Economic complexities and manufacturing growth; the Nigeria experience.

Economic Complexities and Manufacturing Growth; The Nigeria Experience

Obomeghie Adamu Muhammed¹, Obomeghie Adamu Inusa²

¹Auchi Polytechnic, Auchi, Department of Statistics, Edo state, Nigeria

²Auchi Polytechnic, Auchi, Department of Polymer Engineering, Edo state, Nigeria.

DOI: https://dx.doi.org/10.47772/IJRISS.2025.906000138

Received: 26 May 2025; Accepted: 02 June 2025; Published: 03 July 2025

ABSTRACT

The growth of the manufacturing sector of any country represents the level of the country’s drive for industrialization, economic growth and development. Nigeria’s drive for its manufacturing sector growth has been intense for several years with less than the desired achievements. This study therefore examines the impact of economic complexities on Nigeria’s manufacturing sector growth. The data for the study were collected from the Nigeria CBN statistical bulletin as well as, the World Bank’s world development indicator database. Data used ranged from 2010 to 2023. The Robust regressing analysis is used to analyze the data collected. The findings of the study clearly indicates that economic complexities had a positive impact on manufacturing growth within the period under discussion. Based on the findings, it is recommended that Nigeria policy makers in conjunction with industry experts should develop policies such as; offering incentives and support services for manufacturers to explore new export markets and diversify their export portfolios beyond traditional products. Equally, they should support the diffusion of industry 4.0 technologies through training programmes, pilot projects and investment incentives that will help to further diversify the Nigeria economy for a greater manufacturing growth.

Keywords; economic complexities, manufacturing growth, human capital, industrialization, industry 4.0.

INTRODUCTION

Nigeria, acknowledged as Africa’s largest economy by GDP, is characterized by diverse economic arrangements that includes oil and gas, agriculture, manufacturing, services e.t.c. Despite its vast natural resources, the economy faces challenges such as over-reliance on oil exports, infrastructural deficits, and political instability which impact sustainable growth. Nigeria’s GDP was approximately $440 billion in 2022, with an estimated growth rate of about 3.4% (World Bank, 2023). The economy is heavily reliant on oil exports, which account for around 90% of foreign exchange earnings and over 60% of government revenue, making it vulnerable to global oil shocks. (Alabi & Ojediran, 2024).

The Nigeria manufacturing sector contributes approximately 10-12% of Nigeria’s GDP (National Bureau of Statistics, 2023), playing a vital role in economic diversification and employment generation. The sector also provides employment opportunities, especially for youth, and this is crucial for reducing poverty through value addition. Nigeria’s manufacturing sector is still underdeveloped compared to its potential, mainly due to infrastructural deficits, high energy costs, and import dependency. However, continued policy support, infrastructural development, and investment are necessary to unlock this sector’s full potential.(Ogbodo, 2018).

Statement of problem

The manufacturing sector in Nigeria faces a multitude of complex and interconnected challenges that hinder its growth and competitiveness. These challenges includes; Restrictive government policies and fluctuating oil prices that have worsened the foreign exchange crisis, making it difficult for manufacturers to source the necessary foreign exchange for importing raw materials and machinery which has leads to increased production costs and inflation. Equally, persistent inflationary pressures erode consumer purchasing power and increase the operational costs for manufacturing businesses. This makes it challenging to maintain profitability and plan for long-term growth. As well, the fluctuating commodity prices and shifting trade policies create significant uncertainty, impacting demand, production costs, and overall profitability within the sector. The sharp 36% decline in profit margins between 2021 and 2022, alongside a 43.5% drop in export revenues from manufactured goods, illustrates this instability. Elevated interest rates has also increase the cost of borrowing, making it difficult for manufacturers to access capital for investment and expansion.(Onodje &Farayibi, 2020). In a separate study, Ogar, et, al. (2024) noted that; inadequate and unstable electricity supply forces manufacturers to rely on costly diesel-powered generators, significantly increasing overhead costs and reducing their ability to compete with international counterparts. Dilapidated roads and insufficient rail networks hinder the efficient movement of raw materials to factories and finished goods to markets, leading to increased logistics costs and delivery delays. Goods worth billions of naira have been damaged in transit due to poor road conditions. Equally, inefficient port infrastructure causes delays and congestion in clearing imported equipment and materials, further increasing project lead times and operational expenses.

As well, multiple taxation, inappropriate fiscal Policies, limited access to affordable financing, slow rate of technology acquisition, insecurity, supply chain disruptions have also been listed as some of the challenges affecting Nigeria manufacturing growth (Abiola, 2024). Addressing these multifaceted challenges requires a coordinated effort from the government, financial institutions, and the manufacturing sector itself.

Research objectives

To analyze the relationship between economic complexity and the growth rate of the manufacturing sector in Nigeria.

To examine the role of human capital development, as components of economic complexity, in enhancing manufacturing productivity in Nigeria.

To provide recommendations for policymakers on harnessing economic complexity to stimulate manufacturing growth in Nigeria.

Significance of the study.

The analysis of the impact of economic complexities on manufacturing growth in Nigeria holds significant importance for multiple reasons. Understanding this relationship can inform policymakers, investors, and development practitioners about the structural factors influencing Nigeria’s industrial progress and guide strategies to foster sustainable economic development. Equally, analyzing how economic complexities affects manufacturing growth helps identify specific sectors or productive capabilities that can be leveraged or diversified.  For Nigeria, such insights can inform targeted policies to upgrade technological capabilities and diversify its manufacturing base. Nigeria’s economy has historically been reliant on oil exports, making it vulnerable to commodity price shocks. Investigating economic complexity provides a pathway to understanding how to shift towards more complex manufacturing activities, which can generate employment, boost exports, and reduce dependence on primary commodities (Clark & Juma, 2014). This aligns with the broader goal of structural transformation necessary for sustainable development. As well, countries with higher economic complexity are often more attractive to foreign direct investment (FDI) because they possess a diversified and advanced production structure (Matus et, al, 2007). Nigeria’s efforts to enhance its economic complexity could improve its global competitiveness, leading to increased manufacturing investment. By analyzing how economic complexity influences manufacturing growth, stakeholders can identify bottlenecks and opportunities for capacity building, technology transfer, and skill development, which are crucial for inclusive growth (Andrews & Aslam, 2017).

Finally, this study will contribute to the global understanding of how economic complexity relates to industrialization in developing countries. Such research can fill gaps in the literature specific to Nigeria’s economic context, offering tailored insights for policymakers.

LITERATURE REVIEW

Conceptual framework

Hidalgo and Hausmann (2009, 2010), introduced the concept of economic complexity and demonstrated that countries with higher complexity tend to have higher income levels and faster growth. They showed that economic complexity predicts future economic growth better than traditional measures like GDP per capita. They noted further that, expanding a country’s productive capabilities enables it to produce more complex goods. This capability accumulation directly contributes to manufacturing growth by fostering innovation, upgrading existing industries, and diversifying the industrial base.

Human capital refers to the knowledge, skills, experience, and abilities that individuals possess, which can be utilized to produce economic value. It is a vital component of economic development and is particularly significant in the manufacturing sector, where skilled labor and innovation are key drivers of productivity and growth (Nweke, 2023). Human capital encompasses the education, training, health, and competencies of workers that influence their productivity. Investments in human capital (education and vocational training) enhance workers’ efficiency and adaptability, leading to improvements in overall economic output.

More complex economies benefit from knowledge spillovers, which enhance productivity and innovation in manufacturing sectors (Hidalgo & Hausmann, 2009). As countries increase their economic complexity, they tend to transit from primary commodity dependence towards manufacturing and high-value industries (Clark & Juma, 2014). This transition underpins sustained manufacturing growth.

According to Arthur (2021), the principles of complexity economics includes;

• Heterogeneous Agents: Economic actors (individuals, firms, institutions) are diverse in their characteristics, behaviors, and capabilities.
• Interactions and Networks: The economy is a web of interconnected agents whose interactions create emergent patterns and structures. These networks (e.g., trade networks, production networks) are crucial for understanding economic outcomes.
• Adaptive Behavior and Learning: Agents learn from their experiences and adapt their strategies over time. This leads to dynamic and evolving economic landscapes.
• Non-Equilibrium Dynamics: Unlike the focus on equilibrium in neoclassical economics, complexity economics emphasizes that economies are often in a state of flux and disequilibrium. Equilibrium might be a special case, not the norm.
• Emergence: Macro-level patterns and structures arise from the decentralized interactions of micro-level agents. These emergent phenomena (e.g., business cycles, technological lock-in) cannot be easily predicted or understood by analyzing individual agents in isolation.
• -Path Dependence and History: Past events and initial conditions can have long-lasting effects on the development of an economy. “Lock-in” to inferior technologies or development paths can occur.
• Innovation and Novelty: The economy is constantly evolving through the introduction of new technologies, products, and institutions, creating “novelty niches.”

The Concept of Human Capital and Its Role in Manufacturing Growth

Manufacturing growth is often driven by technological advancements, increased productivity and innovation, all of which depend heavily on the quality of human capital. Skilled workers are essential for adopting new technologies, improving processes, and fostering innovation, which collectively contribute to increased manufacturing output. Well-educated and trained workers can operate complex machinery and adopt new production techniques more efficiently (Lucas, 1988). This leads to higher productivity levels within manufacturing firms. Equally, Human capital facilitates research, development and the implementation of new technologies, which are crucial for sustaining manufacturing growth in a competitive global environment (Ihensekhien & Soriwe, 2023).

Keller & Yeaple, (2009) noted that countries with a highly skilled workforce attract more foreign direct investment in manufacturing sectors thereby fostering growth . Human capital plays a central role in driving manufacturing growth by enhancing productivity, facilitating technological adoption, and attracting investment. Building and nurturing a skilled workforce is essential for countries aiming to develop their manufacturing sectors and achieve sustainable economic growth

Theoretical framework

Economic complexity theories offer a different lens through which to understand economic development, growth, and the structure of economies. Unlike traditional neoclassical economics, which often relies on assumptions of perfect rationality and equilibrium, complexity economics views the economy as a dynamic system composed of heterogeneous agents constantly interacting and adapting.

The relationship between economic complexity theories and manufacturing growth is a significant area of study in development economics, as these theories provide insights into how a country’s productive capabilities influence its industrial and economic development. Economic Complexity Theory posits that a country’s level of development depends on the diversity and sophistication of its productive capabilities, which are reflected in its exports and industries (Hausmann, Hwang, & Rodrik, 2007). Countries with complex economies produce a wide array of sophisticated products, often requiring advanced manufacturing capabilities, technology, and skills.

The Economic Complexity Index (ECI) and Product Space

Developed by Hidalgo and Hausmann, (2009), this framework uses international trade data to quantify the “economic complexity” of countries and the “complexity” of products. They defined economic complexity index (ECI) as a measure of the knowledge and capabilities embedded in a country’s export basket. Countries that export a diverse range of sophisticated products (that is, commodities few other countries can make) have higher ECI. Equally, they noted that, Product Complexity Index (PCI) is a measure of the sophistication and knowledge required to produce a product. Products exported by countries with high ECI tend to have high PCI. Finally, they define product space as a network that illustrates the relatedness between different products based on the capabilities required to produce them. Countries tend to diversify into products that are “nearby” in the product space, leveraging their existing capabilities. This theory suggests that the structure of a country’s current production strongly influences its future development path.

Theories of human capital and manufacturing growth are central to understanding economic development and productivity enhancement. The bedrock of human capital theory is often attributed to the works of Gary Becker and Theodore Schultz. Gary Becker (1964), reported in (Holden & Biddle, 2017) in his seminal work “Human Capital,” argued that investments in education, training, and healthcare are analogous to investments in physical capital. Just as a firm invests in new machinery to increase productivity, individuals invest in their own human capital to enhance their future earning potential and productivity. Becker emphasized that these investments yield returns in the form of higher wages and increased output. In the context of manufacturing, this means that a workforce that has invested in skills and knowledge through education and training will be more productive, leading to greater output and efficiency (Holden & Biddle, 2017). Becker’s theory also distinguishes between general-usage skills (transferable across firms) and firm-specific skills, with implications for who bears the cost of training.

Theodore Schultz (1961), reported in (Matache, 2023) also highlighted the significance of human capital. He argued that the observed increases in national output often exceeded what could be explained by increases in traditional factors of production (land, labor, physical capital). He attributed this “residual” growth to improvements in human quality, emphasizing that expenditures on education, health, and even migration for better job opportunities are forms of investment in human capital. Schultz’s work underscored that the productive capacity of a nation’s labor force is a critical driver of economic growth ( Tutor2u, 2021). For manufacturing, this implies that improvements in the overall health, education, and adaptability of the workforce are essential for sustained growth and the ability to respond to market changes (Cook & Klein, 2006)

While the classical human capital theories laid the groundwork, Endogenous Growth Theory provides a more explicit framework for understanding how human capital drives sustained economic growth, particularly in sectors like manufacturing that are heavily influenced by technological progress. (Romer, 2011.) However, the Neoclassical growth models (like the Solow model) suggested that physical capital accumulation eventually faces diminishing returns, leading to a steady state of growth unless there is exogenous (external) technological progress. Endogenous growth theory challenged this by asserting that economic growth is driven by internal factors, primarily the accumulation of human capital, knowledge, and innovation (Popa, 2014)

EMPIRICAL REVIEW

Some researchers have noted that manufacturing is a critical driver of complexity, as more sophisticated manufacturing industries contribute to higher complexity scores. Such studies includes, Alotaibi  & Sallam, (2025) who in their study, measured the effect of the Economic Complexity Index (ECI) on economic development by constructing a composite variable representing economic development. The study utilized data from the period 1991–2021 and demonstrated the existence of a cointegrating relationship between the study variables in both the short and long run using the Autoregressive Distributed Lag (ARDL) methodology through the Bounds Test. The results indicated a positive but statistically insignificant relationship at the 5% significance level between economic complexity and economic development in the short run.

Hoeriyah,  et, al. (2022), analyzed the effect of the economic complexity on economic growth in 86 developing countries by collecting data from  2010-2019. The method of analysis used is the Generalized Method of Moments (GMM) to capture dynamic panel analysis. The estimation results using the System GMM show that economic complexity has a positive effect on economic growth in developing countries.

Lima,  Luciano,  & Frederico (2023) empirically analyzed how manufacturing, dis-aggregated into sub-sectors by research and development (R&D) intensity, influences the level of economic complexity (ECI). For this, two methods were used: 1) the parametric by Panel Dynamic Ordinary Least Squares (PDOLS) and 2) the non-parametric: a) Data Envelopment Analysis (DEA) and b) Malmquist Decomposition. The econometric results suggest that the allocation of workers in the manufacture of high R&D level has a positive impact on the ECI level of all the countries in the sample analyzed, whereas in the sectors of lower R&D there is a greater impact in emerging countries, but lower effects (or negative) on advanced countries.

Empirical evidence consistently indicates a positive relationship between economic complexity and manufacturing growth. Countries that develop more sophisticated and diverse manufacturing sectors tend to experience faster economic growth, higher productivity, and structural transformation. These studies highlight the importance of building capabilities and diversifying export baskets to foster manufacturing expansion and overall development.

Research on Nigeria’s manufacturing sector from 1981 to 2021 found a positive relationship between human capital development (measured by government expenditure on education and health) and manufacturing output. While the statistical significance varied, the study concluded that for the manufacturing sector to achieve steady-state output and satisfactory economic growth, human capital development is essential (Oko, et,al. 2023). This highlights the continued relevance of Becker’s and Schultz’s initial insights into the necessity of investing in people.

A study on Small and Medium-Scale Enterprises (SMEs) in Nigeria (2003-2023) revealed a consistently positive and significant relationship between human capital development (formal education, vocational training, professional development) and SME growth. The findings underscore the importance of continuous skill enhancement and knowledge acquisition for immediate operational improvements and sustained growth in manufacturing-related SMEs (Chidi & Shadare, 2011).

Finally, Bhattacharya & Sahoo, (2012) discovered that developing countries investing in vocational training have experienced accelerated growth in their manufacturing sectors. Human capital theories, from the foundational work of Becker and Schultz to the more contemporary endogenous growth models, consistently demonstrate that investment in people’s knowledge, skills, and health is not merely a cost but a fundamental driver of productivity, innovation, and sustained growth in the manufacturing sector. Recent empirical evidence continues to validate these theoretical underpinnings across diverse economic contexts

METHODOLOGY

Research design

This study employs robust regression techniques, specifically the Huber M-estimator, to estimate the relationship between manufacturing growth and economic complexity in line with similar studies by Khan et, al. (2021) .

Method of data collections

Secondary data were used for the study, the data were collected from the CBN statistical bulletin (2024) and the World Bank’s World Development Indicators database (2024) covering annual data from 2010 to 2023. Data from manufacturing output (MAN), economic complexity index (ECI), human capital index (HCI), exchange rate (ECH), inflation rate (INF) and GDP were retrieved and uded.

The model and variables of the study.

A Robust Least Squares (OLS) regression equation with MAN as the dependent variable and ECI, HCI, GDP, ECH and INF as independent variables, was specified  as follows:

MAN​=β₀+β₁ECI+β₂HCI+β₃GDP+β₄ECH+β₅INF+ϵ​

Where:

MAN = Nigeria manufacturing output data.

ECI  =  Nigeria economic complexity index.

HCI =  Nigeria human capital index.

GDPt= Nigeria’s Gross domestic product.

ECH = Nigeria exchange rate to the dollar.

INF  =  Nigeria inflation rate.

β₀  = Intercept term

β₁,β₂,β₃,β₄  = Coefficients measuring the effect of ECI, HCI, GDP, ECH and INF on MAN, respectively.

ε = Error term capturing unexplained variation.

3.4       Analytical framework.

The analytical framework for the study are hypothesizes in the table 1 below;

Table 1. Hypothesized analytical framework

Source; Authors compilation.

Justification of the chosen method

The robust regression method is preferred in this study because, OLS is highly sensitive to outliers. Even a few extreme data points can disproportionately influence the OLS regression result, leading to biased coefficient estimates and misleading conclusions. Robust regression methods are designed to down-weight the influence of outliers, providing a model that better fits the analysis. Also, Robust regression techniques, particularly those using robust standard errors (like Huber-White standard errors), can provide consistent standard error estimates even in the presence of heteroscedasticity. Robust regression methods also directly address heteroscedasticity in the estimation process.(Pervez & Ali, 2024).

RESULTS

Table 2. Descriptive statistics

Source; Authors compilation from E-view output.

From table 2 which represents the descriptive statistics, it can be seen that GDP has the highest Mean with a value of 444.1393 while ECI has the lowest mean with a value of -1.564286. ECH has the highest standard deviation with a value of 142.2470 while HCI with a value of 0.030598 has the lowest standard deviation.

Table 3 unit root test

Source; Authors compilation from E-view output.

From table 3. which depicts the group unit root test, we can conclude that the variables are stationary at levels from ADF and PP values of 39.3865 and 39.9464 respectively,

while the LLC value of -4.80500 confirms the staionarity at first difference,.

Table 4. Regression output

Source; Authors compilation from E-view output.

Interpretations

From table 4, one may notice that there is a significant positive relationship between economic complexity and manufacturing growth in Nigeria because the p-value of 0.0001 is less than 0.05 which indicates that for every one-unit increase in ECI, the dependent variable MAN is estimated to increase by 16.44417 units, holding other variables constant. This is in conformity with our a priori expectation in line with similar study by Author, (2021).

For human capital index, it is seen that there is a  negative relationship between human capital development and manufacturing growth in Nigeria because the p-value of 0.0001 is less than 0.05 which indicates that for every one-unit increase in HCI, the dependent variable MAN is estimated to decrease by -70.07397 units, holding other variables constant. This is in contrast with our a priori expectation. However, it is in agreement with similar study by Babasanya, et, al. (2025).

In case of GDP, we noticed once more that, there is a statistically significant positive relationship between GDP and manufacturing growth in Nigeria because the p-value of 0.0001 is less than 0.05 indicating that for every one-unit increase in GDP, the dependent variable MAN is estimated to increase by 0.1235 units, holding other variables constant. This is in agreement with our a priori expectation in line with similar study by Oseni & Adegbie, (2020).

There is a significant negative relationship between INF and manufacturing growth in Nigeria because the p-value of 0.0001 is less than 0.05 which indicates that for every one-unit increase in INF, the dependent variable MAN is estimated to decrease by -0.1236 units, holding other variables constant. This is in abidance with our a priori expectation and in conformity with similar studies by (Achugamonu, et, al. (2017).

Finally, with respect to exchange rate, there is a significant positive relationship between exchange rate and manufacturing growth in Nigeria because the p-value of 0.0001 is less than 0.05 which shows that for every one-unit increase in ECH, the dependent variable MAN is estimated to increase by 0.1038 units, holding other variables constant. This is again in contrast with our a priori expectation, however, it is in line with similar study by Enekwe et, al, (2013).

The Rw-squared  of 0.99927 indicates that about 99.9% of the variations in the dependent variable have been accounted for by the independent variables used in the analysis. This further indicates that the model’s explanatory power remains stable and high even when outliers or influential data points are present, providing a more reliable measure of fit in the given situations. This suggests that the predictors are significantly related to the outcome, and the model provides a reasonably good explanation of the data considering robustness.

CONCLUSION

The study concludes that higher economic complexity is associated with stronger manufacturing growth in Nigeria. This implies that if the Nigeria economy has a more diverse and sophisticated production base, capable of producing a wider range of interconnected and higher-value goods, it will have more dynamic and faster-growing manufacturing sectors.

With respect to human capital, one may conclude that human capital is not inherently bad or that less education is better for manufacturing. Instead, the findings signify dysfunctions or specific contextual issues that are preventing the expected positive impact of human capital from materializing such as, severe skill Mismatch / irrelevant education, poor quality of human capital development, brain drain / capital flight of human capital, inefficient labor markets and hiring Practices, lack of complementary investments (Capital-Skill Complementarity). which is capable of leading to a mis-allocation or underutilization of human capital within the manufacturing sector. It further signals that simply increasing the quantity of educated people is not enough; the quality, relevance, utilization, and retention of that human capital, in conjunction with other economic factors, are critical for positive outcomes.

SUGGESTIONS FOR FUTURE RESEARCH

Future research should delve into the specific mechanisms through which economic complexity influences manufacturing growth. That is, what are the channels of influence (e.g., technological spillovers, knowledge diffusion, supply chain development, institutional quality). Also, a significant relationship between manufacturing growth and economic complexity opens up avenues for understanding the structural drivers of industrial development and for formulating policies that foster both economic diversification and a thriving manufacturing sector. The specific nature and implications of this relationship warrant further in-depth investigation.

RECOMMENDATIONS

Policymakers and industry stakeholders could consider the following recommendations;

Evolve and implement policies that encourage entrepreneurship, provide access to funding for startups in emerging sectors, and facilitate the transfer of technology and knowledge. Also, offer incentives and support services for manufacturers to explore new export markets and diversify their export portfolios beyond traditional products.

Equally, to create and strengthen institutions that support applied research relevant to manufacturing, provide funding for collaborative research projects between academia and industry, and incentivize private sector R&D investment through tax credits or grants. Support the diffusion of Industry 4.0 technologies (e.g., automation, robotics, AI, IoT) through training programs, pilot projects, and investment incentives.

Also, to provide support for manufacturers to move up the value chain by investing in skills development, quality standards, and design capabilities. Invest in infrastructure (transport, logistics, energy) that facilitates efficient linkages between different industries and firms. Encourage more students to pursue education in science, technology, engineering, and mathematics to build a strong future workforce for advanced manufacturing. Collaborate with more advanced economies to learn best practices in industrial policy, technology adoption, and skills development.

In the case of human capital, policymakers would need to conduct a deeper analysis to understand the root causes and implement targeted interventions rather than assuming education is detrimental such as, addressing skill mismatch and relevance of education through strengthen vocational and technical education (TVET). Foster industry-academia partnerships by establishing formal and robust partnerships between higher education institutions (universities, polytechnics) and manufacturing companies. Enhancing quality and efficiency of human capital development improve quality assurance in education by strengthening regulatory bodies for education to ensure quality control, accreditation standards. Incentivize retention of skilled labor for key skills by exploring policies that encourage skilled labor to remain in the domestic manufacturing sector.

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