.910	0.157	7.306	2.138	3.139	12.432	5.224
Maximum	0.465	0.593	9.526	5.000	40.167	66.060	62.187
Minimum	−2.552	0.004	5.426	0.000	−17.292	0.195	−0.188
Standard Deviation	0.519	0.109	0.930	0.754	4.830	11.996	5.529
Coefficient of variation (%)	−56.997	69.387	12.732	35.258	153.884	96.499	105.828
Observations	840	840	840	840	840	840	840

presents descriptive statistics, including coefficients of variation, means, and standard deviations for variables such as FDI, FA, NRR, FD, and ECI, indicating that mean-based regressions may not be perfectly reliable. Thus, they have to be augmented by quantile regression via moments. The minimum and maximum values of eci indicate that South Africa had the highest value (0.465) in 2002, while Nigeria had the lowest (−2.552). Also, financial development (FD) data indicate that South Africa had the highest (0.593) in 2019, while the Democratic Republic of Congo had the lowest (0.004) in 1999.

Table 4. Correlation matrix.

	eci	fd	lrgdpp	ins	fdi	nrr	fa
eci	1.000
fd	0.547	1.000
lrgdpp	0.291	0.568	1.000
inst	0.348	0.315	0.090	1.000
fdi	−0.084	−0.027	−0.081	−0.050	1.000
nrr	−0.410	−0.326	0.199	−0.268	0.100	1.000
fa	−0.155	−0.395	−0.688	0.059	0.157	−0.035	1.000

and

Table 5. Variance inflation factor.

Variable	VIF	$\sqrt{\mathit{V}\mathit{I}\mathit{F}}$	Tolerance	${\mathit{R}}^2$
lrgdpp	3.150	1.775	0.318	0.682
fd	2.220	1.490	0.451	0.549
fa	2.080	1.442	0.482	0.518
nrr	1.600	1.265	0.625	0.375
cc	1.210	1.100	0.829	0.171
fdi	1.050	1.025	0.952	0.048
Mean VIF	1.88

highlight the results of the correlation matrix and variance inflation factor (VIF), respectively. The coefficients of the correlation matrix indicate no evidence of severe multicollinearity. According to the literature, multicollinearity is considered a significant issue when the correlation coefficient exceeds 0.8 (Gujarati & Porter, 2010; Komlos, 2026; Siegel & Wagner, 2016). In our study, all coefficients remain below 0.7, suggesting that multicollinearity does not pose a significant threat to our empirical analysis. The VIF coefficients and tolerance coefficients are within the conventional thresholds. VIF coefficients are below 5, while all tolerance estimates exceed 0.1 (Miles, 2014; Studenmund, 2011). Hence, collinearity does not constitute a severe threat to this study’s regressions and estimates. The pairs of variables and the regressions are free of collinearity.

To be consistent with the existing literature,

Table 6. Cross-sectional dependence tests.

Tests	eci	fd	lrgdpp	inst	fdi	nrr	fa
Breusch–Pagan LM	2332.204 ***	3109.830 ***	6584.564 ***	2201.348 ***	895.240 ***	2136.832 ***	1631.557 ***
Pesaran scaled LM	64.321 ***	90.685 ***	208.490 ***	59.885 ***	15.6048 ***	57.697 ***	40.567 ***
Bias-corrected scaled LM	63.766 ***	90.130 ***	207.934 ***	59.329 ***	15.048 ***	57.142 ***	40.012 ***
Pesaran CD	0.575	40.195 ***	52.012 ***	17.398 ***	9.322 ***	23.985 ***	13.599 ***

Note: *** stands for 1 percent level of significance.

provides the findings of the cross-sectional dependence (CD) test. These results affirm robust evidence of interdependence among African countries. Hence, estimators that account for CD are very important to deliver robust and efficient estimates. Also,

Table 7. Pesaran and Yamagata’s (2008) slope homogeneity test.

Models	$\widetilde{∆}$	${\widetilde{∆}}_{\mathit{a}\mathit{d}\mathit{j}}$
$\mathrm{e}\mathrm{c}\mathrm{i}=\mathrm{f}(\mathrm{f}\mathrm{d},\mathrm{l}\mathrm{r}\mathrm{g}\mathrm{d}\mathrm{p}\mathrm{p},\mathrm{i}\mathrm{n}\mathrm{s}\mathrm{t},\mathrm{f}\mathrm{d}\mathrm{i},\mathrm{n}\mathrm{r}\mathrm{r},\mathrm{f}\mathrm{a})$	−39.782 ***	−22.067 ***
$\mathrm{e}\mathrm{c}\mathrm{i}=\mathrm{f}\left({\mathrm{f}\mathrm{d}}^{+},{\mathrm{f}\mathrm{d}}^{-},\mathrm{l}\mathrm{r}\mathrm{g}\mathrm{d}\mathrm{p}\mathrm{p},\mathrm{i}\mathrm{n}\mathrm{s}\mathrm{t},\mathrm{f}\mathrm{d}\mathrm{i},\mathrm{n}\mathrm{r}\mathrm{r},\mathrm{f}\mathrm{a}\right)$	−35.254 ***	−23.503 ***

Note: *** represents a 1 percent level of significance. $\widetilde{∆}$ is the delta and ${\widetilde{∆}}_{adj}$ is the adjusted delta.

presents the findings from Pesaran and Yamagata’s (2008) slope homogeneity test. The results invalidate the assumption of slope homogeneity and support the existence of slope heterogeneity. This finding affirms the necessity of accounting for heterogeneity in the analysis of how eci responds to changes in fd.

4.2. Discussion and Findings

The findings of four estimators, such as Driscoll–Kraay, feasible generalised least squares, Lewbel IV-2SLS, and quantile regression (moment variant), are in two phases in

Table 8. Results of mean-based regressions (Symmetric effect of fd on eci).

Dependent Variable: eci (Economic Complexity)
Variables	Driscoll–Kraay	FGLS	Lewbel 2SLS (IV Estimator)
fd	1.469 ***	1.453 ***	1.594 ***
	(0.000)	(0.000)	(0.000)
lrgdpp	0.139 ***	0.141 ***	0.107
	(0.000)	(0.000)	(0.104)
inst	0.093 ***	0.092 ***	0.095 ***
	(0.000)	(0.000)	(0.000)
fdi	−0.004	−0.004 ***	−0.004
	(0.142)	(0.000)	(0.183)
nrr	−0.014 ***	−0.014 ***	−0.013 ***
	(0.000)	(0.000)	(0.000)
fa	0.012 ***	0.012 ***	0.009
	(0.008)	(0.000)	(0.146)
constant	−2.234 ***	−2.243 ***	−2.025 ***
	(0.000)	(0.000)	(0.000)
Hansen J-Statistic	na	na	0.114
No of Group	30	30	30
Obs	840	840	840

Note: *** denotes 1% level of significance. Values in () are p-values. FGLS: Feasible generalised least squares; 2SLS: two-stage least squares; na: not available. Source: Author’s computations.

,

Table 9. Results of mean-based regressions (Asymmetric effect of fd on eci).

Dependent Variable: eci (Economic Complexity)
Variables	Driscoll–Kraay	FGLS	Lewbel 2SLS (IV Estimation)
${\mathrm{f}\mathrm{d}}^{+}$	1.406 ***	1.395 ***	1.631 ***
	(0.000)	(0.000)	(0.000)
${\mathrm{f}\mathrm{d}}^{-}$	1.477 ***	1.449 ***	1.729 ***
	(0.001)	(0.000)	(0.005)
rgdpp	0.208 ***	0.209 ***	0.158 ***
	(0.000)	(0.000)	(0.009)
inst	0.110 ***	0.109 ***	0.117 ***
	(0.000)	(0.000)	(0.000)
fdi	−0.004 *	−0.003 ***	−0.004
	(0.060)	(0.000)	(0.183)
nrr	−0.017 ***	−0.017 ***	−0.016 ***
	(0.000)	(0.000)	(0.000)
fa	0.012 **	0.012 ***	0.007
	(0.015)	(0.000)	(0.324)
constant	−2.538 ***	−2.547 ***	−2.180 ***
	(0.000)	(0.000)	(0.000)
Asymmetric effect:
${\mathrm{f}\mathrm{d}}^{+}={\mathrm{f}\mathrm{d}}^{-}=0$	25.570 ***	4334.520 ***	13.610 ***
	(0.000)	(0.000)	(0.001)
Hansen J-Statistic	na	na	0.122
No of Group	30	30	30
Obs	840	840	840

Note: ***, **, and * denote 1, 5, and 10 percent levels of significance, respectively. Values in () are p-values. FGLS: Feasible generalised least squares; 2SLS: two-stage least squares; na: not available. Source: Author’s computations.

,

Table 10. Method-of-moment quantile regressions (symmetric effect of fd on eci).

Dependent Variable: eci (Economic Complexity)
Variables	Location	Scale	Quantiles
			Q.10	Q.25	Q.50	Q.75	Q.95
fd	1.469 ***	0.204 **	1.149 ***	1.298 ***	1.478 ***	1.647 ***	1.868 ***
	(0.000)	(0.044)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
rgdpp	0.139 ***	0.005	0.131 ***	0.135 ***	0.139 ***	0.142 ***	0.147 ***
	(0.000)	(0.789)	(0.003)	(0.000)	(0.000)	(0.000)	(0.000)
inst	0.093 ***	−0.034 ***	0.147 ***	0.122 ***	0.091 ***	0.063 ***	0.026
	(0.000)	(0.003)	(0.000)	(0.000)	(0.000)	(0.002)	(0.333)
fdi	−0.004	−0.003 **	0.001	−0.001	−0.004	−0.007 **	−0.010 ***
	(0.128)	(0.017)	(0.724)	(0.648)	(0.116)	(0.023)	(0.009)
nrr	−0.014 ***	0.002 ***	−0.017 ***	−0.016 ***	−0.013 ***	−0.012 ***	−0.009 ***
	(0.000)	(0.008)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
fa	0.0112 ***	−0.006 **	0.021 ***	0.017 ***	0.011 ***	0.006 **	−0.0002
	(0.001)	(0.020)	(0.002)	(0.001)	(0.001)	(0.031)	(0.963)
constant	−2.232 ***	0.342 ***	−2.768 ***	−2.519 ***	−2.216 ***	−1.933 ***	−1.563 ***
	(0.000)	(0.006)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)

Note: *** and ** represent 1 and 5 percent levels of significance, respectively. Figures in () are probability values. Source: Author computations.

and

Table 11. Method-of-moment quantile regressions (asymmetric effect of fd on eci).

Dependent Variable: eci (Economic Complexity)
Variables	Location	Scale	Quantiles
			Q.10	Q.25	Q.50	Q.75	Q.95
${\mathrm{f}\mathrm{d}}^{+}$	1.406 ***	0.567 ***	0.519	0.931 ***	1.427 ***	1.903 ***	2.497 ***
	(0.000)	(0.000)	(0.134)	(0.002)	(0.000)	(0.000)	(0.000)
${\mathrm{f}\mathrm{d}}^{-}$	1.477 ***	1.036 ***	−0.143	0.609	1.516 ***	2.384 ***	3.469 ***
	(0.001)	(0.000)	(0.805)	(0.209)	(0.001)	(0.000)	(0.000)
rgdpp	0.208 ***	0.007	0.196 ***	0.201 ***	0.208 ***	0.214 ***	0.222 ***
	(0.000)	(0.638)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
inst	0.110 ***	−0.044 ***	0.179 ***	0.147 ***	0.108 ***	0.071 ***	0.025
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.002)	(0.411)
fdi	−0.004	−0.005 ***	0.004	0.0004	−0.004	−0.008 ***	−0.013 ***
	(0.144)	(0.000)	(0.261)	(0.898)	(0.127)	(0.004)	(0.000)
nrr	−0.017 ***	0.002 ***	−0.021 ***	−0.019 ***	−0.017 ***	−0.015 ***	−0.012 ***
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
fa	0.012 ***	−0.006 **	0.022 ***	0.017 ***	0.012 ***	0.006 **	−0.00004
	(0.002)	(0.022)	(0.003)	(0.002)	(0.002)	(0.045)	(0.992)
constant	−2.538 ***	0.399 ***	−3.162 ***	−2.873 ***	−2.523 ***	−2.189 ***	−1.771 ***
	(0.000)	(0.001)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)

Note: *** and ** represent 1 and 5 percent levels of significance, respectively. Figures in () are probability values. Source: Author computations.

, namely symmetric and asymmetric dimensions. The data in Table 9 and Table 11 consider the asymmetric effect of FD on ECI, while those in Table 8 and Table 10 focus on the symmetric effect. Hansen-J-Statistics confirm the instruments’ validity in all regressions of Lewbel IV-estimation (see Table 8 and Table 9).

All variants of linear regression (mean- and moment-based, see Table 8 and Table 10) indicate that the FD (financial development) coefficients are significant and positive. These findings validate the position of theories and most empirical work that emphasise the contribution of a well-performing, efficient financial system to enhancing economic complexity, ECI. It implies that African countries’ financial markets and institutions provide financial resources and other support that promote ECI-related initiatives, such as investment in innovative industries, R&D, the knowledge economy, technological advancement, human capital development, and entrepreneurial activities. It suggests that as Africa’s FD improves, ECI benefits as firms, the manufacturing sector, the export sector, and innovative industries attract greater financial support and other technical stimuli from the financial sector, enabling them to adopt more sophisticated production techniques and to manufacture high-tech, globally competitive exports. Hence, African countries should incentivise the financial system to strategically and deliberately pursue financial development, allocating resources more effectively to fund innovative initiatives, increase knowledge intensity, and build a sophisticated manufacturing skill set to engender the production of high-tech, high-quality, competitive products for export. Quantile regression via moments yields more interesting results. The coefficients of FD increase in magnitude and significance as we move from the lower to the upper quantiles (see Table 10). It indicates that as African countries progress from lower to higher levels of financial development (FD), their contributions favour countries with higher levels of the economic complexity index (ECI). Consequently, the impact of FD becomes more beneficial, acting as a catalyst for improvements in ECI in countries with a more sophisticated productive structure than those with lower ECI levels. This finding confirms FD’s influence on ECI in Africa, revealing heterogeneous effects across quantiles. These findings are consistent with the studies’ outcomes of L. K. Chu (2020), Nguyen and Su (2021a, 2021b), Avom et al. (2022), Avom and Ndoya (2024), Njangang and Nvuh-Njoya (2023), and C. O. Olaniyi and Odhiambo (2025a), which establish an increasing effect of FD on ECI. Meanwhile, it is incongruent with the studies by C. O. Olaniyi and Odhiambo (2025b) and Maxwele (2025), which find an adverse effect of FD on ECI.

Consistent with the study’s novelty, the results in Table 9 reveal that FD, financial development, has an asymmetric effect $\left({fd}^{+}\ne {fd}^{-}\right)$ on ECI, economic complexity. These findings are robust, as Driscoll–Kraay, FGLS, and Lewbel’s IV-2SLS estimators confirm that positive (expansionary) and negative (contractionary) shocks in FD have differential impacts on ECI. The Wald test confirms the presence of asymmetry in ECI’s sensitivity to changes in FD. These findings invalidate the prevalence of symmetric, linear effects of FD on ECI reported in existing studies. Prior studies may have exaggerated their empirical analyses and outcomes by failing to account for asymmetry. These insights provide new dimensions of empirical outcomes with deeper and more flexible policy implications that are consistent with real-world fundamentals. The coefficients of both positive and negative change components of FD are positive and significant. It implies that the two components enhance ECI in African countries. Thus, both expansionary and contractionary policies of the African financial sector enhance the productive knowledge and technological capabilities inherent in the productive structure and manufacturing capacities, enabling it to produce sophisticated, high-tech, high-quality, and globally competitive exports. Hence, the two policy dimensions of Africa’s financial systems have significant benefits that enhance economic complexity. These beneficial effects may have some technical interpretations.

On one side of the coin, Africa’s financial sector’s expansionary policies (positive change components of FD) imply that cumulative increases in the allocation of financial resources from financial systems to ECI-related initiatives metamorphose into persistent increases in knowledge intensity, technological capabilities, and sophisticated manufacturing skill sets, thereby spurring the production of a chain of high-quality, high-tech products for exports. Hence, cumulative increases in channelling credit facilities and other technical support from Africa’s financial sector to economic complexity-related initiatives translate into enhancements in productive knowledge and manufacturing capabilities, thereby producing a chain of competitive, high-quality exports. The quantile regression highlights heterogeneous effects: the coefficient for a positive shock to fd is insignificant for countries in quantile 10 (Q.10); however, this becomes significant from quantile 25. The significance increases as we move to higher quantiles. Thus, African countries in the upper quantiles enjoy greater benefits from higher financial development than countries in the lower quantiles. These varied findings suggest that a country’s ability to reap the ECI-enhancing benefits of financial development depends on the quantile it belongs to. Therefore, African nations need to coordinate improvements in their ECI with the ongoing development of their financial systems. This strategic planning is necessary to ensure higher levels of productive knowledge and capacity.

On the other side of the coin, the cumulative decline in credit facilities and other incentives (negative change components of the financial development or contractionary policy of the financial sector) for certain activities leads to expansion in ECI in Africa. These findings reveal that cumulative reductions in Africa’s financial sector’s credit facilities to unproductive or economic complexity-impeding initiatives make more funding available to finance and promote ECI-enhancing initiatives. Thus, strategic and technical adoption and use of contractionary policy by Africa’s financial sectors tend to set the pace and pathway for economic complexity upgrades by phasing out financial sectors’ channelisation of resources into knowledge-killing, technology-harming, innovation-impeding, and unproductive activities in these countries. These processes make more financial resources and technical support available for ECI upgrades, enabling cumulative expansion in economic complexity in the African context, riddled with weak institutions and sharp practices in financial sector dealings. This study has opened and established new insights into how ECI responds to changes in fd by accounting for asymmetries and nonlinearities, which are too obvious to ignore in the operations of Africa’s financial systems. Quantile regression via moments indicates that the contribution of financial development to ECI initiatives is insignificant in African countries at the lower quantiles (10 and 25). Meanwhile, the effects are significant from the middle quantile onward. The statistical properties of the coefficients increase as we move toward the upper quantiles (see Table 11). These findings indicate that African countries with more sophisticated productive structures and higher technical manufacturing capabilities benefit more from further development of the financial system than countries with less economic complexity. These results validate the argument for heterogeneous and conditional distributive effects of FD on ECI, warranting the adoption of quantile regression via the moment approach. Having analysed and interpreted the main findings of the study, we shift attention to provide concise interpretations of control variables.

The coefficient on real income per capita (measured by real GDP per capita) is significant and positive in all variants of the estimators, except in Lewbel’s IV approach, where it is insignificant. These findings imply that higher income in African countries provides greater leverage and resources to finance ECI initiatives that spur more diversified production processes and enhance manufacturing capabilities and knowledge skills, generating a chain of high-quality, high-technology-driven products for export. It suggests that higher income promotes innovative capacity, increases knowledge productivity, advances technology, enhances manufacturing acumen, and shifts people’s preferences and tastes toward more diversified, higher-quality products (C. O. Olaniyi & Odhiambo, 2023, 2025a). These findings reinforce the research outcomes of studies such as Njangang and Nvuh-Njoya (2023), Nguyen and Su (2021b), and Avom and Ndoya (2024). The coefficients for institutional quality (proxied by corruption control) are also positive and significant in most estimators, except at quantile 95 in the quantile regressions. We observe that institutional quality promotes ECI initiatives in the lower quantiles than in the upper quantiles. This finding suggests that African countries in the lower quantiles need stronger institutions than those in the upper quantiles. Building strong and efficient institutions is instrumental to upgrading economic complexity in Africa by promoting the efficient allocation of resources, encouraging firms and stakeholders in the manufacturing sector, incentivising innovative entrepreneurs, and raising awareness of the need to transition from primitive to sophisticated production techniques. These results affirm that building strategic mechanisms to curb corruption in Africa is a key part of developmental plans to improve innovative skills, knowledge intensity, and technological capabilities in manufacturing components to produce globally competitive exports. Our empirical outcomes align with the studies of C. O. Olaniyi and Odhiambo (2025a), Mini et al. (2025), and F. M. Ajide et al. (2025).

The coefficients on natural resource rents (nrr) are consistently negative and significant across all regression variants. These results support the resource curse theory. Resource incomes in African countries constitute impediments to upgrades in ECI initiatives. Thus, consistent flows of resource income may have discouraged stakeholders, policymakers, and governments in African countries from making massive investments in innovative industries, technological capabilities, knowledge intensity, and manufacturing sophistication. They regard resource incomes as free money and manna from heaven that cannot be exhausted, so they see no reason to diversify their economies or reduce their reliance on resources by expanding economic complexity. These results align with studies by K. L. Chu (2023), Njangang and Nvuh-Njoya (2023), Avom and Ndoya (2024), and Ketu et al. (2022). Similarly, the coefficient on foreign direct investment (FDI) is negative and significant in most regressions. FDI inflows to Africa constitute a drag on ECI upgrades. It implies that FDI inflows to Africa make knowledge-based manufacturing capabilities worse off. Thus, FDI inflows weaken technological sophistication and knowledge intensity inherent in the productive structure of a chain of high-tech exports. It highlights that FDI inflows compete with domestic-based knowledge productivity rather than complementing it. This finding may suggest that foreign investors’ focus on exploiting Africa’s market size to profit without adding value to the knowledge intensity and technological components of production systems.

Thus, benefits such as positive externalities, technology transfers, enhanced managerial skills, and other benefits associated with FDI inflows to help facilitate economic diversification and empower Africa’s innovation industries to attain global competitiveness are insignificant in facilitating ECI upgrades. Africa’s countries need to design appropriate institutional support to attract the right FDI that could spur upgrades to ECI-related initiatives. These findings align with the studies by Nguyen and Su (2021b), Neagu et al. (2022), and C. O. Olaniyi and Odhiambo (2025a), which show that FDI inflows weaken economic complexity. Most estimators indicate that foreign aid promotes economic complexity in African countries. These results highlight that most foreign aid flows to initiatives and incentives that spur increases in knowledge productivity, enhance technological sophistication, and facilitate the transition to advanced production techniques in the manufacturing sector to produce quality exportable goods. These findings show that African countries should channel foreign aid to core initiatives such as education, research and development, the knowledge economy, high-powered infrastructure, human capital development, and programmes that enhance ECI-related initiatives. These countries should devise stringent institutional architectures to ensure effective administration and channelisation of foreign aid to core areas that can increase the amount of technological capabilities and productive knowledge inherent in manufacturing components, thereby producing a chain of high-tech, high-quality products for exports. These results are consistent with those of Arpaci-Ayhan (2023) and C. O. Olaniyi and Odhiambo (2025a). These findings highlight that factors such as financial development, corruption control, real income, and foreign aid are instrumental to ECI upgrading initiatives and efforts in African countries. Hence, they are strategic in helping analysts, policymakers, and stakeholders advance knowledge-driven, productive capabilities and technological acumen across production systems and manufacturing sectors.

5. Summary, Conclusions, and Policy Implications

This study contributes to the global discussion by providing the first empirical inquiry into the asymmetric sensitivity of economic complexity (ECI) to changes in financial development (FD). This study’s argument and novelty are grounded in robust asymmetries and nonlinearities in the dynamics of FD data. Most of the operations and transactions in financial markets and institutions in economies with weak institutions and at an early stage of development are shrouded in information gaps among stakeholders, especially between lenders and borrowers. These circumstances give rise to adverse selection and moral hazard, which may impair FD’s contribution to initiatives to increase ECI. Given robust evidence of asymmetries in Africa’s financial systems, this study uses an annual dataset of African economies from 1995 to 2023. The choice of these countries aligns with the argument that the extent of exploitation of informational asymmetries is more severe in economies at an early stage of industrial development. It also follows that information asymmetry and the likelihood of its exploitation are deeper in economies with weak institutions and underdeveloped financial systems. To deliver robust and efficient estimates, this study uses four estimators: Driscoll and Kraay’s nonparametric covariance matrix regression, feasible generalised least squares, Lewbel’s instrumental-variable two-stage least squares, and method-of-moments quantile regression.

The findings provide robust evidence of an asymmetric structure in ECI’s sensitivity to changes in FD. Both positive and negative change components in FD exert significant positive impacts on ECI in African countries. It implies that both expansionary and contractionary financial policies enhance ECI expansion in Africa. These findings have strategic interpretations. On the expansionary side, channelling credit facilities and providing technical support and expertise by Africa’s financial system help build and incentivise ECI initiatives to enhance knowledge intensity and technological capability to manufacture a chain of diverse, sophisticated, high-tech, and high-quality products for export. The positive contribution of the financial sector’s contractionary policy to spur ECI upgrades in Africa suggests that strategic, deliberate, cumulative reductions in channelling financial resources and support for ECI-impeding initiatives spur ECI expansion and make more resources and technical expertise available to promote investment in technology diffusion, knowledge-based manufacturing and productive capacities. Asymmetric analysis of how African countries’ financial sectors’ contractionary policies spur ECI upgrades offers fresh policy perspectives that align with real-world events in financial markets and institutions. It reveals that contractionary policies in Africa’s financial system can prevent inflows of financial resources into initiatives that impede ECI upgrades and, by implication, make more resources available to initiatives that enhance ECI.

These findings are thought-provoking and yield some fascinating policy suggestions for the strategic use of financial development (FD) as an instrument to propel ECI upgrades in the global context and in specific cases of African countries. First, the findings suggest that asymmetric structures are fundamental to the theoretical understanding and policy analysis of leveraging financial development to enhance economic complexity. This study establishes robust evidence of asymmetries, and the strategic differential impacts of positive and negative components of FD on ECI are too obvious to ignore. Analysis of how ECI responds to FD changes may be unrealistic without an appropriate explanation of asymmetric structures and nonlinearities. Secondly, African countries’ financial systems should be more deliberate in their use of expansive policy instruments, channelling more financial resources and providing strategic support to initiatives that promote knowledge intensity, technological capabilities, and sophisticated manufacturing techniques to aid the production of high-tech, globally competitive products for export. Thirdly, Africa’s financial systems need to develop strategies and intentional mechanisms to reduce channelling credit facilities and stop providing support to initiatives and unproductive activities that weaken knowledge-driven productivity and technology diffusion in the manufacturing sector. This policy is essential because we find that contractionary financial-sector policies in African countries enhance ECI. Fourthly, the study’s findings signal that some financial resources from financial sector flows are directed to initiatives and programmes that may impede the expansion of innovative industries and ECI upgrades. Hence, this study suggests that stakeholders in Africa should design institutions to detect, prune, and prevent sharp practices and rent-seeking activities that channel financial sector credit facilities to ECI-inhibiting initiatives, thereby making more funding and technical support available for ECI-related initiatives. Fifthly, all institutional failures and flaws in the financial sector should be identified and addressed to ensure the efficient functioning of Africa’s financial systems, providing more reliable services and technical support to promote initiatives that foster economic complexity. Sixthly, African countries with low ECI rankings and performance should prioritise upgrading ECI to reap the benefits of high levels of financial development; further development of the financial sector without a corresponding improvement in ECI may not yield optimal results.

6. The Research Limitations and Suggestions for Other Scholars

Aside from providing the first empirical scrutiny of asymmetric sensitivity in ECI’s response to changes in FD, this study also has limitations. The highlights are as follows: First, the focus of this study is on entire African countries, but we excluded some countries due to insufficient data on key variables such as economic complexity and financial development. We thereby encourage future research to include more countries as more data becomes available. Secondly, the policy suggestions of this study are more specific and particularly relevant to Africa’s settings in some regards, given the use of data on African economies. We urge future research to consider using datasets from other continents to complement the dataset. Thirdly, this study covers the period of 1995–2023 due to data paucity. Other scholars should consider more recent years to update the knowledge space and provide further insights into developments in the financial sector and economic complexity in Africa. Fourthly, this study acknowledges that other panel data estimators may perform better in the presence of specific econometric issues. Data span limits our choice of estimators. Hence, future studies should consider using an estimator such as the panel smooth transition regression model or the varying-coefficient model to enhance the universal acceptability of the findings.