cluster competition

Volume 4, Number 2, April June 2015

ISSN (Print):2319-9032, (Online):2319-9040

PEZZOTTAITE JOURNALS SJIF (2012): 3.562, SJIF (2013): 5.074, SJIF (2014): 5.857

International Journal of Logistics & Supply Chain Management Perspectives Pezzottaite Journals. 1675 |P a g e

CLUSTERING AND INTERNATIONAL COMPETITION:

A CASE STUDY OF LEATHER FOOTWEAR CLUSTER IN ADDIS ABABA

Dr. Asfaw Wassie32

ABSTRACT

Ethiopia, as many other developing countries in Africa and elsewhere, has moved towards a liberalized trade regime since the

beginning of 1990s after a long period of import substitution. This paper analyses the changes in collective efficiency specifically, the cooperative behavior of Micro, Small, and Medium Enterprises (MSMEs) located in the footwear cluster of

Merkato, Addis Ababa, because of international competition. It also investigates how firms performance is impacted by the changes in collective efficiency. Hence, the hypothesis explored in this paper is that meeting international competitive

pressures require a positive change in collective efficiency specifically, greater local cooperation, both among producers as

well as between producers and their suppliers and buyers. The study draws on qualitative data collected through in-depth

interviews and quantitative responses came from a questionnaire survey covering a sample of 150-shoe making MSMEs to

examine how collective efficiency or more specifically inter-firm ties, both vertical and horizontal, have changed. The

empirical evidence shows that although some external events helped the footwear cluster of Merkato, the increase in collective

efficiency also played an important role in the recent recovery of the cluster. The result also shows that while majority of firms

in the cluster gain from agglomeration economies, the extent of inter-firm co-operation and joint action, and the benefits

arising from it, are highly differentiated. It concludes that external economies, while necessary, are not sufficient to bring

about growth. For growth, joint action, particularly in strategic vertical ties with local suppliers, subcontractors, and external

buyers, is critical. Hence, the positive impact of collective efficiency affects only part of the cluster: those firms, which enter

into cooperative actions, and those, which are able to benefit from some externalities, generated by cooperation. The main

challenge for the future will be to transform the static part of the cluster.

KEYWORDS

Cluster, Collective Efficiency, Footwear, Merkato etc.

INTRODUCTION

Competitiveness is defined as the ability to compete (Ambastha and Momaya, 2004). It is the ability to design, produce and

market products superior to others offered by competitors, by considering price and non-price quality. The search to maintain and

improve competitiveness in this slippery world has increased in importance and receives much attention from political leaders, business people, and scholars (Markusen, 1996). Many methods to improve competitiveness have been proposed and one that

receives particular attention is the concept of a cluster of companies. Clusters are geographic concentrations of interconnected companies, specialized suppliers and service providers, firms in related industries and associated institutions in a particular field

where they compete but also cooperate, and enjoy local externalities (Schmitz 1992). A leading business scholar, Michael Porter (1998b), indicates that a key advantage for an individual member of clustering is that they gain as if they were larger companies. The focus of this paper has grown out of the literature in which collective efficiency has been the central concept. It is defined as

the competitive advantage derived from local external economies and joint action in a cluster (Schmitz 1995). The former are

incidental, while the latter is consciously pursued and the combination of the two varies between clusters and over time.

Industrial cluster scholars argue that clustering may help local firms overcome their growth constraints in order to compete in

distant markets (Giuliani, Pietrobelli & Rabellotti, 2005). A cluster improves small firms competitive advantages so that they are able to compete with fully integrated firms in the international market. By clustering, firm performance is enhanced, as firms

attain benefits from agglomeration economies, including opportunities to utilize innovations in their products and processes

(Porter, 1998). In a cluster, firms also have opportunities to cooperate that may compel them to confront the fast pace of market

demand (Schmitz, 1999). Clustering may also reduce the production and marketing costs for individual enterprises (Schmitz,

1992; Weijland, 1994) to the extent that they can be able to compete in world markets. In other words, clustering helps small firms

improve their competitive advantages, since it helps in overcoming the constraints from diseconomies of scale.

Micro, Small and Medium enterprises (MSMEs) in Sub Saharan Africa in general and in Ethiopia in particular face a number of

constraints, among others, lack of access to markets, finance, business information; lack of business premises; low ability to

acquire skills and managerial expertise; low access to appropriate technology and poor access to quality business infrastructure

(Stevenson & Annette, 2006). Moreover, their potential contribution to economic growth and poverty reduction often remains

unfulfilled because of constrains associated with isolation and informality. Many of these obstacles can be overcome when small-

scale firms are able to share resources, access external economies that lower their operating costs, and undertake joint actions that

32Assistant Professor, College of Business and Economics, Hawassa University, Ethiopia, [email protected]





improve their competitiveness. Spatial proximity facilitates the achievement of such gains, as it is the case for firms operating

within industrial clusters (UNIDO, 2009).

For small enterprises in Sub-Saharan Africa, clusters or agglomerations may offer one means for overcoming some of these

challenges and for creating more innovative and globally integrated industrial sectors (Giuliani et al., 2005; Humphrey & Schmitz,

2000, 2002; McCormick, 1999). Ideally, clustering creates external economies and collective efficiencies that increase the

competitiveness, flexibility, and responsiveness of an industrial sector while providing opportunities for small firms to increase

their production and technological capabilities (Schmitz & Nadvi, 1999). This observation appears to have great relevance to

Ethiopia. While much evidence still needs to be gathered, it is now clear that industrial clusters are becoming common in

Ethiopia, found in a range of sectors and regions. Moreover, some case material suggests that a few of these clusters have already

turned themselves into dynamic clusters by meeting the challenge of intense competition in the local market. This has excited

scholars and the aid and industrial policy community.

Despite the growing interest, detailed information on SME clusters in Africa in general and in Ethiopia in particular remains

limited. Attempts at explicitly studying such forms of industrial organization, as opposed to drawing inferences from studies

carried out for other purposes, are rare, the main exceptions being researches undertaken on selected case studies in Sub-Saharan

Africa by Oyelaran-Oyeyinka and McCormick (2007) and World Bank (2011). In Ethiopia, few studies related to this one have

been conducted on the value of cluster membership by Abdella and Peerlings (2006), on competitiveness and viability by Abdella

M. (2008), on the handloom sub-sector infrastructure by Ayele, G., Moorman, L., Wamisho, K., & Zhang, X. (2009), on cluster

success factors by Ketselamariam H. (2010), and on the governments role in cluster development by Ali M. (2012).

These studies provide important insights into the structure of industrial clusters and how these clusters could help small and

medium domestic enterprises to overcome their size constraints and improve both their sales performance and their access to new

markets. This paper falls within this emerging literature. It also seeks to understand how clustering facilitates responses to

international competitiveness, turning local producers into global competitors. However, the paper pushes the discussion further

by analyzing the nature of inter-firm ties and by presenting a more differentiated understanding of the gains that clustering offers.

Ethiopia, as many other developing countries in Africa and elsewhere, has been moving towards a liberalized trade regime since

the beginning of 1990s after a long period of import substitution. This reform had a significant impact on Addis Ababas footwear sector. Imports grew from 0.2 million pairs in 1997 to 0.90 million pairs in 2001 (LIDI, 2014). Since it is known that

most, if not all, imported leather shoes came from China, it is clear that the China shock took place primarily in 2001. The

imports, however, sharply declined thereafter. The decline in shoe imports is accompanied by the significant increase of the

number of MSMEs in the shoe making industry in Addis Ababa. The number of firms jumped from 500 prior to 2000s to the

current estimation of more than 1500. According to interviews, this can be explained by the fact that although Chinese shoes that

came to Ethiopia had better finishing and were consumers that are more fashionable, quickly learned that they were much less

durable and, hence, began to prefer locally produced shoes. Moreover, the quality of domestically produced shoes was improved,

which would have culminated in the recent surge of exports of the Ethiopian shoes.

The paper focuses on the case study from Merkato Leather Footwear Cluster where, with little exogenous support, local MSMEs

have acquired back a significant share of the local market. This cluster has been identified as an exceptionally successful case in

Africa because of such remarkable recovery from the intense competition from imported Chinese shoes in the late 1990s (Sonobe

et al., 2006). Can this recovery be entirely explained as a windfall gain from changes in the consumer attitude towards Chinese

shoes? Alternatively, are there structural changes occurred in the footwear cluster which have enhanced the ability of domestic

producers to compete? Alternatively, how does a cluster responds to opportunities and crisis of international competition?

This study addresses these questions focusing on changes in external economies and structural changes in vertical and horizontal

relationships between Merkato shoe firms and their suppliers, subcontractors, and buyers. The objective is to assess if

international competition induces greater changes in external economies and cooperation in vertical and horizontal linkages. It

also analyzes if these changes in collective efficiency positively influence firms' performance and together with a favorable

market environment contributes to the cluster's recovery. Furthermore, the paper investigates the heterogeneous behaviour of

firms in the cluster and assesses whether external economies alone are sufficient to ride out major changes in product or factor

markets.

BACKGROUND INFORMATION

Like other countries in the world, there are industrial clusters of micro- and small-scale enterprises in Ethiopia. The most common

types of clusters in Ethiopia are natural clusters. Although the exact number of clusters in Ethiopia is not known, they are

commonly found among labor intensive manufacturing sectors and are mostly located in urban centers, rural towns and tourist

areas. One example of such clusters is the footwear cluster in Merkato, Addis Ababa.

The Merkato footwear cluster is a spontaneously grown agglomeration of MSMEs (natural cluster). Its name reflects its location.

Merkato is the largest open-air market in East Africa located in the city centre of Addis Ababa. The cluster is believed to comprise





above 1500 shoemakers. This cluster is also home of many other related businesses and complementary activities that include

buyers, suppliers of various inputs (soles, leather, shoe accessories), and service providers (repair, machinery rent etc.). The

producers obtain nearly all raw materials needed for the shoe making and services such as machinery and equipment maintenance,

design, and labor supply from the cluster. The majority of firms also sell their products through wholesalers that are mainly

located in the cluster and the vicinity (Sonobe et al., 2006).

This cluster has been functioning for decades and went through difficult times. The socialist ideology and associated command

economy that persisted for about two decades (1975-91) in the country was hostile for private investment and entrepreneurship.

The cluster and the sector at large were stifled as a result. With the change of government in 1991, the country undertook trade

liberalization and extensive policy reforms to transfer the economy into a market oriented one. It also adopted a structural

adjustment program that includes domestic market deregulation and trade opening. Some of these reforms might be helpful for the

revival of the cluster and the private sector at large. However, the domestic market was flooded with imports particularly Chinese-

made shoes following the trade opening. The imported Chinese shoes were less durable but had better finishing, more fashionable

and cheaper than the products produced in the cluster and elsewhere in the country. Throughout the 1990s, the domestic footwear

industry was hit hard. As a result, many firms could not compete and they were forced to close/change or downsize their business

(Sonobe et al., 2006).

The government export promotion strategy that was adopted at the end of the 1990s, consequent industrial, Micro, and Small

Enterprises (MSEs) development strategies consider the leather industry as a priority sector. This is partly justified because

Ethiopia has the largest livestock production in Africa and the 10th largest in the world, which gives the country a comparative

advantage in the raw materials needed for the leather sector. The strategy emphasizes the need to upgrade exports from

unprocessed toward fully processed leather and final products such as footwear, bags, jackets etc. The focus of the support,

however, has been the large footwear firms. The small firms in clusters did not benefit from the government MSE promotion

initiatives mainly because they operate out of the radar of officials.

Despite the absence of support from the government, the Merkato small shoe cluster has made a remarkable recovery in the early

2000s at a time when the large firms continue to struggle for survival and lobby for government support. Although there is no

official record on the number of firms in the cluster, recent studies have shown the increasing expansion of this cluster. Prior to

2000 the number of firms in the cluster was estimated to be around 500. This number increased substantially following the

recovery from the severe import competition and reached about 1000 by 2005 (Sonobe et al., 2006) and is currently estimated to

be above 1500.

This paper analyses whether the recovery is related to the increase in collective efficiency in the cluster. More specifically, it

analyzes the changes in collective efficiency, particularly the cooperative behavior located in the footwear cluster of Merkato,

Addis Ababa, due to the international competition. In other words, it investigates how the cluster has been responding to the

international competition. Hence, the hypothesis explored in this paper is that meeting such international competitive pressure

requires a positive change in collective efficiency and specifically greater local cooperation that ultimately results in improved

performance.

OBJECTIVES OF STUDY

The general objective of the study is to analyze the changes in the collective efficiency, specifically on the cooperative behavior of

MSMEs in a cluster due to international competition.

The specific objectives of the study are to:

Examine clusters responses to the opportunities and challenges of international competition (specifically related to Chinese competition).

Examine the relationship between collective efficiency and the performance of MSMEs in a cluster.

SCOPE AND LIMITATION OF STUDY

The study is limited to the footwear cluster found in Merkato, Addis Ababa. From the methodological point of view, the sample

and context are always issues of concern. Using leather footwear SMEs in Addis Ababa as a target population contributed to the

researchs generalizability but it was also a weakness. Further expansion of this research to large organizations and other clusters would significantly contribute to understanding of the competitiveness of the Footwear Industry in Addis Ababa, Ethiopia.

MATERIALS AND METHODS

This study adopted mixed methods design in which quantitative and qualitative approaches were simultaneously used, but

quantitative approach was the dominant one (QUAN+qual). More specifically a mixed method design is a type of design in which

different, but complementary data were collected on the same topic. In this study, the quantitative, numeric, data were collected





using a questionnaire survey for hypothesis testing. Concurrent with these data, a qualitative (network case study) approach

employing semi structured interviews, informal discussions, and literature surveys was conducted. The purpose of this qualitative

phase was to understand the characteristics of clusters and explore their linkages. The reason for collecting both quantitative and

qualitative data was to bring together the strengths of both forms of research to compare, validate, and corroborate results.

To test the hypothesized model, quantitative data were collected through a semi-structured questionnaire. A total of 175 were

dispatched to a randomly selected sample. Table one show the size distribution in the population and the sample. The

questionnaire was designed by modifying the instrument developed by Nadvi and Schmitz (1994). The survey was conducted to

ascertain whether collective efficiency in terms of external economies and cooperation had increased or decreased over the

previous five years. The major element of the questionnaire deals with the changes in the five constructs namely Access to Market

information (AcMktInfo), Access to Inputs and Services (AcInpSer), cooperation with other local producers (PC), cooperation

with suppliers (SC), and cooperation with local buyers in the domestic market (BC) and the six dependent variables (output, sales,

number of workers, speed of delivery, average price, and net profit). All are measured on a five-point scale ranging from

Increased a lot to decreased a lot.

Items in the questionnaire were checked for the reliability using Cronbachs alpha. Cronbachs alpha values for all items under each constructs were checked, minimum of 0.7 were obtained, and thus the literature considers this value acceptable. The survey

instrument was pre-tested on some entrepreneurs for clarity, and questions were matched with the appropriate factors.

Modifications were made on the pre- test results. The instrument was also checked for its validity. The researcher checked the

content validity of the research instrument. Based on expert judgment, the degree to which a measurement scale contains items

which experts consider to be representative of that which is to be measured. The key informants for the study were the owners or

managers of the enterprises.

For the analysis, collective efficiency and performance index was constructed. (They are composite indices of the external

economies and cooperation variables used for the figures and tables in this paper). Both indices were generated by attaching equal

weights to each external economy and cooperation variable. The five possible values from big increase to big decrease were coded

on a range of +2 to -2 respectively, with no change coded as 0. The index for each firm was then constructed by adding up the

actual values and dividing them by the number of variables.

Ordinary least squares (OLS) regression analysis using a stepwise method was used to test the hypothesized relationships between

performance and the two dimensions of collective efficiency: External economies and Joint action. The assumptions of multiple

regression such as linearity, independence of residuals, and absence of Multicollinearity (Hair et al., 2014) were checked before

running the regression models.

Table-1: Size Distribution of Firms in Population and Sample

Size (Number of workers) Population Sample

Micro (up to 5) 481 80

Small (5 to 17) 280 47

Medium (more than 18) 139 23

Total 900 150

Sources: Authors Compilation

RESULTS AND DISCUSSION

This section starts with description of sample respondents and descriptive analysis of the cluster factors that shows the changes in

the collective efficiency because of international competition during the Chinese shock. This analysis is followed by the

regression analysis with the aim of finding out the relationship between the changes in collective efficiency and the changes in

firm performance.

Characteristics of Sample Respondents

The majority of respondents in this study were firm owners, 130 respondents corresponding to 87% of the sample, and the rest

were managers. Almost all (96%) were male, suggesting a dominant presence of male entrepreneurs in the footwear cluster. The

female representation is relatively better in the small footwear enterprises that constitute 6%. The average age of the Merkato

producers is 34 years old. Surprisingly the average age of the entrepreneurs declines with the increase in the size of the

enterprises. In other words, medium firm owners have the lowest average age of 32. Majority of the producers are experienced in

the shoe making business with 13 years on average. Only 1% of them have less than 2 years of experience. Similar with the age

data, the most experienced entrepreneurs belong to the micro firms. With regards to the entrepreneurs previous relevant experience, majority (94%) of the entrepreneurs were spin offs who had worked at micro (43%), small (17%), and medium (34%)

factories. 81% of all the producers rely on shoe making business as their only source of income.





Table-2: Characteristics of Respondents


Characteristics Items Frequency Percent

Sex

Male 140 93.3

Female 10 6.7

Total 150 100.0

Education

No education 3 2.0

Elementary 56 37.4

Secondary 77 51.3

TVET 14 9.3

Total 150 100

Ethnic Background

Guraghe 132 88.0

Amhara 10 6.7

Tigray 2 1.3

Oromo 2 1.3

Other 4 2.6

Total 150 100

Table-3: Age and Number of Work Experience


About the level of formal education, 2% of them have no

education, 38% completed primary education, and 51% per

cent completed high school. Few entrepreneurs (9%) had

technical and vocational training and with no entrepreneur

having a college degree. As found in many other developing

country clusters (e.g. Agra shoe cluster in India), the

Merkato shoe cluster is characterized by homogeneous social

background. The majority of the entrepreneurs come from

one ethnic group Guraghe that constitutes about 88 per cent of the total number of entrepreneurs in the sample.

Cluster Size of

Enterprise

Entrepreneur

Age

Work

Experience

Merkato

Micro Mean 34.40 14.13

Small Mean 33.96 13.38

Medium Mean 32.87 12.35

Descriptive Analysis of Collective Efficiency

As mentioned above, the five possible values from big increase to big decrease were coded on a range of +2 to -2 respectively,

with no change coded as 0. The index for each firm was then constructed by adding up the actual values and dividing them by the

number of variables. The five-point scale was collapsed in to three categories as Increase, No change, and Decrease to simplify

the presentation of the descriptive analysis.

External Economies: Access to Information about Product and Market

Table-4: Changes in Access to Information about Product and Market (%)


As can be seen from Table 4.30, for the majority of firms there has been an increase in information spillover (knowledge

spillover) in general but with the highest increase in access to design followed by access to buyers information and technology in particular. As design has been one of the major competitive weapons by Chinese, Merkato producers have been giving due

attention to imitating designs and thereby outcompeting Chinese rivals. Hence, according to the qualitative investigation, the

increase in access can be associated with the importance of shoe design as the main instrument to cope up with the pressure from

international competition.

External Economies: Access to Inputs and Services

Table 5: Access to Inputs and Services (%)

Items Increase No Change Decrease

Access to skilled labor 56.0 26.0 16.7

Access to raw material-Leather & sole 55.3 24.7 18.7

Access to second hand machine 57.4 25.3 16.0

Access to new machine 49.3 25.3 24.0

Access to components 53.4 29.3 16.0

Access to finance 11.3 63.3 23.3


The survey results on access to inputs and services are very similar except with access to finance. At least 50% of the respondents

reported an increase in the access to all other inputs other than finance. The highest increment is registered in the access to second

hand machines followed by access to skilled labor and access to raw materials. With respect to finance, however, 63% of the

respondents reported no change in access to finance. ``No change'' here means that it remains at a very low level.

Items Increase No change Decrease

Access to information about design 59.4 28.0 10.3

Access to information about technology 52.7 32.0 13.3

Access to information about buyers 54.7 30.0 14.0





Joint Action: Cooperation with Suppliers

Table-6: Changes in Cooperation with Suppliers (percentages)


Information exchange 45.3 45.3 9.3

Improving quality 46.6 44.7 8.6

Speed of delivery 47.3 42.0 10.0


As shown in Table-4, 32 above, around 45 percent of the firms reported an increase in cooperation with leather and sole suppliers.

Such increases can be observed both in general exchange of information and experience and in specific concerns of improving

quality and speeding up responses. Despite the overall increase, the trend has been less clear as the respondents seem to be divided

between No change and Increase. The decreases were negligible.

Table-7: Cooperation with Suppliers and Size

(Percentage of Firms, which Cooperate a Lot with their Suppliers)


Concerning the differentiation by size, as illustrated in table 7, medium producers seem to work more collaboratively with their

suppliers than their micro and small counterparts. Only in case of speed of delivery that small producers cooperate more with their

suppliers than the medium enterprises. From the qualitative result, it appears that micro producers usually have pure market

relations with dynamic and good quality suppliers. To bargain on delivery and payment conditions, some small firms prefer to buy

components and raw materials from low quality small suppliers.

Cooperation with Buyers

Table-8: Changes in Cooperation with Local Buyers (%)




Technology upgrading 55.4 30.7 14.0

Product development 44.3 40.0 15.0

Cash in advance 39.3 46.7 14.0


Regarding relationships between shoe manufacturers and their buyers there has been a clear trend toward greater cooperation in all

aspects except in cash in advance payment. Table 4.34 above shows that the quality of relationships has tended to improve. 64.7

percent of firms reported an increase in exchange of information and experiences with buyers, and 64.6% reported closer

cooperation in quality improvement. In other areas of cooperation like technology upgrading and product development, the

upward trend was less strong. The lowest increase is registered with respect to cooperation in cash in advance support from local

buyers. Nevertheless, over all five areas examined, an average of 53.76% of firms reported an increase in cooperation with local

buyers.

Table-9: Cooperation with Local Buyers and Size

(Percentage of Firms, which Cooperate a lot with their Suppliers)

Items Micro Small Medium



Technology upgrading 22.5 21.3 39.1

Product development 32.5 34.0 47.8

Cash in advance 10.8 18.3 20.0


Items Micro Small Medium

Information Exchange 17.5 29.8 30.4

Improving Quality 16.3 25.5 30.4

Speed Of Delivery 20.0 36.2 26.1





In comparison across size, as shown in table 9, medium producers do have closer ties with local buyers in all aspects of

cooperation than their micro and small competitors. A relatively large difference between medium producers and smaller ones is

observed, that is, in terms of an increase in cooperation in exchange of information and experiences followed by cooperation in

product development and technology upgrading. However, with respect to cooperation in advance cash payment variations by

firm size appear to be small.

Cooperation with Other Producers

Table-10: Changes in Cooperation with Other Producers (%)

Items Increase No Change Decrease

Information Exchange 20.7 60.0 14.6

Improving Quality 18.7 62.7 18.0

Machine Sharing 15.4 66.0 18.0

Product Development 16.0 62.7 20.7

Design Sharing 14.0 64.7 24.7

Joint Marketing 10.0 65.3 24.0

Joint Input Order 8.0 67.3 24.0


The survey results on horizontal cooperation are very different. As can be seen from Table 10, for the majority of firms there has

been no change in bilateral horizontal cooperation. This is the clearest in the case of joint input order, machine sharing and joint

marketing. The interviews suggest that during the years of most intense crisis horizontal cooperation was particularly low: firms

were excessively involved in their day-to-day survival and were not able to establish cooperative links and invest in joint projects.

In those years, groups of firms, which had previously regularly exchanged information, machines or sometimes orders, ceased to

do so. Besides, the crisis changed profoundly the relationships at cluster level, as many firms went bankrupt and others reduced or

transformed their activities. Since the recovery, however footwear enterprises have begun to build new networks. As can be seen

from same Table, this process is still at an early stage and only horizontal cooperation aimed at information exchange takes place

among a relatively large number of firms. Other forms of horizontal cooperation within the cluster are quite low.

About size differentiation, only in cases of cooperation in quality improvement, joint marketing and product development those

medium producers have improved their relationships with other producers much better than smaller enterprises. In all other

aspects, the change in horizontal cooperation is similar across different size category.

Regression Analysis

In the previous section, a descriptive analysis of the changes occurred in externalities and vertical and horizontal linkages within

the footwear cluster of Merkato since trade liberalization was presented. In what follows whether collective efficiency is related

with performance is assessed using multiple regression analysis. With the methodology discussed in the previous section, for the

regression analysis the following independent variables that measure collective efficiency are built: Access to Market information

(AcMktInfo), Access to Inputs and Services (AcInpSer), Cooperation with other local producers (PC), Cooperation with suppliers

(SC), and Cooperation with local buyers in the domestic market (BC). These variables are treated in the regression model as

continuous. As the subcontracting linkage in the cluster is almost non-existent, Cooperation with subcontractors (CS) is not used

for further analysis.

The dependent variable is a performance indicator obtained with principal component analysis with Varimax rotation method,

estimated as linear combinations of the original variables. In this study, as shown in Table 11, the Bartletts test convincingly rejected the null hypothesis at P-value=0.000, that the samples were selected from populations with equal variances. The Kaiser-

Mayer-Olkin (KMO) measures sample adequacy and reinforces the use of the PCA approach. A small value of the KMO measure

indicates a weak correlation between pairs of scales and consequently PCA is unsuitable for the data reduction process.

Table-11: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy .882

Bartlett's Test of

Sphericity

Approx. Chi-Square 780.996

D.f. 15

Sig. .000


In this study, the KMO result on the performance scale was 0.882 as can be seen from the Table-11. It is suggested that KMO

measures in the region of 0.80 are meritorious (Kaiser, 1974). Therefore, there was not any problem related to sample adequacy in

this study.





Table 12 below presents the solution of principal component analysis with a single component extracted. This component explains

75.7% per cent of the total sample variance and is characterized by the following variables with the highest factor loadings: output

trend, annual sales trend, profit trend, trend of the number of workers, trend of average price and trend of average speed of

delivery. For the extracted component, factor score coefficients for each observation are estimated and then used as the dependent

variable (FAC1) in regression analysis.

Table-12: Component Matrix

Component

1

Number of Workers .928

Total Output in Number of Pairs / Month .913

Vol of Sales in Number of Pairs/Month .888

Average Price of a Pair Of Shoe .888

Net Profit .797

Average Speed of Delivery .797

Extraction Method: Principal Component Analysis

a. 1 Components extracted


Given the initial model composed of FAC1 as response variable and output, sales, profit, number of workers, price and speed of

delivery as repressors, stepwise selection method was used for identifying the relevant variables to predict sample firms' performance. The stepwise selection of dependent variables is a combination of forward and backward procedures. In forward

selection, the first variable considered for entry into the equation is the one with the largest positive or negative correlation with

the dependent variable. The F test for the hypothesis that the coefficient of the entered variable is 0 is then calculated. To

determine whether this variable (and each succeeding variable) is entered, the F value is compared to an established criterion (a

variable is included if the p-value associated with the F statistic is less than or equal to 0.05). If the first variable selected for entry

meets the criterion for inclusion, forward selection continues. The procedure stops when there are no other variables that meet the

entry criterion. Backward selection starts with all variables in the equation and sequentially removes them. Instead of entry

criteria, removal criteria are specified. A variable is removed if the p-value associated with the F statistic is greater than or equal

to 0.10.

In stepwise selection, the first variable is selected in the same manner as in forward selection. If it passes the criterion, the second

variable is selected based on the highest partial correlation. From this point, stepwise selection differs from forward selection: the

first variable is examined to see whether it should be removed as in backward elimination. In the next step, variables not in the

equation are examined for entry. After each step, variables already in the equation are examined for removal. Variable selection

terminates when no more variables meet entry and removal criteria. The three procedures do not always result in the same

equation; when they do, it suggests that the model is robust.

The main findings of the regression analysis are as follows: Access to information about product and market (AcMktInfo),

cooperation with other local shoe firms (PC) cooperation with leather and sole suppliers (SC), cooperation with local buyers (BC)

are positively and significantly related with performance. The R square value from Table 14 indicates that 48.3 % of the variance

in performance is explained by the four variables. Furthermore, as indicated in Table 15, the computed values of the standardized

beta coefficients suggest that horizontal cooperation with other producers is the largest of any repressors with Beta value of 0.312, followed by access to information about market and product and cooperation with local buyers with Beta values of 0.307

and 0.197 respectively. The only variable removed from the model by stepwise selection is AcInpSer representing access to input

and services. Nonetheless, as can be seen from Table 13, AcInpSer is positively and significantly correlated with AcMktInfo and

therefore its contribution to firms' performance in the regression model is indirectly represented by this last variable.

Table-13: Pearson Correlation Result

Variables Performance PC BC AcInpSer AcMktInfo SC

Performance 1.000 .523*** .456*** .506*** .331*** .401***

PC .523*** 1.000 .421*** .278*** .193 .231**

BC .456*** .421*** 1.000 .275*** .196 .234**

AcInpSer .506*** .278*** .275*** 1.000 .705*** .313***

AcMktInfo .331*** .193 .196 .705*** 1.000 .178

SC .401*** .231** .234** .313*** .178 1.000

Note: ***Correlation is significant at 0.01 level,

** Correlation is significant at 0.05 level






Finally, given that several indicators originally measured performance, the robustness of the regression model is also tested

substituting the dependent variable FAC1 with the 6 original variables. This is done by testing OLS 6 separate models in which

the independent variables are always the same while the dependent variables are the 6 performance indicators. The results confirm

the robustness of the original model for at least 4 of the performance variables: sign and level of significance of the beta

coefficients are confirmed for AcMktInfo, PC, BC, and SC, as dependent variables. In other words, production, sales, profits and

employees trends are positively and significantly associated with Access to information about product and market (AcMktInfo),

cooperation with other local shoe firms (PC), cooperation with leather and sole suppliers (SC), and cooperation with local buyers

(BC).

Table-14: Model Summary

Model R R

Square

Adjusted

R Square

Std. Error of

the Estimate

Durbin-Watson F

1 .523a .274 .269 .79841654 55.815

2 .644b .415 .407 .71926841 52.069

3 .673c .453 .441 .69793123 40.243

4 .695d .483 .469 .68070579 1.672 33.850

a. Predictors: (Constant), Pc

b. Predictors: (Constant), Pc, AcMktInfo

c. Predictors: (Constant), Pc, AcMktInfo, Bc

d. Predictors: (Constant), Pc, AcMktInfo, Bc, Sc

e. Dependent Variable: REGR factor score 1 for analysis


Table-15: Coefficientsa


CONCLUSION AND RECOMMENDATION

The main question addressed in this paper concerns the impact of international competition on the footwear industry in Addis

Ababa cluster: Merkato. The impact was quite strong as seen in the large increase in shoe imports and in the huge reduction in the

number of firms and domestic production.

The paper, therefore, assessed if international competitive pressures induced positive changes in external economies and

cooperation in vertical and horizontal linkages that would help firms in the cluster recover from the crisis. The empirical evidence

shows that collective efficiency has indeed increased. The hypothesis that since Chinese Shock shoe firms' performance has been

positively influenced by collective efficiency with other firms is also confirmed by regression analysis on a random sample

stratified by size, covering 150 enterprises located in Merkato.

Model Standardized

Coefficients

T Sig. Collinearity Statistics

Beta Tolerance VIF

1 (Constant) 1.391 .166

Pc .523 7.471 .000 1.000 1.000

2

(Constant) -1.949 .053

Pc .415 6.317 .000 .923 1.083

AcMktInfo .391 5.947 .000 .923 1.083

3

(Constant) -3.302 .001

Pc .333 4.850 .000 .794 1.259

AcMktInfo .353 5.449 .000 .893 1.120

Bc .218 3.182 .002 .795 1.257

4

(Constant) -3.930 .000

Pc .312 4.634 .000 .785 1.274

AcMktInfo .307 4.702 .000 .839 1.192

Bc .197 2.916 .004 .785 1.273

Sc .187 2.912 .004 .868 1.151

Note: a. Dependent Variable: REGR factor score 1 for analysis 1





Cooperation within the cluster contributes to collective efficiency, defined as the comparative advantage derived from local

external economies and cooperation. With regression analysis, the hypothesized positive association between firms' performance

and their cooperative behaviors is accepted.

The model clearly shows that performance is positively related with vertical and horizontal cooperation. For all the independent

variables included in the regression model, which are aggregated indicators of the different types of cooperation, positive and

statistically significant coefficients are found. Therefore, one can conclude that cooperation, which is one of the main components

of collective efficiency, contributes to firms' performance. External economies are other sources of collective efficiency. Out of

the two predictors of external economies, access to information about product and market was found to significantly predict

performance. Even though the second predictor, which is access to inputs and services, was not found to have significant

relationship with performance, it is indirectly represented by the first predictor as the two variables are positively and significantly

correlated with each other.

From what has been said so far it appears that although some external events such as the change in the attitude of customers

towards the quality and durability of the Chinese footwear and the improved local footwear designs imitated from Chinese

imports, the increase in collective efficiency also played an important role in the recent recovery of the cluster. The result also

shows that while majority of the firms in the cluster gain from agglomeration economies, the extent of inter-firm co-operation and

joint action, and the benefits arising from it, are highly differentiated. The positive impact of collective efficiency also affects only

part of the cluster: those firms, which enter into cooperative actions, and those, which are able to benefit from some externalities,

generated by cooperation.

The study concludes that external economies, while necessary, are not sufficient to bring about growth. For growth, joint action,

particularly strategic vertical ties with local suppliers, subcontractors, and external buyers, is critical. This means that MSMEs

cannot just rely on the passive dimension of collective efficiency; they need to enter into joint action with local actors to face new

competitive challenges and to grow.

The main challenge for the future, therefore, will be to transform the static part of the cluster. The study suggests that intervention

from Federal and Regional Micro and Small Development Agencies (MSDEAs), Non-governmental organizations and other

relevant institutions can fill this gap. Such institutions should put some mechanisms in place to assist the less dynamic

manufacturers to 'switch gear' or to enhance and improve their bilateral as well as multilateral cooperative behavior in order to

meet the challenges of the increasingly competitive and rapidly evolving local and international market.

The study also suggests that the blanket approach of policies toward small firms clusters development is not appropriate as firms of different sizes were found to be heterogeneous in various ways and perform differently.

REFERENCES

1. Abdella, M. (2008). The Competitiveness and Viability of Cluster Based Cottage Industries: The Case of Handloom Sector in Ethiopia. Addis Ababa: Ethiopian Development Research.

2. Abdella, M., & Peerlings, J. (2006). Value Added of Cluster Membership for SMEs: Applying Propensity Score Matching to the Handloom Sector in Ethiopia. The Netherlands: Wageningen University.

3. Ali, M. (2012). Government Role in Cluster Development for MSEs: Lessons from Ethiopia (CMI Report).

4. Ambastha, A., & Momaya, K. (2004). Competitiveness of Firms: Review of Theory, Frameworks, and Models. Singapore Management Review, 26(1), 4561.

5. Ayele, G., Moorman, L., Wamisho, K., & Zhang, X. (2009). Infrastructure and Cluster Development: A Case Study of Handloom Weavers in Ethiopia. International Food Policy Research Institute.

6. Giuliani E., Pietrobelli C., & Rabellotti R. (2005). Upgrading in Global Value Chains: Lessons from Latin America Clusters. World Development, 33(4), 549573.

7. Hair, J. F., Hult, T. M., Ringle, C. M., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (1st Edition). Thousand Oaks. California: SAGE Publications, Inc.

8. Humphrey, J., & Schmitz, H. (2000). Governance and Upgrading: Linking Industrial Cluster and Global Value Chain Research (IDS Working Paper). Brighton: University of Sussex.

9. Humphrey, J., & Schmitz, H. (2002). How Does Insertion in Global Value Chain, Affect Upgrading in Industrial Clusters. Regional Studies, 36(9), 10171027.





10. Ketselamariam, H. (2010). Success Factors in Micro and Small Enterprises Cluster Development: Case of Gullele Handloom Clusters in Ethiopia (Thesis). UNISA. South Africa.

11. Markusen, A. (1996). Sticky Places in Slippery Spaces: A Typology of Industrial Districts. Economic Geography, 72, 293313.

12. McCormick, D. (1999). African Enterprise Clusters and Industrialization: Theory and Reality. World Development, 27(9), 15311552.

13. Nadvi, K., & Schmitz, H. (1994). Industrial Clusters in Less Developed Countries: Review of Experiences and Research Agenda (IDS Discussion Paper, 339). Brighton: Institute of Development Studies.

14. Oyelaran-Oyeyinka, B., & McCormick, D. (eds) (2007). Industrial Clusters and Innovation Systems in Africa. Tokyo: United Nations University Press.

15. Porter, M. (1998, November-December). Clusters and the New Economics of Competition. Harvard Business Review, 7790.

16. Porter, M. (1998). Competitive Advantage. London: The Free Press.

17. Schmitz, H. (1992). On the Clustering of Small Firms. IDS Bulletin, 23(3), 2752.

18. Schmitz, H. (1995). Collective Efficiency: Growth Path for Small Scale Industry. Journal of Development Studies, 31(4), 529566.

19. Schmitz, H. (1999). Global Competition and Local Cooperation: Success and Failure in the Sinos Valley, Brazil. World Development, 27(9), 16271650.

20. Schmitz, H., & Nadvi, K. (1999). Clustering and Industrialization: Introduction. World Development, 27(9), 15031514.

21. Sonobe, T., Hu, D., & Otsuka, K. (2002). Process of Cluster Formation in China: A Case Study of a Garment Town. Journal of Development Studies, 39(1), 118-139.

22. Stevenson, L., & Annette, S. (2006). Support for Growth- Oriented Women Entrepreneurs in Ethiopia. International Labor Organization.

23. UNIDO. (2009). Donor Survey of firm level support interventions in Ethiopia. Vienna: UNIDO Publications.

24. Weijland, H. (1994). Trade Networks for Flexible Rural Industry. In Pedersen PO, Sverrison A, van Dijk MP (eds), Flexible Specialization: The Dynamics of Small Scale Industries in the South. London: Intermediate Technology

Publications.

25. (2011). Industrial clusters and Micro and small enterprises in Africa: From Survival to growth. Washington, DC: World Bank.

26. Retrieved from http://www.researchgate.net/publication/254405574_Innovation_performance_and_embeddedness_in_network...

27. Retrieved from http://www.researchgate.net/publication/232641264_Implementation_of_E-commerce_in_Market-Oriented_Cl...

28. Retrieved from http://www.researchgate.net/publication/24085614_The_cutting_Edge_Collective_efficiency_and_internat...

29. Retrieved from http://www.academia.edu/2403622/The_cutting_Edge_Collective_efficiency_and_international_competitive...

*****

cluster competition

Documents