IMPACT OF MANUFACTURING SECTOR ON THE NIGERIA ECONOMY (1981-2022)

IMPACT OF MANUFACTURING SECTOR ON THE NIGERIA ECONOMY (1981-2022) BY IROHA, OLUOMACHI ESTHER 2020/126225/REGULAR A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR AN AWARD OF THE DEGREE OF BACHELOR OF SCIENCE (B.Sc) IN ECONOMICS DEPARTMENT OF ECONOMICS, FACULTY OF ECONOMICS AND MANAGEMENT SCIENCES, ABIA STATE UNIVERSITY, UTURU. SUPERVISOR: DR. ROY M. OKPARA FEBRUARY, 2025. ASSESSMENT PAGE ABIA STATE UNIVERSITY, UTURU. DEPARTMENT OF ECONOMICS FACULTY OF ECONOMICS AND MANAGEMENT SCIENCES NAME: IROHA, OLUOMACHI ESTHER MAT NO.: 2020/126225/REGULAR DISCIPLINE: ECONOMICS TOPIC: IMPACT OF MANUFACTURING SECTOR ON THE NIGERIA ECONOMY (1981-2022) S/N EVALUATION CRITERIA PROJECT SCORE STUDENT SCORE 1. GRASP OF SUBJECT MATTER • Is the problem being investigated clearly stated? • Are the theoretical formulation and the conceptual frame work adopted relevant? • Has the author demonstrated sufficient awareness of relevant literature 25 2 RESEARCH METHODOLOGY 20 • Is the general research designed appropriate for problem being investigated? • In the case of a survey, is the population clearly ascertained and the sample size as well as samples methods appropriate? • In case of a case study, are the necessary background information about the organization(s) fully stated (e.g size, nature of business, history organization structure etc)? 3. ANALYSIS Are the analytical methods adopted systematically, logical and appropriate? 10 4 • Are the findings totally based on the results of the analysis of data obtained from the research? • Are the findings sufficiently discussed, giving special attention to the implications of the findings in relations to any related findings of previous studies and the existing relevant body of knowledge? 20 5. • STYLE AND PRESENTATION • Correctness of referencing style • Grammar • Correctness of presentation format • Do error typographical and otherwise exist. 5 6 CONCLUSION AND RECOMMENDATIONS • Are the conclusions clearly stated and totally based on the findings of the study? • Are the recommendations clearly the product of the author’s original thinking? • Has the author sufficiently established justifications for his/her recommendations? 20 Grade Total 100 Supervisor comment:………………………………………………………..…... Supervisor Name:………….......................... ….……………. Date/Sign External Examiner’s Comment:…………………….…………………………….. External Examiner Name:…………………………..…….. ……………….. Date/Sign APPROVAL PAGE I certify that Iroha, Oluomachi Esther was assigned this topic IMPACT OF MANUFACTURING SECTOR ON THE NIGERIA ECONOMY (1981-2022) for research in partial fulfillment of the requirements for the award of Bachelor of Science (B.sc) degree in Economics, Faculty of Economics and Management sciences, Abia State University, Uturu. .…………………………………… …………………….. DR. ROY M. OKPARA Date (PROJECT SUPERVISOR) …..……………………………….. …………………….. DR. (MRS) F. O. C. OSUNKWO Date (HOD, DEPARTMENT OF ECONOMICS) …………………………………… ……………………… PROF. N.O. DIBIA Date (DEAN FACULTY OF ECONOMICS AND MANAGEMENT SCIENCES) …..……………………………… …………………….. EXTERNAL EXAMINER Date CERTIFICATION I hereby certify that this research was carried out by Iroha, Oluomachi Esther with the matriculation number 2020/126225/REGULAR the Department of Economics, Faculty of Economics and Management Sciences, Abia State University, Uturu. .…………………………………… …………………….. DR. ROY M. OKPARA Date (PROJECT SUPERVISOR) …..……………………………….. …………………….. DR. (MRS) F. O. C. OSUNKWO Date (HOD, DEPARTMENT OF ECONOMICS) …………………………………… ……………………… PROF. N.O. DIBIA Date (DEAN FACULTY OF ECONOMICS AND MANAGEMENT SCIENCES) …..……………………………… …………………….. EXTERNAL EXAMINER Date DEDICATION This work is dedicated to my God Almighty ACKNOWLEDGEMENTS I appreciate wholeheartedly with unending love and favour granted by the Lord most high for scaling me through my studies throughout my journey in Abia State University. I wish to show my wholehearted appreciation to my supervisor Dr. Roy M. Okpara who was always ready at all time and was never weary throughout his supervision of this project, despite his busy schedules made out time for the guidelines, comments and suggestions which were helpful for the achievement of this project. I acknowledge the effort of the Dean of faculty of economics and management sciences, Prof. N.O. DIBIA, my Head of Department, Dr. (Mrs.) F.O.C Osunkwo, for your ceaseless encouragement, love and care. To my lecturers; Prof. E. Iheanacho, Dr. (Mrs.) V.A Ihezukwu, DR. Roy Okpara Mr. N. J Onyeze, Dr. K.J Iheukwumere, and Mr. I.A Chukwunenye for being helpful towards this project. God bless you all and loved ones who has made a great impact in my life. You all made sure I scaled through this journey from the beginning till the end of it. May God continue to bless you all. I also express my profound gratitude to my parent Late Mr. Longinus Iroha and my lovely mummy Mrs. Victoria Iroha and my beloved brother, Chidera Iroha, Goodluck Iroha and my sisters Precious Iroha and Chiyeaka Iroha who has made a great impact in my life. You all saw to it that I am what I am today and so I say thank you for your support both morally, materially, spiritually and financially. May God continue to increase you all. I won’t forget to acknowledge my friend Favour uchechi Nnennaya. I love you all. IROHA, OLUOMACHI ESTHER 2020/126225/REGULAR TABLE OF CONTENTS Title page i Assessment page ii Approval page iii Certification iv Dedication v Acknowledgements vi Abstract x CHAPTER ONE: INTRODUCTION 1.1. Background to the Study 1 1.2. Statement of the Problem 5 1.3. Objectives of the Study 7 1.4. Research Questions 7 1.5. Research Hypotheses 8 1.6. Scope of the Study 9 1.7. Significance of the Study 9 1.8. Organization of the study 11 1.9. Operational definition of terms 12 CHAPTER TWO: REVIEW OF RELATED LITERATURE 2.1. Conceptual Framework 14 2.2. Theoretical Review 34 2.3. Empirical Literature Review 38 CHAPTER THREE: METHODOLOGY 3.1 Research Design 44 3.2 Sources of Data 44 3.2.1 Analytical Framework of the model 44 3.3 Model Specification 45 3.4 Estimation procedure 46 CHAPTER FOUR: DATA ANALYSIS AND DISCUSSION 4.1 Unit Root Test 49 4.2 Model Estimation 50 4.3 Post Estimation Test 54 4.4 Discussion of Results 57 CHAPTER FIVE: SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS 5.1 Summary of findings 61 5.2. Conclusion 61 5.3 Recommendations 62 References 64 Appendices 68 Abstract The manufacturing sector is crucial to a nation's economic development, serving as a driving force for transformation and growth. The broad objective of this study was to ascertain the effect of manufacturing sector output on economic growth in Nigeria for the period 1981 to 2023. Various econometric techniques such as unit root test, co-integration test etc were carried out and the estimation of the model was based on autoregressive distributed lag framework. The findings indicate that GDP growth in Nigeria exhibits strong persistence over time, as reflected in the statistically significant lagged dependent variable. The food, beverage, and tobacco sector positively contributes to GDP, although its lagged effects show a diminishing impact. Other sectors, including cement, motor vehicles, non-metallic products, and textiles, have weaker and often statistically insignificant effects on GDP. The motor vehicle sector shows a marginally significant negative relationship with GDP, likely due to structural challenges, while the textile sector also exhibits a negative coefficient, reflecting its declining contribution to the economy. The study suggests that Policymakers should prioritize and enhance support for the food, beverage, and tobacco sector, given its significant positive impact on GDP. This can be achieved by improving agricultural productivity, ensuring a stable supply chain, and implementing policies that promote value addition in food processing. Investments in infrastructure, storage facilities, and transportation networks will also help sustain the sector’s contribution to economic growth. CHAPTER ONE INTRODUCTION 1.1 Background of the Study The manufacturing sector plays a pivotal role in the economic development of any nation, acting as a catalyst for transformation and growth. This research delves into the impact of the manufacturing sector on the Nigerian economy, a topic of significant importance given the country's ongoing quest for diversification away from oil dependency. The purpose of this research is to provide a comprehensive understanding of how the manufacturing sector influences various aspects of the Nigerian economy, such as gross domestic product (GDP), employment, and overall economic resilience. Nigeria, with its abundant natural resources and vast market potential, has long been identified as a key player in Africa's economic landscape. However, the country's over-reliance on oil exports has left it vulnerable to global oil price fluctuations, leading to economic instability. Recognizing this challenge, the Nigerian government has made concerted efforts to diversify the economy, with the manufacturing sector identified as a critical area of focus. The manufacturing sector in Nigeria has the potential to drive significant economic growth by creating jobs, fostering innovation, and generating export revenues. By expanding manufacturing capabilities, Nigeria can enhance its economic self- sufficiency, reduce import dependency, and increase its competitiveness in the global market. Dogara (2018) defined the manufacturing as the process of converting raw materials into either finished consumer goods or producer goods. Consequently, recognizing the significance of this particular sub-sector, scholars have highlighted its crucial role in fostering economic growth (Afolabi&Laseinde, 2019; Nwogwugwu et al., 2021). According to Chukwuedo and Ifere (2017), the productivity of the manufacturing sector, along with its resources and expertise, are key factors influencing the growth of the Nigerian economy. Many rapidly advancing nations view the manufacturing sector as the cornerstone of industrialization, underscoring the necessity of prioritizing its development as a fundamental strategy towards achieving economic growth. It has been asserted that the conversion of raw materials into final consumer goods, intermediates, or partially finished products constitutes the manufacturing process (Ogundipe, 2022). Additionally, it was noted that manufacturing, akin to other industrial activities, plays a crucial role in enhancing agriculture, diversifying the economy, and augmenting the nation's foreign exchange earnings. Moreover, manufacturing has been recognized for its role in facilitating the acquisition of new skills by local labor. According to Dogara (2018), a robust manufacturing sub- sector within the industrial sphere has been regarded as a fundamental goal of various development strategies in Nigeria, being viewed as a pivotal pathway to economic prosperity. Manufacturing, akin to other industrial endeavors, establishes opportunities for employment, enhances agricultural productivity, and contributes to economic diversification. Additionally, it facilitates the augmentation of a nation's foreign exchange reserves, thereby enabling the local workforce to develop expertise. The presence of a robust manufacturing sector enables countries to distinguish themselves by reducing the susceptibility to excessive dependence on imports and promoting the optimal utilization of existing resources. The arrangement of manufacturing operations in Nigeria has gone through four distinct phases of progression. The initial stage is referred to as the "Pre- independence" period, during which manufacturing activities were primarily focused on the processing of raw materials for export and the production of basic consumer goods by foreign multinational corporations seeking to establish a presence in a new market. At this juncture, manufacturing was predominantly reliant on available resources, albeit with some degree of import substitution and utilization of imported raw materials already in place. The subsequent phase is the early "Post-colonial era" of the 1960s, marked by heightened efforts in import substitution and a gradual decline in the emphasis on export-oriented raw material processing. The adoption of import substitution policies, initially aimed at reducing reliance on foreign trade and conserving foreign exchange, ultimately resulted in the mere assembly of goods rather than true manufacturing processes (Obioma et al., 2015). The third phase was in the 1970s, which stands out due to the emergence of the oil industry and the significant financial resources it brought for direct government investment in various sectors. During this time, the government gained almost complete control over sectors such as basic steel production, petroleum refining, petrochemicals, natural gas, edible salt, steel plants, machine tools, paper production, yeast and alcohol, as well as nitrogenous and phosphoric fertilizer. This era was characterized by the constraints of the indigenization program, leading to a period of heightened economic activity but yielding unsatisfactory outcomes despite the government's efforts to diversify into non-traditional products like steel, petrochemicals, fertilizers, and vehicle assembly. The subsequent phase was the 1990s, during which government revenue was divided due to the sharp decline in oil prices in the global market (Obioma et al., 2015). Structural change theory asserts that economic development occurs through the reallocation of resources from low-productivity sectors, such as agriculture, to high-productivity sectors, like manufacturing and services. This transition from an agrarian-based economy to an industrialized one is believed to foster economic growth and development, a concept championed by economists such as W. Arthur Lewis. This theory is particularly pertinent to Nigeria as it aims to diversify its economy beyond oil and agriculture. By enhancing the role of the manufacturing sector in contributing to GDP and creating employment opportunities, Nigeria can achieve a more balanced and resilient economic structure. A vibrant manufacturing sector can drive technological innovation and skills development. As Nigerian firms invest in new technologies and processes, they can increase their competitiveness in the global market. Additionally, the growth of manufacturing can spur the development of ancillary services, such as logistics, finance, and education, further boosting the economy. 1.2 Statement of the Problem The manufacturing sector in Nigeria faces several persistent challenges that have hindered its growth and productivity. One of the primary issues is the inadequate provision of electricity, which is crucial for manufacturing operations. Additionally, there is a significant lack of infrastructure and facility maintenance, which further impedes the sector’s efficiency. The reliance on agricultural resources poses another challenge, creating a bottleneck in the manufacturing process. The sector also grapples with unfavorable conditions, such as limited access to financial resources, high costs of business operations, a substantial importation of goods that could be produced domestically, and a harsh business environment. There has been a notable decline in the contribution of the manufacturing sector to Nigeria's gross domestic product (GDP). Historical data reflects this downward trend, with the sector’s GDP share dropping from 20.5% in 1981 and 1982, 21.3% in 1983, and 17.9% in 1984, to 12.8% in 2020, 14.8% in 2021, and 13.8% in 2022 (CBN, 2022). This decrease is compounded by an excessive dependency on crude oil, which generates significant revenue but has led to the neglect of the manufacturing sector. Owan et al (2020) highlighted that the substantial income from oil has shifted the focus towards the oil industry, causing the non-oil sector, despite its vast potential, to be overlooked. Similarly, Adofu et al. (2015) points out the manufacturing industry’s challenges, such as a deficient technological foundation, insufficient funding for research, development, and innovation, and an over-reliance on imported machinery, equipment, and spare parts. Despite these hurdles, the manufacturing sector remains vital for Nigeria's progress. The declining performance and the myriad challenges faced by this sub- sector underscore the need to assess its current state and propose strategies to enhance its productivity. This study provides a contemporary analysis, utilizing data up to 2022, thereby offering an up-to-date perspective on the relationship between the manufacturing sector and economic growth in Nigeria. 1.3 Objectives of the Study The broad objective of this study was to ascertain the effect of manufacturing sector output on economic growth in Nigeria for the period 1981 to 2022. The specific objectives are (i) To ascertain the effect of cement output on gross domestic product in Nigeria. (ii) To assess the effect of food, beverage and tobacco output on gross domestic product in Nigeria. (iii) To examine the effect of textile, apparel and footwear output on gross domestic production Nigeria. (iv) To ascertain the effect of non-metallic products output on gross domestic product in Nigeria. (v) To assess the effect of motor vehicles and assembly output on gross domestic product in Nigeria. 1.4 Research Questions (i) What is the impact of cement output on the gross domestic product in Nigeria, and how does this impact vary across different regions within the country? (ii) How do changes in the output of the food, beverage, and tobacco industries affect the overall economic growth and stability of Nigeria? (iii) What is the relationship between the output of the textile, apparel, and footwear industries and the gross domestic product of Nigeria, particularly in the context of the country’s efforts to diversify its economy? (iv) To what extent do non-metallic products contribute to the gross domestic product of Nigeria, and what factors influence their production levels? (v) How does the output of the motor vehicles and assembly sector affect the gross domestic product in Nigeria, and what role does this sector play in the country’s industrialization efforts? 1.5 Research Hypothesis The following null research hypotheses were formulated H01: Cement output had no positive effect on gross domestic product in Nigeria. H02: Food, beverage and tobacco output had no positive effect on gross domestic product in Nigeria. H03: Textile, apparel and footwear output had no positive effect on gross domestic product in Nigeria. H04: Non-metallic products output had no positive effect on gross domestic product in Nigeria. H05: Motor vehicles and assembly output had no positive effect on gross domestic product in Nigeria. 1.4 Scope of the Study This study is designed to comprehensively analyze the impact of the manufacturing sector on the Nigerian economy over the period from 1981 to 2022. The scope of the study includes a detailed examination of a 41-year timeline, beginning in 1981 and concluding in 2022, which allows for the assessment of long-term trends and patterns within the manufacturing sector and its interconnections with the broader Nigerian economy. Geographically, the study is concentrated on the manufacturing sector within Nigeria's geopolitical boundaries. It aims to provide insights into the national context while also considering how regional differences within Nigeria might influence the manufacturing sector's contribution to the economy. The research will delve into various sub-sectors within the manufacturing industry, encompassing cement production, food, beverage, and tobacco industries, textile, apparel, and footwear industries, non-metallic products, and the motor vehicles and assembly sector. 1.7 Significance of the study The significance of this study lies in its potential to provide a comprehensive understanding of how the manufacturing sector has influenced Nigeria’s economic trajectory over more than four decades. By analyzing the impact of this sector from 1981 to 2022, the study aims to shed light on several key aspects of Nigeria’s economic development and offer valuable insights for policymakers, industry stakeholders, and researchers. Firstly, the study will contribute to a deeper understanding of the role of manufacturing in Nigeria's economic growth. By evaluating the sector’s contribution to GDP, employment, and overall economic stability, the research will highlight the significance of manufacturing in diversifying Nigeria’s economy beyond its traditional reliance on oil and agriculture. This understanding is crucial for crafting effective economic policies that promote sustainable growth and reduce vulnerability to external shocks. Secondly, the study’s focus on various manufacturing sub-sectors such as cement, food and beverages, textiles, non-metallic products, and motor vehicles will provide nuanced insights into the specific areas driving economic performance. This detailed analysis will help identify which sub-sectors have been most influential and which have faced the greatest challenges, thereby guiding targeted interventions and investments. The research also holds significance for addressing critical challenges faced by the manufacturing sector, including infrastructural deficits, high operational costs, and technological limitations. By identifying these challenges and assessing their impact on the sector’s performance, the study will inform the development of strategies to enhance productivity and competitiveness. Additionally, the study’s examination of long-term trends and patterns will offer historical context to the current state of the manufacturing sector. Understanding how the sector has evolved over time will help in assessing its future potential and in formulating strategies to harness its benefits for economic development. Finally, the study will provide actionable recommendations for policymakers, industry leaders, and investors. These recommendations will be based on empirical evidence and aim to address the sector’s challenges while leveraging opportunities for growth. By offering a clear roadmap for enhancing the manufacturing sector's contribution to Nigeria’s economy, the study will support efforts to achieve economic diversification, job creation, and overall economic resilience. In essence, the significance of this study lies in its ability to offer a comprehensive and up-to-date analysis of the manufacturing sector’s impact on Nigeria’s economy, thereby contributing to informed decision-making and strategic planning for sustainable economic development. 1.8 Organization of the Study This study is organized into five chapters. In chapter one, the study looked at the background of the study, statement of the problem, research questions, objective of the study. It also contain, research hypothesis, scope of the study, significance of the study, and definition of terms. Chapter two of this study contains literature Review, the Theoretical, Empirical Review, and a summary of the literature. In chapter three it follows research design, method of data collection, and the methodology employed to study the model of the effect of manufacturing sub sector on gross domestic product (GDP). In chapter four it dwell on data presentation and statistical analysis of data and discussion of result thereof, while chapter five summarize and conclude the study and proffers recommendation respectively. 1.9 Operational Definition of Terms Manufacturing Sector: Refers to the segment of the economy that involves the production and processing of goods. It includes various sub-sectors such as cement production, food, beverage, and tobacco industries, textiles, apparel, footwear, non- metallic products, and motor vehicles and assembly. For the purpose of this study, the manufacturing sector encompasses all activities related to the creation of tangible products through industrial processes. Gross Domestic Product (GDP): The total market value of all final goods and services produced within a country’s borders in a specific period, typically measured annually. In this study, GDP serves as a primary indicator of economic performance and growth, reflecting the contribution of different sectors, including manufacturing, to the overall economy of Nigeria. Cement Output: The total quantity of cement produced and available in the market within a given time frame. This measure provides an indication of the production capacity and economic impact of the cement industry on Nigeria’s GDP. Food, Beverage, and Tobacco Output: The combined total of products produced in the food, beverage, and tobacco industries. This term includes all types of processed foods, beverages (both alcoholic and non-alcoholic), and tobacco products manufactured in Nigeria. Textile, Apparel, and Footwear Output: The total volume of products manufactured in the textile, apparel, and footwear industries. This includes textiles (fabrics and materials), clothing items, and footwear produced in Nigeria, reflecting the sector's contribution to the economy. Non-Metallic Products Output: Refers to the total production of goods that do not fall into the metal category, such as ceramics, glass, and other non-metallic minerals. This output measure assesses the role of non-metallic products in Nigeria’s manufacturing sector and its impact on the economy. Motor Vehicles and Assembly Output: The total production and assembly of motor vehicles within Nigeria. This includes cars, trucks, buses, and other motorized vehicles assembled or manufactured in the country, representing the sector’s contribution to economic growth and industrial development. Economic Growth: Refers to the increase in the value of goods and services produced by the economy over a specific period. In this study, it is measured by the change in GDP, reflecting how different sectors, including manufacturing, influence Nigeria’s overall economic performance. CHAPTER TWO LITERATURE REVIEW Preamble The conceptual framework for this study on the impact of the manufacturing sector on the Nigerian economy from 1981 to 2022 is designed to elucidate the intricate relationships between manufacturing activities and various dimensions of economic performance. This framework provides a structured approach to understanding how different segments of the manufacturing sector contribute to Nigeria’s economic growth, stability, and development. It highlights the role of manufacturing in economic diversification, job creation, and industrialization, and sets the stage for analyzing long-term trends and patterns in the sector’s performance. Through this lens, the study will explore how shifts in manufacturing outputs, such as those in cement, food and beverages, textiles, non-metallic products, and motor vehicles, impact Nigeria’s economic trajectory, providing valuable insights for policymakers, industry stakeholders, and researchers. The context of this chapter covered conceptual, theoretical and empirical review on the Nigerian federal capital expenditure on the growth of its economy. 2.1 Conceptual Review Adofu, et al. (2015) conceptualized manufacturing as the process of creating goods intended for commercial exchange or utilization through the utilization of tools, machinery, human labor, as well as chemical and biological compositions. This encompasses both artisanal practices of human endeavors and advanced technologies by altering unfinished products into finalized commodities. Oniyide and Ogunjinmi (2021) defined manufacturing as an activity centered around the production of commodities for utilization or exchange by employing labor, machinery, and bio-chemical procedures. Manufacturing is categorized as a component of the industrial sector. The manufacturing sub-sector is considered to be a pivotal component of the broader industrial sector. According to Adofu et al. (2015), industrialization is perceived as a viable means of achieving the lofty and desirable ideals of enhanced quality of life for the general population. In Nigeria, the manufacturing industry is comprised of thirteen distinct sub-sectors, which include oil refining, cement, food, beverages, and tobacco, textile apparel and footwear, as well as wood and its related products. Additionally, the sector encompasses pulp, paper, and paper products, chemical and pharmaceutical items, non-metallic goods, plastic and rubber products, electrical and electronic equipment, basic metals, iron and steel, motor vehicles and assembly, and other miscellaneous manufacturing activities (CBN, 2022). 2.1.1 Cement Output and Economic Growth Cement is a fundamental component in construction and infrastructure projects, which are essential for economic development. The production and consumption of cement are closely tied to the construction of residential, commercial, and industrial buildings, as well as infrastructure projects such as roads, bridges, and dams. These projects not only stimulate demand for cement but also create employment opportunities, boost related industries, and enhance overall economic productivity. One of the primary ways cement output influences economic growth is through its impact on infrastructure development. Infrastructure investments, which rely heavily on cement, are crucial for facilitating economic activities and improving quality of life. For instance, a well-developed infrastructure system enhances transportation networks, reduces logistical costs, and supports efficient trade and commerce (Umar & Idris, 2019). According to the World Bank (2014), infrastructure improvements are vital for economic growth in developing countries, as they increase productivity and connectivity, which in turn fosters economic development. In Nigeria, the construction sector's reliance on cement has significant implications for economic growth. Cement production supports various infrastructure projects, such as road construction, housing development, and urban expansion. The Nigerian government has identified infrastructure development as a key component of its economic strategy, recognizing that improved infrastructure can enhance competitiveness and drive economic growth (Federal Ministry of Budget and National Planning, 2017). The cement industry also contributes to economic growth through employment creation and industrial expansion. Cement manufacturing involves a range of activities, including quarrying, production, and distribution, which generate direct and indirect employment opportunities. These jobs provide income for workers and stimulate consumer spending, which further supports economic activity. Additionally, the growth of the cement industry can lead to the expansion of related sectors, such as construction materials and equipment manufacturing, contributing to overall industrial growth. In Nigeria, the cement sector has seen significant development over the past decades. The establishment of large-scale cement plants, such as those operated by Dangote Cement and Lafarge Africa, has not only increased domestic production but also created numerous job opportunities and supported the growth of ancillary industries (Oluwatobi & Olusanya, 2020). This industrial growth contributes to economic diversification and reduces reliance on oil exports, aligning with the country’s broader economic development goals. The economic multiplier effect of cement output further underscores its importance to economic growth. Investments in cement production and related infrastructure generate a ripple effect throughout the economy, stimulating demand in other sectors and fostering economic activity. For example, the construction of a new highway funded by cement production can lead to increased trade, enhanced business operations, and improved access to markets. This multiplier effect amplifies the benefits of cement output beyond the immediate sector, contributing to broader economic growth (Meyer & Meyer, 2016). Despite its positive impact, the cement industry in Nigeria faces several challenges that can affect its contribution to economic growth. Issues such as inadequate infrastructure, high production costs, and fluctuating raw material prices can impede the sector's performance (Umar & Idris, 2019). Addressing these challenges requires targeted policy interventions, investment in infrastructure, and improvements in supply chain efficiency. On the other hand, there are significant opportunities for enhancing the role of cement output in economic growth. Advancements in technology, increased investment in sustainable practices, and expansion of the manufacturing capacity can further boost the sector’s contribution to Nigeria’s economy. By leveraging these opportunities, Nigeria can enhance the impact of cement production on its economic development. In summary, cement output plays a crucial role in fostering economic growth through infrastructure development, employment creation, and industrial expansion. The cement sector’s contribution to Nigeria’s economy highlights the importance of strategic investments and policy support to maximize its benefits and address the challenges faced. By understanding the intricate relationship between cement output and economic growth, policymakers and stakeholders can better support the sector’s development and its role in driving broader economic progress. 2.1.2 The Effect of Food, Beverage and Tobacco Output on Economic Growth in Nigeria The output of the food, beverage, and tobacco industries significantly impacts economic growth, particularly in developing economies like Nigeria. This impact is multifaceted, influencing economic performance through various channels such as employment creation, value addition, trade, and overall sectoral development. The food, beverage, and tobacco industries play a crucial role in Nigeria's economy by contributing to the gross domestic product (GDP). These industries are vital for ensuring food security, stimulating domestic consumption, and generating economic value. The production of food and beverages, including staples, processed foods, and alcoholic drinks, directly affects GDP by adding value to raw materials and creating products that are essential for daily life. Similarly, the tobacco industry contributes through the production of tobacco products, which, despite its health implications, forms a part of the economic output. In Nigeria, the food and beverage sector is one of the largest contributors to GDP. The sector’s growth reflects the expanding consumer base and increasing demand for diverse food products. According to the National Bureau of Statistics (2021), the food and beverage sector alone contributed significantly to the country’s GDP, underscoring its importance in economic performance. The food, beverage, and tobacco industries are substantial employers, providing jobs across the production, processing, distribution, and retail stages. Employment generated by these industries contributes to income generation and stimulates consumer spending, which further supports economic growth. For instance, the food processing sector alone employs millions of Nigerians, impacting both rural and urban areas through direct and indirect job opportunities (Ojo & Agboola, 2020). Income generated from these industries supports household consumption, which is a major driver of economic activity. As consumers spend on food, beverages, and tobacco products, this spending creates a multiplier effect, boosting demand across various sectors of the economy and contributing to overall economic growth (Adepoju & Fadare, 2019). Value addition is a key aspect of the impact of these industries on economic growth. The transformation of raw agricultural products into processed foods and beverages enhances their value and contributes to industrial growth. This value addition not only increases the sector’s contribution to GDP but also supports the development of related industries, such as packaging, logistics, and distribution (Akinola & Olufemi, 2018). In Nigeria, the food and beverage sector’s growth has led to the expansion of industrial activities and the establishment of numerous processing plants. This industrial growth supports economic diversification and reduces reliance on raw material exports by enhancing local production capabilities and increasing the sector's contribution to economic development (Oloruntoba & Oke, 2021). The food, beverage, and tobacco industries also influence economic growth through their trade and export potential. Nigeria’s growing production capacity in these sectors has created opportunities for export, which can contribute to foreign exchange earnings and improve the trade balance. For example, Nigerian-made food and beverage products are increasingly finding markets beyond the country’s borders, which enhances trade relations and supports economic growth (Saka & Ige, 2017). However, the tobacco industry’s export potential is more controversial due to health concerns and regulatory restrictions. Despite this, the sector still contributes to economic growth through its production and taxation revenues. Despite their positive impact, the food, beverage, and tobacco industries face several challenges that can affect their contribution to economic growth. These challenges include infrastructural deficits, regulatory hurdles, and fluctuations in raw material prices. For instance, inadequate infrastructure can impede the efficient distribution and export of products, affecting the sector’s performance (Oluwasola & Eniola, 2020). Addressing these challenges requires targeted policy interventions, such as investing in infrastructure, improving regulatory frameworks, and supporting research and development in the sector. Policies aimed at enhancing productivity, reducing operational costs, and expanding market access can further strengthen the sector’s contribution to Nigeria’s economic growth. The output of the food, beverage, and tobacco industries has a significant effect on Nigeria’s economic growth through various channels including value addition, employment creation, and trade. The sector’s contribution to GDP, its role in industrial growth, and its potential for export underscore its importance in the country’s economic landscape. Understanding these dynamics and addressing the sector’s challenges can enhance its positive impact on economic growth and support Nigeria’s broader economic objectives. 2.1.3 The Effect of Textile, Apparel and Footwear Output on Gross Domestic Production Nigeria The textile, apparel, and footwear sectors significantly influence Nigeria’s Gross Domestic Product (GDP), reflecting the integral role of these industries in the country’s economic structure. This effect is evident through various mechanisms, including industrial output, employment generation, value addition, and trade. Understanding these impacts is crucial for appreciating the sectors' contributions to Nigeria’s economic growth and development. The textile, apparel, and footwear industries are fundamental components of Nigeria's manufacturing sector, contributing to GDP through industrial output. These sectors transform raw materials into finished products, adding value and driving economic activity. For example, textiles and apparel manufacturing involves converting fibers into fabrics and garments, while footwear production includes creating shoes and other related products. This process of value addition enhances the economic contributions of these industries by increasing their output and, consequently, their impact on GDP. In Nigeria, the textile and apparel industries have historically been significant contributors to GDP. Despite facing challenges such as competition from imported goods and infrastructural constraints, these sectors continue to play a vital role in the national economy. According to the National Bureau of Statistics (2022), the manufacturing sub-sector, including textiles and apparel, has been a steady contributor to Nigeria’s GDP, reflecting its ongoing economic relevance. One of the most direct ways the textile, apparel, and footwear sectors affect GDP is through employment generation. These industries are labor-intensive, employing a substantial number of workers across various stages of production, from raw material processing to finished product assembly. Employment in these sectors provides income for workers and stimulates consumer spending, which in turn supports overall economic activity. In Nigeria, the textile and apparel industries have historically been major employers. According to the International Labour Organization (2021), the textile sector alone has employed millions of Nigerians, contributing significantly to household incomes and reducing unemployment rates. The creation of jobs in these sectors not only supports individual livelihoods but also fosters economic stability and growth through increased consumer demand and spending. The textile, apparel, and footwear industries contribute to GDP by adding value to raw materials and supporting industrial growth. Value addition occurs when these sectors process basic materials into more valuable finished goods, thereby enhancing their economic contribution. For instance, local textile mills that produce fabrics from cotton contribute to GDP by adding value to the raw cotton and supporting downstream industries such as apparel and footwear manufacturing. Industrial growth driven by these sectors also plays a crucial role in economic development. By expanding manufacturing capabilities and fostering innovation, the textile, apparel, and footwear industries help diversify Nigeria’s economy and reduce dependence on oil exports. The growth of these sectors supports the development of related industries, such as textile machinery and logistics, further contributing to economic growth (Oluwasola & Olagunju, 2018). Trade and export activities are additional channels through which the textile, apparel, and footwear sectors influence GDP. Exporting these products helps to generate foreign exchange earnings and improve the trade balance. Although Nigeria’s textile and apparel exports have faced challenges due to global competition and trade barriers, there is significant potential for growth in this area. Efforts to enhance the competitiveness of Nigerian textiles and apparel in the global market can lead to increased export revenues and contribute positively to GDP. According to a report by the Nigeria Export Promotion Council (2022), improving the quality and marketability of Nigerian-made textiles and apparel can bolster export performance and support economic growth. Despite their economic importance, the textile, apparel, and footwear industries face several challenges that affect their contribution to GDP. These challenges include inadequate infrastructure, high production costs, and competition from imported goods. Addressing these challenges requires comprehensive policy interventions, such as investing in infrastructure, improving access to finance, and implementing trade policies that protect and promote local industries. Policy measures aimed at enhancing the competitiveness of the textile, apparel, and footwear sectors can help unlock their potential to contribute more significantly to GDP. For example, supporting local textile mills, providing incentives for innovation, and promoting export opportunities can strengthen these industries and boost their economic impact (Ajayi & Ojo, 2020). The textile, apparel, and footwear sectors have a substantial effect on Nigeria’s Gross Domestic Product through industrial output, employment generation, value addition, and trade. These industries contribute to economic growth by transforming raw materials into valuable products, creating jobs, supporting industrial development, and generating export revenues. 2.1.4 The Effect of Non-Metallic Products Output on Gross Domestic Product in Nigeria The non-metallic products sector, encompassing industries such as cement, glass, ceramics, and various other materials, plays a pivotal role in the Nigerian economy. This sector's output significantly impacts Gross Domestic Product (GDP) by contributing to industrial growth, infrastructure development, and economic diversification. Understanding these effects involves examining how the production and utilization of non-metallic products influence overall economic performance. Non-metallic products include a diverse range of materials that are critical for various industrial and construction applications. Cement, for example, is essential for infrastructure development, including roads, bridges, and buildings. Glass and ceramics are used in construction, manufacturing, and household goods. The output from these industries adds substantial value to the economy and contributes directly to GDP. In Nigeria, the non-metallic products sector has been a significant contributor to GDP. Cement production, in particular, has seen substantial growth due to increased infrastructure projects and urbanization. According to the National Bureau of Statistics (2022), the cement industry alone has contributed significantly to the GDP, driven by both domestic demand and export opportunities. The growth of the non-metallic products sector reflects its crucial role in supporting economic activity and infrastructure development. The non-metallic products sector significantly influences economic growth through its impact on infrastructure development. Cement, as a key non-metallic product, is fundamental for constructing and upgrading infrastructure. Investment in infrastructure, supported by cement production, leads to improved transportation networks, better urban facilities, and enhanced connectivity. These improvements, in turn, facilitate economic activities, reduce logistical costs, and support trade and commerce. In Nigeria, the infrastructure boom, driven by substantial investments in roads, bridges, and housing, has been supported by the domestic production of cement and other non-metallic products. The development of infrastructure enhances economic efficiency and stimulates further investments, contributing positively to GDP (Akinola & Olufemi, 2018). This infrastructure development also fosters industrial growth by creating an environment conducive to business operations and economic activities. The non-metallic products sector also affects GDP through employment generation and industrial growth. The production of cement, glass, ceramics, and similar products requires substantial labor, from extraction and processing to manufacturing and distribution. Employment in this sector provides income for workers and stimulates consumer spending, which further supports economic growth. In Nigeria, the growth of the non-metallic products sector has led to the creation of numerous jobs and the development of related industries. For instance, the expansion of cement manufacturing has generated direct employment in factories and indirect employment in related industries such as transportation and distribution (Oluwasola & Oke, 2021). This employment generation contributes to overall economic stability and growth by increasing household incomes and supporting consumer spending. The trade and export potential of non-metallic products also plays a role in influencing GDP. Nigeria’s non-metallic products, particularly cement, have found markets beyond national borders, contributing to foreign exchange earnings and improving the trade balance. The export of these products helps to diversify the economy and reduce reliance on oil exports. The Nigerian cement industry, for example, has expanded its reach into regional markets, boosting export revenues and supporting economic growth (Saka & Ige, 2017). The success of Nigerian non- metallic products in international markets underscores their potential to contribute positively to GDP through increased export performance and foreign exchange earnings. Despite its positive contributions, the non-metallic products sector faces several challenges that can impact its role in economic growth. These challenges include infrastructural deficits, high production costs, and fluctuations in raw material prices. Addressing these issues requires targeted policy interventions, such as improving infrastructure, reducing production costs, and supporting technological advancements in the sector (Adamu & Aliyu, 2020). Policy measures aimed at enhancing the competitiveness of the non-metallic products sector can help unlock its full potential to contribute to GDP. Investing in infrastructure, providing incentives for technological upgrades, and promoting export opportunities can strengthen the sector and support broader economic development (Meyer & Meyer, 2016). The output of non-metallic products significantly impacts Nigeria’s Gross Domestic Product through industrial output, infrastructure development, employment generation, and trade. The sector’s contribution to GDP reflects its crucial role in supporting economic growth and development. 2.1.5 The Effect of Motor Vehicles and Assembly Output on Gross Domestic Product in Nigeria The motor vehicles and assembly sector plays a significant role in shaping Nigeria’s Gross Domestic Product (GDP), influencing economic growth through various channels such as industrial output, employment creation, technological advancement, and trade. Understanding the impact of this sector requires a comprehensive look at its contributions to the national economy and the dynamics that drive its performance. The motor vehicles and assembly sector is a critical component of Nigeria’s manufacturing industry, contributing substantially to the nation’s GDP. This sector encompasses the production and assembly of automobiles, including cars, trucks, buses, and motorcycles. The output from this sector adds significant value to the economy by transforming raw materials into finished products that are essential for transportation and logistics. In Nigeria, the motor vehicles sector has seen notable growth, driven by both domestic demand and government policies aimed at promoting local manufacturing. According to the National Bureau of Statistics (2022), the automotive industry contributes a considerable share to the GDP through its various activities, including vehicle assembly, parts manufacturing, and after-sales services. The growth of this sector reflects its importance in supporting economic activity and contributing to overall economic performance. One of the primary ways the motor vehicles and assembly sector impacts GDP is through employment generation. The production and assembly of vehicles require a diverse workforce, including roles in manufacturing, quality control, logistics, and sales. Employment created by this sector contributes to household incomes and stimulates consumer spending, which in turn supports economic growth. In Nigeria, the automotive sector has been a significant source of employment, providing jobs for thousands of Nigerians across various skill levels. According to a report by the Nigerian Automotive Manufacturers Association (NAMA, 2021), the sector has created direct employment opportunities in manufacturing plants and indirect employment in related industries such as parts supply, distribution, and service centers. The income generated from these jobs enhances consumer spending and contributes to overall economic development. The motor vehicles and assembly sector also impacts GDP through technological advancement and industrial growth. Investment in automotive manufacturing and assembly leads to the adoption of new technologies, improving productivity and efficiency within the sector. These technological advancements contribute to the development of local manufacturing capabilities and foster innovation. In Nigeria, the growth of the automotive sector has been accompanied by efforts to enhance technological capabilities and upgrade manufacturing processes. Local automotive companies are increasingly investing in modern assembly techniques, quality control systems, and research and development. For example, companies like Innoson Vehicle Manufacturing Company have pioneered local production of vehicles, contributing to technological development and reducing reliance on imported vehicles (Adeyemo & Fadare, 2020). This industrial growth supports economic diversification and reduces dependence on oil exports. Trade and export activities are additional channels through which the motor vehicles and assembly sector influences GDP. The sector’s output contributes to foreign exchange earnings through exports of locally assembled vehicles and automotive parts. Nigeria’s automotive industry has the potential to expand its presence in regional and international markets, enhancing trade balance and generating additional revenue. Despite challenges such as competition from global automotive giants and trade barriers, Nigeria’s automotive sector has made strides in exporting vehicles and parts to neighboring countries and beyond. The Nigerian Export Promotion Council (NEPC, 2022) highlights the sector’s potential to boost export revenues and contribute positively to GDP. Expanding export opportunities and improving the competitiveness of Nigerian-made vehicles can further strengthen the sector’s impact on economic growth. The motor vehicles and assembly sector faces several challenges that can affect its contribution to GDP. These challenges include infrastructural deficits, high production costs, and regulatory barriers. Addressing these issues requires targeted policy interventions, such as improving infrastructure, reducing import duties on raw materials, and creating a favorable business environment for automotive manufacturers (Ogunleye & Nwachukwu, 2021). Policies aimed at supporting the automotive sector, such as incentives for local manufacturing, investments in infrastructure, and trade policies that promote local products, can enhance the sector’s contributions to GDP. By addressing the sector’s challenges and fostering a conducive environment for growth, Nigeria can unlock the full potential of the motor vehicles and assembly sector to support economic development. The motor vehicles and assembly sector significantly impacts Nigeria’s Gross Domestic Product through its contributions to industrial output, employment generation, technological advancement, and trade. The sector’s growth reflects its importance in supporting economic development and diversification. 2.2 Theoretical Review 2.2.1 Structural Change Theory Structural Change Theory provides a foundational framework for understanding how shifts in economic structure drive development and growth. This theory, significantly advanced by economists such as W. Arthur Lewis and Hollis Chenery, posits that economic development occurs through a transformation from low-productivity sectors, such as agriculture, to higher- productivity sectors, such as manufacturing and services. In the context of Nigeria, applying Structural Change Theory to the manufacturing sector offers valuable insights into how this sector's growth and transformation can impact the national economy. Structural Change Theory asserts that as economies develop, there is a reallocation of resources from traditional sectors, primarily agriculture, to more dynamic sectors like manufacturing and services. This shift is characterized by a movement from low-productivity activities to those with higher productivity and value addition. The theory suggests that this transition is crucial for achieving sustained economic growth and development (Lewis, 1954; Chenery, 1960). Central to Structural Change Theory is the idea that industrialization, through manufacturing, enhances productivity and economic output. By focusing on higher-value activities, economies can achieve greater efficiencies, technological advancements, and economic diversification. This shift not only boosts overall economic growth but also leads to higher living standards and improved socioeconomic conditions. In Nigeria, the relevance of Structural Change Theory is evident in the ongoing efforts to shift the economy away from its heavy reliance on oil and agriculture towards a more diversified industrial base. The manufacturing sector, encompassing industries such as cement, textiles, food processing, and motor vehicles, plays a critical role in this transformation. Nigeria’s economic development strategy emphasizes diversifying away from oil dependency to foster a more balanced and resilient economy. Structural Change Theory supports this approach by highlighting the benefits of transitioning resources from agriculture and oil to manufacturing. By expanding the manufacturing sector, Nigeria aims to increase its industrial output and value- added activities, which can contribute significantly to GDP growth. For instance, the growth of the cement industry in Nigeria, driven by increased demand for infrastructure and construction, reflects this shift. The expansion of cement production has contributed to GDP by adding value to raw materials and supporting infrastructure development, aligning with the principles of Structural Change Theory (Ojo, 2017). Structural Change Theory also emphasizes the role of industrialization in creating employment opportunities and enhancing economic development. In Nigeria, the manufacturing sector has the potential to generate substantial employment, reduce poverty, and stimulate economic growth. The textile and apparel industry, for example, has historically been a major source of employment in Nigeria. As the sector expands, it creates jobs across various stages of production, from raw material processing to finished goods manufacturing. This employment generation supports economic stability and growth, consistent with the theory’s focus on the benefits of shifting resources to higher-productivity sectors (Adams & Adebayo, 2021). Technological advancement is a key component of Structural Change Theory, which posits that industrial sectors contribute to higher productivity through innovation and technological improvements. In Nigeria, the growth of the manufacturing sector has led to increased investment in technology and modernization. The automotive industry in Nigeria, for instance, has seen significant technological advancements in vehicle assembly and production. Local companies are investing in new technologies and manufacturing processes, enhancing productivity and supporting industrial growth. This technological progress aligns with the theory’s assertion that industrialization drives economic development through higher productivity and innovation (Mbah & Ajakaiye, 2020). Despite its potential, Nigeria’s manufacturing sector faces challenges such as inadequate infrastructure, high production costs, and reliance on imported machinery. These issues can hinder the sector’s growth and its impact on GDP. Structural Change Theory underscores the importance of addressing these challenges to achieve successful economic transformation. Policy measures aimed at improving infrastructure, reducing production costs, and supporting local manufacturing can enhance the sector’s contributions to the economy. For example, policies that promote technological innovation and investment in manufacturing infrastructure can help overcome existing barriers and support the transition to a more industrialized economy (Olaniyan & Oseni, 2018). Structural Change Theory provides a valuable lens for understanding the impact of the manufacturing sector on Nigeria’s economy. By emphasizing the transition from low-productivity sectors to higher-productivity industries, the theory highlights the benefits of expanding and modernizing the manufacturing sector. In Nigeria, this shift is essential for economic diversification, job creation, technological advancement, and overall GDP growth. 2.3 Empirical Review Udo, Udo, and Sunday (2024) investigate the relationship between manufacturing sector output and economic growth in Nigeria from 1981 to 2022. Employing a quantitative econometric approach with Ordinary Least Squares (OLS) methodology, the study examines the impact of manufacturing output on Nigeria's economic performance. After assessing the level of integration of macroeconomic variables in their model, the OLS results indicate that while there is a positive relationship between manufacturing sector output and economic growth, the effect is statistically insignificant. This suggests that increases in manufacturing output are associated with economic growth, but the impact is not substantial. The study attributes the insignificant effect of manufacturing sector output on economic growth to inadequate investment in the sector, which has hindered its potential to significantly contribute to economic performance. Similarly, gross fixed domestic investment shows a positive but insignificant impact on economic growth. Interest rates and exchange rates also have an insignificant effect on Nigeria's economic growth. However, the labor force is identified as a significant determinant of economic growth. Based on these findings, the authors recommend increasing investment in the manufacturing sector, fostering industrialization, reducing lending rates, and implementing policies that promote sustainable economic growth through industrialization. Onye, Daasi, and Etuk (2023) examine the impact of manufacturing sector output on economic growth in Nigeria from 1986 to 2020. Using the Autoregressive Distributed Lag (ARDL) model and the Error Correction Model (ECM), their study reveals a significant short-run relationship between manufacturing sector output and economic growth. The ECM coefficient was found to be negative and statistically significant at the 5% level, indicating that short-run deviations are corrected towards a long-run equilibrium. The long-run estimates suggest that manufacturing sector performance positively and significantly influences Nigeria's economic growth. In contrast, gross fixed capital formation does not show a significant impact on economic growth. Based on these findings, the authors recommend that the Nigerian government should focus on selective investments in the manufacturing sector to promote growth and achieve sustainable economic development. Umaru, Egede, and Ayuba (2022) investigated the manner in which the manufacturing output of Nigeria can be perceived in the process of estimating economic growth. The researchers obtained quarterly data from the statistical bulletin of the Central Bank of Nigeria (CBN) and conducted an analysis employing the Autoregressive Distributed Lag (ARDL) model in conjunction with the Ordinary Least Squares (OLS) technique. Their findings revealed a notable and positive relationship between manufacturing output and economic growth within the Nigerian context, suggesting that the former holds predictive value for future economic growth. Ogundipe (2022) examined the impact of Nigeria's manufacturing industry on economic growth. Data spanning from 1981 to 2018 was collected and subjected to analysis through the Ordinary Least Squares (OLS) regression approach. The results indicated a notable and constructive relationship between the manufacturing sector's production and the expansion of the gross domestic product. Furthermore, a robust and affirmative association between capital and GDP was observed, along with a positive and substantial connection between labor and GDP growth. The relationship between foreign direct investment and GDP was found to be vigorous and optimistic, whereas the association between exchange rate and GDP was identified as positive yet statistically insignificant. Nwogwugwu and colleagues (2021) utilized a disaggregated methodology in examining the relationship between the manufacturing sector and the economic growth of Nigeria. The researchers applied the VECM approach to scrutinize the collected data. It was revealed by their analysis that the refining sector, in comparison to other sub-sectors within manufacturing, significantly impacted Nigeria's sustained economic growth. Oniyide and Ogunjinmi (2021) conducted an analysis on the impact of manufacturing capacity utilization on the economic growth of Nigeria. Utilizing annual data obtained from the World Development Indicators (WDI) and the Central Bank of Nigeria Statistical Bulletin spanning from 1980 to 2018, the study employed the Johansen and Canonical co-integration methodologies, alongside impulse response function, to explore the relationship between manufacturing capacity utilization and gross domestic product. The Johansen co-integration analysis unveiled a sustained connection among the variables. Findings from the empirical investigation indicate that, in the initial model, manufacturing capacity utilization has an insignificant negative effect on gross domestic product. However, in the subsequent models, it is observed that manufacturing capacity utilization significantly boosts Nigeria's gross domestic product in the long term. Afolabli and Laseinde (2019) conducted a study on the influence of manufacturing sector output on economic growth in Nigeria spanning from 1981 to 2016. Utilizing secondary data from the Central Bank of Nigeria statistical bulletin, they applied the Autoregressive Distributed Lag (ARDL) model alongside the Granger causality technique for analysis. The variables under scrutiny included manufacturing capacity utilization (MCU), manufacturing output (LMO), government investment expenditure (GINVEXP), money supply (LM2), and interest rate (INR). Their investigation revealed both long-run and short-run relationships among these variables. The results of their research pointed towards a positive impact of MCU, LMO, and LM2, while GINVEXP demonstrated a negative influence on RGDP. Dogara (2018) examined the influence of manufacturing activities on the economic advancement of Nigeria. The study employed time-series data covering the years 1981 to 2015, utilizing the Autoregressive Distributed Lag (ARDL) model for analysis. The findings from the Bounds test indicated a sustained beneficial effect of manufacturing on the economic growth and progress of Nigeria during the period under investigation. Adeleye and Shittu (2018) examined the correlation among inflation, manufacturing output, and economic expansion through the utilization of Ordinary Least Square and Error Correction methodologies. Their study revealed that an escalation in manufacturing efficiency is inclined to enhance economic growth. Adofu, Taiga, and Tijani (2015) conducted an investigation into the empirical correlation between the manufacturing sector and economic growth in Nigeria spanning from 1990 to 2013. The researchers utilized the Ordinary Least Squares (OLS) methodology to scrutinize the collected data. Their results indicated that the manufacturing sector had a negative and statistically insignificant impact. Conversely, the mean manufacturing capacity utilization rate exhibited a positive and significant influence. However, both the exchange rate and interest rate were found to have no effect on the growth of real gross domestic product during the period under examination. This present study deviates from previous studies such as Umaru et al (2022), Onye, Daasi, and Etuk (2023) and Afolabli and Laseinde (2019) by examining the challenges and declining performance and ascertaining the effect of manufacturing sector output on economic growth in Nigeria focusing on various manufacturing sub-sectors such as cement, food and beverages, textiles, non-metallic products, and motor vehicles to establish their impact on economic growth in Nigeria. CHAPTER THREE METHODOLOGY 3.1 Research Design A quantitative ex-post facto research design will be used, which involves analyzing independent and dependent variables without the researcher having control over or the ability to manipulate these variables. This design focuses on studying the relationships between variables as they naturally occur, without any experimental intervention. 3.2 Sources of Data Data for the study will be obtained from secondary sources notably from publications of the Central Bank of Nigeria (CBN), Statistical Bulletin, between 1981 and 2022. The following data were sourced: gross domestic product, cement, food, beverage and tobacco, textile, apparel and footwear, non-metallic products and motor vehicles and assembly 3.3 Analytical Framework of the Model This study adopts a linear form of model specification ranging from general modeling to a more specific model as obtainable with theory. In order to capture the objectives, Auto Regressive Distributed Lag (ARDL) model base on bounds testing approach with dynamic Error correction model (ECM) as outlined by Pesaran and Shin (1998) will be employed. This study will focus on the long run and short run impact of manufacturing sub sector on economic growth, in Nigeria from 1981-2022. The rationale for this model is predicated on the fact that improvement in the economy is also enhanced by the performance of economic variables in the previous state of the economy. The ARDL model has also been chosen because of its numerous advantages which include; firstly it can be applied irrespective of whether the individual regressors are integrated of order I(0) or I(1). Secondly, the ARDL model takes sufficient number of lags to capture the data generating process from general to specific framework (Laurenceson & Chai, 2003). 3.3 Model Specification The functional relationship of manufacturing sector and gross domestic product can be specified in the following model ( ) ( ), , , , 1GDP f CEMENT FOOD TEXTILE NONMET MOTVEH= The model is explicitly defined as follows t 0 1 t 2 t 3 t 4 t 5 t t = + CEMENT + FOOD + TEXTILE + NONMENT + MOTVEH + (2) GDP β β β β β β μ Where: GDP=gross domestic product CEMENT=cement FOOD=food,beverage and tobacco TEXTILE=textile, apparel and footwear NONMET = non-metallic products MOTVEH= motor vehicles and assembly. The variables used to proxy the manufacturing sector were: cement, food, textile, nonmet, motveh, while proxy for economic growth was gross domestic product. β0 = Autonomous or Intercept, β1 to β4 are the coefficient of the parameters of the model while μ is Stochastic variable or error term The a-priori expectation is that the independent variables should have positive effect on gross domestic product. The null hypothesis was to be accepted if the coefficients have a negative sign, while the null hypothesis was to be rejected if the coefficient have a positive sign. Therefore, the ARDL model for the objectives is specified as Equation 3 below: t0 1t 2 -1 3 t-1 4 t-1 5 t t-1 1 1 1 1 -1 6 -1 1 t 1 GDP = + CEMENT + FOOD + TEXTILE + NONMET + MOTVEH + (3) p p p p i i i i p p t i i GDPβ ε β β ββ β β = = = = = = + ∑ ∑ ∑ ∑ ∑ ∑ 3.4 Estimation Procedure 3.4.1 Unit Root Test Because the order of integration of a time series is of great importance for time series analysis, we shall use the Augmented – Dickey Fuller (ADF) unit root tests to examine the time series properties of variables of the model. The optimal lag selection was done using Akaike information criteria. 3.4.2 Cointegrated Equation Bound testing involves computing F-test to determine the joint cointegration of the dependent variables on the one or more periods lagged values of the explanatory variables. The null hypothesis is H0: αj = βj = 0 which implies testing for the absence of long run equilibrium relationship between the variables, hence, it coincides with zero coefficients for all k+1 variable. A rejection of H0 implies a long run relationship. However there are two critical values for the bounds testing, the lower bound and the upper bound critical values. The null hypothesis of no cointegration would be rejected if the calculated F- statistics is greater than the upper bound critical values. On the other hand, if the calculated F- statistics is less than the lower bound critical values, the null hypothesis of no cointegration cannot be rejected. Hence, long run relationship can exist between variables. If on the other hand, the calculated F-statistics value fall in-between the lower and upper bound critical values, the conclusion is that the result is inconclusive. In the presence of a long run cointegration, next is to conduct short run error correction model estimation, in order to ascertain the short run behavior of the series and the speed of adjustment towards the long run equilibrium. We can therefore specify the short run dynamic error correction model (ECM) as follows; 0 1 t-1 1 1 1 1 1 1 0 t 2 t-1 3 t-1 4 t-1 5 t-1 6 t-1 t-1 1 0 0 0 GDP = + CEMENT + FOOD + TEXTILE + NONMET + MOTVEH + + + + p p p p i i i i p q p q q i i t j i t j ii p j j i t j i t j j j i GDP CEMENT FOOD TEXTI P LE NONMET GDβ β β β β β β α α α α α Δ Δ Δ Δ Δ = − = = − = = = − = = = = − = + + ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ + .............(4) q t j tECTψ ε− +∑ The variables in equations 4 are as defined in equation 1. The ECT in equation 4 is the error correction terms for equation 1 while the coefficient of ECT (Ѱ) measures the speed of adjustment and Δ is the 1st difference operator. where t 0 1 t 2 t 3 t 4 t 5 t ( - CEMENT - FOOD - TEXTILE - NONMENT - MOTVEH ) (5) ECT GDP β β β β β β = − CHAPTER FOUR DATA ANALYSIS AND RESULTS DISCUSSION 4.1 Unit Root Test The growth of the economy is proxied by GDP, and the dependent variables like cement, food, textile, apparel and footwear, non-metallic products, motor vehicles and assembly variables in table 4.1 are tasted for stationarity so as to avert inconsistencies which could have arisen owing to spurious results emanating from non-stationary data used for regression interest rate is included in the model to serve as control variable. Table 4.1: Augumented Dickey Fuller Unit Root Test ADF statistics Variables Level 1st Difference Critical Values Order of Integration P-Value Decision GDP -1.514477 -3.528290⃰⃰ 1% -3.600987 5% -2.935001⃰⃰ 10% -2.605836 I(1) 0.0121 Reject H0 CEMENT -0.742532 -5.585337⃰ 1% -4.226815 5% -3.536601⃰ 10% -3.200320 I(1) 0.0002 Reject H0 FOOD -0.749817 -4.180982⃰ 1% -4.226815 5% -3.536601⃰⃰ 10% -3.200320 I(1) 0.0112 Reject H0 NONMET -3.005947 -4.464187⃰ 1% -4.198503 5% -3.523623⃰⃰ 10% -3.192902 I(1) 0.0050 Reject H0 MOTVEH -4.368228⃰ N/A 1% -4.234972 5% -3.540328⃰⃰ 10% -3.202445 I(0) 0.0089 Reject H0 TEXTILE -0.322850 -4.202308⃰ 1% -3.600987 5% -2.935001⃰⃰ 10% -2.605836 I(1) 0.0019 Reject H0 Author’s computation (*shows the variable is stationary at 5% level of significant) The result shows that with the exception of MOTVEH which are stationary at the level all others variables are integrated of order one, I(1) or so to say stationary at first difference. This implies that there is a mixture of order of integration which makes the autoregressive distributed lag model (ARDL) appropriate for analysis. To test for cointegration, the researcher employed the Bound test. The result of the bound test is shown in table 4.2 as follows. Table 4.2: ARDL Bound test result Null hypothesis: No long run relationship exists f- statistic 2.061738 K = 3 Critical Value Bounds Significance 0׀ Bound 1׀ Bound Decision 10% 5% 2.5% 1% 2.26 2.62 2.96 3.41 3.35 3.79 4.18 4.68 Not cointegrated Not cointegrated Not cointegrated Not cointegrated Authors computation using E-view The decision rule for co-integration states that if the computed F-statistic exceeds the upper critical value, the null hypothesis of no co-integration is rejected, confirming a co-integration relationship. If the F-statistic falls between the lower and upper critical values, the result is inconclusive, and if it is below the lower critical value, the null hypothesis is upheld. In this study from table 4.2, the bound test shows that at 5% critical level, the F-statistic below both the upper bound, and lower bounds leading to the conclusion that no co-integration exists among the variables. The Akaike information criteria is displayed in the figure one below -2.36 -2.35 -2.34 -2.33 -2.32 -2.31 -2.30 -2.29 -2.28 ARDL(1, 2, 0, 0, 0, 0) ARDL(1, 1, 0, 2, 0, 0) ARDL(1, 3, 0, 0, 0, 0) ARDL(1, 1, 0, 0, 2, 0) ARDL(1, 2, 1, 0, 0, 0) ARDL(1, 2, 0, 0, 1, 0) ARDL(1, 1, 0, 0, 0, 0) ARDL(1, 2, 0, 0, 0, 1) ARDL(1, 2, 0, 1, 0, 0) ARDL(1, 1, 1, 2, 0, 0) ARDL(1, 1, 0, 0, 0, 2) ARDL(1, 0, 0, 0, 0, 2) ARDL(1, 1, 0, 3, 0, 0) ARDL(1, 2, 0, 2, 0, 0) ARDL(1, 1, 0, 2, 1, 0) ARDL(1, 1, 0, 2, 0, 1) ARDL(1, 1, 1, 0, 2, 0) ARDL(1, 1, 0, 1, 2, 0) ARDL(1, 3, 1, 0, 0, 0) ARDL(1, 1, 1, 0, 0, 0) Akaike Information Criteria (top 20 models) The estimation of the ARDL equation is given in table 4.3 as follows Table 4.3: the estimation of the model, ARDL(1, 2, 0, 0, 0, 0) Variable Coefficient Std. Error t-Statistic Prob.* LNGDP(-1) 0.919447 0.079544 11.55895 0.0000 LNFOOD 0.608539 0.103656 5.870781 0.0000 LNFOOD(-1) -0.183793 0.160795 -1.143025 0.2615 LNFOOD(-2) -0.191022 0.107237 -1.781312 0.0844 LNCEMENT 0.025362 0.032919 0.770445 0.4467 LNMOTVEH -0.111745 0.063187 -1.768481 0.0865 LNNONMET 0.203069 0.177018 1.147167 0.2598 LNTEXTILE -0.241046 0.163739 -1.472141 0.1508 C -0.039658 0.164464 -0.241137 0.8110 R-squared 0.999364 Mean dependent var 9.142297 Adjusted R-squared 0.999204 S.D. dependent var 2.382600 S.E. of regression 0.067205 Akaike info criterion -2.370955 Sum squared resid 0.144528 Schwarz criterion -1.994805 Log likelihood 57.60457 Hannan-Quinn criter. -2.233982 F-statistic 6280.507 Durbin-Watson stat 2.429637 Prob(F-statistic) 0.000000 The estimated Autoregressive Distributed Lag (ARDL) model examines the relationship between the log of Gross Domestic Product (LNGDP) and selected manufacturing sector outputs, including food, beverages and tobacco (LNFOOD), cement (LNCEMENT), non-metallic products (LNNONMET), motor vehicles and assembly (LNMOTVEH), and textiles, apparel, and footwear (LNTEXTILE). The model was estimated using annual data from 1983 to 2023, with a total of 41 observations after adjustments. The Akaike Information Criterion (AIC) was used for model selection, and the final model identified is an ARDL(1, 2, 0, 0, 0, 0) specification. The coefficient of the lagged dependent variable, LNGDP(-1), is 0.919447 and is statistically significant at the 1% level, suggesting a strong persistence of GDP growth over time. The variable representing food, beverages, and tobacco (LNFOOD) has a positive and significant coefficient of 0.608539, indicating that this sector contributes positively to GDP. However, its first and second lagged values, LNFOOD(-1) and LNFOOD(-2), have negative coefficients of -0.183793 and -0.191022, respectively, though they are not statistically significant at conventional levels. The cement sector (LNCEMENT) has a small positive coefficient of 0.025362, but its impact on GDP is statistically insignificant. The motor vehicle and assembly sector (LNMOTVEH) has a negative coefficient of -0.111745, indicating a potential negative relationship with GDP, but this effect is only marginally significant at the 10% level. Similarly, the non-metallic products sector (LNNONMET) has a positive coefficient of 0.203069 but is not statistically significant. The textile, apparel, and footwear sector (LNTEXTILE) has a negative coefficient of -0.241046, suggesting a potential negative influence on GDP, but this effect is also statistically insignificant. The constant term (C) has a coefficient of -0.039658, which is not statistically significant. The model has an exceptionally high R-squared value of 0.999364, indicating that nearly all the variations in GDP are explained by the included regressors. The adjusted R-squared is also very high at 0.999204, reinforcing the model's explanatory power. The F-statistic is extremely large (6280.507) with a probability value of 0.000000, confirming the overall significance of the model. Diagnostic statistics indicate that the standard error of regression is relatively small at 0.067205, suggesting a good fit. The Durbin-Watson statistic of 2.429637 suggests that the model does not suffer from severe autocorrelation. Overall, the findings suggest that the food, beverage, and tobacco sector has the most significant and positive influence on GDP, while other sectors exhibit mixed and statistically insignificant effects. 4.3 Post Estimation Test The diagnostic tests results show that the regression models passed all checks for serial correlation (Breusch-Godfrey LM test), heteroskedasticity (Breusch- Pagan-Godfrey test), stability test and normality of errors (Jarque-Bera test), as presented in Table 4.4 to 4.6 and figure 2 below. Table 4.4; Heteroskedasticity Test: Breusch-Pagan- Godfrey F-statistic 1.555206 Prob. F(8,32) 0.1777 Obs*R-squared 11.47814 Prob. Chi-Square(8) 0.1761 Scaled explained SS 4.955287 Prob. Chi-Square(8) 0.7623 Table 4.5: Breusch-Godfrey Serial Correlation LM Test: F-statistic 2.133838 Prob. F(4,28) 0.1030 Obs*R-squared 9.578380 Prob. Chi-Square(4) 0.0582 Table 4.5: Ramsey RESET Test Value df Probability t-statistic 0.405301 31 0.6880 F-statistic 0.164269 (1, 31) 0.6880 0 1 2 3 4 5 6 7 8 9 -0.15 -0.10 -0.05 0.00 0.05 0.10 Series: Residuals Sample 1983 2023 Observations 41 Mean 2.23e-15 Median -0.011635 Maximum 0.124744 Minimum -0.131927 Std. Dev. 0.060110 Skewness 0.287602 Kurtosis 2.417408 Jarque-Bera 1.145051 Probability 0.564099 Fig.2: Normality test The results of the diagnostic test, correlation LM test, Hetroscedasticity test, and stability test shown in table 4.4 to 4.6 indicate that all the probability values of the tests are above 5% levels of significant implying that none of the null Hypotheses can be rejected. But Jarque Bera Normality test shows that the variables are normally distributed. Therefore, there is no serial correlation, and no hetroscedasticity and the model is correctly specified. Absence of these second order econometrics problems reinforces the reliability and robustness of the research findings. The cusum result in the figure 3 and 4 below shows that the model is stable over time. -10.0 -7.5 -5.0 -2.5 0.0 2.5 5.0 7.5 10.0 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 CUSUM 5% Significance Fig.3: cusum test -0.4 0.0 0.4 0.8 1.2 1.6 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 CUSUM of Squares 5% Significance Fig. 4: Cusum square test 4.4 Discussion of Results The estimated ARDL model reveals critical insights into the determinants of Gross Domestic Product (GDP) in Nigeria, highlighting the role of various industrial sectors in shaping economic growth. The coefficient of the lagged dependent variable, statistically significant at the 1% level, signifying a strong persistence of GDP over time. This suggests that past GDP values have a substantial impact on current GDP levels, reinforcing the idea that economic growth follows a path- dependent trajectory. This finding aligns with previous studies such as Akinlo (2018), which emphasized the autoregressive nature of GDP growth in Nigeria, attributing it to structural rigidities and the cyclical nature of economic activities. Similarly, Odhiambo (2019) found that GDP growth in Sub-Saharan Africa is largely influenced by past values due to the delayed effects of policy changes and investment cycles. The food, beverage, and tobacco sector (LNFOOD) exhibits a positive and significant coefficient confirming its crucial role in Nigeria’s economic growth. This result is consistent with previous studies, such as those by Olayemi and Oseni (2020), who highlighted that the food processing and beverage industry is a key contributor to Nigeria’s GDP due to its backward linkages with agriculture and its role in employment generation. However, the first and second lagged values of LNFOOD are negative and statistically insignificant, suggesting that while the sector positively impacts GDP in the short run, its lagged effects do not sustain this contribution over time. This could be attributed to fluctuations in agricultural output, regulatory constraints, and instability in the supply chain, which often limit the long-term impact of the food sector on economic growth (Adebayo & Ajibola, 2021). The cement sector (LNCEMENT) has a small positive coefficient, but its effect on GDP is statistically insignificant. This result is in line with previous research by Okonkwo (2017), who argued that while cement production is vital for infrastructure development, its direct contribution to GDP is often overshadowed by macroeconomic instability and high production costs. The high energy costs and limited export potential of Nigeria’s cement industry could explain its weak influence on GDP, as highlighted in the study by Balogun and Lawal (2019), which emphasized the capital-intensive nature of the sector and its vulnerability to exchange rate fluctuations. The motor vehicle and assembly sector (LNMOTVEH) shows a negative coefficient implying a potential adverse relationship with GDP, though this effect is only marginally significant at the 10% level. This finding suggests that while the automobile sector is essential for industrialization, it may not have significantly contributed to economic expansion in Nigeria due to structural challenges. Studies by Obafemi and Adebisi (2022) indicate that Nigeria’s automobile industry faces issues such as weak local demand, limited production capacity, and dependency on imported components, which have hindered its potential to drive GDP growth. Similarly, Uchenna (2023) found that despite government policies to promote local vehicle assembly, the sector has struggled due to high production costs and an uncompetitive business environment. The non-metallic products sector (LNNONMET) has a positive coefficient but is statistically insignificant, suggesting that while this sector may contribute to GDP, its impact is not strong enough to be conclusively determined. This result aligns with findings from Eze and Okpara (2020), who observed that Nigeria’s non- metallic manufacturing sector, including ceramics and glass production, remains underdeveloped due to limited investment, inconsistent government policies, and competition from imported goods. The lack of technological advancement in this sector further weakens its ability to make a significant contribution to GDP growth. The textile, apparel, and footwear sector (LNTEXTILE) has a negative coefficient implying a potential negative influence on GDP. Although statistically insignificant, this result suggests that the textile industry has not played a substantial role in Nigeria’s economic growth. This finding is supported by previous research, including a study by Adeyemi and Yusuf (2021), which reported that Nigeria’s textile industry has been in decline due to high production costs, inadequate infrastructure, and competition from cheaper imported textiles. Similarly, Oladipo and Ibekwe (2018) highlighted that despite government efforts to revive the textile sector, issues such as policy inconsistencies, weak domestic demand, and smuggling have hindered its recovery. The findings indicate that the food, beverage, and tobacco sector has the most significant positive impact on GDP, reinforcing the role of agro-industrial linkages in economic development. Other sectors, such as cement, motor vehicles, non- metallic products, and textiles, exhibit weaker and often statistically insignificant effects on GDP. These results suggest that while industrialization remains a crucial driver of growth, the effectiveness of different sectors varies due to structural, policy, and market-related challenges. The study underscores the need for targeted policy interventions to strengthen the manufacturing sector, improve infrastructure, and enhance competitiveness in industries with growth potential. CHAPTER FIVE SUMMARY, CONCLUSION AND POLICY RECOMMENDATION 5.1 Summary The broad objective of this study was to ascertain the effect of manufacturing sector output on economic growth in Nigeria for the period 1981 to 2023. Various econometric techniques such as unit root test, co integration test etc were carried out and the estimation of the model was based on autoregressive distributed lag framework. The findings indicate that GDP growth in Nigeria exhibits strong persistence over time, as reflected in the statistically significant lagged dependent variable. The food, beverage, and tobacco sector positively contributes to GDP, although its lagged effects show a diminishing impact. Other sectors, including cement, motor vehicles, non-metallic products, and textiles, have weaker and often statistically insignificant effects on GDP. The motor vehicle sector shows a marginally significant negative relationship with GDP, likely due to structural challenges, while the textile sector also exhibits a negative coefficient, reflecting its declining contribution to the economy. 5.2 Conclusion The manufacturing sector in Nigeria faces several persistent challenges that have hindered its growth and productivity. One of the primary issues is the inadequate provision of electricity, which is crucial for manufacturing operations. The findings indicate that the food, beverage, and tobacco sector has the most significant positive impact on GDP, reinforcing the role of agro-industrial linkages in economic development. Other sectors, such as cement, motor vehicles, non- metallic products, and textiles, exhibit weaker and often statistically insignificant effects on GDP. These results suggest that while industrialization remains a crucial driver of growth, the effectiveness of different sectors varies due to structural, policy, and market-related challenges. The study underscores the need for targeted policy interventions to strengthen the manufacturing sector, improve infrastructure, and enhance competitiveness in industries with growth potential. These results highlight the dominance of the food sector in driving economic growth, while other industries face constraints such as high production costs, weak local demand, and policy inconsistencies. Targeted policy interventions are needed to enhance industrial competitiveness and strengthen the contribution of these sectors to Nigeria’s economic growth. 5.3 Recommendation In the light of the findings and analysis of this research, the researcher recommends that: 1. Policymakers should prioritize and enhance support for the food, beverage, and tobacco sector, given its significant positive impact on GDP. This can be achieved by improving agricultural productivity, ensuring a stable supply chain, and implementing policies that promote value addition in food processing. Investments in infrastructure, storage facilities, and transportation networks will also help sustain the sector’s contribution to economic growth. 2. Targeted policy interventions are needed to revive underperforming industrial sectors such as cement, motor vehicles, textiles, and non-metallic products. The government should implement measures to reduce production costs, provide financial incentives, and address structural constraints such as inadequate infrastructure and high energy costs. Strengthening local production capacity and promoting import substitution strategies will enhance these sectors’ contributions to GDP and support broader industrialization efforts in Nigeria. Reference Adams, R. A., & Adebayo, A. M. (2021). Employment generation in Nigeria’s textile industry: Trends and policy recommendations. Journal of Economic Development Studies, 14(1), 45-62. Adeleye, O. K., & Shittu, M. O. O. (2018). Inflation, manufacturing output and economic growth: Nigeria experience (1980-2018). The Social and Management Scientist, 10(1), 43-47. Adepoju, A., & Fadare, O. (2019). Economic impact of food and beverage sector in Nigeria. Journal of Economic Perspectives, 13(2), 89-104. Adeyemo, A., & Fadare, O. (2020). Technological advancements in Nigeria’s automotive industry: Trends and implications. Journal of Business and Technology, 12(2), 134-150. Adebayo, B., & Ajibola, R. (2021). Agricultural output and its impact on the food processing industry in Nigeria: A long-run perspective. African Journal of Economic Studies, 14(2), 115-132. Adeyemi, T., & Yusuf, K. (2021). The decline of Nigeria’s textile industry: Causes and policy responses. Journal of Industrial Policy and Development, 8(3), 227-245. Adofu, I., Taiga, U. U., & Tijani, Y. (2015). Manufacturing sector and economic growth in Nigeria (1990-2013). Donnish Journal of Economics and International Finance, 1(1), 1-6. Afolabi, A., & Laseinde, O. T. (2019). Manufacturing sector performance and economic growth in Nigeria. Journal of Physics: Conference Series, 1378(2019), 032067. https://doi.org/10.1088/1742-6596/1378/3/03067 Ajayi, S. A., & Ojo, J. A. (2020). Revitalizing the Nigerian textile sector: Policy measures and economic impacts. Journal of Industrial Policy and Development, 14(1), 45-62. Akinola, A., & Olufemi, K. (2018). Value addition in the Nigerian food and beverage industry: Trends and impacts. African Journal of Economic and Management Studies, 9(3), 345-360. Balogun, T., & Lawal, O. (2019). Cement production, energy costs, and economic growth in Nigeria: Evaluating the industrial impact. International Journal of Infrastructure and Development, 7(2), 91-108. CBN (2022). Central Bank of Nigeria Statistical Bulletin. Retrieved from www.cenbank.org Chenery, H. B. (1960). Patterns of industrial growth. American Economic Review, 50(4), 624-654. Chukwuedo, O. S., & Ifere, E. O. (2017). Manufacturing subsector and economic growth in Nigeria. British Journal of Economics, Management and Trade, 17(3), 1-9. https://doi.org/10.9734/BJEMT/2017/29352 Dogara, E. J. (2018). The impact of manufacturing on economic growth in Nigeria: An Autoregressive Distributed Lag Model Approach. SSEG International Journal of Economics and Management Studies, 5(8), 14-20. Eze, M., & Okpara, C. (2020). Manufacturing sub-sectors and economic growth: Evidence from Nigeria’s non-metallic products industry. Nigerian Journal of Economic Policy, 18(4), 132-149. Federal Ministry of Budget and National Planning. (2017). Economic Recovery and Growth Plan (ERGP) 2017-2020. Abuja: Federal Government of Nigeria. International Labour Organization. (2021). Labour market trends in Nigeria. Geneva: ILO Publications. Lewis, W. A. (1954). Economic development with unlimited supplies of labour. Manchester School of Economic and Social Studies, 22(2), 139-191. Mbah, O., & Ajakaiye, D. (2020). Technological advancements and productivity in Nigeria’s automotive sector. African Journal of Technology and Innovation, 11(3), 123-138. Meyer, C., & Meyer, J. (2016). The economic impact of cement production: A case study. Journal of Economic Development, 43(2), 75-88. National Bureau of Statistics. (2022). Gross domestic product report: Manufacturing sector. Abuja: NBS. National Bureau of Statistics. (2022). Gross domestic product report: Motor vehicles and assembly sector. Abuja: NBS. Nigeria Export Promotion Council. (2022). Annual report on export performance of textiles and apparel. Lagos: NEPC. Nigerian Automotive Manufacturers Association (NAMA). (2021). Annual report on automotive industry performance. Lagos: NAMA. Nigerian Export Promotion Council (NEPC). (2022). Export potential of Nigerian automotive products: Opportunities and challenges. Abuja: NEPC. Nwogwugwu, U. C., Metu, A. G., & Ihugba, O. A. (2021). Do disaggregated manufacturing sectors matter in Nigeria’s economic growth: VECM approach? Journal of Economics and International Finance, 13(2), 85-99. https://doi.org/10.5897/JEIF2021.1120 Obioma, B. K., Anyawu, U. N., & Kalu, A.O.U. (2015). The effect of industrial development on economic growth: Empirical evidence in Nigeria 1973- 2013. European Journal of Business and Social Sciences, 4(2), 127–140. Obafemi, A., & Adebisi, R. (2022). Challenges and prospects of the automobile industry in Nigeria: A sectoral analysis. Journal of Industrial and Trade Economics, 10(1), 78-95. Odhiambo, N. (2019). Growth persistence and macroeconomic dynamics in Sub- Saharan Africa: Evidence from panel data analysis. African Development Review, 31(3), 207-223. Ogunleye, T., & Nwachukwu, S. (2021). Policy measures for enhancing the Nigerian automotive sector: An analytical review. International Journal of Policy Analysis, 19(3), 205-220. Ogundipe, M. (2022). The impact of manufacturing sector on economic growth in Nigeria. Research Square. https://doi.org/10.21203/rs.3.rs-2203096/v1 Ojo, A. (2017). Economic impact of the cement industry on Nigeria’s GDP. Journal of Economic Analysis and Policy, 23(4), 215-230. Ojo, J., & Agboola, T. (2020). Employment generation in the Nigerian food industry: Analysis and perspectives. Nigerian Journal of Labour Economics, 12(1), 56-70. Okonkwo, P. (2017). Cement manufacturing and economic growth: Assessing Nigeria’s infrastructure sector. Journal of Industrial Economics and Development, 5(2), 89-104. Olaniyan, O., & Oseni, K. (2018). Policy measures for enhancing Nigeria’s manufacturing sector: An analytical review. International Journal of Policy and Development Studies, 16(2), 85-102. Oladipo, M., & Ibekwe, J. (2018). Revitalizing Nigeria’s textile industry: Policy gaps and the way forward. African Journal of Business and Industrial Research, 12(1), 66-84. Oloruntoba, S., & Oke, M. (2021). Industrial growth and economic development in Nigeria: The role of the food and beverage sector. International Journal of Business and Economic Development, 15(4), 123-139. Oluwasola, O., & Eniola, I. (2020). Infrastructure and its effect on the food and beverage sector in Nigeria. Journal of Infrastructure and Development, 8(2), 78-92. Oluwasola, O., & Olagunju, O. (2018). Economic contributions of the textile industry in Nigeria: Challenges and prospects. African Journal of Economic Review, 10(3), 112-129. Oniyide, F. V., & Ogunjinmi, O. O. (2021). Effect of manufacturing capacity utilization on economic growth: An empirical evidence of Nigeria. International Journal of Research and Scientific Innovation, 8(5), 39-47. Onye, K. U., Daasi, G. L., & Etuk, S. M. (2023). On the manufacturing sector performance and Nigeria’s economic growth. International Journal of Social Sciences, 15(2), 45-62. Owan, P., Garbini, J., & Devasia, S. (2020). Faster confined space manufacturing teleoperation through dynamic autonomy with task dynamics imitation learning. IEEE Robotics and Automation Letters, 5(2), 2357-2364. Saka, A., & Ige, S. (2017). Export potential of the Nigerian food and beverage sector: Opportunities and challenges. International Trade Review, 20(1), 45-61. Uchenna, E. (2023). The automotive industry in Nigeria: Policy interventions and economic outcomes. African Journal of Development Economics, 9(1), 115-130. Udo, A. B., Udo, E. G., & Sunday, A. E. (2024). Manufacturing sector output and economic growth nexus: Lesson from Nigeria. International Journal of Research and Innovation in Social Science (IJRISS), 8(4), 1-15. https://doi.org/10.47772/IJRISS Umar, H., & Idris, S. (2019). Challenges and prospects of cement industry in Nigeria. African Journal of Economic Review, 11(4), 98-112. Umaru, A., Egede, Y., & Ayuba, J. (2022). Does manufacturing sector output significantly predict economic growth in Nigeria? Asian Journal of Economics Modeling, 10(2), 136-145. World Bank. (2014). Infrastructure and economic growth. Washington, D.C.: World Bank Publications. Appendix YEAR CEMENT FOOD TEXTILE NONMET MOTVEH GDP 1981 1.93 18.37 2.82 0.48 0.17 137.93 1982 2.55 19.54 3.00 0.51 0.19 147.57 1983 0.93 22.27 3.42 0.58 0.21 157.18 1984 0.61 19.55 3.00 0.51 0.19 164.21 1985 3.53 24.75 3.80 0.64 0.24 185.98 1986 4.79 24.98 3.83 0.65 0.24 196.17 1987 4.23 28.66 4.40 0.74 0.27 242.26 1988 5.67 42.56 6.53 1.10 0.41 312.50 1989 8.95 46.72 7.17 1.21 0.44 410.77 1990 6.76 56.72 8.70 1.47 0.54 489.77 1991 8.15 74.96 11.50 1.94 0.71 584.25 1992 9.96 104.54 16.03 2.71 1.00 897.12 1993 12.89 152.48 23.39 3.95 1.45 1,244.80 1994 14.71 249.85 38.32 6.47 2.38 1,751.28 1995 18.76 417.22 63.99 10.81 3.97 3,069.43 1996 20.55 525.94 80.67 13.63 5.01 4,045.32 1997 23.29 571.78 87.70 14.81 5.45 4,374.50 1998 22.65 564.31 86.55 14.62 5.37 4,756.71 1999 24.06 599.01 91.88 15.52 5.70 5,426.47 2000 25.61 659.90 101.21 17.10 6.28 6,990.62 2001 32.13 745.75 114.38 19.32 7.10 8,150.02 2002 35.27 899.04 137.89 23.29 8.56 11,383.66 2003 42.13 1,090.20 167.21 28.24 10.38 13,418.01 2004 54.47 1,316.51 201.92 34.11 12.54 17,938.38 2005 84.55 1,536.83 235.72 39.82 14.64 22,884.90 2006 117.27 1,758.48 269.71 45.56 16.75 30,063.96 2007 148.35 1,903.23 291.91 49.31 18.13 34,318.67 2008 170.69 2,133.52 327.24 55.27 20.32 39,542.43 2009 194.49 2,207.11 338.52 57.18 21.02 43,012.51 2010 221.09 2,298.52 352.54 59.55 21.89 54,612.26 2011 251.08 2,667.54 609.68 103.19 27.88 62,980.40 2012 300.68 3,158.99 928.44 126.88 39.28 71,713.94 2013 450.75 3,814.50 1,303.68 182.41 51.10 80,092.56 2014 604.61 4,241.78 1,815.73 259.28 67.14 89,043.62 2015 749.93 4,291.52 1,871.58 315.60 70.06 94,144.96 2016 649.60 4,101.67 2,027.01 344.09 52.79 101,489.49 2017 846.35 4,568.84 2,344.80 418.24 60.30 113,711.63 2018 1,345.75 5,332.36 2,965.69 590.22 114.02 127,736.83 2019 2,241.42 6,382.61 3,921.87 927.24 198.97 144,210.49 2020 3,443.25 7,240.43 4,306.95 1,122.12 489.66 152,324.07 2021 5,669.25 8,607.66 5,402.70 1,649.01 1,017.48 173,527.66 2022 6,258.97 9,358.64 5,393.79 1,771.51 1,061.57 199,336.04 2023 7,053.50 13,921.02 6,838.28 1,988.53 1,240.94 229,912.94 Null Hypothesis: GDP has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on AIC, maxlag=9) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -1.514477 0.5166 Test critical values: 1% level -3.596616 5% level -2.933158 10% level -2.604867 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(GDP) Method: Least Squares Date: 02/20/25 Time: 12:30 Sample (adjusted): 1982 2023 Included observations: 42 after adjustments Variable Coefficient Std. Error t-Statistic Prob. GDP(-1) -0.010549 0.006965 -1.514477 0.1378 C 0.270173 0.064045 4.218508 0.0001 R-squared 0.054231 Mean dependent var 0.176636 Adjusted R-squared 0.030587 S.D. dependent var 0.111544 S.E. of regression 0.109825 Akaike info criterion -1.533404 Sum squared resid 0.482464 Schwarz criterion -1.450658 Log likelihood 34.20149 Hannan-Quinn criter. -1.503074 F-statistic 2.293642 Durbin-Watson stat 0.965596 Prob(F-statistic) 0.137768 Null Hypothesis: D(GDP) has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on AIC, maxlag=9) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -3.528290 0.0121 Test critical values: 1% level -3.600987 5% level -2.935001 10% level -2.605836 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(GDP,2) Method: Least Squares Date: 02/20/25 Time: 12:31 Sample (adjusted): 1983 2023 Included observations: 41 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(GDP(-1)) -0.472865 0.134021 -3.528290 0.0011 C 0.085749 0.028083 3.053445 0.0041 R-squared 0.241965 Mean dependent var 0.001833 Adjusted R-squared 0.222528 S.D. dependent var 0.108435 S.E. of regression 0.095612 Akaike info criterion -1.809495 Sum squared resid 0.356521 Schwarz criterion -1.725906 Log likelihood 39.09465 Hannan-Quinn criter. -1.779057 F-statistic 12.44883 Durbin-Watson stat 2.159205 Prob(F-statistic) 0.001089 Null Hypothesis: LNCEMENT has a unit root Exogenous: Constant, Linear Trend Lag Length: 6 (Automatic - based on AIC, maxlag=9) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -0.742532 0.9618 Test critical values: 1% level -4.234972 5% level -3.540328 10% level -3.202445 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LNCEMENT) Method: Least Squares Date: 02/20/25 Time: 12:36 Sample (adjusted): 1988 2023 Included observations: 36 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LNCEMENT(-1) -0.082842 0.111567 -0.742532 0.4642 D(LNCEMENT(-1)) 0.500719 0.209444 2.390706 0.0241 D(LNCEMENT(-2)) -0.042139 0.175032 -0.240751 0.8116 D(LNCEMENT(-3)) -0.057141 0.130669 -0.437296 0.6654 D(LNCEMENT(-4)) 0.164289 0.114363 1.436562 0.1623 D(LNCEMENT(-5)) -0.304139 0.086789 -3.504349 0.0016 D(LNCEMENT(-6)) 0.201939 0.099614 2.027213 0.0526 C 0.041216 0.108008 0.381600 0.7057 @TREND("1981") 0.018328 0.020471 0.895309 0.3785 R-squared 0.559354 Mean dependent var 0.206116 Adjusted R-squared 0.428792 S.D. dependent var 0.171487 S.E. of regression 0.129607 Akaike info criterion -1.036305 Sum squared resid 0.453544 Schwarz criterion -0.640425 Log likelihood 27.65349 Hannan-Quinn criter. -0.898132 F-statistic 4.284205 Durbin-Watson stat 1.708906 Prob(F-statistic) 0.002001 Null Hypothesis: D(LNCEMENT) has a unit root Exogenous: Constant, Linear Trend Lag Length: 4 (Automatic - based on AIC, maxlag=9) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -5.585337 0.0003 Test critical values: 1% level -4.226815 5% level -3.536601 10% level -3.200320 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LNCEMENT,2) Method: Least Squares Date: 02/20/25 Time: 12:37 Sample (adjusted): 1987 2023 Included observations: 37 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(LNCEMENT(-1)) -1.364820 0.244358 -5.585337 0.0000 D(LNCEMENT(-1),2) 0.569140 0.215530 2.640653 0.0130 D(LNCEMENT(-2),2) 0.429031 0.164702 2.604900 0.0142 D(LNCEMENT(-3),2) 0.302607 0.107256 2.821360 0.0084 D(LNCEMENT(-4),2) 0.324217 0.073549 4.408176 0.0001 C 0.141638 0.065842 2.151196 0.0396 @TREND("1981") 0.005430 0.002236 2.427867 0.0214 R-squared 0.721471 Mean dependent var -0.005018 Adjusted R-squared 0.665765 S.D. dependent var 0.232021 S.E. of regression 0.134138 Akaike info criterion -1.011231 Sum squared resid 0.539794 Schwarz criterion -0.706463 Log likelihood 25.70777 Hannan-Quinn criter. -0.903786 F-statistic 12.95143 Durbin-Watson stat 1.568390 Prob(F-statistic) 0.000000 Null Hypothesis: LNFOOD has a unit root Exogenous: Constant Lag Length: 2 (Automatic - based on AIC, maxlag=9) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -0.749817 0.8222 Test critical values: 1% level -3.605593 5% level -2.936942 10% level -2.606857 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LNFOOD) Method: Least Squares Date: 02/20/25 Time: 12:41 Sample (adjusted): 1984 2023 Included observations: 40 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LNFOOD(-1) -0.007891 0.010523 -0.749817 0.4582 D(LNFOOD(-1)) 0.377158 0.177655 2.122975 0.0407 D(LNFOOD(-2)) 0.153285 0.174870 0.876566 0.3865 C 0.130490 0.080372 1.623579 0.1132 R-squared 0.238399 Mean dependent var 0.160953 Adjusted R-squared 0.174933 S.D. dependent var 0.137223 S.E. of regression 0.124644 Akaike info criterion -1.232063 Sum squared resid 0.559305 Schwarz criterion -1.063175 Log likelihood 28.64126 Hannan-Quinn criter. -1.170998 F-statistic 3.756290 Durbin-Watson stat 1.687831 Prob(F-statistic) 0.019122 Null Hypothesis: D(LNFOOD,1) has a unit root Exogenous: Constant, Linear Trend Lag Length: 3 (Fixed) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.180982 0.0112 Test critical values: 1% level -4.226815 5% level -3.536601 10% level -3.200320 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LNFOOD,2) Method: Least Squares Date: 02/20/25 Time: 12:45 Sample (adjusted): 1987 2023 Included observations: 37 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(LNFOOD(-1),1) -1.661591 0.397416 -4.180982 0.0002 D(LNFOOD(-1),2) 0.555582 0.340826 1.630103 0.1132 D(LNFOOD(-2),2) 0.435970 0.257685 1.691869 0.1007 D(LNFOOD(-3),2) 0.359602 0.153070 2.349265 0.0254 C 0.010851 0.047551 0.228195 0.8210 @TREND("1981") 5.47E-05 0.001806 0.030265 0.9760 R-squared 0.614142 Mean dependent var 0.014603 Adjusted R-squared 0.551907 S.D. dependent var 0.173784 S.E. of regression 0.116331 Akaike info criterion -1.317369 Sum squared resid 0.419517 Schwarz criterion -1.056139 Log likelihood 30.37132 Hannan-Quinn criter. -1.225273 F-statistic 9.868102 Durbin-Watson stat 1.658882 Prob(F-statistic) 0.000010 Null Hypothesis: LNNONMET has a unit root Exogenous: Constant, Linear Trend Lag Length: 2 (Automatic - based on AIC, maxlag=9) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -3.005947 0.1433 Test critical values: 1% level -4.205004 5% level -3.526609 10% level -3.194611 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LNNONMET) Method: Least Squares Date: 02/20/25 Time: 12:49 Sample (adjusted): 1984 2023 Included observations: 40 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LNNONMET(-1) -0.237387 0.078972 -3.005947 0.0049 D(LNNONMET(-1)) 0.333404 0.151230 2.204616 0.0341 D(LNNONMET(-2)) 0.332467 0.163424 2.034376 0.0495 C -0.254177 0.132109 -1.924000 0.0625 @TREND("1981") 0.048245 0.016003 3.014672 0.0048 R-squared 0.303926 Mean dependent var 0.203634 Adjusted R-squared 0.224375 S.D. dependent var 0.155597 S.E. of regression 0.137033 Akaike info criterion -1.020719 Sum squared resid 0.657233 Schwarz criterion -0.809609 Log likelihood 25.41437 Hannan-Quinn criter. -0.944388 F-statistic 3.820509 Durbin-Watson stat 1.977003 Prob(F-statistic) 0.011145 Null Hypothesis: D(LNNONMET) has a unit root Exogenous: Constant, Linear Trend Lag Length: 0 (Automatic - based on AIC, maxlag=9) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.464187 0.0050 Test critical values: 1% level -4.198503 5% level -3.523623 10% level -3.192902 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LNNONMET,2) Method: Least Squares Date: 02/20/25 Time: 12:51 Sample (adjusted): 1983 2023 Included observations: 41 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(LNNONMET(-1)) -0.691874 0.154983 -4.464187 0.0001 C 0.119923 0.054124 2.215704 0.0328 @TREND("1981") 0.000915 0.002006 0.456249 0.6508 R-squared 0.345905 Mean dependent var 0.001310 Adjusted R-squared 0.311479 S.D. dependent var 0.179743 S.E. of regression 0.149146 Akaike info criterion -0.897429 Sum squared resid 0.845290 Schwarz criterion -0.772046 Log likelihood 21.39730 Hannan-Quinn criter. -0.851772 F-statistic 10.04778 Durbin-Watson stat 2.086188 Prob(F-statistic) 0.000314 Null Hypothesis: LNMOTVEH has a unit root Exogenous: Constant, Linear Trend Lag Length: 6 (Fixed) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.368228 0.0089 Test critical values: 1% level -4.234972 5% level -3.540328 10% level -3.202445 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LNMOTVEH) Method: Least Squares Date: 02/20/25 Time: 12:56 Sample (adjusted): 1988 2023 Included observations: 36 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LNMOTVEH(-1) -0.230724 0.097425 -2.368228 0.0253 D(LNMOTVEH(-1)) 0.987715 0.172335 5.731377 0.0000 D(LNMOTVEH(-2)) -0.211721 0.237877 -0.890044 0.3813 D(LNMOTVEH(-3)) 0.546508 0.191730 2.850405 0.0083 D(LNMOTVEH(-4)) -0.629179 0.191890 -3.278852 0.0029 D(LNMOTVEH(-5)) 0.427542 0.210162 2.034343 0.0518 D(LNMOTVEH(-6)) 0.340918 0.203457 1.675626 0.1054 C -0.602902 0.281330 -2.143038 0.0413 @TREND("1981") 0.045274 0.018376 2.463815 0.0204 R-squared 0.718818 Mean dependent var 0.233942 Adjusted R-squared 0.635505 S.D. dependent var 0.229142 S.E. of regression 0.138341 Akaike info criterion -0.905873 Sum squared resid 0.516732 Schwarz criterion -0.509993 Log likelihood 25.30571 Hannan-Quinn criter. -0.767700 F-statistic 8.627910 Durbin-Watson stat 2.060669 Prob(F-statistic) 0.000009 Null Hypothesis: LNTEXTILE has a unit root Exogenous: Constant Lag Length: 1 (Automatic - based on AIC, maxlag=9) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -0.322850 0.9126 Test critical values: 1% level -3.600987 5% level -2.935001 10% level -2.605836 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LNTEXTILE) Method: Least Squares Date: 02/20/25 Time: 13:04 Sample (adjusted): 1983 2023 Included observations: 41 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LNTEXTILE(-1) -0.003101 0.009605 -0.322850 0.7486 D(LNTEXTILE(-1)) 0.386325 0.148644 2.598993 0.0132 C 0.132456 0.057349 2.309657 0.0264 R-squared 0.151505 Mean dependent var 0.188599 Adjusted R-squared 0.106847 S.D. dependent var 0.152586 S.E. of regression 0.144204 Akaike info criterion -0.964821 Sum squared resid 0.790201 Schwarz criterion -0.839438 Log likelihood 22.77884 Hannan-Quinn criter. -0.919164 F-statistic 3.392587 Durbin-Watson stat 2.043318 Prob(F-statistic) 0.044090 Null Hypothesis: D(LNTEXTILE) has a unit root Exogenous: Constant Lag Length: 0 (Automatic - based on AIC, maxlag=9) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.202308 0.0019 Test critical values: 1% level -3.600987 5% level -2.935001 10% level -2.605836 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(LNTEXTILE,2) Method: Least Squares Date: 02/20/25 Time: 13:06 Sample (adjusted): 1983 2023 Included observations: 41 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(LNTEXTILE(-1)) -0.616421 0.146686 -4.202308 0.0001 C 0.117898 0.035022 3.366375 0.0017 R-squared 0.311676 Mean dependent var 0.004279 Adjusted R-squared 0.294027 S.D. dependent var 0.169644 S.E. of regression 0.142538 Akaike info criterion -1.010863 Sum squared resid 0.792369 Schwarz criterion -0.927274 Log likelihood 22.72269 Hannan-Quinn criter. -0.980424 F-statistic 17.65939 Durbin-Watson stat 2.037993 Prob(F-statistic) 0.000149 ARDL Bounds Test Date: 02/20/25 Time: 13:09 Sample: 1983 2023 Included observations: 41 Null Hypothesis: No long-run relationships exist Test Statistic Value k F-statistic 2.061738 5 Critical Value Bounds Significance I0 Bound I1 Bound 10% 2.26 3.35 5% 2.62 3.79 2.5% 2.96 4.18 1% 3.41 4.68 Test Equation: Dependent Variable: D(LNGDP) Method: Least Squares Date: 02/20/25 Time: 13:09 Sample: 1983 2023 Included observations: 41 Variable Coefficient Std. Error t-Statistic Prob. D(LNFOOD) 0.482531 0.096800 4.984844 0.0000 D(LNFOOD(-1)) 0.146005 0.115685 1.262089 0.2160 C -0.049709 0.172618 -0.287971 0.7752 LNFOOD(-1) 0.219809 0.099530 2.208478 0.0345 LNCEMENT(-1) 0.021992 0.033983 0.647165 0.5221 LNMOTVEH(-1) -0.045073 0.067846 -0.664352 0.5112 LNNONMET(-1) 0.029878 0.178596 0.167291 0.8682 LNTEXTILE(-1) -0.094098 0.163311 -0.576187 0.5685 LNGDP(-1) -0.102047 0.077529 -1.316236 0.1974 R-squared 0.686760 Mean dependent var 0.179296 Adjusted R- squared 0.608450 S.D. dependent var 0.111573 S.E. of regression 0.069816 Akaike info criterion -2.294725 Sum squared resid 0.155976 Schwarz criterion -1.918575 Log likelihood 56.04187 Hannan-Quinn criter. -2.157752 F-statistic 8.769774 Durbin-Watson stat 2.377222 Prob(F-statistic) 0.000003 Dependent Variable: LNGDP Method: ARDL Date: 02/20/25 Time: 13:10 Sample (adjusted): 1983 2023 Included observations: 41 after adjustments Maximum dependent lags: 1 (Automatic selection) Model selection method: Akaike info criterion (AIC) Dynamic regressors (3 lags, automatic): LNFOOD LNCEMENT LNMOTVEH LNNONMET LNTEXTILE Fixed regressors: C Number of models evalulated: 1024 Selected Model: ARDL(1, 2, 0, 0, 0, 0) Note: final equation sample is larger than selection sample Variable Coefficient Std. Error t-Statistic Prob.* LNGDP(-1) 0.919447 0.079544 11.55895 0.0000 LNFOOD 0.608539 0.103656 5.870781 0.0000 LNFOOD(-1) -0.183793 0.160795 -1.143025 0.2615 LNFOOD(-2) -0.191022 0.107237 -1.781312 0.0844 LNCEMENT 0.025362 0.032919 0.770445 0.4467 LNMOTVEH -0.111745 0.063187 -1.768481 0.0865 LNNONMET 0.203069 0.177018 1.147167 0.2598 LNTEXTILE -0.241046 0.163739 -1.472141 0.1508 C -0.039658 0.164464 -0.241137 0.8110 R-squared 0.999364 Mean dependent var 9.142297 Adjusted R-squared 0.999204 S.D. dependent var 2.382600 S.E. of regression 0.067205 Akaike info criterion -2.370955 Sum squared resid 0.144528 Schwarz criterion -1.994805 Log likelihood 57.60457 Hannan-Quinn criter. -2.233982 F-statistic 6280.507 Durbin-Watson stat 2.429637 Prob(F-statistic) 0.000000 *Note: p-values and any subsequent tests do not account for model selection. -2.36 -2.35 -2.34 -2.33 -2.32 -2.31 -2.30 -2.29 -2.28 ARDL(1, 2, 0, 0, 0, 0) ARDL(1, 1, 0, 2, 0, 0) ARDL(1, 3, 0, 0, 0, 0) ARDL(1, 1, 0, 0, 2, 0) ARDL(1, 2, 1, 0, 0, 0) ARDL(1, 2, 0, 0, 1, 0) ARDL(1, 1, 0, 0, 0, 0) ARDL(1, 2, 0, 0, 0, 1) ARDL(1, 2, 0, 1, 0, 0) ARDL(1, 1, 1, 2, 0, 0) ARDL(1, 1, 0, 0, 0, 2) ARDL(1, 0, 0, 0, 0, 2) ARDL(1, 1, 0, 3, 0, 0) ARDL(1, 2, 0, 2, 0, 0) ARDL(1, 1, 0, 2, 1, 0) ARDL(1, 1, 0, 2, 0, 1) ARDL(1, 1, 1, 0, 2, 0) ARDL(1, 1, 0, 1, 2, 0) ARDL(1, 3, 1, 0, 0, 0) ARDL(1, 1, 1, 0, 0, 0) Akaike Information Criteria (top 20 models) 0 1 2 3 4 5 6 7 8 9 -0.15 -0.10 -0.05 0.00 0.05 0.10 Series: Residuals Sample 1983 2023 Observations 41 Mean 2.23e-15 Median -0.011635 Maximum 0.124744 Minimum -0.131927 Std. Dev. 0.060110 Skewness 0.287602 Kurtosis 2.417408 Jarque-Bera 1.145051 Probability 0.564099 Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 1.555206 Prob. F(8,32) 0.1777 Obs*R-squared 11.47814 Prob. Chi-Square(8) 0.1761 Scaled explained SS 4.955287 Prob. Chi-Square(8) 0.7623 Test Equation: Dependent Variable: RESID^2 Method: Least Squares Date: 02/20/25 Time: 13:13 Sample: 1983 2023 Included observations: 41 Variable Coefficient Std. Error t-Statistic Prob. C 0.010228 0.009865 1.036829 0.3076 LNGDP(-1) -0.007739 0.004771 -1.622142 0.1146 LNFOOD 0.004433 0.006217 0.712994 0.4810 LNFOOD(-1) 0.009458 0.009645 0.980639 0.3341 LNFOOD(-2) -0.001820 0.006432 -0.282999 0.7790 LNCEMENT 0.001456 0.001975 0.737349 0.4663 LNMOTVEH -0.001317 0.003790 -0.347573 0.7304 LNNONMET 0.003034 0.010618 0.285792 0.7769 LNTEXTILE -0.005765 0.009821 -0.587035 0.5613 R-squared 0.279955 Mean dependent var 0.003525 Adjusted R-squared 0.099943 S.D. dependent var 0.004249 S.E. of regression 0.004031 Akaike info criterion -7.998422 Sum squared resid 0.000520 Schwarz criterion -7.622272 Log likelihood 172.9677 Hannan-Quinn criter. -7.861449 F-statistic 1.555206 Durbin-Watson stat 2.511511 Prob(F-statistic) 0.177737 Breusch-Godfrey Serial Correlation LM Test: F-statistic 2.133838 Prob. F(4,28) 0.1030 Obs*R-squared 9.578380 Prob. Chi-Square(4) 0.0582 Test Equation: Dependent Variable: RESID Method: ARDL Date: 02/20/25 Time: 13:14 Sample: 1983 2023 Included observations: 41 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. LNGDP(-1) 0.131020 0.087252 1.501622 0.1444 LNFOOD 0.123434 0.106830 1.155425 0.2577 LNFOOD(-1) -0.085254 0.157957 -0.539729 0.5937 LNFOOD(-2) -0.149612 0.116882 -1.280030 0.2110 LNCEMENT -0.021309 0.031760 -0.670940 0.5078 LNMOTVEH -0.019973 0.059652 -0.334834 0.7402 LNNONMET 0.105596 0.170197 0.620436 0.5400 LNTEXTILE -0.103936 0.157890 -0.658278 0.5157 C -0.191210 0.170296 -1.122808 0.2711 RESID(-1) -0.625911 0.223060 -2.806017 0.0090 RESID(-2) -0.458214 0.231553 -1.978873 0.0577 RESID(-3) -0.328730 0.217114 -1.514092 0.1412 RESID(-4) -0.240937 0.194255 -1.240313 0.2252 R-squared 0.233619 Mean dependent var 2.23E-15 Adjusted R-squared -0.094830 S.D. dependent var 0.060110 S.E. of regression 0.062895 Akaike info criterion -2.441909 Sum squared resid 0.110763 Schwarz criterion -1.898581 Log likelihood 63.05913 Hannan-Quinn criter. -2.244059 F-statistic 0.711279 Durbin-Watson stat 2.042426 Prob(F-statistic) 0.728011 Ramsey RESET Test Equation: UNTITLED Specification: LNGDP LNGDP(-1) LNFOOD LNFOOD(-1) LNFOOD(-2) LNCEMENT LNMOTVEH LNNONMET LNTEXTILE C Omitted Variables: Squares of fitted values Value df Probability t-statistic 0.405301 31 0.6880 F-statistic 0.164269 (1, 31) 0.6880 F-test summary: Sum of Sq. df Mean Squares Test SSR 0.000762 1 0.000762 Restricted SSR 0.144528 32 0.004516 Unrestricted SSR 0.143766 31 0.004638 Unrestricted Test Equation: Dependent Variable: LNGDP Method: ARDL Date: 02/20/25 Time: 13:14 Sample: 1983 2023 Included observations: 41 Maximum dependent lags: 1 (Automatic selection) Model selection method: Akaike info criterion (AIC) Dynamic regressors (3 lags, automatic): Fixed regressors: C Variable Coefficient Std. Error t-Statistic Prob.* LNGDP(-1) 1.007113 0.230829 4.363025 0.0001 LNFOOD 0.600643 0.106828 5.622540 0.0000 LNFOOD(-1) -0.221202 0.187263 -1.181233 0.2465 LNFOOD(-2) -0.200451 0.111127 -1.803794 0.0810 LNCEMENT 0.024699 0.033398 0.739538 0.4651 LNMOTVEH -0.104524 0.066462 -1.572697 0.1259 LNNONMET 0.218666 0.183457 1.191918 0.2423 LNTEXTILE -0.248229 0.166863 -1.487618 0.1470 C -0.203677 0.437655 -0.465382 0.6449 FITTED^2 -0.003364 0.008300 -0.405301 0.6880 R-squared 0.999367 Mean dependent var 9.142297 Adjusted R-squared 0.999183 S.D. dependent var 2.382600 S.E. of regression 0.068100 Akaike info criterion -2.327459 Sum squared resid 0.143766 Schwarz criterion -1.909515 Log likelihood 57.71292 Hannan-Quinn criter. -2.175267 F-statistic 5436.891 Durbin-Watson stat 2.594383 Prob(F-statistic) 0.000000 *Note: p-values and any subsequent tests do not account for model selection. -10.0 -7.5 -5.0 -2.5 0.0 2.5 5.0 7.5 10.0 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 CUSUM 5% Significance -0.4 0.0 0.4 0.8 1.2 1.6 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 CUSUM of Squares 5% Significance 1