




































 
 

 

13 
© 2019 by the authors; licensee Asian Online Journal Publishing Group 
 

Economy 
Vol. 6, No. 1, 13-24, 2019 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/journal.502.2019.61.13.24 

© 2019 by the authors; licensee Asian Online Journal Publishing Group 

    
 

 
 
 
Cluster Development in a Transforming Economy: The Case of Motorcycle Spare 
Parts Firms in Nnewi, Anambra State of Nigeria 

 
Tobechi F. Agbanike1    

Augustine C. Osigwe2    

Denis N. Yuni3   

Thank-God C. Onoja4     

Sunday A. Okwor5     

 

 
( Corresponding Author) 

 
1,3,4,5Department of Economics and Development Studies, Alex Ekwueme Federal University, Ndufu-Alike, Ebonyi 
State, Nigeria. 

 
2Research and Training Division National Institute for Legislative Studies, National Assembly, Abuja, Nigeria. 

 

 
Abstract 

This paper examined the impact of cluster development in Nnewi, Anambra State of Nigeria. The 
estimated parsimonious model revealed that capital and labour were significant determinants of 
sales made by the firms while the cluster dummy variable was insignificant. This insignificance of 
the cluster dummy variable implied that, in terms of total sales, there was no significant difference 
between firms in the cluster and firms outside the cluster. For the profit model, we found that 
capital, labour and the cluster dummy were significant at 1% level. Capital, labour and cluster 
dummy have a positive relationship with firm profit. The positive coefficient of the cluster dummy 
variable indicated that the profit of firms in the cluster was significantly higher than that of the 

firms outside the cluster by about ₦31,050. It was therefore concluded that cluster residency made 
a significant difference in firm profit and recommended that government should encourage cluster 
development to accelerate the transformation of the economy. 

 
Keywords: Cluster, Economy, Firms, Motorcycle, Nnewi, Nigeria. 

JEL Classification: B21; L2; R3. 
 

Citation | Tobechi F. Agbanike; Augustine C. Osigwe; Denis N. 
Yuni; Thank-God C. Onoja; Sunday A. Okwor (2019). Cluster 
Development in a Transforming Economy: The Case of Motorcycle 
Spare Parts Firms in Nnewi, Anambra State of Nigeria. Economy, 
6(1): 13-24. 
History:  
Received: 23 April 2019 
Revised: 31 May 2019 
Accepted: 3 July 2019 
Published: 6 September 2019 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Acknowledgement: All authors contributed to the conception and design of 
the study. 
Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 14 
2. Literature Review ............................................................................................................................................................................ 16 
3. Data and Methods ............................................................................................................................................................................ 19 
4. Data Analysis and Presentation of Empirical Results .............................................................................................................. 20 
5. Conclusion ......................................................................................................................................................................................... 22 
References .............................................................................................................................................................................................. 22 
 

 

 

 

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http://asianonlinejournals.com/index.php/Economy/article/view/971
http://orcid.org/0000-0001-9063-7176
http://orcid.org/0000-0001-6528-3521
http://orcid.org/0000-0002-7468-4105
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http://orcid.org/0000-0002-1502-8618
http://asianonlinejournals.com/index.php/Economy/article/view/971
http://orcid.org/0000-0001-9063-7176
http://orcid.org/0000-0001-6528-3521
http://orcid.org/0000-0002-7468-4105
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http://asianonlinejournals.com/index.php/Economy/article/view/971
http://orcid.org/0000-0001-9063-7176
http://orcid.org/0000-0001-6528-3521
http://orcid.org/0000-0002-7468-4105
http://orcid.org/0000-0003-3746-4186
http://orcid.org/0000-0002-1502-8618
http://asianonlinejournals.com/index.php/Economy/article/view/971
http://orcid.org/0000-0001-9063-7176
http://orcid.org/0000-0001-6528-3521
http://orcid.org/0000-0002-7468-4105
http://orcid.org/0000-0003-3746-4186
http://orcid.org/0000-0002-1502-8618


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Contribution of this paper to the literature 
This study contributes to the existing literature by examining the impact of cluster development in 
Nnewi, Anambra State of Nigeria. 

 
1. Introduction 
1.1. Background to the study 

In terms of official documentation, the concept of cluster is relatively new in Nigeria. Cluster initiatives began 
to gain prominence in the country in early 2007 when the Federal Government embraced the cluster model, 
recommended by the Federal Ministry of Commerce and Industry (FMCI) as a new Industrial Development 
Strategy for Nigeria. FMCI (2007) remarked that the concept of cluster was not completely a policy shift but a re-
focusing of implementation strategy to accelerate industrialization. According to Iwuagwu (2011) the strategy, 
therefore, proffered key steps, needed to position the country on the path to rapid industrialization and thus 
achieving its vision of becoming one of the twenty largest economies in the World by the year 2020. 

FMCI (2007) documented that the concept of cluster would operate on five planks: Free Trade Zones; 
Industrial Parks; Industrial Clusters; Enterprise Zones1; and, Incubators2. Free Trade Zones was defined as fertile 
land for economic activities usually located in the proximity of seaports or international airports (both entry and 
exit points). In such zones, goods are brought in or taken out of the country without the usual payment of duties 
crosschecked. This is because the zones are considered to be aside customs‟ jurisdiction. Therefore the federal 
government would create more of such zones throughout the country to beef the existing ones, while the zones 
would grant special motivations to attract FDIs. Industrial parks were described as mega parks covering areas of 
between 30 - 50 square kilometres for large manufacturing enterprises with great value addition in the production 
of goods. According to FMCI (2007) it was planned that at least one park located in each of the six geo-political 
zones of the country. Each of these parks was to focus on processing products which the zone has comparative and 
competitive advantages over others. 

Economic activities are clustered to create a controlled environment for industrialization to flourish, face 
especially in the presence of chronic infrastructural deficits. Nigeria does not only have a number of large industrial 
estates and complexes but also witnessed the spontaneous development of small clusters across the country. The 
latter includes Computer Village in Otigba, Lagos, the auto and industrial spare parts fabricators in Nnewi, the 
leather tannery in Kano and the footwear, leatherworks, and garment cluster in Aba. There are approximately 25 
free trade zones licensed by the federal government (Chete et al., 2014). 

Since independent in 1960, swift economic development through industrialization has remained a mirage in 
Nigeria. Ever since, diverse approaches to industrial development have been adopted by different administration. In 
addition, different economic development policies which have bearing on the industrial sector have also been 
adopted in the past five decades. Among these policies are: Import Substitution Strategy (ISS), Indigenization 
Policy and the Structural Adjustment Program (SAP). In the opinion of Iwuagwu (2011) the challenges of the 
industrial sector remained unresolved despite adoption of these policies. Nigeria strayed further away from 
industrialization in the wake of the policies.  

Amakom (2006) earlier observed that despite successive governments‟ efforts to encourage industrialization, 
minimal positive results have been achieved and the industrial sector remains poorly developed. Available statistics 
from the Central Bank of Nigeria (CBN) (2015) indicates that the manufacturing sub-sector on the average 
accounted for 5.19% of Nigeria‟s RGDP between 1981 and 1985. Between 1986 and 1990, it averaged 6.81% and 
later declined to 4.46% from 1991 to 1995; between 1996 and 2000, it nosedived to 3.60%; and marginally increased 
to 3.65%; from 2001 to 2005. The manufacturing sub-sector contribution to the RGDP averaged at 4.08% between 
2006 and 2010 and slightly increased to 4.20% from the year 2011 to 2014 Figure 1.  
 

 
Figure-1. Manufacturing sector contribution to real GDP (percentage). 

                        Source: Authors‟ analysis based on data from CBN (2015). 
 

1.1.1. Nigeria Industrial Revolution Plan  
The Nigeria Industrial Revolution Plan (NIRP) released in January, 2014, is a five-year plan to rapidly build up 

industrial capacity and improve Nigeria‟s competitiveness. It aimed at increasing the contribution of manufacturing 
to GDP from its 4 per cent value in 2013, to 6 per cent by 2015, and finally above10 per cent by 2017. The NIPR is 
driven by the desire to a process of intense industrialization, based on sectors where Nigeria has comparative 
advantages, such as the agro-allied sectors; metals and solid minerals-related sectors; oil and gas related industries; 
as well as construction, light manufacturing and services. The NIRP also addresses the numerous issues that have 

                                                             
1 A specific geographical area that has been designated by a governmental authority (usually federal). Businesses within the enterprise zone are entitled to 
receive various types of financial aid. These include tax benefits, special financing and other incentives designed to encourage businesses to establish and 
maintain a presence within the specified zone. Enterprise zones are often established in low-income areas or places that are recovering from disaster. Business 
are encouraged, through cost savings, to open their doors and hire local residents within these areas in order to stimulate economic growth. 
2 Incubators create room for business development services covering key development areas, access to an extensive international network of key industry 
players, as well as facilitate investor contacts.  
 



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held back the Nigerian non-oil sector for years; it addresses the high cost of funding and lack of long-term finance 
in Nigeria; it builds up industrial infrastructure and power for industry; provides industrial skills; links innovation 
and industry; improves our investment climate; strengthens product standards; and promotes local patronage 
(Ministry of Industry Trade and Investment, 2014). The NIRP is expected to drive the following outcomes: 

 Job creation. 

 Economic and revenue diversification. 

 Import substitution. 

 Export diversification. 

 Broadened government tax base. 
 

1.1.2. Brief Background Story of Nnewi 
Nnewi is a major industrial and commercial hub in Nigeria. High commercial undertakings in the town attract 

many merchants. Nnewi is home to the first car producing firm in Nigeria and 'NASENI M1' - the first locally 
produced motorcycle - was manufactured in the town.  

In the context of manufacturing, Nnewi is loosely described as the Japan of Africa. This is because it hosts local 
firms, including Cutix and ADswitch, Cento Group of Companies, Coscharis Group of Companies, Ibeto Group of 
Companies, Omata Holdings Ltd, Uru Industries Ltd, Ejiamatu Group of Companies, Innoson Group of 
Companies, Louis Carter Group, John White Industries, Ebunso Nig. Ltd, Chikason Group, and so on. Most of 
these firms double as dealers who market some of their products (typically motor parts) through their distribution 
channels. 
 

1.2. Statement of the Problem  
Nigeria‟s low level of industrialization is linked to its colonization. Efforts of the colonial masters towards 

industrializing the country could at best be described as abysmal (AIAE, 2006). They focused mainly on excavating 
and exporting to Britain, raw materials that served as industrial inputs and importing to Nigeria finished goods 
that sold at exorbitant prices. In other words, they showed commitment to the execution of any effective 
industrialization policy. AIAE (2006) corroborates the foregoing as follows; 

The genesis of the poor state of our national economy and low level of 
industrialization is traceable to the colonial era. The colonial masters paid 
lip service to manufacturing and industrialization. Their major concern 
was to extract as much primary products as possible for use by industries in 
their home countries. In other words, there was no deep-seated commitment 
for the implementation of what passed for colonial industrialization policy 
(AIAE, 2006). 

A number of significant barriers hamper the growth of the Nnewi cluster. These include: lack of necessary 
infrastructure, absence of skilled workforce, failure to systematically connect with stakeholders both inside and 
outside of their networks (including businesses and universities), conservatism in business enterprises, bribe and 
corrupt practices, volatile local business environment, poor policy implementation, inefficient public administration 
and policy inconsistency. Other challenges confronting cluster development in Nnewi include but not limited to: 
lack of access to credit, high interest rate, high tariff and declining sales (see, AIAE (2006)). 

Furthermore, due to the massive revenue from crude oil, some regimes abandoned other crucial economic 
sectors, manufacturing inclusive. This negligence aggravated the state of the national economy. Instead of being 
proactive, different administrations focused mainly on crude oil revenue without any deliberate effort and 
commitment to ensuring the execution of spirited industrial policies for economic development. Thus, the belief 
that crude oil abundance is a curse rather than a blessing to Nigeria; a phenomenon popularly referred to as the 
Dutch disease. 

The persistent insecurity of lives and property in the country occasioned by armed robbery, assassinations, 
kidnappings and, more recently, bombings in Northern parts of the country, constitute major hindrances to cluster 
development. Such anti-social and anti-growth activities do not encourage entrepreneurs to perform optimally; 
they rather discourage the inflow of FDI into the country. A case in point was the closure of some 
telecommunication industries‟ services in the northern part of the country due to insecurity in the area.  

Based on the foregoing, the following research objectives shall guide this study:  
 

1.3. Research Objectives 
This study examines the impact of cluster development on a transforming economy with specific reference to 

motorcycle spare parts firms in Nnewi, Anambra State of Nigeria. To achieve this, the following specific objectives 
are pursued: 

i) To identify the difference in performance between motorcycles spare parts firms in cluster and outside 
cluster in Nnewi, Anambra state. 

ii) To assess the impacts of cluster residency on the performance of motorcycle spare parts firms in Nnewi, 
Anambra state.  

iii) To examine the enabling conditions and barriers to the growth of motorcycle spare parts cluster in Nnewi, 
Anambra state. 

iv) To determine the effects of government policies on the motorcycle spare parts cluster in Nnewi, Anambra 
state. 

 

1.4. Justification of the Study 
This study is motivated by the increasing consciousness on the significance of motorcycle spare parts business 

in the socioeconomic development of Nigeria. For instance, the business is capable of improving people‟s income 
and creating more employment opportunities. The outcome of this study will be of immense benefit to Nigerians as 



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it will to focus on the contributions of motorcycle spare parts business and the informal sector in general to socio-
economic development of the people. Last year, the federal government of Nigeria advised state governments 
across the country and the Federal Capital Territory (FCT) to ban3 motorcycles as a means of commercial 
transportation. Prior to the advice, many states4 had implemented the „OKADA‟ ban policy in some of their major 
cities. The policy, no doubt, will have ripple effects on motorcycle spare parts business, at least, starting with the 
fall in the demand for motorcycle and its spare parts by those who use it for transportation business. Against this 
backdrop, this study, among other objectives, examined the effects of government policies on the motorcycle spare 
parts cluster in Nnewi, Anambra state. 

As identified in the literature, cluster of factories contribute to economic development, at least in the area of job 
creation (see, Sparks and Barnett (2010)). The alarming rate of unemployment in Nigeria with its agonizing effects 
has become a subject of concern to both the government and other stakeholders in the economy. According to the 
NBS (2012) with the current unemployment rate of 23.9 per cent and unemployed youth population of 20.3 million, 
Nigeria still generates about 4.5 million new entrants into the labour market annually. Even though the 
government has been making efforts to address this ugly trend, the status quo still remains. Expectedly, when 
firms perform well in terms of sale and profit, their capacity to employ more labour force and pay taxes increases. 
However, the ban on „OKADA‟ discourages manufacturing and employment of labour. Given the unemployment 
situation in the country, we investigate, among other things, the difference in performance (sale and profit) 
between motorcycle spare parts firms in cluster and outside cluster in Nnewi, Anambra State. The findings of this 
study will provide evidence for policy purposes. 

Previous studies have examined the effects of industrial clusters on the Nigerian economy (see for example, 
(Oyelaran-Oyeyinka, 2001; Uzor, 2004; Abiola, 2006; Amakom, 2006; Iwuagwu, 2011)). The specific objectives of 
these studies differ markedly on account of many factors, ranging from the typology of cluster of interest to 
location of cluster. Of all these studies, Amakom (2006) and Oyelaran-Oyeyinka (2001) are of particular interest to 
this research. Amakom (2006) attempted to find out if the same problems that bedeviled other industries that are 
scattered all over the country are also affecting those in the industrial clusters whereas Oyelaran-Oyeyinka (2001) 
investigated the basis for long-term sustainable development of industrial clusters in Lagos, Nigeria and compared 
the metropolitan clusters with the Nnewi cluster, located within a rural setting in a homogeneous ethnic 
community. None of these studies examined the impact of cluster residency on the performance of motorcycle spare 
parts firms in Nnewi. This makes the present study relevant and timely. 

Finally, the review on the rationales for industrial cluster presented in section 2.2 indicates a plethora of 
reasons supporting cluster-focused economic development strategy. Among these reasons are: access to markets, 
technological spill overs, potentials to enable more entrepreneurs to participate in industrial production, lowering 
the capital barriers to entry, ensuring competitiveness of firms and fast-tracking industrial and economic growth, 
boosting innovation, entrepreneurship, wages, and business specialization. To know the extent to which the Nnewi 
motorcycle spare parts cluster delivers on a number of these, it is necessary to conduct a study of this nature.   
 

2. Literature Review 
2.1. Conceptual Issues 

Industrial cluster simply refers to the concentration of economic activities of a certain sector or group of firms 
that produce similar and closely related goods in a given location. According to Porter (2000) clusters „represent a 
new way of thinking‟ about economic growth at all levels, but which requires new roles for companies, government 
agencies, universities and other organizations in enhancing competitiveness. In a study carried out for the World 
Bank on Ethiopia, Ali (2012) strongly argues that cluster development programmes have become increasingly 
widespread tools in fostering innovation and growth of a competitive private sector in developing countries. To 
Schmitz and Nadvi (1999) industrial clusters provide a wide range of advantages that enable enterprises to become 
competitive and profitable. 

Martin and Sunley (2003) note that clustering has two dimensions. First, the functional dimension which 
includes local inter-firm linkages and forward and backward linkages with such interconnected agents as input 
suppliers and output buyers. Such linkages, they maintain, often result in social interrelationships that are 
expressed through trust and collaborative networks that develop over a long period of time. Second, the physical 
dimension that points to the physical co-location of enterprises close to one another (geographic proximity) in the 
cluster. They argued that while geographic proximity helps stimulate the functional dimensions of clustering, it 
does not offer a direct view about the nature and strength of local inter-firm linkages and social networks.  

Ali (2012) use a diagram to depict an example of composition of an industrial cluster. In the diagram, he shows 
that industrial cluster involves the supply of both raw materials and machinery within a group of related firms. 
This group of firms could have common service providers in terms of designing, maintenance, finance, training and 
so on. It is also clear from the diagram that there are public and private institutions whose activities affect the 
operations of the firms. These activities could be in the areas of policies, research or even in the other forms of co-
operations. Lastly, the firms could get their outputs across to the final consumer directly or through the help of 
selling agents Figure 2. 
 

                                                             
3 Three main reasons adduced by the government for the ban policy are; high rate of crippling and fatal accidents per unit of distance travelled, increased rate 
of crime and Okada business has been criticized for causing or exacerbating traffic congestion in the cities where they operate 
4 These states include, Lagos, Imo, Niger, Rivers, Delta, Enugu, Edo, Kano, Plateau, Kaduna.  

 



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Figure-2. Examples of composition of an industrial cluster. 

                           Source: Ali (2012) . 
 

To Clar et al. (2008) different approaches have been developed towards facilitating clusters. They 
acknowledged that four main schools of thought have emerged in this regard Table 1. The Californian and Nordic 
schools of thought are tied to economic systems while the Industrial and Porter‟s industrial cluster are more 
generic. According to them, the OECD5 countries have engaged in a good deal of experimentation with respect to 
the various approaches. Table 1 situates the Nnewi motorcycle spare parts cluster within the hybrid of industrial 
and Porter‟s industrial cluster. 
 

Table-1. Four schools of thought on clustering. 

School of thought Characteristics 

Industrial district External economies, mutual trust and a positive atmosphere of cooperation 
between companies, leading to incremental innovations. 

Californian school Vertical disintegration, reduced transaction costs and a specialized local labour 
market, plus conventions, informal rules, and habits, leading to greater 
collaboration among companies. 

Nordic school Innovation as the basis for obtaining competitiveness for firms, regions and 
nations, learning is seen as mainly a localised process, information is relatively 
globally mobile, knowledge is remarkably spatially rooted. 

Porter‟s industrial cluster External economies strengthened by proximity and better access to input factors, 
local rivalry, and local customers; collective improvement of the competitiveness of 
companies and creation of opportunities for the establishment of new niche 
companies to support the expansion of local supply chains and add more value to 
the cluster. 

       Source: EC (2002). 

 

2.2. Rationales for Industrial Cluster 
There seems to be a consensus6 on the relevance of clustering as an alternative strategy for industrial 

development in developing countries. For example, Adam Smith chronicled the economic gains available to firms 
through the division of labour, an indisputable characteristic of industrial clustering. Following this, Marshall 
(1920) recognised that industrial clusters enjoy at least three well-known major benefits, namely, access to 
markets, labour market pooling, and technological spill overs (Krugman, 1991). Extending Marshall‟s view, 
Schmitz (1995) and Schmitz and Nadvi (1999) observe that embedded in these benefits are the potentials to enable 
more entrepreneurs to participate in industrial production that may otherwise be inaccessible to them. These 
benefits have been branded „collective efficiencies‟ in the literature. Ruan and Zhang (2009) identified a further 
important collective efficiency of clustering mechanism. To them, clustering can assist in lowering the capital 
barriers to entry through division of the production processes among firms, thus enabling more firms with 
otherwise limited capital to enter the production process/chain. Cluster development is usually considered as one 
of the ways of ensuring competitiveness of firms and fast-tracking industrial and economic growth (see for 
example, (Mwamila and Diyamett, 2011; Brakman and Van Marrewijk, 2013)).  

More so, clustering is believed to offer unique opportunities to engage in the wide array of domestic linkages 
between users and producers, between the knowledge-producing sector. 
(Universities and R&D Institutes) and the goods and services-producing sectors of an economy that stimulates 
learning and innovation (Nadvi, 1995; Nadvi and Schmitz, 1997; Meyer-Stamer, 1998a). 

Clusters drive productivity and innovation. Firms that are located within a cluster can transact more 
efficiently, share technologies and knowledge more readily, operate more flexibly, start new businesses more easily, 
and perceive and implement innovations more rapidly. They can also efficiently access “public goods” such as pools 
of specialized skilled employees, specialized infrastructure, technological knowledge, and others. Clusters embody 
traditional notions such as input-output linkages, among others. Because of the importance of physical proximity in 
reaping cluster benefits, clusters are often regional instead of national except in small countries (Porter, 2009). 

                                                             
5 OECD means Organisation for Economic Cooperation and Development and is usually considered as the club of the rich countries. 
6 See the studies by Meyer-Stamer (1998b); Weijland (1999); Bell and Albu (1999); Porter (2000); Gordon and McCann (2000); Fujita et al. (2001); Sonobe and 
Otsuka (2011); Nakabayashi (2006) and Ruan and Zhang (2009). 



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Clusters and cluster strategies have the potential to accelerate regional economic growth and assist in 
economic restructuring. At a time of tepid growth, cluster strategies possess documented power to help power 
regional economic growth by boosting innovation, entrepreneurship, wages, employment and business 
specialization. At a time of shaken confidence in past growth models, cluster frameworks point to the centrality to 
national wellbeing of practical economic systems in regions and so offer a fresh paradigm for new thoughts on 
national economic management. And finally, as a policy framework, clusters provide a practical tool for policy 
coordination and possibly increased return on public investments. Clusters deliver significant productivity 
advantages to groups of firms, suppliers and related actors and institutions that draw mutual advantage from 
localization (Muro and Bruce, 2010). 

In the Nigerian context, Iwuagwu (2011) argues that clustering would permit greater focusing of public 
resources as infrastructural facilities would be concentrated in identified locations, especially for industrial and 
commercial purposes. According to him, because of geographic proximity of firms as well as financial and other 
business institutions, clustering would enhance the effectiveness of the innovation process required to kick start 
Nigeria‟s industrial take-off. It would also encourage localization of economies and enhance the likelihood of inter-
firm technology and information transfers as well as motivate companies in Nigeria to go into product 
specialization and adoption of new technologies. 
 

2.3. Cluster Development and Economic Growth 
Empirical literature on the role and importance of clusters in supporting economic development is mixed and 

still emerging. There is a huge body of empirical literature supporting clustering as the key determinant of 
economic development (see for example, (Audretsch and Feldman, 1996; European Commission, 2002; Lee et al., 
2017; Zeibote, 2018)). Several international bodies have been advocating for cluster development. They include the 
OECD, the World Bank, the Inter-American Development Bank (IADB), the United Nations Conference on Trade 
and Development, the World Economic Forum, and the United Nations Industrial Development Organization. As 
noted by Choe and Roberts (2011) advocacy has helped to move the debate from the academic sphere to the realm 
of policy and business. 

According to UNIDO (2010) cluster concept has gained prominence as an economic policy tool aimed at 
fostering innovation and growth of a competitive private sector in developing countries. UNIDO (2010) maintains 
that, recently, donors and development agencies have paid increasing attention to the potential of cluster initiatives 
to bring about pro-poor effects. The organization is of the view that thriving clusters can generate employment, 
income and opportunities for the local community and become drivers of broad-based local economic development. 
UNIDO (2010) further state that in the framework of private-sector development initiatives, cluster-based 
interventions have gained momentum. According to the organization, three main arguments can be advanced to 
explain the focus on clusters as targets of development assistance: i) Collective efficiency gains; ii) Spatial proximity 
effects; and iii) Pro-poor potential.  

Using Arizona as a case study, Waits (2000) presented a practical evidence of the benefits from cluster-based 
economic development strategy. He asserted that clusters of world-class firms in related industries are the most 
important economic development clusters in the global economy. According to him, these clusters, rather than 
individual companies or simple industries, are source of jobs, income and export growth.  This is what Arizona has 
found to be the case. He maintains that, as a result of cluster-focused economic analysis and strategy development, 
Arizona has a better understanding of its economy and economic development clusters. The state also has a viable 
approach - cluster working groups and organizations - for putting industries together to design policies, address 
common problems and implement initiatives. 

Oyelaran-Oyeyinka (2001) investigates the basis for long-term sustainable development of industrial clusters in 
Lagos, Nigeria, comparing the metropolitan clusters with the Nnewi cluster, located in a rural homogeneous ethnic 
community. According to him, the characteristics of clustering examined are: the forms and intensity of inter-firm 
linkages, including the formation of trade networks and the role of business associations. He found a significant 
level of collaboration among firms in sharing utilities and modest forms of subcontracting non-core activities 
among Lagos firms, but this is not prevalent in Nnewi. He further reveal that the Lagos clusters have relatively 
high proportions of educated manpower. However, this important asset is underemployed in a situation of low 
growth rate of demand for quality products. In addition, the study shows that the firms in Nnewi, on the other 
hand, are owned by semi-illiterates who came from trading backgrounds into manufacturing. Networks such as 
industry associations were found to be playing vital roles such as information providers and as links to the global 
market even though the benefits are still latent. Ethnic and kinship ties were identified to play a prominent role at 
Nnewi while social networks and non-family ties were found to be more important in the Lagos clusters. The study 
suggested that non-economic factors exert profound influence on the evolving forms of industrial organisations in 
late industrialisation. 

Uzor (2004) examined the cluster concept and its impact on private sector development in Africa in general and 
on SME development in Nigeria in particular. The findings of the study indicate that there are no effective 
institutions supporting cluster development in Africa in general and in Nigeria in particular. Competition and 
learning were considered in the study as two important elements needed in African clusters given that they are the 
benchmark for the private sector development in the region. Limitations of the clusters in the region were classified 
into; i) level of education of the entrepreneurs, ii) weak government, institutional support and market development, 
and iii) financial constraints of the SMEs needed for expansion. The study recommended the need for partnership 
building among the state, institutions and the private sectors for SME development. Such relationship plays an 
important role in economic development generally, especially in infrastructure and in capacity building. More so, 
the study added that policy objective for the support of technical education and training is imperative in the South 
East region and in Nigeria generally. 

Amakom (2006) attempted to find out if the same problems facing other industries scattered all over the 
country are also affecting those in the industrial clusters which the government claims to be more environment 
friendly. The study employed data from the UNIDO survey 2006 on leather industry in Kano industrial cluster. 



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Also the study employed Probit regression and found that the inability of the leather sector to meet its target is 
due to low efficiency caused by poor technical know-how cum high cost of doing business (high indirect costs as a 
result of inadequate of physical infrastructure) which resulted to low capacity utilization. According to Amakom, 
this finding is not at variance with the findings of studies involving other firms scattered all over the country. 
Therefore it would be erroneous to accept any hypothesis that firms within the industrial cluster have friendlier 
environment.  

A study by Abiola (2006) focused on three key elements: the origin of investment in manufacturing technology; 
the technological learning strategy and mechanism; and the technological capabilities acquired. The study which 
was titled Knowledge, Technology and Growth: The Case Study of Nnewi Auto Parts Cluster in Nigeria 
discovered that majority of firms implemented technical change simply to improve old processes. It was revealed in 
the study that foreign technological linkages currently form the basis of development. Abiola identified the major 
obstacles to getting or procuring the foreign inputs for production of machinery and automotive components in 
Nnewi cluster as follows: tariff and non-tariff, finance, custom formalities, information and security of lives. 
 

2.4. Industrial Clusters in Different Countries and Industrial Regions of the World 
In Latin America, the Brazilian Shoe Cluster of Sinos Valley is remarkable. Brazil is one of the world‟s leading 

producers of leather shoes and the bulk of its export-oriented leather shoes are produced in the Sinos Valley 
industrial cluster. Three factors have been noted for Sinos Valleys‟ economic success. They are: i) backward 
linkages with local input suppliers; ii) forward linkages with producers and buyers, mainly export agents; and iii) 
the tactical intervention of local support institutions in aiding the cluster‟s capacity to move into higher value 
added product markets. 

Clusters in East Asia have been successful as a result of the following; i) imitation and assimilation of foreign 
technologies, ii) the formation of geographically dense industrial clusters consisting of a large number of small 
enterprises producing similar and related products and, iii) the advent of manifold innovations leading to progress 
in the industrial structures. For instance, China and Taiwan modelled their industrial development after that of 
Japan. 

Pakistan‟s industrial cluster located in Siatkot produces surgical equipment and is export-oriented. Siatkot 
Surgical Instrument Industrial Cluster‟s success is enormous as a result of the producers‟ ability to meet high 
export standards. Also, it adopted in no small measure designs and technical experts from Britain besides the 
development of technical institutions for the training of workers.  
 

3. Data and Methods 
The study adopted the survey method to evaluate the impacts of cluster of motorcycle spare parts factories in 

Nnewi on the economy of Anambra state. The population of the study includes all motorcycle spare parts factories 
operating in Nnewi; Anambra State. Some of these firms are clustered within 21 unequal zones (clustered firms) 
while others are outside the cluster (non-clustered firms). The purposive sampling technique was adopted in the 
selection of three zones – zone 6 (300 dealers), zone 13 (200 dealers) and zone 10 (100 dealers). The selected zones 
have characteristics that typically represent the firms in the cluster. For instance, the largest number of dealers is 
found in zone six and it is considered as the hot spot of the cluster. Zone 10 possesses the direct opposite of the 
characteristics of zone 6 whereas zone 13 represents the moderate of zones 6 and 10. The Yaro Yamane‟s formula 

(Yamane, 1967),
 

2
(1 )

N
n

N e



, (n = sample size, N = population and e = error margin) was employed to develop 

a representative sample size of 384 motorcycle spare parts from the population of the three zones. In addition, 384 
non-clustered motorcycle spare parts factories were randomly7 sampled to serve as a control. In all, 768 motorcycle 
spare parts factories were surveyed. The validity and reliability of the instruments were tested to ensure efficiency.  

The study employed the t-test distribution technique, multiple regressions and descriptive analysis to achieve 
its objectives. To achieve the first objective of identifying the difference in performance of firms in cluster and the 
non-clustered firms, the t-test of significance was employed. The specific performance indicators examined are; 
volume of sales, capital, labour and profit.  

For the second objective of assessing the impact of cluster residency on the performance of motorcycle spare 
parts firms in Nnewi, Anambra state, three multiple regression models (with profit and sales as dependent 
variables) were estimated. The compact version of the estimated econometric equation is presented as: 

0

1

n

i i i

i

Y X 



      

Where Yi is the dependent variable (sales or profit), 0 is the constant term or the intercept, i  represents the 

coefficient of the determinants and iX  the explanatory variables which include: cluster dummy (1 if firm is in a 

cluster and 0 otherwise), capital, labour, experience of firm head, years of education of firm head, age of firm head 
and access to credit dummy (1 if firm has access to credit and 0 otherwise). n represents the number of explanatory 

variables while   stands for the error or residual term.  

The third and fourth objectives which were to examine the enabling conditions and barriers to the growth of 
motorcycle spare parts cluster and to determine the effects of government policies on the motorcycle spare parts 
cluster in Nnewi, Anambra state were achieved by employing the likhert scale. The likhert scale was used in 
ranking the responses whereas the means were derived to show the average position of the respondents. 
 
 
 

                                                             
7 Each individual firm was chosen randomly and entirely by chance, such that each one of them had the same probability of being chosen. 



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4. Data Analysis and Presentation of Empirical Results 
Data collected first underwent manual editing. This was followed by the coding of the completed questionnaire, 

data entering, processing and analysis, using the appropriate statistical packages. The results of the analysis are 
presented in Appendix 1 and discussed in the next section. 
 

4.1. Differences in Performance of Firms in Cluster and Non-Cluster in Nnewi, Anambra State 
In terms of sales, the mean value of small firms in the cluster stood at N125, 112 whereas that of non-clustered 

small firms amounted to N121, 084.2. This difference was not statistically significant, judging from the absolute t 
value of 0.34. The mean value of sales of medium firms in the cluster was N319, 877 as against that of firms outside 
the cluster which was N316, 577.1. The absolute t value of 1.98 indicate that the difference in sales of the two 
categories being compared was statistically significant. Similarly, the mean value of sale of the large firms was N4, 
444, 094 while that of the non-clustered firms came up to N4, 422, 870. This difference in mean sales value of large 
firms in cluster and outside cluster is statistically significant. 
 

 
Figure 3. Overall firm size – sales. 

                                     Source: Authors‟ analysis based on data from field study. 
 

For the capital of the different firm sizes, no significant difference was found between firms within and outside 
cluster.  
 

 
Figure-4. Overall firm size – capital. 

                            Source: Authors‟ analysis based on data from field study. 
 

Similarly, no significant difference was found in the labour of clustered small and large firms and non-clustered 
small and large firms. However, a significant difference exists in clustered medium firms and non-clustered medium 
firms.  
 

 
  Figure-5. Overall firm size – labour. 
                                    Source: Authors‟ analysis based on data from field study. 
 

The mean profit of the three categories of firms in the cluster was significantly different from the mean profit 
of those outside the cluster.  
 

 
Figure-6. Overall firm size – profit. 

                                      Source: Authors‟ analysis based on data from field study. 
 



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4.2. Impacts of Cluster Residency on the Performance of Motorcycle Spare Parts Firms in Nnewi, Anambra State 
The study estimated multiple regression models to show the impacts of cluster residency on output and profit 

of the firms. A dummy variable that connotes 1 if firm is in cluster and 0 otherwise was considered as the key 
variable. Other control variables were also considered. They include: capital, labour, experience of firm head, years 
of education of firm head, age of firm head and access to credit dummy. In all, three models consisting of two 
equations each were estimated. The first model is the complete model while the second and third represent its 
parsimony. The regression results of the three models are presented in Appendix 2. 

The results show that the probability of the F-statistics of the models is less than 0.05 implying that the overall 
model is significant in each case. The R square values (ranging from 39% to 62%) indicated that the models are 
reasonably of good fit considering the cross-sectional nature of the data. The mean variance inflation factor for all 
the models emerged quite low while all the individual variables showed negligible presence of multi-colinearity. 
The problem of heteroscedasticity was automatically addressed within the software.  

In specific, the results from model 1 show that capital, experience of the firm head and access to credit are 
significant determinants of the sales of the firms. The said variables are positively related to sales, implying that 
increase in capital base of the firm and experience of the firm head leads to increase in sales. The results further 

reveal that firms that have access to credit have a significant difference in sales of about ₦50,493 over firms that do 
not access credit. Interestingly, when the two least significant variables (age and years of education of firm head) 
were dropped in the second model, capital, experience of the firm head and access to credit remained significant 
determinants of sales of the firm. In the third model, where capital, labour and the cluster dummy were retained, 
capital and labour emerged significant while the cluster dummy remained insignificant. The cluster dummy 
variable which is the variable of interest was not significant in the three models. This showed that in terms of total 
sales, there is no significant difference between firms in the cluster and the non-clustered firms.  

For the profit regressions, the results showed that only capital, labour and the cluster dummy were significant 
at 1% significant level. Experience of firm head was significant at 10% significant level while years of education of 
firm head, age of firm head and access to credit were not significant at all. Again, capital, labour and cluster dummy 
have a positive relationship with firm profit. The positive coefficient of the cluster dummy variable indicated that 

the profit of firms in the cluster is significantly higher than the non-clustered firms by about ₦33,351. In addition 
to capital, labour and the cluster dummy, experience of the firm head became significant in the second model. 
Noteworthy is the fact that the cluster dummy variable was significant in all profit equations in the three models, 
unlike in the case of the sales equations. This therefore implies that though residing in clusters might not make a 
significant difference in total sales, it definitely makes a significant difference in profit. This could be explained by 
the fact that firms in clusters enjoy economies of scale and sometimes scope that reduce their overall cost of doing 
business, hence improving profits. 
 

4.3. Enabling Conditions and Barriers to the Growth of Motorcycle Spare Parts Cluster in Nnewi 
On the average, the respondents were indifferent to government‟s devotion to the cluster but disagreed that 

the government frequently attends to the needs of the cluster residents. They disagreed that the current location of 
the cluster in terms of distance to the market is beneficial. The respondents agreed that the road network of the 
cluster contributes positively to firm output. They also strongly agreed that the cluster encouraged localization of 
firms. They were indifferent as to whether taxation is lower for those in the cluster and disagreed that shops are 
unaffordable Table 2. 
 

Table-2. Enabling conditions and barriers to the growth of motorcycle spare parts cluster in Nnewi. 

Description  Likert scale Conclusion 

The government is very devoted to the cluster. 4 Indifferent 
The government frequently attends to the needs of the cluster residents. 2 Disagree 
The current location of the cluster is beneficial in terms of sourcing raw 
materials. 

4 Agree 

The current location of the cluster is beneficial in terms of distance to the market. 2 Disagree 
The road network of the cluster contributes positively to firm output. 4 Agree 
The cluster has encouraged localization of firms and industries. 5 Strongly agree 
Taxation is lower for those in the cluster. 3 Indifferent 
Shops are unaffordable. 2 Disagree 

Business is easier now in the cluster. 4 Agree 
Shops are available. 4 Agree 
Clusters are the best option for development of small firms. 5 Strongly agree 
Health care services are available within the cluster. 4 Agree 
There is adequate electricity supply. 2 Disagree 
There is access to credit and other banking services. 4 Agree 
Security in the cluster is adequate. 4 Agree 

        Source: Authors‟ analysis based on data from field study. 

 
On the average, they agreed on the following: the current location of the Cluster is beneficial in terms of 

sourcing raw materials; business is easier in the cluster; shops are available; healthcare services are available within 
the cluster; there is access to credit and other banking services; and security in the cluster is adequate. Further, 
they strongly agreed that clusters are the best options for the development of small firms and disagreed that there 
is adequate electricity supply in the cluster. 
 

4.4. Impact of Government Policies on the Motorcycle Spare Parts Cluster in Nnewi 
Our field study indicated that „OKADA‟ ban policy has negatively affected motorcycle spare parts cluster in 

Nnewi, Anambra state. The average likert scale showed that the respondents strongly agree on this negative effect. 
The respondents agreed on three key issues as follows; “infrastructure is well developed in the cluster”, “the 



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initiatives of the government are meant at improving factory performance”, and “government supports in 
addressing the challenges of the residents in the cluster”. However, they disagreed that the government has done 
everything within its ability to optimise the functions of the cluster Table 3. 
 

 Table-3. Government policies on the motorcycle spare parts cluster in Nnewi. 

Description  Likert scale Conclusion 

The „OKADA‟ ban policy has negatively affected motorcycle spare parts cluster. 5 Strongly agree 
Infrastructure is well developed in the cluster. 4 Agree 
The initiatives of the government are meant at improving factory performance. 4 Agree 
Government supports in addressing the challenges of the residents in the cluster. 4 Agree 
The government has done all in its power to optimise the functions of the cluster. 2 Disagree 

         Source: Authors‟ analysis based on data from field study. 

 

5. Conclusion 
This paper examined the impact of cluster development on a transforming economy using motorcycle spare 

parts firms in Nnewi, Anambra State of Nigeria as a case study. In terms of sales, the mean value of medium firms, 
and large firms in the cluster stood at 319, 877 Naira and 4, 444, 094 Naira, respectively, whereas that of the non-
clustered medium, and large firms were in turns, 316, 577.1 Naira and 4, 422, 870 Naira. The differences in mean 
value of sales of medium and large firms were found to be statistically significant, judging from their absolute t-
values of 1.98 and 2.09, respectively. The difference between the mean values of sales of small firms in the cluster 
and non-clustered small firms was statistically insignificant with an absolute t-value of 0.34. For the capital of the 
different firm sizes, no significant difference was found between firms within and outside the cluster. Similarly, no 
significant difference was found in the labour of clustered small and large firms and non-clustered small and large 
firms. However, a significant difference (t-value of 1.91) existed in labour of clustered medium firms and non-
clustered medium firms. The mean profit of the three categories of firms in the cluster (small - 25,789.54 Naira, 
medium - 159,104.4 Naira and large - 390,383 Naira) was significantly different from those outside the cluster 
(small - 25,438.79 Naira, medium – 142,901.9 and large – 375,498.6 Naira). 

In this study, three models consisting of two equations each were estimated. The first model was the complete 
model while the second and third represented its parsimony. The results from model 1 showed that capital, 
experience of the firm head and access to credit were significant determinants of the sales of the firms. The results 

further revealed that firms that access credit have significant difference in sales of about ₦50,493 over firms that do 
not access credit. In the absence of the two least significant variables (age and years of education of firm head) 
capital, experience of the firm head and access to credit remained significant. In the third model, capital and labour 
were still significant while the cluster dummy variable remained insignificant. The cluster dummy variable which is 
the variable of interest was not significant in the three models, implying that in terms of total sales, there is no 
significant difference between firms in the cluster and the non-clustered firms. 

For the profit regressions, it was discovered that capital, labour and the cluster dummy were significant at 1% 
level while experience of firm head was significant at 10%. However, years of education of firm head, age of firm 
head and access to credit dummy variable were not significant at all levels. Capital, labour and cluster dummy had a 
positive relationship with firm profit. The positive coefficient of the cluster dummy variable indicated that the 

profit of firms in the cluster was significantly higher than the non-clustered firms by about ₦33,351. In the second 
profit model, in addition to capital, labour and the cluster dummy, experience of the firm head became significant at 
5% level. Noteworthy is the fact that the cluster dummy variable was significant in all profit equations across the 
three models unlike in the case of the sales equations, thereby, implying that cluster residency makes a significant 
difference in firm profit. This could be explained by the fact that firms in clusters enjoy economies of scale and 
sometimes scope that reduce their overall cost of doing business, hence improving profits.  

On the enabling conditions and barriers to the growth of motorcycle spare parts cluster in Nnewi, it was 
discovered that the respondents on the average were indifferent regarding the government‟s devotion to the cluster 
but disagreed that the government frequently attends to the needs of the cluster residents. They disagreed that the 
current location of the cluster in terms of distance to the market is beneficial. However, the respondents agreed 
that the road network of the cluster contributes positively to firm performance and strongly agreed that the cluster 
has encouraged localization of firms and industries in Nnewi. Finally, they were indifferent as to whether taxation 
is lower for those in the cluster and disagreed that shops were unaffordable. 
 

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Appendix-1. Descriptive statistics of firms in cluster and non-clustered firms. 

Variables Firm size Cluster -mean Non cluster -mean Absolute  t-value 

Sales Small firms 125,112 121,084.2 0.3360 

Medium firms 319,877 316,577.1 1.9809** 
Large firms 4,444,094 4,422,870 2.0949** 

overall 812,610.5 613,153.8 1.3615 
Capital Small firms 110,265.3 118,241.6 0.4194 

Medium firms 280,981.9 279,997.4 0.0161 
Large firms 581,060.5 901,700.8 1.2432 

overall 261,569.1 303,664.9 0.6848 
Labour Small firms 2.89 2.62 0.6282 

Medium firms 18.14894 17.0339 1.9057* 
Large firms 36.60714 31.95 0.7196 

overall 18.61458 18.44792 0.9121 
Profit Small firms 25,789.54 25,438.79 1.0912 

Medium firms 159,104.4 142,901.9 2.1503** 
Large firms 390,383 375,498.6 2.2640** 

overall 139,907.8 124,886.1 2.1669** 
                        * implies significant at 10%, ** implies significant at 5% & *** implies significant at 1%. 

 



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Appendix-2. Regression results of the three models. 

Variables Model 1 Model 2 Model 3 

Sales Profit Sales Profit Sales Profit 

Cluster 
dummy 

63799.3 
(1.47) 

33351.4*** 
(2.78) 

27069.4 
(1.33) 

33440.3*** 
(2.77) 

27480.9 
(1.36) 

31050*** 
(2.72) 

Capital 1.700647 *** 
(3.14) 

23.51677 *** 
(2.66) 

1.611556*** 
(3.97) 

23.52155*** 
(2.66) 

1.606348*** 
(3.97) 

23.76177*** 
(2.71) 

Labour -4548.209 
(-1.11) 

15263*** 
(2.93) 

-4548.209 
(-1.14) 

15470.4*** 
(2.97) 

31097.43** 
(2.25) 

16884.7*** 
(2.95) 

Experience of 
firm head 

42641.91 ** 
(2.03) 

40576* 
(1.89) 

39993.58)**   
(2.01) 

43059.28 ** 
(1.99) 

  

Years of 
education 

of firm head 

20259.1 
(1.58) 

46734.7 
(0.71) 

    

Age of firm 
head 

17549.8 
(1.02) 

2769.428 
(0.10) 

    

Access to 
credit dummy 

50,492.6 ** 
(2.10) 

17,4196 
(0.85) 

40576 
(2.09) ** 

31850 
(1.27) 

  

Constant 15563 
(1.34) 

86423 
(1.19) 

18982 
(1.55) 

-74319.9 
(-0.55) 

50492.6 
(2.10) 

-10979.9 
(0.98) 

R square 0.3924 0.6165 0.4399 0.6154 0.4957 0.6085 
F probability 0.0046 0.0000 0.0029 0.0000 0.0004 0.0000 

Mean VIF 1.25 1.25 1.06 1.06 1.03 1.03 
                * implies significant at 10%, ** implies significant at 5% & *** implies significant at 1%. 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

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