SMEs and the Economic Growth: A Comparative Study of Clustering Techniques in SMEs Data Analysis

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1 SMEs and the Economic Growth: A Comparative Study of Clustering Techniques in SMEs Data Analysis Dr. Karim Mardaneh The Business School, University of Ballarat k.mardaneh@ballarat.edu.au Abstract Regional economic planning of small-to-medium enterprises (SMEs) requires a thorough understanding of the industry structure and the size of business. The main body of the literature regarding SMEs is focused on formation and growth, as well as success and failure (Dejardin & Fritsch, 2010). Some studies have considered clustering regional areas based on functional specialisation but only a few studies have considered industry structure and the size of business (Okamuro, 2006). This area of study may require large data sets and sophisticated clustering techniques, which have not been used in SMEs research. Using the Australian data and a large data set for regional (non-metropolitan) areas, this current study attempts to investigate the relationship between the economic growth of geographical areas with the industry structure and size of the businesses within those areas. For this the study uses Ward s, the k-means, global k-means, and the modified global k-means clustering algorithms to cluster the Statistical Local Areas (SLA), and compares the function of these algorithms to identify the algorithm that performs the clustering task of the SMEs data more efficiently. Resulting analysis of this comparative study demonstrates that the modified global k-means algorithm outperforms the other algorithms examined. Key words: firm size, business location, regional and metropolitan, industry structure, clustering INTRODUCTION The current study compares various clustering techniques by examining the relationship between the industry structure and the size of business with economic growth using Australian regional (non-metropolitan) areas data. Some studies (e.g. Pagano, 2003) have examined firm size and industry structure, however, such studies have not considered in combination the role of both industry structure and the size of business in economic growth. The study uses four clustering techniques on Statistical Local Area (SLA) regions to examine the performance of these clustering methods on small-to-medium enterprises (SMEs) data sets. Researchers have been using clustering techniques for pattern recognition (e.g. Beer and Clower, 2009), however, there is a gap in the literature in terms of application of clustering methods on issues related to SMEs. Cluster analysis has been used by contemporary researchers as a method of analysis when the number of observations in a particular field is fairly large (Freestone, Murphy and Jenner, 2003). This current study adopts cluster analysis and uses four methods of Ward s clustering (Ward, 1963), the k-means (Hartigan and Wong, 1979), global k-means (Likas, Vlassis, Verbeek, 2003), and the modified global k-means (Bagirov and Mardaneh, 2006) algorithms to cluster the SLAs based on the industry structure and size of the businesses within those areas and compares the function of these algorithms to identify a clustering algorithm that is more suitable for clustering task of the SMEs data. By doing this the study addresses the gap in understanding the role of industry structure and size of business in economic growth and the cluster analysis of the SMEs data sets. 1

2 LITERATURE REVIEW This review of the literature relevant to the topic and this paper provides a brief summary based on two broad parameters: industry structure and size of business, as well as cluster analysis. Changes in industry structure influences economic growth, however, understanding the change requires a thorough consideration of the role of the industry structure and size of business (Micro, Small, Medium, Large). Previous studies in this area have mainly focused on formation and growth (Dobbs and Hamilton, 2007; Mueller, et al. 2008; Hudson, et al., 2001; Beugelsdijk, 2007; Sierdjan, 2007; Koster, 2007; Armington and ACS, 2002; Pagano and Schivardi, 2000), and some studies focus on issues such as the organisational attitude to change, and success and failure factors (Walker and Brown, 2004; Agarwal and Audretsch, 2001; Gray, 2002; Feser et al. 2008; Dejardin and Fritsch, 2010). There are only a few studies that consider industry structure and the size of business (Okamuro and Hobayashi, 2006; Pagano, 2003; Pagano and Schivardi, 2000), however, they do not identify the drivers of the economic growth in relation to these factors. Delgado et al. (2010) suggest that regions with an industry structure which enables wealth creating initiatives (exports) while complementing this with consumption-led growth (driven by population growth) will perform better in economic terms. Distribution of a region's economic activity across industries is considered a major determinant of the region's level of income, resilience of its local economy and its ability to grow (Australian Government Department of Transport and Regional Services, 2003). Some researchers (see, for example, Beer and Maude, 1995) use cluster analysis to examine changes in economic functions of towns and to analyse socio-economic factors. Clustering or cluster analysis is a challenging problem and different algorithms for clustering have been proposed. The cluster analysis deals with the problem of organising a collection of patterns or objects into clusters based on similarity so that objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters (Bagirov, 2008; Bagirov and Mardaneh, 2006). In this study clustering technique collects SLAs into clusters in a way that SLA regions within a cluster are similar to each other, but they are different from the regions in the other clusters. Clustering algorithms have been used in different business disciplines however, Ward s clustering has been widely used in personality and consumer behaviour (Greeno, Sommers and Kernan, 1973; Kernan and Bruce, 1972) as well as marketing and economics studies (Eliashberg, Lilien, and Kim, 1995; Blin and Cohen, 1997; Doyle and Saunders, 1985). The k-means method on the other hand has been used only in a few marketing studies (Calantone and Sawyer, 1978; Moriarty and Venkatesan, 1978; Schaninger, Lessig, and Panton, 1980) Clustering has been also used in economics studies. Smith (1965) suggests that industry employment or occupational data have been analysed to establish groups of towns with similar functional specialisation. The purpose is to divide towns into a series of classes in which functional similarity is maximised within, but minimised between, the groups. Beer and Maude (1995) examine changes in the economic 2

3 functions of Australia s regional cities to understand the changing role of regional cities within the national urban system. They use Ward s cluster analysis to examine change in their economic functions between Beer and Clower (2009) use Ward s cluster analysis method to examine the changing role of regional cities within the Australian urban system. Freestone et al. (2003) investigate urban employment and spatial trends in Australian urbanisation. They use factor analysis and the Ward s cluster analysis in their study. Sorensen and Weinand (1991) use factor analysis and Ward s clustering algorithm to study regional wellbeing in Australia and demonstrate complex spatial variations in socio-economic well-being in regional areas. Ho and Hung (2008) use a model that integrates analytic hierarchy process, Ward s cluster analysis and correspondence analysis to examine how a graduate institute develops effective marketing strategies. Wong and Saunders (1993) use factor analysis and Ward s clustering technique to investigate the related themes in marketing and to identify groups of similar companies. So far, Ward s clustering method has been used by researchers in business discipline especially related to regional development and the k-means method has been mostly used in information technology and data mining studies. The k-means algorithm has only been used in regional economics studies by this author in a previous paper (Mardaneh, 2010). The study strives to explore whether the k-means algorithm and its variations could provide a better tool for regional economics studies than the Ward s clustering algorithm. The framework of the study is based on SMEs, the two variables (industry structure and size of business) and the comparative experiment of the four algorithms. Finding the algorithm that is more efficient and does the clustering analysis of SMEs data better, could help with further understanding the industry structure and the size of business (SMEs), and could provide valuable information regarding regional economic growth. OBJECTIVE, DATA AND ANALYTICAL METHODOLOGY Using the regional Australian data this current study examines the influence of the industry structure and size of the businesses on economic growth of SLAs. To measure this, the study uses individual weekly income as a proxy for economic growth and assumes that SLAs with more people in $1000 and over income level must have a particular industry structure and business sizes. For this, the study clusters SLAs based on the industry structure and the three business sizes (micro, small, medium). Clustering is conducted three times (once for each size of business) using the k-means, global k-means, modified global k-means and the Ward s clustering algorithms. Results are compared to identify a clustering algorithm that is more suitable for clustering the SMEs data. The data for this study was collected and prepared from the Australian Bureau of Statistics (ABS, 2007) using data for Counts of Australian Businesses, including Entries and Exits, June 2003-June 2007 which includes Businesses by Industry division by SLA by Employment size ranges. This is provided as categories of data for businesses by industry division (See appendix A for the list of industries). The data exhibits sixteen industry types and the number of employees at each SLA based on size of businesses. The ABS classifies size of businesses as micro business (1-4 employees), small business (5-19 employees), medium business ( employees), 3

4 and large business (200 and over employees). The study adopts this classification, however, does not include large businesses (200 and over) as the relevant data were too sparse. For the same reason the Electricity, Gas and Water supply industry are excluded from the analysis as well. Since the study focuses on regional (nonmetropolitan) geographical areas and due to the fact that the industry structure and number of different business sizes in regional areas are very different to metropolitan areas, the study excludes metropolitan data, and by doing so avoids the skewness in analysis. After removing metropolitan SLAs and the outliers (extreme values in data set) 661 regional SLAs are used for the analysis. For analysis purposes the study considers two individual weekly income levels: $1000- $1999 and $2000 and over. Percentage of people under these two income levels per SLA is considered. In the next stage median of this percentage for each income level across all SLAs are calculated (11.8% and 1.9% for each income level respectively). SLAs above median within both income levels are considered as SLAs having higher level of economic growth and are labelled as category 1. The rest of SLAs are considered as SLAs with lower level of economic growth and labelled as category 2. SLAs in the data are made up of samples with 3 firm sizes (1-4, 5-19, ) and 15 industry types which complete the data set. To identify the industry type (s) and the business size (es) that have a higher or lower level of contribution to economic growth of a SLA (allocating a SLA to categories 1 or 2) clustering analysis is conducted. Results of the analysis are shown in Tables 1 to 3 for three different business sizes. For clustering the k-means, global k-means, modified global k-means and the Ward s clustering algorithms were applied on three SMEs data sets. Results of the analysis are shown in Tables 4 to 6. CLUSTERING ALGORITHMS This section provides a brief review of the clustering algorithms used in this study. Clustering algorithms can be used to analyse large data sets comprising a myriad of economic and social variables for large samples. They seek to group samples with similar characteristics and ensure maximum statistical separation from other contrasting clusters. This is a process of pattern recognition which simplifies understanding of these large data sets. In one classification, clustering algorithms are classified as hierarchical or iterative algorithms. Hierarchical methods start with a set of clusters and put each sample in data in an individual cluster. Clusters are then successively merged to form a hierarchy of clusters (Guha, et al., 2001). Iterative methods start by dividing observations into some predetermined number of clusters. Observations are then reassigned to clusters until some decision rule terminates the process (Punj and Stewart, 1983). Ward s clustering algorithm is hierarchical, while the k-means and its variations are iterative algorithms. The Ward s algorithm The Ward s algorithm is a hierarchical algorithm and seeks to group a set of n members which are called subsets or groups in relation to an objective function value. The method seeks to unite two of these n subsets to reduce the number of subsets to n-1, in a way that minimises the change in the objective function s value. The n-1 resulting subsets then are examined to determine if a third member should be grouped 4

5 with the first pair. If necessary this procedure can be continued until all n members of the original array are in one group (Ward, 1963). The k-means algorithm The k-means algorithm considers each sample (SLAs in this study) in a data as a point n in n-dimensional space ( R ) and chooses k centres (also called centroids) and assigns each point to the cluster nearest the centre. The centre is the average of all the points in the cluster, that is, its coordinates are the arithmetic mean for each dimension separately over all the points in the cluster. The k-means algorithm is known to be an efficient clustering algorithm, but it is sensitive to the choice of starting points (Bagirov, 2008). The global k-means algorithm The global k-means algorithm has been proposed to improve global search properties of k-means algorithms. The global k-means algorithm (Likas et al., 2003) computes clusters successively. At the first iteration of this algorithm the centroid of the set A is computed and in order to compute k-partition of the k-th iteration this algorithm uses centres of k-1 clusters from the previous iteration (Likas et al., 2003). The modified global k-means algorithm This algorithm computes clusters incrementally and to compute k-partition of a data set it uses k-1cluster centres from the previous iteration. An important step in this algorithm is the computation of a starting point for the k-th cluster centre. This starting point is computed by minimising so-called auxiliary cluster function. (Bagirov and Mardaneh, 2006) Empirical studies of the performance of clustering algorithms suggest that one of the iterative clustering methods (e.g. k-means clustering) is preferable to other hierarchical methods (e.g. Ward s clustering), (Punj and Stewart, 1983). The k-means appears to be more efficient in many studies (see, for example, Mezzich,1978; Milligan, 1980; Bayne et al., 1980) if a non-random starting point is specified. As a clustering algorithm includes more and more observations, its performance tends to deteriorate. This effect is probably the result of outliers beginning to come into the solution. The k-means appears to be more robust than any of the hierarchical methods with respect to the presence of outliers. The more efficient version of the k-means algorithm (modified global k-means) (as it is shown later in next section) could better cluster the SMEs data, and could help with further understanding the industry structure and the size of business. ANALYSIS AND RESULTS The analysis to produce the results outlined in the paper clustered SLAs based on the industry type and the three business sizes (1-4, 5-19, ). Industry, cluster category and the cluster centroids values are reported in Tables 1 to 3. In these tables the industry and size of businesses only have been reported if the difference between Cluster Centroids value in cluster category 1 and 2 is at least 0.1 and not lower. In Tables 1 to 3 industry type/size of businesses (variables) with higher cluster centroids value in cluster category 1 are considered as variables which are responsible for higher level of contribution to economic growth and industry type/size of 5

6 businesses with higher cluster centroids value in cluster category 2 are considered as variables which are responsible for lower level of contribution to economic growth. Table 1 Higher/lower level of industry contribution in economic growth (firm size 1-4) Cluster category Industry 1 2 Cluster Centroids Higher level of contribution to economic growth Construction Retail Trade Property and Business Services Personal and other Services Cultural and Recreational Services Lower level of contribution to economic growth Mining Communication Services Wholesale Accommodation, Cafes and Restaurants Health and Community Services Manufacturing Transport and Storage Agriculture, Forestry and Fishing Considering Tables 1 to 3, Construction, Retail Trade, and Personal and other Services industries indicate a higher level of contribution to economic growth in all three sizes. Agriculture, Forestry and Fishing, Wholesale and Communication Services industries show a lower level of contribution to economic growth in all three sizes. Table 2 Higher/lower level of industry contribution in economic growth (firm size 5-19) Cluster category Industry 1 2 Cluster Centroids Higher level of contribution to economic growth Retail Trade Construction Property and Business Services Transport and Storage Health and Community Services Personal and other Services Lower level of contribution to economic growth Communication Services Cultural and Recreational Services Wholesale Accommodation, Cafes and Restaurants Agriculture, Forestry and Fishing Property and Business Services industry shows a higher level of contribution to economic growth in both 1-4 and 5-19 size, however it shows a lower level of contribution to economic growth in size. Cultural and Recreational Services industry shows a higher level of contribution in both 1-4 and sizes however it shows a lower level of contribution for size Transport and Storage industry shows a higher level of contribution in both 5-19 and size, however it shows a lower level of contribution for 1-4 size. 6

7 Table 3 Higher/lower level of industry contribution in economic growth (firm size ) Cluster category Industry 1 2 Cluster Centroids Higher level of contribution to economic growth Retail Trade Accommodation, Cafes and Restaurants Construction Transport and Storage Cultural and Recreational Services Finance and Insurance Personal and other Services Lower level of contribution to economic growth Communication Services Mining Wholesale Manufacturing Property and Business Services Agriculture, Forestry and Fishing Health and Community Services industry shows a higher level of contribution in 5-19 size but it shows a lower level of contribution in 1-4 size. Finance and Insurance industry only shows a higher level of contribution in Accommodation, Cafes and Restaurants industry shows a higher level of contribution in size , however it shows a lower level of contribution in other two sizes. Both Mining and Manufacturing industries show a lower level of contribution in both 1-4 and sizes. For clustering analysis the study applied the k-means, global k-means, modified global k-means and the Ward s clustering algorithms on data sets of business sizes to identify the algorithm that is efficient in clustering SMEs data. For this the study calculated the objective function value and the CPU time spent for the calculation to cluster the data sets. Clustering was conducted for 2, 5, 10, 15, and 20 cluster numbers for a better comparison. Results for the analysis including the objective function values and CPU time spent for the calculation for four algorithms are shown in tables 4 to 6. The analysis of this study was conducted on a Intel Core 2 Duo, 2.99 GHz, PC. were used for the analysis. Tables 4 to 6 represent the number of clusters (N), values of the 5 objective function (ƒ 10 ) and CPU time spent for the analysis (t) for multi-start k- means (MSKM), global k-means (GKM), modified global k-means (MGKM), and Ward s clustering (WARD) clustering algorithms. Performance of the algorithms Data set 1: This data set includes micro businesses with 1-4 employees across 15 industry types. 7

8 Table 4 Data 1: Comparative values for algorithms (firm size 1-4) N MSKM GKM MGKM WARD 5 ƒ 10 t Results presented in Table 4 shows that MGKM algorithm outperforms both MSKM and GKM when the number of clusters N 10. With any number of clusters MGKM outperforms WARD and WARD gives the worst results compared to all the other algorithms. GKM requires less CPU time however its solutions are not better. MGKM requires more CPU time particularly when the number of clusters increases (N 10). Similarly CPU time for MSKM and GKM and increases as the number of clusters N increase. CPU time for WARD is almost constant for any cluster number N as it is a hierarchical algorithm and does not go through iterations like the other three algorithms. Data set 2: The data set includes micro medium businesses with 5-19 employees across 15 industry types. Table 5 Data 2: Comparative values for algorithms (firm size 5-19) N MSKM GKM MGKM WARD 5 ƒ 10 t Results presented in Table 5 shows that MGKM algorithm outperforms both MSKM and GKM when the number of clusters N 10. With any number of clusters MGKM outperforms WARD. MGKM requires more CPU time particularly when the number of clusters increases (N > 5). Similarly CPU time for MSKM and GKM increases as the number of clusters N increase. CPU time for WARD is almost constant for any cluster number N. Data set 3: The data set includes micro medium businesses with employees across 15 industry types. Table 6 Data 3: Comparative values for algorithms (firm size ) N MSKM GKM MGKM WARD 5 ƒ 10 t Table 6 shows some mixed performance results for MSKM, GKM and MGKM when the number of clusters N 5, however the variation in this mix is only very small. In some cases (e.g. N=2, 15) MSKM has performed slightly better than MGKM, 8

9 however the difference in performance is minimal and not significant. With any number of clusters MGKM outperforms WARD. MGKM requires more CPU time for all the cluster numbers N. CPU time for GKM and MGKM increases as the number of clusters N increase. CPU time for WARD is almost constant for any cluster number N. Finding a clustering algorithm that could help with more efficient cluster analysis of the SMEs data is important. A more efficient clustering algorithm helps with a more accurate and precise grouping of the data points (geographical areas in this study) based on their similarity. This in turn helps with finding the shared characteristics between members (data points) of a cluster. Understanding these characteristics provides us with a diagnostic of the factors that generate those characteristics. In this current study such an understanding helps with identifying the role that each combined industry and business size could play in economic growth or decline of the geographical areas. This will also show whether they have a higher or lower contribution to the economic growth. CONCLUSION The study presented the numerical results on 3 data sets. These results clearly show that the modified global k-means algorithm is more efficient for solving clustering problems in used data sets. It outperforms simple k-means, global k-means and Ward s clustering algorithms. However the proposed algorithm requires more computational efforts than the global k-means algorithm. Cluster analysis revealed clusters of industries associated with each industry structure and the size of business. Some industries appear to be dominant in more than one cluster. The resulting analysis uses SMEs data sets which suggest that the Modified global k-means clustering algorithm is most promising among all tested algorithms. Our findings provide a better way of clustering using a more efficient clustering algorithm and as a result providing a better understanding of the industry structure and the size of businesses in regional areas. This has some policy implications for future economic planning and focus on SMEs for regional areas. This will provide ways in identifying significant factors that need further investigation using qualitative methods to ascertain the importance of the clusters and their relationship to SMEs. REFERENCES Agarwal R. and Audretsch D. B. (2001) Does Entry Size Matter? The Impact of The Life Cycle and Technology on Firm Survival, The Journal of Industrial Economics 49, Armington C. and Acs Z. J. (2002) The determinants of regional variation in New Firm Formation, Regional Studies 36, Australian Bureau of Statistics. (2007) Counts of Australian Businesses, including Entries and Exits, Jun 2003 to Jun 2007, Cat. No Canberra: ABS Australian Government Department of Transport and Regional Services. (2003) Information Paper 49: Focus on Regions: No.1: Industry structure,

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13 Appendix 1: List of the industries Agriculture, Forestry and Fishing; Mining; Manufacturing; Electricity, Gas, and Water Supply; Construction; Wholesale Trade; Retail Trade; Accommodation Cafes and Restaurants; Industry type Transport, and Storage; Communication Services; Finance and Insurance; Property and Business Services; Education; Health and Community Services; Cultural and Recreational Services; Personal and other Services. Source: ABS (2007) 13