Export Diversification Drive: the Role of Nigerian Manufacturing Sector

Export Diversification Drive: the Role of Nigerian Manufacturing Sector | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Export Diversification Drive: the Role of Nigerian Manufacturing Sector Abiola Olawale M This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4394927/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This research delves into the enigmatic relationship between Nigeria's manufacturing sector and the nation's drive for export diversification . Leveraging a regression analysisand time series data from 1985 to 2022, it paints a nuanced picture of their complex interplay. The analysis confirms the stationarity of all variables at first differenced . Additionally, the Johansen co-integration test reveals a long-run equilibrium relationshipbetween them, suggesting that while their short-term fluctuations may diverge, they are ultimately bound by a deeper interdependence. The analysis exposes a weak and negative associationbetween the two, hinting at the meagre contribution of the manufacturing sector to export diversification during the studied period. This underscores the need for a critical reevaluation and targeted interventions to unlock the sector's potential as a powerful engine of export growth. Therefore, the study advocates for a paradigm shift in approach. Instead of government’s piecemeal efforts, it should champion the creation of a robust and vibrant manufacturing ecosystem, pulsating with innovation and productivity. This vision will envision modern factories humming with cutting-edge processes, meticulously crafting high-quality goods capable of holding their own on the global stage. From value-added agricultural products to sophisticated machinery, the potential portfolio of Nigerian exports is vast and brimming with promise. Development Economics Export Diversification Manufacturing sector Value added 1. INTRODUCTION Like many developing nations, Nigeria has embarked on a determined journey to diversify its economy, with a particular focus on bolstering its manufacturing and processing industries. This strategic shift stems from the profound understanding that a diversified economic bedrock fosters both stability and robust growth. Since attaining independence, Nigeria has grappled with the formidable challenge of export diversification. This struggle largely arises from the skewed structure of its exports, heavily reliant on a single commodity: oil. This persistent dependence has rendered the country's economy vulnerable to the whims of global oil price fluctuations, hindering its resilience. Recognizing the critical need for a more diversified and sustainable export base, the government has implemented various trade policies and initiatives. However, achieving this elusive goal has proven to be a persistent challenge. Export diversification is a multifaceted strategy that serves as the cornerstone of economic resilience and sustained growth. It encompasses not only the expansion of a nation's export activities but also recognizes the pivotal role played by the manufacturing sector in driving overall economic diversification. This approach holds particular significance for countries like Nigeria, aiming to break free from her dependence on a limited range of exports, such as oil, and cultivate a more robust and diversified economic landscape. The manufacturing sector occupies a central position in the context of export diversification. By adding substantial value to raw materials, this sector facilitates the creation of a diverse array of finished goods suitable for export. By promoting the export of such manufactured products, the country can shield itself from the volatility of commodity prices and external market fluctuations, thereby enhancing economic stability. According to data from the National Bureau of Statistics (NBS) for the year 2021, the manufacturing sector made a modest contribution of 5.2% to the real Gross Domestic Product (GDP) and accounted for 9% of the total export revenue. However, a striking observation emerges: only 4% of Nigeria's manufacturing output found its way into the export market in 2021. This already modest export share further contracted to a mere 2% during the initial three quarters of 2022. These statistics highlight the significant challenges and constraints faced by the Nigerian manufacturing industry in achieving meaningful export diversification. The minimal share of this sector in the export market underscores the urgency of scrutinizing the nation's export diversification efforts and the role of its manufacturing sector, which possesses the potential to be a powerful engine for export diversification. Given the current low contribution of the manufacturing sector to overall economic growth, this research project assumes an imperative nature. By highlighting the intricate link between a thriving manufacturing sector and export diversification drive (Benjamin, 2017; Wan, Kazmi, and Wong, 2022), this research project delves into the critical question: can Nigeria unlock the potential of its manufacturing sector to propel its export diversification and ultimately drive sustainable economic development? 1.0. STATEMENT OF THE PROBLEM The crucial value of any research lies in its capacity to address critical societal issues. In the context of Nigeria's export diversification, addressing the underperformance of the manufacturing sector becomes an imperative endeavour. To fulfill the ambitious mandate of national industrialization, deliberate government efforts are needed to empower this vital sector. The lagging performance of the Nigerian manufacturing sector, failing to capitalize on its inherent potential, exposes a pressing need to investigate the underlying causes of its stagnation. In today's interconnected world, where competition transcends national borders, Nigeria cannot afford to fall behind in revitalizing its manufacturing sector. This research project aims to shed light on the crucial role the manufacturing sector can play in propelling Nigeria's export diversification journey. 1.3. RESEARCH OBJECTIVES This study shall be guided by the following objectives: i. To examine the relationship between Nigeria's manufacturing sector and its national export diversification strategy ii. To investigate factors hindering the Nigerian manufacturing sector's full potential, leading to its underwhelming contribution to national GDP. iii. To identify actionable solutions and policy recommendations to revitalize the Nigerian manufacturing sector. 1.4. RESEARCH QUESTIONS This research work will enable the researcher to come up with answers to the following research questions namely: i. What is the intricate relationship between Nigeria's manufacturing sector and its national export diversification strategy? ii. What are the factors hindering the Nigerian manufacturing sector's full potential, leading to its underwhelming contribution to national GDP? iii. What are the actionable solutions and policy recommendations to revitalize the Nigerian manufacturing sector? 2. LITERATURE REVIEW This section embarks on a critical journey through both theoretical and empirical research, examining the intricate relationship between the manufacturing sector and export diversification. Our aim is twofold: first, to illuminate the theoretical foundations underpinning this crucial connection, and second, to assess the current landscape of relevant empirical literature. Furthermore, we will navigate the unique contribution this study seeks to make, pinpointing existing gaps in the literature that our research endeavours to bridge. By meticulously synthesizing theoretical frameworks and scrutinizing prior research, this section lays the groundwork for the subsequent analysis and findings, paving the way for a nuanced understanding of our study's objectives and methodology. 2.1. CONCEPTUAL REVIEW Now, let's look at the different ideas that shed light on the study's central question. 2.1.1. NIGERIA’S EXPORT PRODUCTS STRUCTURE Nigeria's export profile is characterized by the dominance of primary products, which often have lower value compared to processed goods. The country heavily relies on the export of commodities such as crude petroleum, petroleum gas, refined petroleum, cocoa, rubber, palm oil, sesame seeds, cashew nuts, and groundnuts. While these products constitute a significant portion of Nigeria's export revenue, they are generally considered to have less value in the international market. The primary nature of these exports poses challenges, as they are susceptible to price fluctuations in the global market for commodities. For instance, the prices of crude oil, Nigeria's major export, are highly volatile and subject to geopolitical and economic factors. In addition to the mentioned primary products, Nigeria's exports span various categories, including animal and vegetable by-products, animal hides, animal products, arts and antiques, chemical products, foodstuffs, footwear and headwear, instruments, machines, metals, mineral products, miscellaneous items, paper goods, plastic and rubbers, precious metals, stone and glass, textiles, transportation equipment, vegetable products, and wood products. Despite the diversity in export categories, there is a common challenge associated with the low value addition to these products before exportation. The lack of significant processing and value addition limits the earning potential from exports, as the country often sells raw materials rather than finished goods. This situation results in a lower influx of foreign exchange compared to if the products underwent further processing or manufacturing within the country. Based on the National Bureau of Statistics (NBS) data, the monetary value of Nigeria's export products has been experiencing a persistent decline. This trend underscores the challenges faced by the country's export sector and raises concerns about the sustainability of its economic growth model. The declining monetary value of Nigeria's export products serves as a call to action for policymakers to prioritize initiatives that will strengthen the resilience and sustainability of the country's export sector. A more diversified and value-added approach to exports will not only enhance foreign exchange earnings but also contribute to the overall economic development and prosperity of the nation. 2.1.2. NIGERIA’S EXPORTS DIVERSIFICATION EFFORTS Export diversification plays a pivotal role in driving economic growth, offering a strategy that involves broadening the range of exported goods to expand the export basket. The fundamental concept behind export diversification is the differentiation of items based on various attributes such as appearance, quality, design, taste, choice, and function. This approach seeks to enhance the variety and composition of a country's export portfolio, reducing reliance on a narrow set of products. Since gaining independence in 1960, the Nigerian government has implemented various developmental plans and macroeconomic policy frameworks aimed at developing the non-oil sector to diversify the economy. These policies, ranging from protectionism to trade liberalization and export promotion, have sought to ensure a diversified structure for the country's export earnings. Agencies such as the Nigerian Export Promotion Council and the Nigerian Export-import Bank (NEXIM) were established to implement these policies effectively. One notable initiative prior to the Structural Adjustment Programs (SAP) was the National Accelerated Food Production Project (NAFPP) launched in 1972. The primary objective of this policy was to increase the nation's agricultural output by imparting better farming practices to farmers. Financial challenges associated with the program led to the establishment of the Nigeria Agricultural and Cooperative Bank (NACB) in 1973. This World Bank-backed initiative aimed to provide lending facilities to small-scale farmers who lacked access to other financing options, with joint funding from the World Bank and the federal government. The establishment of the Export Processing Zone (EPZ), as outlined in Decree No. 34 of 1991, marked a significant effort to further the trajectory of export diversification in the country. This afforded both domestic and foreign businesses the opportunity to manufacture or assemble goods exclusively for export, enjoying exemptions from the customary fees and documentation required for standard import and export activities. By creating this specialized economic zone, Nigeria aimed to encourage international and domestic businesses to engage in export-oriented activities with increased flexibility and reduced bureaucratic constraints, fostering a conducive environment for export diversification initiatives. The EPZ serves as a mechanism to attract investment, stimulate economic activities, and enhance the nation's competitiveness in the global market. Following the restoration of democracy in 1999, Nigeria witnessed a rapid transformation in its non-oil sector, primarily driven by heightened state support for small and medium-sized enterprises (SMEs) to boost both finished and raw material exports. In this period, the various administrations implemented policies with the overarching goal of fostering economic diversification, reflecting a commitment to reduce the nation's overreliance on oil-related revenues (Adeloye, 2012). The policies introduced during this period aimed to create an environment conducive to diversification, fostering the growth of industries beyond the oil sector. Notably, there was a concerted effort to enhance support for SMEs, recognizing their pivotal role in driving export activities. As a result of these strategic measures, non-oil exports experienced substantial growth, surging from $1 billion in 2006 to $2.3 billion in 2010, according to industry reports and briefings. This positive trajectory reflected the success of targeted policies in encouraging a more diversified economic landscape and reducing dependence on oil revenue. The Zero Oil Initiative, established by the Nigerian Export Promotion Council in collaboration with the Ministry of Budget and National Planning in 2016, aims to boost exports, diversify the economy away from oil dependency, and fortify the nation's foreign reserves. This strategy, a response to the 2016 economic downturn and an effort to reduce over-reliance on crude oil exports, was integrated into the Economic Recovery and Growth strategy. The NEPC identified 22 products across various industries for enhanced export focus, considering Nigeria's comparative advantage and ease of production (NGF repository). Also in 2016, the government implemented the prohibition on rice imports to boost domestic rice production, coupled with a N40 billion funding boost for rice producers through the "Anchor Borrowers Programme" (ABP). The ABP aimed to assist small- and medium-sized farmers in increasing their output and supplying feedstock to agro-processors. Governor Godwin Emefiele of the Central Bank of Nigeria emphasized that the program's goal was to diversify the economy and establish an ecosystem, moving away from a mono-product economy. Another significant effort to boost food production was the introduction of the Green Alternative policy (2016–2020). The Ministry of Agriculture asserts that this strategy will serve as a new pivot for economic diversification, fostering inclusive growth and overall economic development. These initiatives underscore the government's commitment to transforming the agricultural sector and diversifying the economy away from overreliance on oil. 2.1.3. MANUFACTURING AND NIGERIAN MANUFACTURING SECTOR Manufacturing – the process of transforming raw materials into finished goods through human labour, machinery, and chemical processes (Ogundipe, 2022) – is not merely a production cycle; it's an engine of progress. Beyond the creation of consumer items, intermediates, and semi-finished commodities, manufacturing fuels advancement. It leverages advanced technology and machinery (Okon, 2017) to create goods and services that elevate human well-being and raise living standards (World Bank, 2023). This transformative power makes manufacturing indispensable for economic prosperity. It forms the bedrock for producing goods and services, creating employment opportunities, and generating substantial income, ultimately laying the foundation for a thriving nation. The manufacturing sector acts as a catalyst for economic development, accelerating structural transformation and economic diversification (Egbiku Joshua, 2018). This empowers nations to leverage their resource endowments and reduce reliance on foreign aid. By providing finished products and raw materials, industries weave a tapestry of growth, development, and sustainability. Moreover, the productivity of the non-manufacturing sector is intricately linked to the growth of the manufacturing counterpart, highlighting the crucial role manufacturing sector plays in fostering a holistic economic ecosystem. The sector's impact extends beyond mere production. It spurs the growth of ancillary industries and supply chains, weaving intricate connections with raw material suppliers, logistics providers, and distribution networks. This interconnected ecosystem nurtures economic interdependencies, contributing to the development of a robust industrial landscape. Furthermore, manufacturing aids export diversification by crafting goods for international markets, diminishing dependence on a limited range of commodities. This broadening of export capabilities augments foreign exchange earnings, fostering a more balanced trade profile and enhancing global competitiveness. Nigeria's manufacturing sector can steer technological innovation and adoption, fostering heightened efficiency, elevated product quality, and enhanced global competitiveness. Embracing technological advancements positions Nigerian manufacturing on a trajectory of sustainable growth and global relevance. Moreover, the country’s industrial sector's expansion will act as a catalyst for heightened demand across various services, including banking, insurance, and other professional services. This phenomenon fuels the rapid expansion of the service sector, establishing a symbiotic relationship between manufacturing and services sector. This interconnection not only propels economic growth but also enriches the overall development of the nation. The positive spillover effects of the manufacturing sector extend beyond economic realms. By generating employment opportunities, it addresses issues related to unemployment and poverty, fostering social development and stability. The sector's ability to create wealth and contribute to government revenue empowers the state to invest in essential public services, infrastructure, and social welfare programs, further enhancing the overall quality of life for the populace. The manufacturing sector is not just a cog in the economic machine; it is the engine that drives progress. By harnessing its transformative power, Nigeria can unlock its full potential, diversify its export landscape, and achieve sustainable economic growth, paving the way for a brighter future for its citizens. 2.1.4. TRANSITIONING FROM SIMPLE TO SMART PRODUCTION SYSTEMS To truly unlock the potential of Nigeria's manufacturing sector, a decisive shift from basic production systems to smart product systems is paramount. This transformative leap involves the seamless integration of advanced technologies and data-driven strategies into the manufacturing DNA, unleashing a new era of efficiency, flexibility, and scalability . Smart production systems are woven from the cutting-edge threads of the Internet of Things (IoT), artificial intelligence (AI), robotics, and data analytics . These technologies weave a symphony of intelligence, interconnectivity, and automation, fundamentally redefining the production landscape. Imagine IoT sensors whispering crucial machine data in real-time , enabling predictive maintenance and minimizing downtime. AI algorithms become sage advisors , optimizing production schedules and resource allocation with unerring precision. The agility granted by smart production systems is a game-changer in today's dynamic marketplace. Consumer preferences pirouette on a dime, and market conditions can shift like desert sands. Smart technologies empower manufacturers to flex and adapt with nimble grace , adjusting production processes swiftly to meet evolving demands. Data-driven insights, gleaned from a treasure trove of consumer behaviour, market trends, and operational data, illuminate the path for informed decision-making, guiding manufacturers towards profitable horizons. But the benefits of this transformative voyage extend far beyond operational efficiency. Smart production systems will catapult Nigeria's manufacturing to a global stage of competitiveness , attracting foreign investment and propelling economic growth. This technological embrace will unleash a ripple effect, fostering skill development as the workforce learns to waltz with cutting-edge tools and processes. As Nigeria embarks on the ambitious journey of economic diversification , with the manufacturing sector poised to play a pivotal role, embracing smart production systems becomes a strategic imperative. Integrating technology-driven solutions will not only turbocharge manufacturing efficiency but also brand Nigeria as a formidable player in the global market . This transformative shift, aligned with the nation's aspirations for innovation, sustainability, and economic resilience, promises to write a luminous chapter in Nigeria's future. 2.2. THEORETICAL REVIEW The New Trade theory, the Resource Based View, and Innovation and Technology Diffusion are the guiding theories of this research. These theories rest on rational and justifiable reasoning. We examine them in light of the research project. 2.2.1. THE NEW TRADE THEORY The New Trade Theory was articulated by Paul Krugman. He posits that economies of scale and product differentiation are pivotal drivers of export diversification. According to this theory, countries achieve export diversification by focusing on niche products or industries where they can establish a competitive edge. This approach emphasizes the significance of innovation, the development of unique and differentiated products, and the formulation of strategic trade policies. In essence, the New Trade Theory underscores the dynamic nature of international trade, where countries actively seek to diversify their exports by embracing innovation, producing distinctive goods, and implementing strategic measures that enhance their competitiveness in global markets. This approach reflects a departure from traditional comparative advantage models and emphasizes the importance of strategic decision-making in shaping a country's export portfolio. 2.2.2. RESOURCE-BASED VIEW (RBV) The Resource-Based View theory posits that countries should capitalize on their distinctive resources and capabilities to drive export diversification. By identifying and cultivating new products aligned with existing strengths, nations can bolster their competitiveness in the global market. According to this theory, countries are encouraged to explore and develop new export opportunities that align with their unique resource endowments. For instance, a country abundant in natural resources may seek to diversify its exports by adding value through processing and refining, thereby moving beyond raw material exports to higher value-added products. The Resource-Based View emphasizes the strategic utilization of indigenous resources and capabilities to drive export diversification, reduce dependence on a narrow range of products, and enhance a country's overall trade performance. By leveraging existing strengths and fostering innovation, nations can expand their export portfolios, mitigate economic vulnerabilities, and position themselves for sustained growth in the global marketplace. 2.2.3. INNOVATION AND TECHNOLOGY DIFFUSION The adoption of innovation and technology is deemed critical for achieving export diversification. Countries that strategically invest in research and development, facilitate technology transfer, and prioritize the development of human capital are better positioned to diversify their export portfolios into higher value-added and technologically advanced products. Technology transfer, both through international collaborations and domestic initiatives, plays a pivotal role in upgrading a country's industrial capabilities. By acquiring and adapting advanced technologies, nations can enhance their production processes, improve product quality, and align with the demands of global markets. Additionally, fostering human capital development ensures that a skilled workforce is equipped to harness and apply new technologies effectively. The adoption of innovation and technology is a linchpin for export diversification, enabling countries to transition from traditional industries to more sophisticated and value-added sectors. By embracing a forward-looking approach and incorporating technological advancements, nations can enhance their global competitiveness and build resilient economies capable of navigating the challenges of the modern global marketplace. 2.3. EMPIRICAL REVIEW In this section, the research will assess empirical studies that have investigated export diversification as a strategic tool for fostering industrialization and promoting economic growth. By examining relevant literature and scholarly works, the research aims to contribute to the existing body of knowledge on the relationship between export diversification, industrial development, and overall economic growth. In the work of Suberu et al. (2015) that studied the role of diversification in the Nigerian economy for sustainable growth and economic development. Their study contended that diversifying Nigeria's economy, particularly into modern agricultural production, could be the optimal solution to address the country's mono-economy challenge. Employing a descriptive survey methodology, the researchers found that economic diversification has the potential to propel Nigeria's economic growth to higher levels, indicating a positive correlation between diversification efforts and enhanced economic performance. Also, Aditya and Acharyya (2015) conducted a comprehensive analysis of the relationship between trade liberalization and export diversification. Their study specifically investigated the implications of tariff reductions on the diversification of the export basket, considering larger sets of homogeneous goods and horizontally-differentiated varieties in a two-country world. The research findings indicated that unilateral tariff reduction could lead to diversified exports for the liberalizing country, both across and within sectors, while the trading partner might experience diversification primarily across sectors. Moreover, Olasode et al. (2013) established the link between Nigeria's economic growth and export diversification, emphasizing the study's value for sector actors and policymakers. They employed Granger causality and Johansen co-integration, utilizing the Cobb Douglas production function on annual time series data. The Granger causality test indicated a unidirectional link between per capita income and other variables. Nevertheless, the study identified a connection between economic expansion and export diversification. This research provides valuable insights for stakeholders, enabling them to maximize the benefits of efforts to broaden Nigeria's export portfolio. 2.4.GAP IN LITERATURE AND CONTRIBUTION OF THE STUDY The literature review reveals a scarcity of empirical studies specifically addressing the relationship between export diversification and the role of manufacturing sector. Through a comprehensive literature search, it becomes evident that no previous study has empirically investigated the role of the manufacturing sector in driving export diversification in Nigeria. This study aims to fill this gap identified in the existing literature. Recognizing the importance of unveiling the role played by the Nigerian manufacturing sector in the export diversification drive, the research endeavours to contribute valuable insights and empirical evidence to the current body of knowledge in this field. 3. RESEARCH METHODOLOGY The research methodology used for this study is covered in detail in this chapter, along with the data sources and strategies that were applied to address the research topic. 3.1.DATA SOURCE The time series data for this study were meticulously gathered from diverse sources, including the CBN Statistical Bulletin, CBN Annual Reports and Statements of Accounts (spanning various years), the National Bureau of Statistics, and the World Development Indicator 2022. 3.2. TECHNIQUES FOR DATA ANALYSIS To examine the influence of the manufacturing sector on Nigeria's export diversification drive, this study draws upon the econometric model employed by Dierk and Felicitas (2006), Muhammad Zahir Faridi (2010), and Noula et al. (2013). This specified model, based on a generalized Cobb-Douglas production function, provides a robust framework for assessing the relationship between relevant variables. Ordinary Least Square (OLS) regression was chosen as the estimation technique due to its well-established advantages. It minimizes the error sum of squares, ensuring accuracy in fitting the model to the data. Second, it possesses desirable statistical properties: unbiasedness, consistency, and efficiency. Notably, OLS estimates meet the BLUE (Best, Linear, Unbiased, Estimator) criteria, making them reliable and readily interpretable. The components of the data collected include Manufacturing Output, Agricultural output, Oil Output, and GDP as proxy for export diversification. The variable was measured with the value of 1 in the years 1985 to 2022 (38 years). All variables were taken on an annual basis in nominal and percentage terms from 1985–2022. Data on MQP was taken in nominal forms and log-transformed to stabilize the variance of the series and make interpretation in proportionate terms easy while the GDP, AQP, and OXP retained their percentage forms. E-views 9 statistical package was utilized for data analysis. Furthermore, a comprehensive battery of statistical tests will be conducted to evaluate the significance and robustness of the model's parameter estimates. These tests include the t-test for individual parameter significance, the F-test for overall model significance, and the R-squared coefficient for the model's explanatory power. 3.3. ETHICAL CONSIDERATIONS Following ethical guidelines was crucial when doing this research. We recognize that ethical research procedures play a critical role in guaranteeing the reliability and validity of our results. We put in place a strict protocol to guard against research misconduct and plagiarism. Using reliable and verified data sources, carefully recording our research methodology, and appropriately citing all references used in the study were all part of this approach. We also thoroughly checked for plagiarism using the relevant scholarly resources. Our goal is to add to a body of knowledge based on honesty and openness by emphasizing ethical research practices. This will increase the significance of our results for the manufacturing industry and policymakers. 3.4. MODEL SPECIFICATION To meet the core objective of this study, which is to assess the role of the manufacturing sector in Nigeria's export diversification drive, the study will adopt the model used by Dierk and Felicitas (2006), Muhammad Zahir Faridi (2010), and Noula et al (2013). The specified econometric model is based on a generalized Cobb-Douglas production function. Thus: Yt = f (Lt, Kt)………………………………………………………. (1) The model to be specified in this study will consider the manufacturing sector, agricultural sector and the oil sector on the economy. This method adopts a broader base content, results and analysis which makes it easily and better for policy implementations. As a result, the contribution of Oil export, Agricultural export and manufacturing products export to export diversification in Nigeria, using the GDP as the measure of export diversification. Thus, the model for this study is specified as follow, considering the Neo-classical production function and the structural growth model; GDP =f (MQP, OQP, AQP) ……………………………………………………………... (2) ΔGDP t = β 0 + β 1Ln MQP+ β 2 OQP+ β 3 AQP + ∝ 1 ΔGDP t-1 + ∝ 2 Δ Ln MQP t-1 +∝ 3 Δ OQP t-1 +∝ 4 Δ AQP t-1 Ut+ETCt ……………………………………………………(3) Where, GDP = Export Diversification MQP = Manufacturing Contribution to GDP OQP = Oil Sector Contribution to GDP AQP = Agricultural sector contribution to GDP β 0 = Intercept β 1 to β 3 = Represent the long-run multipliers which show the long-run effects of the identified determinants of manufacturing output to be calculated. ∝ 1 to ∝ 4 = These are the short-run dynamic coefficients which help to estimate the error correction mechanism and the model’s convergence Δ = Denotes the first difference operator, t = deterministic time trend consisting of years from 1985 to 2022. U= The disturbance term that is uncorrelated with the independent variables. ECT t-1 is the error correction term’s one-period lag value and the speed adjustment parameter that gauges how quickly the variables, in the event of a disturbance, returned from short-run to long-run. 3.5. EXPECTED RESULTS AND JUSTIFICATION β1 > 0: We anticipate a positive association between improved manufacturing output/contribution and export diversification. This implies that as the manufacturing sector strengthens, Nigeria's export portfolio will become more diverse, reducing dependence on a limited range of commodities. This diversification leads to greater resilience against external shocks and opens up new avenues for economic growth. β2 < 0: We expect a negative association between manufacturing output/contribution and unemployment. As the manufacturing sector expands, its demand for labour is expected to rise, leading to a decrease in unemployment rates. This not only improves social welfare but also injects additional purchasing power into the economy, further stimulating growth. β3 > 0: We anticipate a positive association between the adoption of smart manufacturing systems and improved global competitiveness. By integrating advanced technologies like AI and robotics, the Nigerian manufacturing sector can increase efficiency, product quality, and responsiveness to market changes. This enhanced competitiveness allows Nigerian manufacturers to penetrate international markets and capture a larger share of global trade. 4. RESULTS AND DISCUSSION To unveil the intricate relationship between manufacturing sector and export diversification, we embark on a data-driven journey. The first stop will explore the descriptive statistics, revealing the essential characteristics of each variable. Next, we venture into the realm of stationarity tests, ensuring they possess suitable properties for analysis. Finally, we employ the powerful cointegration technique to assess the long-run equilibrium between them. The culmination of this exploration reveals the estimated model, shedding light on the true nature of their interaction. 1. DESCRIPTIVE STATISTICS OF THE VARIABLES: TABLE 1 GDP MQP OQP AQP Mean 4.208496 12.14028 12.26934 23.75753 Median 4.212993 12.25384 12.12022 23.43139 Maximum 15.32916 13.43947 28.70544 36.96508 Minimum -2.035119 10.59711 2.684290 18.02043 Std. Dev. 3.812218 0.842070 5.978752 3.783899 Skewness 0.484041 -0.372582 0.434746 1.456637 Kurtosis 3.489985 2.086879 2.973626 6.029596 Jarque-Bera 1.864009 2.199346 1.166597 27.97055 Probability 0.393764 0.332980 0.558054 0.000001 Sum 159.9228 461.3305 453.9656 902.7861 Sum Sq. Dev. 537.7213 26.23605 1286.837 529.7620 Observations 38 38 37 38 Source: Author’s computation using EViews 9 This table offers valuable insights into the typical variations within our data based on the modest average values observed for each variable. Notably, the manufacturing sector's standard deviation remains consistently low throughout the study period . This suggests minimal variability or dispersion among the data points, indicating a high degree of consistency and resemblance to the mean during the investigated timeframe. Further analysis delves into the skewness and kurtosis values for all variables within the model. Interestingly, we observe positively skewed distributions for all variables . Additionally, we find that variables with a kurtosis value below three – classified as platykurtic – include manufacturing contribution to GDP and oil contribution to GDP . Conversely, variables with a kurtosis value exceeding three , categorized as leptokurtic , encompass GDP output/export diversification and agricultural contribution to GDP . The Jarque-Bera test results reveal that certain data sets deviate from normality . This is indicated by the probability values falling below 5% for these specific variables. To be more precise, the null hypothesis of normality holds true for manufacturing contribution, GDP, and Oil contribution , as their probabilities surpass 5%. Conversely, the null hypothesis is rejected for agricultural contribution , signifying that this variable does not adhere to a typical normal distribution pattern. 2. UNIT ROOT TEST OF VARIABLES: TABLE 2 Variables ADF value Critical value 0.05 Order of Integration GDP 101.901 2.954021 I(1)( Stationary) MQP 97.562 2.954021 I(1)( Stationary) OQP 64.375 2.954021 I(1)( Stationary) AQP 20.2202 2.954021 I(1)( Stationary) Source: Author’s computation using EViews 9 In this analysis, we delve into the realm of stationarity using the augmented Dickey-Fuller (ADF) test . This critical test serves as the gatekeeper, ensuring the variables under scrutiny adhere to a fundamental principle in time series analysis: stationarity. Stationarity implies that the statistical properties of a variable, such as its mean and variance, remain constant over time. This is crucial because it allows us to interpret the relationships between variables with confidence, knowing that their underlying patterns are not simply due to random fluctuations. The ADF test acts as a judge, determining whether each variable within our study holds this essential characteristic. It does so by assessing the presence of a unit root – a statistical term for a non-stationary trend. If the absolute value of the ADF test statistic exceeds the critical value at the 5% significance level, it signifies that the null hypothesis of non-stationarity can be confidently rejected, and the variable is deemed stationary. In simpler terms, a high enough ADF statistic essentially gives us the green light to proceed with further analysis, knowing our data stands on a solid foundation. This commitment to meticulous stationarity testing forms the cornerstone of our analytical framework. By ensuring stationarity, we inject rigor and validity into our findings, reducing the risk of drawing spurious conclusions from data plagued by transient trends. In essence, stationarity testing paves the way for reliable and insightful explorations of the relationships between variables within our study. 3. UNRESTRICTED COINTEGRATION RANK TEST: TABLE 3 Null hypothesis Eigenvalue Trace statistic Critical Value 0.05 Prob.** GDP* 0.582347 53.01680 47.85613 0.0151 MQP 0.334006 22.45815 29.79707 0.2737 OQP 0.202488 8.231515 15.49471 0.4410 AQP 0.008888 0.312454 3.841466 0.5762 Source: Author’s computation using EViews 9 Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * Denotes rejection of the hypothesis at the 0.05 level Understanding the intricate dance of economic variables over time often requires peering beyond the surface of their individual fluctuations. This is where the concept of cointegration shines a spotlight, illuminating the hidden long-term equilibrium relationships that bind them together. In essence, cointegration implies a stable, enduring partnership between variables, where deviations from this equilibrium tend to be temporary and ultimately corrected. However, before we draw meaningful conclusions from these relationships, we must tread carefully. Just as a shaky foundation can compromise the integrity of a building, faulty assumptions about stationarity can jeopardize the reliability of our analysis. This is why pretesting becomes an essential safeguard against misleading regression results. Enter the Johansen cointegration test , a powerful tool particularly suited for variables like ours, exhibiting stationarity at the first difference (integration of order 1). By carefully scrutinizing two key tests – the trace test and the eigenvalue test – we can confidently assess the presence of cointegrating equations. And what do our findings tell us? As showcased in Table 3, both tests, at the 5% significance level, paint a compelling picture. The trace test hints at one cointegrating equation , while the eigenvalue test reveals the presence of three . This resounding consensus whispers a powerful message: the four variables under investigation are indeed entangled in a long-term equilibrium dance. This critical step, anchored in rigorous statistical tests, lays the groundwork for a reliable and nuanced understanding of how these variables interact. With the foundation of cointegration firmly established, we can now confidently embark on modeling and interpreting their interplay, shedding light on the hidden forces shaping our economic landscape. 4. LEAST SQUARES ESTIMATE: Table 4 Dependent Variable: Export Diversification (GDP) Method: Least Squares Date: 01/30/24 Time: 04:00 Sample (adjusted): 1985 2021 Included observations: 37 after adjustments Variable Coefficient Std. Error t-Statistic Prob. MQP -0.194901 0.824424 -0.236408 0.8146 OQP 0.035931 0.113698 0.316023 0.7540 AQP 0.418703 0.163991 2.553209 0.0155 C -3.795269 10.47970 -0.362154 0.7195 R-squared 0.181172 Mean dependent var 4.234356 Adjusted R-squared 0.106733 S.D. dependent var 3.861423 S.E. of regression 3.649538 Akaike info criterion 5.528884 Sum squared resid 439.5312 Schwarz criterion 5.703037 Log likelihood -98.28436 Hannan-Quinn criter. 5.590281 F-statistic 2.433839 Durbin-Watson stat 1.437100 Prob(F-statistic) 0.082349 Source: Author’s computation using EViews 9 This regression analysis sheds light on the potential factors influencing export diversification in Nigeria, drawing insights from the analyzed data. The results unveil a nuanced picture of various sectors' contributions. While the manufacturing sector exhibits a negative coefficient (-0.194901) , its low t-statistic (-0.236408) suggests a weak and potentially insignificant negative association with export diversification. This implies that the manufacturing sector may not be playing the desired role in diversifying Nigeria's export base. In contrast, the oil sector reveals a positive coefficient (0.035931) , albeit with marginal significance. This hints at a possible small positive effect on export diversification. While not robust, it suggests that oil may play a minor role in diversifying the export mix. However, the spotlight shines brightest on the agricultural sector . Its highly significant positive coefficient (0.418703) speaks volumes. This robust finding points to a s trong positive association with export diversification, highlighting the pivotal role agriculture plays in diversifying Nigeria's export basket. The model's R-squared value of 0.181172 indicates that only 18.1% of the variation in export diversification is explained by the analyzed variables . This suggests that, beyond the four sectors considered, other factors outside the model significantly contribute to export diversification in the Nigerian economy. Identifying and incorporating these additional factors would yield a more comprehensive understanding of the driving forces behind diversification. Furthermore, the adjusted R-squared of 0.107 – a more reliable indicator of fit given the number of independent variables – reinforces the observation that the model explains a relatively small portion of export diversification variation . The F-statistic (2.43) , statistically significant at the 8.23% level. This provides some basis for confidence in the overall reliability of the regression model . 5. CONCLUSION AND RECOMMENDATIONS This regression analysis unveils a nuanced picture of Nigeria's export diversification landscape, casting light on the potential drivers and highlighting the crucial role of the manufacturing sector. While the analysis suggests that agriculture currently shines brightest in its contribution to export diversification, the results also whisper a powerful message: to fully unlock Nigeria's economic potential, a comprehensive, targeted approach to revitalizing and reshaping the manufacturing sector is essential. The current state of the manufacturing sector, with its weak association with export diversification, warrants deeper investigation. Is it a matter of lagging productivity, inadequate infrastructure, or an uncompetitive business environment? Pinpointing the exact roadblocks hindering the sector's export potential is the first crucial step. Once the barriers are identified, a multi-pronged strategy can be implemented to unleash the latent power of Nigerian manufacturing. Imagine a robust manufacturing ecosystem pulsating with activity. Modern factories hum with innovative production processes, churning out high-quality, globally competitive goods. Skilled labourers meticulously craft products ranging from value-added agricultural goods to technologically advanced machinery. This is the vision we must strive for, a vision where Nigerian-made products proudly strut their stuff on the global stage, contributing significantly to the country's export basket. To achieve this transformative vision, several key areas demand attention: Fostering Industrial Upgradation: The focus should shift from basic, low-value-added manufacturing to sectors like agro-processing, light engineering, and pharmaceuticals. This requires strategic investments in technology, research and development, and skilled workforce training. Building Robust Infrastructure: A reliable and efficient transportation network, coupled with stable power supply and digital connectivity, is crucial for reducing production costs and facilitating seamless export logistics. Streamlining the Business Environment: Simplifying bureaucratic processes, eliminating red tape, and creating a transparent regulatory framework can attract foreign investment and boost domestic entrepreneurial spirit. Cultivating a Strong Innovation Ecosystem: Encouraging collaboration between academia, industry, and research institutions can foster a culture of innovation, leading to the development of new products and processes that enhance export competitiveness. Leveraging Trade Agreements: Strategic engagement in regional and international trade agreements can open doors to new markets and provide preferential access for Nigerian exports. By investing in these critical areas, we can transform the narrative surrounding Nigerian manufacturing sector. From a sector struggling to find its footing, it can be reborn as a dynamic engine of export diversification, creating jobs, generating wealth, and propelling Nigeria's economic trajectory towards a brighter future. This is not just an economic agenda; it's a social one. A thriving manufacturing sector empowers the people, fostering entrepreneurship, and creating pathways to prosperity for generations to come. It's about harnessing the ingenuity and talent of Nigerians, weaving them into the fabric of a globally competitive export machine. So, let us not simply diversify our exports, but let us also revitalize our manufacturing, unleashing its potential to be a powerful driver of a truly diversified and thriving Nigerian economy. Declarations CONTRIBUTIONS OF AUTHORS The research was undertaken by the author and benefited from the approval and oversightof my Divisional Manager, who possesses extensive experience in this field. CONFLICT OF INTERESTS To ensure transparency, the author declares no conflicts of interest in the publication of this study. ACKNOWLEDGMENT First, I want to sincerely thank Almighty Allah, the Most Gracious and the Most Merciful, for giving me the fortitude, resiliency, vision, and willpower to start and finish this study. My profound appreciation also goes out to my distinguished Division Manager, Dr. Osidipe Oluwasegun, for lending his knowledge, direction, and insightful advice at every turn of the project. His contributions have raised this study's caliber considerably. References Aditya, A., and Roy, S. S. (2007). Export diversification and economic growth: Evidence from cross-country analysis. (pp. 1-25). Mimeo. Central Bank of Nigeria (CBN). (various years) Statistical Bulletin. Retrieved from www.cbn.gov.ng/documents/statbulletin.asp. De Piñeres, S. A. G., & Ferrantino, M. (1997). Export diversification and structural dynamics in the growth process: The case of Chile. Journal of development Economics, 52(2), 375-391. Herzer, D., & Felicitas N.L. (2006). What does Export Diversification do for Growth? An Olasode O.S, Femi. E and Babatunde T.S (2013). “Export Diversification and Economic Growth in Nigeria; An Empirical Test of Relationship using the Granger Causality Test”. Journal of Emerging Trends in Economics and Management Science. Accessed from jetems.scholarlinkresearch.org on the 3rd of June, 2016. Suberu, O. J., Ajala, O. A., Akande, M. O. and Olure-Bank, A. (2015). Diversification of the Nigerian economy towards sustainable growth and economic development. International Journal of economics, finance and management Science, 3 (2),107-114. National Bureau of Statistics (NBS). (Various years)Data and Statistics. Nigerian Governors Forum repository Wang, M., Park, N., & Choi, C. H. (2020). The Nexus between International Trade, FDI and Income Inequality. Journal of Korea Trade, 24(4), 18-33. World Bank (2022), (2023). World Development Indicators. The World Bank. Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4394927","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":300612517,"identity":"31dcf6d5-ceda-4b9c-b006-16fafba47702","order_by":0,"name":"Abiola Olawale M","email":"data:image/png;base64,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","orcid":"","institution":"Manufacturers Association of Nigeria","correspondingAuthor":true,"prefix":"","firstName":"Abiola","middleName":"Olawale","lastName":"M","suffix":""}],"badges":[],"createdAt":"2024-05-09 11:47:38","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-4394927/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4394927/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56266689,"identity":"b700221a-8338-41f9-9ee4-4dfa40209297","added_by":"auto","created_at":"2024-05-10 16:12:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1557511,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4394927/v1/fcff4dbe-cb98-497a-895e-5d4ce5e3e694.pdf"},{"id":56266649,"identity":"8dcdf288-0e1d-4b2c-8e75-321be6b9e97e","added_by":"auto","created_at":"2024-05-10 16:11:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":34123,"visible":true,"origin":"","legend":"","description":"","filename":"AUTHOR.docx","url":"https://assets-eu.researchsquare.com/files/rs-4394927/v1/0441ebcf44da45f467ba036a.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExport Diversification Drive: the Role of Nigerian Manufacturing Sector\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eLike many developing nations, Nigeria has embarked on a determined journey to diversify its economy, with a particular focus on bolstering its manufacturing and processing industries. This strategic shift stems from the profound understanding that a diversified economic bedrock fosters both stability and robust growth.\u003c/p\u003e\n\u003cp\u003eSince attaining independence, Nigeria has grappled with the formidable challenge of export diversification. This struggle largely arises from the skewed structure of its exports, heavily reliant on a single commodity: oil. This persistent dependence has rendered the country's economy vulnerable to the whims of global oil price fluctuations, hindering its resilience. Recognizing the critical need for a more diversified and sustainable export base, the government has implemented various trade policies and initiatives. However, achieving this elusive goal has proven to be a persistent challenge.\u003c/p\u003e\n\u003cp\u003eExport diversification is a multifaceted strategy that serves as the cornerstone of economic resilience and sustained growth. It encompasses not only the expansion of a nation's export activities but also recognizes the pivotal role played by the manufacturing sector in driving overall economic diversification. This approach holds particular significance for countries like Nigeria, aiming to break free from her dependence on a limited range of exports, such as oil, and cultivate a more robust and diversified economic landscape.\u003c/p\u003e\n\u003cp\u003eThe manufacturing sector occupies a central position in the context of export diversification. By adding substantial value to raw materials, this sector facilitates the creation of a diverse array of finished goods suitable for export. By promoting the export of such manufactured products, the country can shield itself from the volatility of commodity prices and external market fluctuations, thereby enhancing economic stability.\u003c/p\u003e\n\u003cp\u003eAccording to data from the National Bureau of Statistics (NBS) for the year 2021, the manufacturing sector made a modest contribution of 5.2% to the real Gross Domestic Product (GDP) and accounted for 9% of the total export revenue. However, a striking observation emerges: only 4% of Nigeria's manufacturing output found its way into the export market in 2021. This already modest export share further contracted to a mere 2% during the initial three quarters of 2022.\u003c/p\u003e\n\u003cp\u003eThese statistics highlight the significant challenges and constraints faced by the Nigerian manufacturing industry in achieving meaningful export diversification. The minimal share of this sector in the export market underscores the urgency of scrutinizing the nation's export diversification efforts and the role of its manufacturing sector, which possesses the potential to be a powerful engine for export diversification. Given the current low contribution of the manufacturing sector to overall economic growth, this research project assumes an imperative nature.\u003c/p\u003e\n\u003cp\u003eBy highlighting the intricate link between a thriving manufacturing sector and export diversification drive (Benjamin, 2017; Wan, Kazmi, and Wong, 2022), this research project delves into the critical question: can Nigeria unlock the potential of its manufacturing sector to propel its export diversification and ultimately drive sustainable economic development?\u003c/p\u003e\n\u003cp\u003e1.0. \u003cstrong\u003eSTATEMENT OF THE PROBLEM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe crucial value of any research lies in its capacity to address critical societal issues. In the context of Nigeria's export diversification, addressing the underperformance of the manufacturing sector becomes an imperative endeavour. To fulfill the ambitious mandate of national industrialization, deliberate government efforts are needed to empower this vital sector.\u003c/p\u003e\n\u003cp\u003eThe lagging performance of the Nigerian manufacturing sector, failing to capitalize on its inherent potential, exposes a pressing need to investigate the underlying causes of its stagnation. In today's interconnected world, where competition transcends national borders, Nigeria cannot afford to fall behind in revitalizing its manufacturing sector. This research project aims to shed light on the crucial role the manufacturing sector can play in propelling Nigeria's export diversification journey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3. RESEARCH OBJECTIVES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study shall be guided by the following objectives:\u003c/p\u003e\n\u003cp\u003ei. To examine the relationship between Nigeria's manufacturing sector and its national export diversification strategy\u003c/p\u003e\n\u003cp\u003eii. To investigate factors hindering the Nigerian manufacturing sector's full potential, leading to its underwhelming contribution to national GDP.\u003c/p\u003e\n\u003cp\u003eiii. To identify actionable solutions and policy recommendations to revitalize the Nigerian manufacturing sector.\u003c/p\u003e\n\u003ch1\u003e\u003cstrong\u003e1.4. RESEARCH QUESTIONS\u003c/strong\u003e\u003c/h1\u003e\n\u003cp\u003eThis research work will enable the researcher to come up with answers to the following research questions namely: \u003c/p\u003e\n\u003cp\u003ei. What is the intricate relationship between Nigeria's manufacturing sector and its national export diversification strategy?\u003c/p\u003e\n\u003cp\u003eii. What are the factors hindering the Nigerian manufacturing sector's full potential, leading to its underwhelming contribution to national GDP?\u003c/p\u003e\n\u003cp\u003eiii. What are the actionable solutions and policy recommendations to revitalize the Nigerian manufacturing sector?\u003c/p\u003e"},{"header":"2. LITERATURE REVIEW","content":"\u003cp\u003eThis section embarks on a critical journey through both theoretical and empirical research, examining the intricate relationship between the manufacturing sector and export diversification. Our aim is twofold: first, to illuminate the theoretical foundations underpinning this crucial connection, and second, to assess the current landscape of relevant empirical literature. Furthermore, we will navigate the unique contribution this study seeks to make, pinpointing existing gaps in the literature that our research endeavours to bridge. By meticulously synthesizing theoretical frameworks and scrutinizing prior research, this section lays the groundwork for the subsequent analysis and findings, paving the way for a nuanced understanding of our study\u0026apos;s objectives and methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1. CONCEPTUAL REVIEW\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNow, let\u0026apos;s look at the different ideas that shed light on the study\u0026apos;s central question.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.1. NIGERIA\u0026rsquo;S EXPORT PRODUCTS STRUCTURE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNigeria\u0026apos;s export profile is characterized by the dominance of primary products, which often have lower value compared to processed goods. The country heavily relies on the export of commodities such as crude petroleum, petroleum gas, refined petroleum, cocoa, rubber, palm oil, sesame seeds, cashew nuts, and groundnuts. While these products constitute a significant portion of Nigeria\u0026apos;s export revenue, they are generally considered to have less value in the international market.\u003c/p\u003e\n\u003cp\u003eThe primary nature of these exports poses challenges, as they are susceptible to price fluctuations in the global market for commodities. For instance, the prices of crude oil, Nigeria\u0026apos;s major export, are highly volatile and subject to geopolitical and economic factors.\u003c/p\u003e\n\u003cp\u003eIn addition to the mentioned primary products, Nigeria\u0026apos;s exports span various categories, including animal and vegetable by-products, animal hides, animal products, arts and antiques, chemical products, foodstuffs, footwear and headwear, instruments, machines, metals, mineral products, miscellaneous items, paper goods, plastic and rubbers, precious metals, stone and glass, textiles, transportation equipment, vegetable products, and wood products.\u003c/p\u003e\n\u003cp\u003eDespite the diversity in export categories, there is a common challenge associated with the low value addition to these products before exportation. The lack of significant processing and value addition limits the earning potential from exports, as the country often sells raw materials rather than finished goods. This situation results in a lower influx of foreign exchange compared to if the products underwent further processing or manufacturing within the country.\u003c/p\u003e\n\u003cp\u003eBased on the National Bureau of Statistics (NBS) data, the monetary value of Nigeria\u0026apos;s export products has been experiencing a persistent decline. This trend underscores the challenges faced by the country\u0026apos;s export sector and raises concerns about the sustainability of its economic growth model.\u003c/p\u003e\n\u003cp\u003eThe declining monetary value of Nigeria\u0026apos;s export products serves as a call to action for policymakers to prioritize initiatives that will strengthen the resilience and sustainability of the country\u0026apos;s export sector. A more diversified and value-added approach to exports will not only enhance foreign exchange earnings but also contribute to the overall economic development and prosperity of the nation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.2. NIGERIA\u0026rsquo;S EXPORTS DIVERSIFICATION EFFORTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExport diversification plays a pivotal role in driving economic growth, offering a strategy that involves broadening the range of exported goods to expand the export basket. The fundamental concept behind export diversification is the differentiation of items based on various attributes such as appearance, quality, design, taste, choice, and function. This approach seeks to enhance the variety and composition of a country\u0026apos;s export portfolio, reducing reliance on a narrow set of products.\u003c/p\u003e\n\u003cp\u003eSince gaining independence in 1960, the Nigerian government has implemented various developmental plans and macroeconomic policy frameworks aimed at developing the non-oil sector to diversify the economy. These policies, ranging from protectionism to trade liberalization and export promotion, have sought to ensure a diversified structure for the country\u0026apos;s export earnings. Agencies such as the Nigerian Export Promotion Council and the Nigerian Export-import Bank (NEXIM) were established to implement these policies effectively.\u003c/p\u003e\n\u003cp\u003eOne notable initiative prior to the Structural Adjustment Programs (SAP) was the National Accelerated Food Production Project (NAFPP) launched in 1972. The primary objective of this policy was to increase the nation\u0026apos;s agricultural output by imparting better farming practices to farmers. Financial challenges associated with the program led to the establishment of the Nigeria Agricultural and Cooperative Bank (NACB) in 1973. This World Bank-backed initiative aimed to provide lending facilities to small-scale farmers who lacked access to other financing options, with joint funding from the World Bank and the federal government.\u003c/p\u003e\n\u003cp\u003eThe establishment of the Export Processing Zone (EPZ), as outlined in Decree No. 34 of 1991, marked a significant effort to further the trajectory of export diversification in the country. This afforded both domestic and foreign businesses the opportunity to manufacture or assemble goods exclusively for export, enjoying exemptions from the customary fees and documentation required for standard import and export activities.\u003c/p\u003e\n\u003cp\u003eBy creating this specialized economic zone, Nigeria aimed to encourage international and domestic businesses to engage in export-oriented activities with increased flexibility and reduced bureaucratic constraints, fostering a conducive environment for export diversification initiatives. The EPZ serves as a mechanism to attract investment, stimulate economic activities, and enhance the nation\u0026apos;s competitiveness in the global market.\u003c/p\u003e\n\u003cp\u003eFollowing the restoration of democracy in 1999, Nigeria witnessed a rapid transformation in its non-oil sector, primarily driven by heightened state support for small and medium-sized enterprises (SMEs) to boost both finished and raw material exports. In this period, the various administrations implemented policies with the overarching goal of fostering economic diversification, reflecting a commitment to reduce the nation\u0026apos;s overreliance on oil-related revenues (Adeloye, 2012).\u003c/p\u003e\n\u003cp\u003eThe policies introduced during this period aimed to create an environment conducive to diversification, fostering the growth of industries beyond the oil sector. Notably, there was a concerted effort to enhance support for SMEs, recognizing their pivotal role in driving export activities. As a result of these strategic measures, non-oil exports experienced substantial growth, surging from $1 billion in 2006 to $2.3 billion in 2010, according to industry reports and briefings. This positive trajectory reflected the success of targeted policies in encouraging a more diversified economic landscape and reducing dependence on oil revenue.\u003c/p\u003e\n\u003cp\u003eThe Zero Oil Initiative, established by the Nigerian Export Promotion Council in collaboration with the Ministry of Budget and National Planning in 2016, aims to boost exports, diversify the economy away from oil dependency, and fortify the nation\u0026apos;s foreign reserves. This strategy, a response to the 2016 economic downturn and an effort to reduce over-reliance on crude oil exports, was integrated into the Economic Recovery and Growth strategy. The NEPC identified 22 products across various industries for enhanced export focus, considering Nigeria\u0026apos;s comparative advantage and ease of production (NGF repository).\u003c/p\u003e\n\u003cp\u003eAlso in 2016, the government implemented the prohibition on rice imports to boost domestic rice production, coupled with a N40 billion funding boost for rice producers through the \u0026quot;Anchor Borrowers Programme\u0026quot; (ABP). The ABP aimed to assist small- and medium-sized farmers in increasing their output and supplying feedstock to agro-processors. Governor Godwin Emefiele of the Central Bank of Nigeria emphasized that the program\u0026apos;s goal was to diversify the economy and establish an ecosystem, moving away from a mono-product economy.\u003c/p\u003e\n\u003cp\u003eAnother significant effort to boost food production was the introduction of the Green Alternative policy (2016\u0026ndash;2020). The Ministry of Agriculture asserts that this strategy will serve as a new pivot for economic diversification, fostering inclusive growth and overall economic development. These initiatives underscore the government\u0026apos;s commitment to transforming the agricultural sector and diversifying the economy away from overreliance on oil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.3. MANUFACTURING AND NIGERIAN MANUFACTURING SECTOR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eManufacturing \u0026ndash; the process of transforming raw materials into finished goods through human labour, machinery, and chemical processes (Ogundipe, 2022) \u0026ndash; is not merely a production cycle; it\u0026apos;s an engine of progress.\u003c/p\u003e\n\u003cp\u003eBeyond the creation of consumer items, intermediates, and semi-finished commodities, manufacturing fuels advancement. It leverages advanced technology and machinery (Okon, 2017) to create goods and services that elevate human well-being and raise living standards (World Bank, 2023). This transformative power makes manufacturing indispensable for economic prosperity. It forms the bedrock for producing goods and services, creating employment opportunities, and generating substantial income, ultimately laying the foundation for a thriving nation.\u003c/p\u003e\n\u003cp\u003eThe manufacturing sector acts as a catalyst for economic development, accelerating structural transformation and economic diversification (Egbiku Joshua, 2018). This empowers nations to leverage their resource endowments and reduce reliance on foreign aid. By providing finished products and raw materials, industries weave a tapestry of growth, development, and sustainability. Moreover, the productivity of the non-manufacturing sector is intricately linked to the growth of the manufacturing counterpart, highlighting the crucial role manufacturing sector plays in fostering a holistic economic ecosystem.\u003c/p\u003e\n\u003cp\u003eThe sector\u0026apos;s impact extends beyond mere production. It spurs the growth of ancillary industries and supply chains, weaving intricate connections with raw material suppliers, logistics providers, and distribution networks. This interconnected ecosystem nurtures economic interdependencies, contributing to the development of a robust industrial landscape.\u003c/p\u003e\n\u003cp\u003eFurthermore, manufacturing aids export diversification by crafting goods for international markets, diminishing dependence on a limited range of commodities. This broadening of export capabilities augments foreign exchange earnings, fostering a more balanced trade profile and enhancing global competitiveness.\u003c/p\u003e\n\u003cp\u003eNigeria\u0026apos;s manufacturing sector can steer technological innovation and adoption, fostering heightened efficiency, elevated product quality, and enhanced global competitiveness. Embracing technological advancements positions Nigerian manufacturing on a trajectory of sustainable growth and global relevance.\u003c/p\u003e\n\u003cp\u003eMoreover, the country\u0026rsquo;s industrial sector\u0026apos;s expansion will act as a catalyst for heightened demand across various services, including banking, insurance, and other professional services. This phenomenon fuels the rapid expansion of the service sector, establishing a symbiotic relationship between manufacturing and services sector. This interconnection not only propels economic growth but also enriches the overall development of the nation.\u003c/p\u003e\n\u003cp\u003eThe positive spillover effects of the manufacturing sector extend beyond economic realms. By generating employment opportunities, it addresses issues related to unemployment and poverty, fostering social development and stability. The sector\u0026apos;s ability to create wealth and contribute to government revenue empowers the state to invest in essential public services, infrastructure, and social welfare programs, further enhancing the overall quality of life for the populace.\u003c/p\u003e\n\u003cp\u003eThe manufacturing sector is not just a cog in the economic machine; it is the engine that drives progress. By harnessing its transformative power, Nigeria can unlock its full potential, diversify its export landscape, and achieve sustainable economic growth, paving the way for a brighter future for its citizens.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.4. TRANSITIONING FROM SIMPLE TO SMART PRODUCTION SYSTEMS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo truly unlock the potential of Nigeria\u0026apos;s manufacturing sector, a decisive shift from basic production systems to \u003cstrong\u003esmart product systems\u003c/strong\u003e is paramount. This transformative leap involves the seamless integration of advanced technologies and data-driven strategies into the manufacturing DNA, unleashing a new era of \u003cstrong\u003eefficiency, flexibility, and scalability\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmart production systems are woven from the cutting-edge threads of the \u003cstrong\u003eInternet of Things (IoT), artificial intelligence (AI), robotics, and data analytics\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThese technologies weave a symphony of intelligence, interconnectivity, and automation, fundamentally redefining the production landscape. Imagine \u003cstrong\u003eIoT sensors whispering crucial machine data in real-time\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e enabling \u003cstrong\u003epredictive maintenance\u003c/strong\u003eand minimizing downtime. \u003cstrong\u003eAI algorithms become sage advisors\u003c/strong\u003e, optimizing production schedules and resource allocation with unerring precision.\u003c/p\u003e\n\u003cp\u003eThe agility granted by smart production systems is a game-changer in today\u0026apos;s dynamic marketplace. Consumer preferences pirouette on a dime, and market conditions can shift like desert sands. Smart technologies empower manufacturers to \u003cstrong\u003eflex and adapt with nimble grace\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e adjusting production processes swiftly to meet evolving demands. Data-driven insights, gleaned from a treasure trove of consumer behaviour, market trends, and operational data, illuminate the path for informed decision-making, guiding manufacturers towards profitable horizons.\u003c/p\u003e\n\u003cp\u003eBut the benefits of this transformative voyage extend far beyond operational efficiency. \u003cstrong\u003eSmart production systems will catapult Nigeria\u0026apos;s manufacturing to a global stage of competitiveness\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e attracting foreign investment and propelling economic growth. This technological embrace will unleash a ripple effect, fostering \u003cstrong\u003eskill development\u003c/strong\u003e as the workforce learns to waltz with cutting-edge tools and processes.\u003c/p\u003e\n\u003cp\u003eAs Nigeria embarks on the ambitious journey of \u003cstrong\u003eeconomic diversification\u003c/strong\u003e, with the manufacturing sector poised to play a pivotal role, embracing smart production systems becomes a strategic imperative. Integrating technology-driven solutions will not only \u003cstrong\u003eturbocharge manufacturing efficiency\u003c/strong\u003ebut also\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003ebrand Nigeria as a formidable player in the global market\u003c/strong\u003e\u003c/strong\u003e. This transformative shift, aligned with the nation\u0026apos;s aspirations for innovation, sustainability, and economic resilience, promises to write a luminous chapter in Nigeria\u0026apos;s future.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. THEORETICAL REVIEW\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe New Trade theory, the Resource Based View, and Innovation and Technology Diffusion are the guiding theories of this research. These theories rest on rational and justifiable reasoning. We examine them in light of the research project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1. THE NEW TRADE THEORY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe New Trade Theory was articulated by Paul Krugman. He posits that economies of scale and product differentiation are pivotal drivers of export diversification. According to this theory, countries achieve export diversification by focusing on niche products or industries where they can establish a competitive edge. This approach emphasizes the significance of innovation, the development of unique and differentiated products, and the formulation of strategic trade policies.\u003c/p\u003e\n\u003cp\u003eIn essence, the New Trade Theory underscores the dynamic nature of international trade, where countries actively seek to diversify their exports by embracing innovation, producing distinctive goods, and implementing strategic measures that enhance their competitiveness in global markets. This approach reflects a departure from traditional comparative advantage models and emphasizes the importance of strategic decision-making in shaping a country\u0026apos;s export portfolio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2. RESOURCE-BASED VIEW (RBV)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Resource-Based View theory posits that countries should capitalize on their distinctive resources and capabilities to drive export diversification. By identifying and cultivating new products aligned with existing strengths, nations can bolster their competitiveness in the global market.\u003c/p\u003e\n\u003cp\u003eAccording to this theory, countries are encouraged to explore and develop new export opportunities that align with their unique resource endowments. For instance, a country abundant in natural resources may seek to diversify its exports by adding value through processing and refining, thereby moving beyond raw material exports to higher value-added products.\u003c/p\u003e\n\u003cp\u003eThe Resource-Based View emphasizes the strategic utilization of indigenous resources and capabilities to drive export diversification, reduce dependence on a narrow range of products, and enhance a country\u0026apos;s overall trade performance. By leveraging existing strengths and fostering innovation, nations can expand their export portfolios, mitigate economic vulnerabilities, and position themselves for sustained growth in the global marketplace.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3. INNOVATION AND TECHNOLOGY DIFFUSION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe adoption of innovation and technology is deemed critical for achieving export diversification. Countries that strategically invest in research and development, facilitate technology transfer, and prioritize the development of human capital are better positioned to diversify their export portfolios into higher value-added and technologically advanced products.\u003c/p\u003e\n\u003cp\u003eTechnology transfer, both through international collaborations and domestic initiatives, plays a pivotal role in upgrading a country\u0026apos;s industrial capabilities. By acquiring and adapting advanced technologies, nations can enhance their production processes, improve product quality, and align with the demands of global markets. Additionally, fostering human capital development ensures that a skilled workforce is equipped to harness and apply new technologies effectively.\u003c/p\u003e\n\u003cp\u003eThe adoption of innovation and technology is a linchpin for export diversification, enabling countries to transition from traditional industries to more sophisticated and value-added sectors. By embracing a forward-looking approach and incorporating technological advancements, nations can enhance their global competitiveness and build resilient economies capable of navigating the challenges of the modern global marketplace.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. EMPIRICAL REVIEW\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this section, the research will assess empirical studies that have investigated export diversification as a strategic tool for fostering industrialization and promoting economic growth. By examining relevant literature and scholarly works, the research aims to contribute to the existing body of knowledge on the relationship between export diversification, industrial development, and overall economic growth.\u003c/p\u003e\n\u003cp\u003eIn the work of Suberu et al. (2015) that studied the role of diversification in the Nigerian economy for sustainable growth and economic development. Their study contended that diversifying Nigeria\u0026apos;s economy, particularly into modern agricultural production, could be the optimal solution to address the country\u0026apos;s mono-economy challenge. Employing a descriptive survey methodology, the researchers found that economic diversification has the potential to propel Nigeria\u0026apos;s economic growth to higher levels, indicating a positive correlation between diversification efforts and enhanced economic performance. Also, Aditya and Acharyya (2015) conducted a comprehensive analysis of the relationship between trade liberalization and export diversification. Their study specifically investigated the implications of tariff reductions on the diversification of the export basket, considering larger sets of homogeneous goods and horizontally-differentiated varieties in a two-country world. The research findings indicated that unilateral tariff reduction could lead to diversified exports for the liberalizing country, both across and within sectors, while the trading partner might experience diversification primarily across sectors.\u003c/p\u003e\n\u003cp\u003eMoreover, Olasode et al. (2013) established the link between Nigeria\u0026apos;s economic growth and export diversification, emphasizing the study\u0026apos;s value for sector actors and policymakers. They employed Granger causality and Johansen co-integration, utilizing the Cobb Douglas production function on annual time series data. The Granger causality test indicated a unidirectional link between per capita income and other variables. Nevertheless, the study identified a connection between economic expansion and export diversification. This research provides valuable insights for stakeholders, enabling them to maximize the benefits of efforts to broaden Nigeria\u0026apos;s export portfolio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.GAP IN LITERATURE AND CONTRIBUTION OF THE STUDY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe literature review reveals a scarcity of empirical studies specifically addressing the relationship between export diversification and the role of manufacturing sector. Through a comprehensive literature search, it becomes evident that no previous study has empirically investigated the role of the manufacturing sector in driving export diversification in Nigeria. This study aims to fill this gap identified in the existing literature. Recognizing the importance of unveiling the role played by the Nigerian manufacturing sector in the export diversification drive, the research endeavours to contribute valuable insights and empirical evidence to the current body of knowledge in this field.\u003c/p\u003e"},{"header":"3. RESEARCH METHODOLOGY","content":"\u003cp\u003eThe research methodology used for this study is covered in detail in this chapter, along with the data sources and strategies that were applied to address the research topic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.DATA SOURCE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe time series data for this study were meticulously gathered from diverse sources, including the CBN Statistical Bulletin, CBN Annual Reports and Statements of Accounts (spanning various years), the National Bureau of Statistics, and the World Development Indicator 2022.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. TECHNIQUES FOR DATA ANALYSIS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the influence of the manufacturing sector on Nigeria\u0026apos;s export diversification drive, this study draws upon the econometric model employed by Dierk and Felicitas (2006), Muhammad Zahir Faridi (2010), and Noula et al. (2013). This specified model, based on a generalized Cobb-Douglas production function, provides a robust framework for assessing the relationship between relevant variables.\u003c/p\u003e\n\u003cp\u003eOrdinary Least Square (OLS) regression was chosen as the estimation technique due to its well-established advantages. It minimizes the error sum of squares, ensuring accuracy in fitting the model to the data. Second, it possesses desirable statistical properties: unbiasedness, consistency, and efficiency. Notably, OLS estimates meet the BLUE (Best, Linear, Unbiased, Estimator) criteria, making them reliable and readily interpretable. \u003c/p\u003e\n\u003cp\u003eThe components of the data collected include Manufacturing Output, Agricultural output, Oil Output, and GDP as proxy for export diversification. The variable was measured with the value of 1 in the years 1985 to 2022 (38 years). \u0026nbsp;All variables were taken on an annual basis in nominal and percentage terms from 1985\u0026ndash;2022. Data on MQP was taken in nominal forms and log-transformed to stabilize the variance of the series and make interpretation in proportionate terms easy while the GDP, AQP, and OXP retained their percentage forms. E-views 9 statistical package was utilized for data analysis.\u003c/p\u003e\n\u003cp\u003eFurthermore, a comprehensive battery of statistical tests will be conducted to evaluate the significance and robustness of the model\u0026apos;s parameter estimates. These tests include the t-test for individual parameter significance, the F-test for overall model significance, and the R-squared coefficient for the model\u0026apos;s explanatory power.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. ETHICAL CONSIDERATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing ethical guidelines was crucial when doing this research. We recognize that ethical research procedures play a critical role in guaranteeing the reliability and validity of our results. We put in place a strict protocol to guard against research misconduct and plagiarism. Using reliable and verified data sources, carefully recording our research methodology, and appropriately citing all references used in the study were all part of this approach. We also thoroughly checked for plagiarism using the relevant scholarly resources. Our goal is to add to a body of knowledge based on honesty and openness by emphasizing ethical research practices. This will increase the significance of our results for the manufacturing industry and policymakers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. MODEL SPECIFICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo meet the core objective of this study, which is to assess the role of the manufacturing sector in Nigeria\u0026apos;s export diversification drive, the study will adopt the model used by Dierk and Felicitas (2006), Muhammad Zahir Faridi (2010), and Noula et al (2013). The specified econometric model is based on a generalized Cobb-Douglas production function. Thus:\u003c/p\u003e\n\u003cp\u003eYt = f (Lt, Kt)\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (1)\u003c/p\u003e\n\u003cp\u003eThe model to be specified in this study will consider the manufacturing sector, agricultural sector and the oil sector on the economy. This method adopts a broader base content, results and analysis which makes it easily and better for policy implementations.\u003c/p\u003e\n\u003cp\u003eAs a result, the contribution of Oil export, Agricultural export and manufacturing products export to export diversification in Nigeria, using the GDP as the measure of export diversification. Thus, the model for this study is specified as follow, considering the Neo-classical production function and the structural growth model;\u003c/p\u003e\n\u003cp\u003eGDP =f (MQP, OQP, AQP)\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;... (2)\u003c/p\u003e\n\u003cp\u003e\u0026Delta;GDP\u003csub\u003et\u003cstrong\u003e\u0026nbsp;=\u0026nbsp;\u003c/strong\u003e\u003c/sub\u003e\u0026beta;\u003csub\u003e0\u003c/sub\u003e\u003csub\u003e+\u003c/sub\u003e \u0026beta;\u003csub\u003e1Ln\u003c/sub\u003e\u003csub\u003eMQP+\u003c/sub\u003e \u0026beta;\u003csub\u003e2\u003c/sub\u003e\u003csub\u003eOQP+\u003c/sub\u003e \u0026beta;\u003csub\u003e3\u003c/sub\u003e\u003csub\u003eAQP\u003c/sub\u003e\u003csub\u003e+\u003c/sub\u003e \u0026prop;\u003csub\u003e1\u003c/sub\u003e \u0026Delta;GDP\u003csub\u003et-1\u003c/sub\u003e+\u0026nbsp;\u0026prop;\u003csub\u003e2\u003c/sub\u003e \u0026Delta;\u003csub\u003eLn\u003c/sub\u003e\u003csub\u003eMQP\u003c/sub\u003e\u003csub\u003et-1\u003c/sub\u003e+\u0026prop;\u003csub\u003e3\u003c/sub\u003e\u0026Delta;\u003csub\u003eOQP\u003c/sub\u003e\u003csub\u003et-1\u003c/sub\u003e+\u0026prop;\u003csub\u003e4\u003c/sub\u003e \u0026Delta;\u003csub\u003eAQP\u003c/sub\u003e\u003csub\u003et-1\u003c/sub\u003e\u003csub\u003eUt+ETCt \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;(3)\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eWhere,\u003c/p\u003e\n\u003cp\u003eGDP = Export Diversification\u003c/p\u003e\n\u003cp\u003eMQP = Manufacturing Contribution to GDP\u003c/p\u003e\n\u003cp\u003eOQP = Oil Sector Contribution to GDP\u003c/p\u003e\n\u003cp\u003eAQP = Agricultural sector contribution to GDP\u003c/p\u003e\n\u003cp\u003e\u0026beta;\u003csub\u003e0\u003c/sub\u003e = Intercept\u003c/p\u003e\n\u003cp\u003e\u0026beta;\u003csub\u003e1\u0026nbsp;\u003c/sub\u003eto\u0026nbsp;\u0026beta;\u003csub\u003e3 =\u003c/sub\u003e Represent the long-run multipliers which show the long-run effects of the identified determinants of manufacturing output to be calculated.\u003c/p\u003e\n\u003cp\u003e\u0026prop;\u003csub\u003e1\u0026nbsp;\u003c/sub\u003eto\u0026nbsp;\u0026prop;\u003csub\u003e4\u0026nbsp;\u003c/sub\u003e= These are the short-run dynamic coefficients which help to estimate the error correction mechanism and the model\u0026rsquo;s convergence\u003c/p\u003e\n\u003cp\u003e\u0026Delta; = Denotes the first difference operator,\u003c/p\u003e\n\u003cp\u003et = deterministic time trend consisting of years from 1985 to 2022.\u003c/p\u003e\n\u003cp\u003eU= The disturbance term that is uncorrelated with the independent variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eECT t-1 is the error correction term\u0026rsquo;s one-period lag value and the speed adjustment parameter that gauges how quickly the variables, in the event of a disturbance, returned from short-run to long-run.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. EXPECTED RESULTS AND JUSTIFICATION \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u0026beta;1 \u0026gt; 0:\u003c/strong\u003e We anticipate a \u003cstrong\u003epositive association\u003c/strong\u003e between improved manufacturing output/contribution and export diversification. This implies that as the manufacturing sector strengthens, Nigeria\u0026apos;s export portfolio will become more diverse, reducing dependence on a limited range of commodities. This diversification leads to greater resilience against external shocks and opens up new avenues for economic growth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026beta;2 \u0026lt; 0:\u003c/strong\u003e We expect a \u003cstrong\u003enegative association\u003c/strong\u003e between manufacturing output/contribution and unemployment. As the manufacturing sector expands, its demand for labour is expected to rise, leading to a decrease in unemployment rates. This not only improves social welfare but also injects additional purchasing power into the economy, further stimulating growth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026beta;3 \u0026gt; 0:\u003c/strong\u003e We anticipate a\u003cstrong\u003epositive association\u003c/strong\u003e between the adoption of smart manufacturing systems and improved global competitiveness. By integrating advanced technologies like AI and robotics, the Nigerian manufacturing sector can increase efficiency, product quality, and responsiveness to market changes. This enhanced competitiveness allows Nigerian manufacturers to penetrate international markets and capture a larger share of global trade.\u003c/p\u003e"},{"header":"4. RESULTS AND DISCUSSION ","content":"\u003cp\u003e\u003cstrong\u003eTo unveil the intricate relationship between manufacturing sector and export diversification, we embark on a data-driven journey. The first stop will explore the descriptive statistics, revealing the essential characteristics of each variable. Next, we venture into the realm of stationarity tests, ensuring they possess suitable properties for analysis. Finally, we employ the powerful cointegration technique to assess the long-run equilibrium between them. The culmination of this exploration reveals the estimated model, shedding light on the true nature of their interaction.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. DESCRIPTIVE STATISTICS OF THE VARIABLES: TABLE 1\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eMQP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eOQP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAQP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;4.208496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;12.14028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;12.26934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;23.75753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;4.212993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;12.25384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;12.12022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;23.43139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Maximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;15.32916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;13.43947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;28.70544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;36.96508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Minimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e-2.035119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;10.59711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;2.684290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;18.02043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Std. Dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;3.812218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.842070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;5.978752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;3.783899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Skewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.484041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.372582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.434746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;1.456637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Kurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;3.489985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;2.086879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;2.973626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;6.029596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Jarque-Bera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;1.864009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;2.199346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;1.166597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;27.97055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Probability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.393764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.332980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.558054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.000001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Sum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;159.9228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;461.3305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;453.9656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;902.7861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Sum Sq. Dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;537.7213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;26.23605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;1286.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;529.7620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Author\u0026rsquo;s computation using EViews 9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis table offers valuable insights into the \u003cstrong\u003etypical variations\u003c/strong\u003e within our data based on the \u003cstrong\u003emodest average values\u003c/strong\u003e observed for each variable. Notably, the \u003cstrong\u003emanufacturing sector\u0026apos;s standard deviation remains consistently low throughout the study period\u003c/strong\u003e. This suggests \u003cstrong\u003eminimal variability or dispersion\u003c/strong\u003e among the data points, indicating a \u003cstrong\u003ehigh degree of consistency and resemblance to the mean\u003c/strong\u003e during the investigated timeframe.\u003c/p\u003e\n\u003cp\u003eFurther analysis delves into the \u003cstrong\u003eskewness and kurtosis values\u003c/strong\u003e for all variables within the model. Interestingly, we observe \u003cstrong\u003epositively skewed distributions for all variables\u003c/strong\u003e. Additionally, we find that \u003cstrong\u003evariables with a kurtosis value below three\u003c/strong\u003e \u0026ndash; classified as \u003cstrong\u003eplatykurtic\u003c/strong\u003e \u0026ndash; include \u003cstrong\u003emanufacturing contribution to GDP and oil contribution to GDP\u003c/strong\u003e. Conversely, \u003cstrong\u003evariables with a kurtosis value exceeding three\u003c/strong\u003e, categorized as\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eleptokurtic\u003c/strong\u003e, encompass \u003cstrong\u003eGDP output/export diversification and agricultural contribution to GDP\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eJarque-Bera test results\u003c/strong\u003e reveal that certain data sets \u003cstrong\u003edeviate from normality\u003c/strong\u003e. This is indicated by the \u003cstrong\u003eprobability values falling below 5%\u003c/strong\u003e for these specific variables. To be more precise, the \u003cstrong\u003enull hypothesis of normality holds true for manufacturing contribution, GDP, and Oil contribution\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e as their probabilities surpass 5%. Conversely, the \u003cstrong\u003enull hypothesis is rejected for agricultural contribution\u003c/strong\u003e, signifying that this variable does not adhere to a typical normal distribution pattern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. UNIT ROOT TEST OF VARIABLES: TABLE 2\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eADF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCritical value\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrder of Integration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e101.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2.954021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eI(1)( Stationary)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eMQP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e97.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2.954021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eI(1)( Stationary)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eOQP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e64.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2.954021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eI(1)( Stationary)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eAQP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e20.2202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2.954021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eI(1)( Stationary)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Author\u0026rsquo;s computation using EViews 9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this analysis, we delve into the realm of stationarity using the \u003cstrong\u003eaugmented Dickey-Fuller (ADF) test\u003c/strong\u003e. This critical test serves as the gatekeeper, ensuring the variables under scrutiny adhere to a fundamental principle in time series analysis: stationarity.\u003c/p\u003e\n\u003cp\u003eStationarity implies that the statistical properties of a variable, such as its mean and variance, remain constant over time. This is crucial because it allows us to interpret the relationships between variables with confidence, knowing that their underlying patterns are not simply due to random fluctuations.\u003c/p\u003e\n\u003cp\u003eThe ADF test acts as a judge, determining whether each variable within our study holds this essential characteristic. It does so by assessing the presence of a unit root \u0026ndash; a statistical term for a non-stationary trend. If the absolute value of the ADF test statistic exceeds the critical value at the 5% significance level, it signifies that the null hypothesis of non-stationarity can be confidently rejected, and the variable is deemed stationary. In simpler terms, a high enough ADF statistic essentially gives us the green light to proceed with further analysis, knowing our data stands on a solid foundation.\u003c/p\u003e\n\u003cp\u003eThis commitment to meticulous stationarity testing forms the cornerstone of our analytical framework. By ensuring stationarity, we inject rigor and validity into our findings, reducing the risk of drawing spurious conclusions from data plagued by transient trends. In essence, stationarity testing paves the way for reliable and insightful explorations of the relationships between variables within our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. UNRESTRICTED COINTEGRATION RANK TEST: TABLE 3\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNull hypothesis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEigenvalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrace statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCritical Value 0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProb.**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eGDP*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.582347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;53.01680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;47.85613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.0151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eMQP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.334006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;22.45815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;29.79707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.2737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eOQP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.202488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;8.231515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;15.49471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.4410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eAQP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.008888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.312454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;3.841466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;0.5762\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Author\u0026rsquo;s computation using EViews 9\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Trace test indicates 1 cointegrating eqn(s) at the 0.05 level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;* Denotes rejection of the hypothesis at the 0.05 level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUnderstanding the intricate dance of economic variables over time often requires peering beyond the surface of their individual fluctuations. This is where the concept of \u003cstrong\u003ecointegration\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eshines a spotlight, illuminating the hidden \u003cstrong\u003elong-term equilibrium relationships\u003c/strong\u003e that bind them together. In essence, cointegration implies a\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003estable, enduring partnership\u003c/strong\u003e between variables, where deviations from this equilibrium tend to be temporary and ultimately corrected.\u003c/p\u003e\n\u003cp\u003eHowever, before we draw meaningful conclusions from these relationships, we must tread carefully. Just as a shaky foundation can compromise the integrity of a building, faulty assumptions about stationarity can jeopardize the reliability of our analysis. This is why \u003cstrong\u003epretesting becomes an essential safeguard\u003c/strong\u003e against misleading regression results.\u003c/p\u003e\n\u003cp\u003eEnter the \u003cstrong\u003eJohansen cointegration\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003etest\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e a powerful tool particularly suited for variables like ours, exhibiting stationarity at the first difference (integration of order 1). By carefully scrutinizing two key tests \u0026ndash; the \u003cstrong\u003etrace test\u003c/strong\u003e and the \u003cstrong\u003eeigenvalue test\u003c/strong\u003e \u0026ndash; we can confidently assess the presence of cointegrating equations.\u003c/p\u003e\n\u003cp\u003eAnd what do our findings tell us? As showcased in Table 3, both tests, at the 5% significance level, paint a compelling picture. The trace test hints at \u003cstrong\u003eone cointegrating equation\u003c/strong\u003e, while the eigenvalue test reveals the presence of \u003cstrong\u003ethree\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e This resounding consensus whispers a powerful message: \u003cstrong\u003ethe four variables under investigation are indeed entangled in a long-term equilibrium dance.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis critical step, anchored in rigorous statistical tests, lays the groundwork for a reliable and nuanced understanding of how these variables interact. With the foundation of cointegration firmly established, we can now confidently embark on modeling and interpreting their interplay, shedding light on the hidden forces shaping our economic landscape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. LEAST SQUARES ESTIMATE: Table 4\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"86.61257606490872%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eDependent Variable: Export Diversification (GDP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.18255578093306%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eMethod: Least Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.430020283975658%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.18255578093306%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eDate: 01/30/24 \u0026nbsp; \u0026nbsp; \u0026nbsp;Time: 04:00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.430020283975658%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"70.18255578093306%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003eSample (adjusted): 1985 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.430020283975658%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"86.61257606490872%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eIncluded observations: 37 after adjustments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.945233265720084%\" valign=\"bottom\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.010141987829615%\" valign=\"bottom\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.227180527383368%\" valign=\"bottom\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.430020283975658%\" valign=\"bottom\"\u003e\n \u003cp\u003et-Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003eProb. \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.945233265720084%\" valign=\"bottom\"\u003e\n \u003cp\u003eMQP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.010141987829615%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.194901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.227180527383368%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.824424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.430020283975658%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.236408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.8146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.945233265720084%\" valign=\"bottom\"\u003e\n \u003cp\u003eOQP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.010141987829615%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.035931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.227180527383368%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.113698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.430020283975658%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.316023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.7540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.945233265720084%\" valign=\"bottom\"\u003e\n \u003cp\u003eAQP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.010141987829615%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.418703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.227180527383368%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.163991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.430020283975658%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.553209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.945233265720084%\" valign=\"bottom\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.010141987829615%\" valign=\"bottom\"\u003e\n \u003cp\u003e-3.795269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.227180527383368%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.47970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.430020283975658%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.362154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.7195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.945233265720084%\" valign=\"bottom\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.010141987829615%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.181172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.657200811359026%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mean dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.234356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.945233265720084%\" valign=\"bottom\"\u003e\n \u003cp\u003eAdjusted R-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.010141987829615%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.106733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.657200811359026%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; S.D. dependent var\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.861423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.945233265720084%\" valign=\"bottom\"\u003e\n \u003cp\u003eS.E. of regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.010141987829615%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.649538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.657200811359026%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Akaike info criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.528884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.945233265720084%\" valign=\"bottom\"\u003e\n \u003cp\u003eSum squared resid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.010141987829615%\" valign=\"bottom\"\u003e\n \u003cp\u003e439.5312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.657200811359026%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Schwarz criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.703037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.945233265720084%\" valign=\"bottom\"\u003e\n \u003cp\u003eLog likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.010141987829615%\" valign=\"bottom\"\u003e\n \u003cp\u003e-98.28436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.657200811359026%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Hannan-Quinn criter.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.590281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.945233265720084%\" valign=\"bottom\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.010141987829615%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.433839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.657200811359026%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Durbin-Watson stat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.437100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.945233265720084%\" valign=\"bottom\"\u003e\n \u003cp\u003eProb(F-statistic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.010141987829615%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.082349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.227180527383368%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.430020283975658%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.387423935091277%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Author\u0026rsquo;s computation using EViews 9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis regression analysis sheds light on the potential factors influencing \u003cstrong\u003eexport diversification\u003c/strong\u003e in Nigeria, drawing insights from the analyzed data. The results unveil a nuanced picture of various sectors\u0026apos; contributions. While the \u003cstrong\u003emanufacturing sector\u003c/strong\u003e exhibits a \u003cstrong\u003enegative coefficient\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(-0.194901)\u003c/strong\u003e, its low \u003cstrong\u003et-statistic (-0.236408)\u003c/strong\u003e suggests a \u003cstrong\u003eweak and potentially insignificant negative association\u003c/strong\u003e with export diversification. This implies that the manufacturing sector may not be playing the desired role in diversifying Nigeria\u0026apos;s export base.\u003c/p\u003e\n\u003cp\u003eIn contrast, the \u003cstrong\u003eoil sector\u003c/strong\u003e reveals a \u003cstrong\u003epositive coefficient (0.035931)\u003c/strong\u003e, albeit with marginal significance. This hints at a \u003cstrong\u003epossible small positive effect\u003c/strong\u003e on export diversification. While not robust, it suggests that oil may play a minor role in diversifying the export mix.\u003c/p\u003e\n\u003cp\u003eHowever, the spotlight shines brightest on the \u003cstrong\u003eagricultural sector\u003c/strong\u003e. Its \u003cstrong\u003ehighly significant positive coefficient (0.418703)\u003c/strong\u003e speaks volumes. This robust finding points to a \u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003etrong positive association\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewith export diversification, highlighting the pivotal role agriculture plays in diversifying Nigeria\u0026apos;s export basket.\u003c/p\u003e\n\u003cp\u003eThe model\u0026apos;s \u003cstrong\u003eR-squared value of 0.181172\u003c/strong\u003e indicates that only \u003cstrong\u003e18.1% of the variation in export diversification is explained by the analyzed variables\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e This suggests that, beyond the four sectors considered, \u003cstrong\u003eother factors outside the model significantly contribute to export diversification\u003c/strong\u003e in the Nigerian economy. Identifying and incorporating these additional factors would yield a more comprehensive understanding of the driving forces behind diversification.\u003c/p\u003e\n\u003cp\u003eFurthermore, the \u003cstrong\u003eadjusted R-squared of 0.107\u003c/strong\u003e \u0026ndash; a more reliable indicator of fit given the number of independent variables \u0026ndash; reinforces the observation that the model explains a \u003cstrong\u003erelatively small portion of export diversification variation\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThe \u003cstrong\u003eF-statistic (2.43)\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e statistically significant at the 8.23% level. This provides some\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ebasis for confidence in the overall reliability of the regression model\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"5. CONCLUSION AND RECOMMENDATIONS","content":"\u003cp\u003eThis regression analysis unveils a nuanced picture of Nigeria's export diversification landscape, casting light on the potential drivers and highlighting the crucial role of the manufacturing sector. While the analysis suggests that agriculture currently shines brightest in its contribution to export diversification, the results also whisper a powerful message: to fully unlock Nigeria's economic potential, a comprehensive, targeted approach to \u003cstrong\u003erevitalizing and reshaping the manufacturing sector\u003c/strong\u003e is essential.\u003c/p\u003e\n\u003cp\u003eThe current state of the manufacturing sector, with its weak association with export diversification, warrants deeper investigation. Is it a matter of lagging productivity, inadequate infrastructure, or an uncompetitive business environment? Pinpointing the exact roadblocks hindering the sector's export potential is the first crucial step. Once the barriers are identified, a multi-pronged strategy can be implemented to unleash the latent power of Nigerian manufacturing.\u003c/p\u003e\n\u003cp\u003eImagine a robust manufacturing ecosystem pulsating with activity. Modern factories hum with innovative production processes, churning out high-quality, globally competitive goods. Skilled labourers meticulously craft products ranging from value-added agricultural goods to technologically advanced machinery. This is the vision we must strive for, a vision where Nigerian-made products proudly strut their stuff on the global stage, contributing significantly to the country's export basket.\u003c/p\u003e\n\u003cp\u003eTo achieve this transformative vision, several key areas demand attention:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFostering Industrial Upgradation:\u003c/strong\u003e The focus should shift from basic, low-value-added manufacturing to sectors like agro-processing, light engineering, and pharmaceuticals. This requires strategic investments in technology, research and development, and skilled workforce training.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBuilding Robust Infrastructure:\u003c/strong\u003e A reliable and efficient transportation network, coupled with stable power supply and digital connectivity, is crucial for reducing production costs and facilitating seamless export logistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStreamlining the Business Environment:\u003c/strong\u003e Simplifying bureaucratic processes, eliminating red tape, and creating a transparent regulatory framework can attract foreign investment and boost domestic entrepreneurial spirit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCultivating a Strong Innovation Ecosystem:\u003c/strong\u003e Encouraging collaboration between academia, industry, and research institutions can foster a culture of innovation, leading to the development of new products and processes that enhance export competitiveness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLeveraging Trade Agreements:\u003c/strong\u003e Strategic engagement in regional and international trade agreements can open doors to new markets and provide preferential access for Nigerian exports.\u003c/p\u003e\n\u003cp\u003eBy investing in these critical areas, we can transform the narrative surrounding Nigerian manufacturing sector. From a sector struggling to find its footing, it can be reborn as a dynamic engine of export diversification, creating jobs, generating wealth, and propelling Nigeria's economic trajectory towards a brighter future.\u003c/p\u003e\n\u003cp\u003eThis is not just an economic agenda; it's a social one. A thriving manufacturing sector empowers the people, fostering entrepreneurship, and creating pathways to prosperity for generations to come. It's about harnessing the ingenuity and talent of Nigerians, weaving them into the fabric of a globally competitive export machine. So, let us not simply diversify our exports, but let us also revitalize our manufacturing, unleashing its potential to be a powerful driver of a truly diversified and thriving Nigerian economy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCONTRIBUTIONS OF AUTHORS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was undertaken by the author and benefited from the approval and oversightof my Divisional Manager, who possesses extensive experience in this field.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure transparency, the author declares no conflicts of interest in the publication of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, I want to sincerely thank Almighty Allah, the Most Gracious and the Most Merciful, for giving me the fortitude, resiliency, vision, and willpower to start and finish this study.\u003c/p\u003e\n\u003cp\u003eMy profound appreciation also goes out to my distinguished Division Manager, Dr. Osidipe Oluwasegun, for lending his knowledge, direction, and insightful advice at every turn of the project. His contributions have raised this study\u0026apos;s caliber considerably.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eAditya, A., and Roy, S. S. (2007).\u0026nbsp;\u003c/strong\u003eExport diversification and economic growth: Evidence from cross-country analysis. (pp. 1-25). Mimeo.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCentral Bank of Nigeria (CBN).\u003c/strong\u003e (various years) Statistical Bulletin. Retrieved from www.cbn.gov.ng/documents/statbulletin.asp.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDe Pi\u0026ntilde;eres, S. A. G., \u0026amp; Ferrantino, M. (1997).\u0026nbsp;\u003c/strong\u003eExport diversification and structural dynamics in the growth process: The case of Chile. Journal of development Economics, 52(2), 375-391.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHerzer, D., \u0026amp; Felicitas N.L. (2006).\u003c/strong\u003e What does Export Diversification do for Growth? An\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOlasode O.S, Femi. E and Babatunde T.S (2013).\u003c/strong\u003e \u0026ldquo;Export Diversification and Economic Growth in Nigeria; An Empirical Test of Relationship using the Granger Causality Test\u0026rdquo;. Journal of Emerging Trends in Economics and Management Science. Accessed from jetems.scholarlinkresearch.org on the 3rd of June, 2016.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSuberu, O. J., Ajala, O. A., Akande, M. O. and Olure-Bank, A. (2015).\u003c/strong\u003e Diversification of the Nigerian economy towards sustainable growth and economic development. International Journal of economics, finance and management Science, 3 (2),107-114.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNational Bureau of Statistics (NBS).\u0026nbsp;\u003c/strong\u003e(Various years)Data and Statistics.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNigerian Governors Forum repository\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWang, M., Park, N., \u0026amp; Choi, C. H. (2020).\u0026nbsp;\u003c/strong\u003eThe Nexus between International Trade, FDI and Income Inequality. Journal of Korea Trade, 24(4), 18-33.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWorld Bank (2022), (2023).\u0026nbsp;\u003c/strong\u003eWorld Development Indicators. The World Bank.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Manufacturers Association of Nigeria","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Export Diversification, Manufacturing sector, Value added","lastPublishedDoi":"10.21203/rs.3.rs-4394927/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4394927/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eThis research delves into the enigmatic relationship between Nigeria's manufacturing sector and the nation's drive for export diversification\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eLeveraging a regression analysisand time series data from 1985 to 2022, it paints a nuanced picture of their complex interplay. The analysis confirms the stationarity of all variables at first differenced\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eAdditionally, the Johansen co-integration test reveals a\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003elong-run equilibrium relationshipbetween them, suggesting that while their short-term fluctuations may diverge, they are ultimately bound by a deeper interdependence. The analysis exposes a weak and negative associationbetween the two, hinting at the meagre contribution of the manufacturing sector to export diversification during the studied period. This underscores the need for a critical reevaluation and targeted interventions to unlock the sector's potential as a powerful engine of export growth. Therefore, the study advocates for a paradigm shift in approach. Instead of government’s piecemeal efforts, it should champion the creation of a robust and vibrant manufacturing ecosystem, pulsating with innovation and productivity. This vision will envision modern factories humming with cutting-edge processes, meticulously crafting high-quality goods capable of holding their own on the global stage. From value-added agricultural products to sophisticated machinery, the potential portfolio of Nigerian exports is vast and brimming with promise.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Export Diversification Drive: the Role of Nigerian Manufacturing Sector","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-10 16:08:10","doi":"10.21203/rs.3.rs-4394927/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"26cbdd19-3fab-41d3-aa71-a2b376c9b547","owner":[],"postedDate":"May 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31775463,"name":"Development Economics"}],"tags":[],"updatedAt":"2024-05-10T16:08:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-10 16:08:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4394927","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4394927","identity":"rs-4394927","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}