NEW VENTURES AND INDUSTRIAL CLUSTERING: A CASE STUDY OF TAIWAN

Ent

Meng-chun Liu, Tain-Jy Chen and Ming-Wen Hu*

The main purpose of this paper is to investigate the role of industrial clusters in the formation and development of new ventures as well as entrepreneurship. We take Taiwan as an example; the empirical works are conducted first by a look at the contingency table of incumbents and new firms’ regional distribution to see their geographic linkage. Then a Tobit regression was performed to decide the determinants of Taiwan’s manufacturing new ventures. Our empirical results show that industrial clustering and R&D intensity are important facilitators of new ventures. The policy implication from our results is that promotion of the spatial agglomeration of firms is a useful means of nurturing startups. This study further suggests that inter-firm linkages play a critical role within an industrial cluster, indicating that government support for the incubation of new businesses should be targeted towards the building of networks. In order to assist in matching local networks to the global production system, the aggressive promotion of spatial agglomeration is necessary in order to facilitate mutual learning and knowledge spillovers. Developing countries in particular may need to design policies aimed at leveraging the capabilities of multinationals in establishing both local and cross-border networks for learning.

Key Words: New Ventures, Clustering, Spatial Proximity, Inter-Firm Networks, repreneurship.

INTRODUCTION

New venture is the center of economic progress, especially for developing countries lacking natural endowments. Drawing on Hoang and Antoncic (2003) recent review of (social) network-based research in entrepreneurship, we synonymize entrepreneurial activity with new venture creation. There are many definitions for an entrepreneur, even more for the concept of entrepreneurship. Ranging from defining entrepreneurship simply as the creation of new enterprises (Low, 2001) to regarding entrepreneurship as a spirit to deploy the resources in pursuit of an opportunity, independent of the origin or ownership of such resources (Johannisson, 1998). While Bygrave and Hofer (1991: 14) defined the

* Dr Meng-chun Liu is the Deputy Director of the International Economy Division, CIER. Professor Tain-Jy Chen is the President of the Chung-Hua Institution for Economic Research (CIER). Dr Ming-Wen Hu is a Professor at the Tamkang University and corresponding author for this paper. Address for correspondence: Department of Industrial Economics, Tamkang University, Taipei 25137, Taiwan. Email: humw@mail.tku.edu.tw.

entrepreneurial process as “involving all the functions, activities, and action associated with the perceiving of opportunities and the creation of organisations to pursue them”. Hébert and Link (1989) define the entrepreneur as someone who specializes in taking responsibility for and making judgmental decisions that affect the location, the form, and the use of goods, resources, or institutions. In any event, entrepreneurs are dynamic and willing to launch new ventures and to take necessary risks. Because of asymmetric information and lack of knowledge, an economy is normally characterized by hidden profit opportunities that have not yet been uncovered. Entrepreneurs are capable of identifying these opportunities and engaging in proper activities to realise the gains. Although the personal characteristics of individuals are important constituents of entrepreneurship, some environmental factors have been identified as necessary for the incubation of new ventures, including political, economic and cultural factors. In recent years, some studies have noted that external networks can represent an alternative mode of organisation that enables entrepreneurial growth. It was argued that spatial proximity and long-term relations between firms are very important in improving market competitiveness within a region, in which important roles are played by social, reputation, cooperative, knowledge, innovative and technology networks (Lechner and Dowling, 2003). Apparently, the external networks embodied in the industrial clusters provide the firms in the region with a close supplier-chain relationship, common technologies and customers, and a labor market pooling. Industrial clusters can also be a practical means of linking neighborhood firms to the regional and even global economy. Industrial clusters may effectively reduce the barriers of market entry and further incubate new ventures. A few studies have indicated that an industrial cluster stimulates market entry into a region in which a number of firms have accumulated. This is because an industrial cluster is connected with suppliers of intermediate inputs, and is capable of creating knowledge spillover effects (Marshall, 1890). Industrial clustering effectively reduces the market entry barriers and drives a high rate of market entrance, which in turn, is associated with a high rate of innovation, which improves productivity. The main purpose of this paper is to investigate the role of industrial clusters in the formation and development of new ventures as well as entrepreneurship. Taiwan, once regarded as a newly industrialized economy, has long been known for its abundant entrepreneurship resources. The empirical evidence for this study is to be obtained from a case study of the manufacturing industry in Taiwan. The remainder of this paper is arranged as follows. Brief literature review surrounding industrial clusters and entrepreneurship is presented in Section 2. In Section 3, we introduce the measurement of industrial clusters, and suggest the need for a content analysis of industrial clusters in order to better understand the relationship between new ventures and industrial clustering. This section also presents a general picture, in the form of a case study of industrial clustering in Taiwanese manufacturing industries for the year 1992 and 1994. We then look at the patterns of market entry over the periods from 1992-95 and 1992-97. Section 4 employs a quantitative model to examine the role of industrial clustering in reducing market entry barriers and incubating new ventures. The empirical results are presented in Section 5. The findings of this study are summarised in the concluding section.

LITERATURE REVIEW There are many advantages for a new venture to lunch within an industrial cluster. Within an industrial cluster, collective economic actors who are closely connected to one another possess shared technological narratives, affluent mutual trust and a willingness to cooperate (Solvell and Zander 1998: 409). Organisations involved in research and development (R&D), such as universities and government-sponsored research institutes, function as an intermediary source of new technology (Lundvall 1992: 13-14). As noted by Schumpeter (1934), innovation underlines entrepreneurship, and all of the above factors are conducive to innovation. Industrial clustering also stimulates competition and emulation between individuals in their entrepreneurial achievements. Competition between classmates is often observed in the locations that are famous for industrial clustering, such as the Silicon Valley in the US and Hamamatsu in Japan, with achievements by alumni also being capable of inspiring younger graduates to embark on the course of entrepreneurial activities. Some factors underscore the inter-firm network embodied in regional clusters. First of all, drawing on Lechner and Dowling’s (2000) study of the biotechnology cluster in Munich, Germany, we note that regional culture influences corporate culture in terms of both cooperation and competition. Given the time and energy constraints, spatial proximity is critical to building trust between firms over time, which mainly stems from frequent interactions and face-to face contact. Thus, it is argued that a network of entrepreneurial firms is regionally embedded, and the successful development of the industrial cluster will determine the competitiveness of firms within the cluster. Lechner and Dowling (2003) also emphasize the importance of external relations to the growth of entrepreneurial firms by examining the case of the IT cluster in Munich, Germany. As noted in Lechner and Dowling (2003), inter-firm networks do not work in a virtual space in which spatial proximity is unimportant. Both spatial proximity and long-term relations determine the quality of the relationship and the competitive advantage of firms, which are derived from close relations. Generally, the network relationship of firms enables them to enjoy advantages in terms of social networks, cooperative networks, marketing networks and knowledge-innovation networks. The nature of collectiveness of the cluster provides the firms within the cluster with some advantages. As in the earlier studies, the emergence of industrial clusters is recognised here as shaping and driving the competitiveness of firms within clusters at both national and global levels. This may be attributable to agglomeration externalities, three types of which are classified by Glaeser et al., (1992). The first type is Marshall-Arrow-Romer externality which highlights industrial specialisation within a region. Each firm in the cluster enjoys the benefit from saving investment costs by specialising within a narrow area of the value-added chain. Similar firms within the clusters find ways to differentiate themselves by locating unique market niches that have not been filled by other firms. Small firms can join together to fill a large contract that none of them could undertake alone, and as such, these small businesses can be more competitive and successful in the long run by becoming a part of a dynamic cluster which fosters competition, collaboration and innovation amongst the participants. The second type is Porter externality, which arises from regional specialisation and the

differentiation of products. This effect stems mainly from local rivalry between firms, which further fosters the rapid diffusion of knowledge and adoption of new ideas. In addition, the development of industrial clusters may lead to simultaneous competition and collaboration in offering innovative products and services, and to further establish a sustainable competitive advantage in the dimensions of technology, workforce, production methods, delivery time, quality, and resource procurement. The third type of externality is diversity externality, which is stimulated by the interchange of ideas between various types of activities within the region. As noted in Jovanovic and Rob (1989), new ideas can be derived from heterogeneous knowledge across firms and people. Industrial agglomeration externalities enable firms within a cluster to enjoy higher growth and competitiveness, and these three types of externality have pointed to some important dimensions in the competitiveness of industrial clusters. This is because firms within a region can share a common dependence on research, innovation, knowledge and regional industry-specific assets. An industrial cluster also reduces the risks for business ventures. Labour pooling is an important feature of industrial clustering since it cuts the costs of hiring and discharging workers. Pooling of skilled workers also makes the mobilisation of managerial resources much easier. Industrial clusters are often supported by a venture capital industry which finances the start-ups and helps innovators to realise their gains by organising proper production processes. Moreover, industrial clustering provides industry-specific information, particularly that which is pertinent to technology, and which thus prevents an entrepreneur from making huge mistakes. An industrial cluster also represents a learning region which spawns innovation. Chell and Baines (2000) indicated that ‘weak tie’ networking is part of fundamental entrepreneurial behavior, since the interchange of entrepreneurial contact, knowledge and confidence are necessary in the pursuit of opportunities through the mobilization of resources, which include raising capital, labour and effort, for ventures with uncertainties. Hoang and Antoncic (2003: 178) summarize the key findings around networks as determinants of new ventures formation. They are fund-raising benefits for new ventures; interorganizational linkages within emerging industry signal that spur new venture formations; and early legitimating activities affected persistence of entrepreneurial activity. In short, new ventures may be regarded as the inherent underpinning of network activity. In the following section, in order to examine this assumption, we measure the degree of spatial agglomeration for each manufacturing industry, along with the market entry rate. NEW VENTURES AND INDUSTRIAL CLUSTERING FOR TAIWANESE MANUFACTURING INDUSTRIES Measurement of New Ventures for Taiwanese Manufacturing Industries According to Wennekers and Thurik (1999: 34), new entry is the act of launching a new venture. Drawing also from Geroski (1995: 431), who presents a ‘stylized result’ that high rates of entry are often associated with high rates of innovation and increases in efficiency in his brief survey

of recent empirical work on entry, we regard start-ups as new ventures. Table 1 summarizes the patterns of market entry in each Taiwanese manufacturing sector over two periods 1992-95 and 1992-97, in terms firm size and the industrial distribution of new entrants. Table 1 Pattern of New Ventures in Taiwanese Manufacturing Unit: %

1992-95 1994-97

Industrial Sectors Large Firms Small Firms Large Firms Small Firms

Food Manufacturing 3.202 96.798 3.285 96.715 Textile mill products 3.544 96.456 3.887 96.113 Wearing apparel & accessories 3.327 96.673 4.403 95.597 Leather, fur products 4.294 95.706 4.615 95.385 Wood & bamboo products 0.761 99.239 0.792 99.208 Furniture & fixtures 1.200 98.800 0.875 99.125 Pulp, paper & paper products 0.704 99.296 0.847 99.153 Printing processing 0.734 99.266 1.131 98.869 Chemical materials 3.670 96.330 4.428 95.572 Chemical products 2.094 97.906 1.875 98.125 Petroleum & coal products 9.524 90.476 7.692 92.308 Rubber products 1.987 98.013 2.303 97.697 Plastic products 1.198 98.802 0.976 99.024 Non-metallic mineral products 3.365 96.635 3.093 96.907 Basic metal 2.384 97.616 2.191 97.809 Fabricated metal products 0.537 99.463 0.680 99.320 Machinery & equipments 0.932 99.068 0.907 99.093 Electrical &electronic machinery 4.201 95.799 3.426 96.574 Transport equipments 2.903 97.097 3.135 96.865 Precision instruments 2.770 97.230 2.796 97.204 Miscellaneous industry 1.487 98.513 1.503 98.497 Total 1.952 98.048 1.956 98.044 Source: Calculated by this study Based on early studies, two types of innovation stand out from a microeconomic perspective, ‘entrepreneurial innovation’ and ‘managed innovation’. The emergence of entrepreneurial innovation relies mainly on the economic opportunities brought about by new technologies and scientific development. In this case, the entry of small, dynamic and rapidly growing firms is responsible for such innovation. In contrast to the case of entrepreneurial innovation, managed innovation is made by existing firms in the market and, as argued by Schumpeter (1942), this type of innovation tends to be dominated by large firms with a monopoly in the market. In some sectors, such as petroleum & coal products and leather & fur products, some large startup firms are not clearly associated with industrial sectors with capital-intensity. The large shares of entry firms in three industry sectors, fabricated metal products, machinery & equipments, and electrical & electronic machinery may demonstrate that these booming industrial sectors attract large share of new entry firms and absorb larger share of employees. There is also clear evidence that the startup firms tend to be SMEs in a significant share. Parts of new-entry firms make R&D investments in their correspondingly established years. Such new-entry firms,

especially associated with small in size, may be regarded as entrepreneurial innovation firms. As is apparent from Table 1, some sectors deeply attract the entries of entrepreneurial firms. These industrial sectors not only include high-tech industries, such as electrical & electronic machinery, chemical materials, and chemical products, but also include traditional and heavy industries, such as leather & fur products, and rubber products, indicating that the entry of entrepreneurial firms cannot be simply attributed to the industrial characteristics in R&D intensity. Measurement of Industrial Clusters for Taiwanese Manufacturing Industries This subsection set outs to measure the level of spatial concentration of Taiwanese manufacturing industries at town level for 1992 and 1994. The geographical agglomeration index comprises of the intra-industry distribution of employment by plants, as the geographical Herfindahl index. The geographical agglomeration index is defined as follows:

∑= 2xGH where GH denotes the geographical Herfindahl index, x is locational share of employment for each industry. In the case of perfect agglomeration, GH approaches 1. Based on the geographical Herfindahl index, we calculate the level of GH at town level and the 2-digit Chinese Standard of Industrial Classifications (CSIC) codes for 21 manufacturing industries. In contact to Table 1, Table 2 gives data on the industrial geographical agglomeration, number of incumbent firms for 1992 and 1994, and the entry rates of large firms and SMEs for 1992-95 and 1994-1997. In regard to industrial agglomeration, petroleum & coal products industry reaches an extraordinary high level in 1992. Against the background that Taiwan’s energy consumption mainly relies on the imports, the firms of such industry generally gather together in some harbor regions. Next to petroleum & coal products industry, non-metallic mineral products, basic metal, transport equipments, and chemical material are significantly higher than other sectors in terms of geographical agglomeration indicator. Table 2 shows that the entry-rates across industries for 1992-95 and 1994-1997 range from 15.7% and 19.1% (wood & bamboo products) to 56.4% and 53% (electrical &electronic machinery), and the average of entry rates achieves at 37.4% and 35.1%. Apart from petroleum & coal products industry, wearing apparel & accessories, and electrical &electronic machinery drive a comparatively high entry rate of large firms. Table 2 Industrial Spatial Agglomeration and Entry Rates in Taiwanese Manufacturing Sector

Industrial Sectors GH* Indicators （1992）

1992-95 Total Firm

GH* Indicators （1994）

1994-97 Total Firm

Food Manufacturing 0.0107 20.7 0.0102 20.0 Textile mill products 0.0190 31.4 0.0190 30.3 Wearing apparel & accessories 0.0184 47.0 0.0186 39.3 Leather, fur products 0.0254 32.9 0.0326 31.5 Wood & bamboo products 0.0123 15.7 0.0132 19.1 Furniture & fixtures 0.0145 37.5 0.0140 27.2 Pulp, paper & paper products 0.0133 30.9 0.0130 28.8 Printing processing 0.0389 48.2 0.0397 47.6

Chemical materials 0.0335 32.6 0.0358 26.5 Chemical products 0.0205 25.7 0.0212 21.0 Petroleum & coal products 0.3119 43.8 0.2004 21.0 Rubber products 0.0256 26.9 0.0244 28.7 Plastic products 0.0125 32.8 0.0132 29.5 Non-metallic mineral products 0.0298 24.4 0.0276 24.1 Basic metal 0.0486 34.6 0.0448 30.1 Fabricated metal products 0.0192 47.8 0.0179 48.8 Machinery & equipments 0.0181 41.4 0.0180 36.3 Electrical &electronic machinery 0.0242 56.4 0.0246 53.0 Transport equipments 0.0204 38.6 0.0205 32.3 Precision instruments 0.0259 37.1 0.0255 29.0 Miscellaneous industry 0.0128 33.3 0.0184 27.8 Total number of new firms - 25,822 - 24,277 Average Entry Rates (%) - 37.4 - 35.1 Note: * GH indicator denotes geographical Herfindahl index as the way to measure spatial agglomeration for each

dustrial sector. inSource: calculated by this study.

Spatial Distribution of New ventures and Incumbents The above sections underline the role of spatial agglomeration in new firm formation rate. In addition, variances in market entry rates may be explained by industrial concentration, market growth/market room, scale disadvantage, and R&D investments. However, the above research does not yet make sure whether new firm start-ups are significantly correlation with their incumbents in terms of the geographical deployment. This section aims to provide more specific evidence to underline the industrial agglomeration effect in driving new firm formation. We perform a comparative approach to two types of industries in terms of locational choices of new firm start-ups. According to the degree of correlation of new firm start-ups with their incumbents in terms of spatial distribution, we intend to identify two types of industries. The first type of industries denotes such industrial sector that the new firms intend to locate their production bases close to their incumbent counterparts. The others are regarded as the second type of industries. Based on the networking attributions of industrial clusters, if other condition is given, new firms should be attracted to locate themselves into the clusters in order to share a common dependence on research, innovation, knowledge and regional industry-specific assets, demonstrating the evolution process of industrial clusters. The comparison of two types of industrial clusters in their entry rates may underpin the argument that the spatial agglomeration effect of firms can effectively drive new firm formation. Two-stage approach is utilized to examine the hypothesis. First of all, test for independence and Pearson correlation index are performed to identify some specific industrial sectors of Taiwan, in which new firms are similar to their incumbents in spatial distributions cross 336 towns. The χ2 test procedure is used to test the hypothesis of independence of new firms and their incumbents in spatial distributions. The observed distributions are presented in Table 3, which is known as a contingency table.

Table 3 2 x 336 Contingency Table of Probabilities for an industrial sector Region 1 …Region j… Region 336 Total New Firms π1.1 …π1j… π1.336 π1. Incumbents π2.1 …π2j… π2.336 π2. Total π.1 …π.j… π.336 1 In the contingency table test, χ2 is employed in a goodness-of-fit test for the 93 manufacturing industries. The null hypothesis is that new firms are independent from their incumbents in terms of spatial distribution. Let πij denote the underlying bivariate probability distribution of firm group i in region j. The firm group is new firms if i=1, and firm group the incumbents if i=2. Let πi and πj similarly denote the marginal probability distribution of firm group i and region j. It should be noted that the measures for spatial distributions of incumbents are based on the numbers of employees. The null hypothesis of statistical independence may be stated precisely:

Ho: πij=πi . πj The Chi-square statistics for examining the null hypothesis is

χ2=∑∑−2 336

1

2)(

i ji

jiij nππ

πππ,

where n is the sum of the number new firms and the incumbents. Furthermore, the Pearson correlation indexes are utilized to confirm the positive correlation between new firms and incumbents in their spatial distributions. According to both Chi-square statistics and the Pearson correlation indexes, we can choose some industries as the first group, of which Chi-square statistics are significant at 5% and Pearson correlation indexes are positive. In the second stage, we examine if the first group of industrial sectors are higher than the second group in the market entry rates for the two periods. Table 4 shows that in the first period of 1992-95, the first group and the second group are consist of 24 and 69 industrial sectors, respectively. Their average market entry rates for four years are 0.57 and 0.40, respectively. The first group is significantly higher than second group at the significance of 10%. Over the second period of 1995-97, number of industrial sectors in first group increases up to 29, and the number of second group decrease to 64. Their average of market entry rates for three years reaches 0.40 and 0.25. The average market entry of first group is significantly higher than that of second group at the significance of 5%. The empirical evidence from Taiwan’s manufacturing sector indicates that spatial agglomeration of incumbents, new firm formation, and locational choice are of high interdependence.

Table 4 Average Entry Rates for 1992-95 and 1995-97 Period Group Number of Average Entry Rate St.d t-valuea

Industrial Sectors 1 24 0.57 0.61 1992-95 2 69 0.40 0.25 1.95

1 29 0.40 0.49 1995-97 2 64 0.25 0.18 2.23

Source: calculated by authors. Note: The t-values are corresponding to the null hypothesis that the average entry rate of group 1 sectors is larger

equal to that of group 2 sectors. or

In brief, the empirical evidence from Taiwan’s manufacturing industrial sectors generally supports the above arguments that the significant role played by the industrial agglomeration reduces entry barriers. Industrial clusters facilitate the information diffusion of markets and technologies and also benefit firms in the labor market pooling. These advantages from industrial clusters help new entry firms in overcoming entry barriers and also attract firm to access the industrial clusters. The determinants of new-firm entry rate for each industrial sector cannot be totally explored by Table 4. The next section applies a quantitative approach to this issue. DETERMINANTS OF MARKET ENTRY FOR TAIWANESE MANUFACTURING INDUSTRIES

In this section, we utilise the data bank of manufacturing plant Census Survey at 3 digital CSIC codes for 93 industries to empirically examine the factors affecting firms’ market entry decisions over two periods of 1992-95 and 1992-97. Entryr, the dependent variable in this empirical model, is the entry ratio of number of startup firms to the number of incumbents for each manufacturing sector. Considering that the dependent variable is a censored number in nature, we employ a Tobit regression in this study. The empirical models are set as follows:

),,,,,4,,(1 INDSMARRMESIEXIRDCRKLGHFEntryr = . (1) ),,,,,4,,(2 INDSMAGRMESIEXIRDCRKLGHFEntryr = . (2)

The explanatory variables used in the above model are: the geographical agglomeration index (GH), capital to labors ratio (KL), Industrial Concentration (CR4), industrial R&D intensity (IRD), industrial export intensity (IEX), scale disadvantage ratio (SDR), market room (MARR), market growth (MAGR), and industrial scale (INDS). The explanatory variables used in the above model and the hypothetical impacts on Entryr are explained as follows: Geographical agglomeration index (GH) GH, the geographical Herfindahl index, is used to measure the extent of industrial agglomeration for each industrial sector. Firms within industrial clusters share a common dependence on innovation, knowledge and regionally industrial assets. The spatial agglomeration also facilitates the interchange of entrepreneurial contact, knowledge and confidence, which are necessary in pursuing market and innovative opportunities. And due to the networking relationship embodied on industrial clusters built on their common background can generate reputation networks, which automatically lead to trust-based business relationship. Based on the above research on entrepreneurship, networking and industrial clustering, this paper utilizes the level of industrial clustering simply as a proxy for network strength. We assume that an industry associated with higher spatial agglomeration will have stronger network ties, leading to a higher rate of market entry. Thus, the geographical agglomeration an

industry is, the higher market entry rate it has. Therefore, we hypothesize that the GH variable has a positive impact on the Entry variable. Capital Intensity (KL) Capital Intensity (KL) is the ratio of book value of fixed capital stock to labor compensation in an industry. Most literature generally regard capital-labor ratio as a type of market entry barriers (Dunne and Robers, 1991; Rosenbaum and Lamort, 1992). This is because capital goods, such as machinery and factories are characterized by their natural indivisibility and inflexibility. Morrison (1997) argued that capital actually has a quasi-fixity nature, and that where a firm’s production technology is more capital-intensive, it may suffer from higher sunk costs in natures. We expect that industry with higher capital labor ratio will place greater entry barriers and therefore hypothesize that the KL variable will have a negative impact on Entry variable. Industrial Concentration (CR4) Concentration is defined as the share of the four largest firms measured for 1992 as a proxy for the degree of industrial concentration. Where an industry has a higher concentration, this suggests that it is more likely to be dominated by a few firms. In contrast, firms within the industry with a lower CR4 value have much less monopolistic power and are therefore more likely to engage in keen competition. Chen, Chuang and Yang (2002) used between 1991-1996 Taiwan’s Consensus Databank to examine the entry and exit of manufacturing firms. They suggest that the increase in industrial concentration ratio over the period lead to the decline in market entry. However, based on Jeong and Masson’s (1990) research on Korea’s market entry, high concentration may lead to expectation of either cooperation or of retaliation for new entry firms, indicating that the effect of CR4 is uncertain on market entry. Based on the above studies, the coefficient of CR4 is not sure in the entry equation. Industrial R&D intensity (IRD) IRD is designed to measure the extent of opportunity innovation. R&D intensity is used as a proxy for the knowledge stock of an industry. An industry with high R&D intensity may have a large room for new entry firms to differentiate their products. There is also situation where technological break through uplifting scale limits of production hence enhances entry. Such is so called entry-facilitating effect (Mukhopadhyay, 1985). Early studies, such as Orr (1974), provided some empirical evidence to suggest that a high intensity of R&D symbolizes the entry barrier in an industry. By contrast, Geoski (1995) observes a positive correlation between the rate of innovation and the rate of entry to an industry. As pointed out by Marsili (2002), whether innovation in technologies of high or increasing opportunities is associated with entrepreneurial activities is dependent on the nature of knowledge. Without any solid stock of technologies, Taiwan’s industries heavily rely on the transferred technological from overseas, and the innovation activities also focus on product engineering and continuous processes, characterized by low technological entry barriers in knowledge and scale. Accordingly, we expect that industries with higher R&D intensity will have lower entry barriers and therefore hypothesize that the IRD variable will have a positive impact on Entryr variables.

Industrial Export Intensity (IEX) This study also examine whether industries with high export orientation tend to drive the new-firm entry. The empirical result seems to counter the presumption in based on some early studies. Utilizing Taiwanese firm data for 1986 and the frontier production function, Aw and Batra (1998) suggest that the productivity-export correlation differs significantly across firms depending on their investment in technology. In addition, Aw and Hwang (1995) suggested the existence of a significant linkage between firm-level productivity and exporting activities, and developed an empirical model to shed light to the value-added differences between export-oriented and domestic market-oriented firms in the electronics industry in Taiwan, confirming the importance of learning mechanism from export markets. Both studies may suggest that export markets enable to attract the new firm entry because of strong learning effects in improving productivity and technological diffusion. However, in comparison with the learning effect of export markets, the keen competitive pressure may yields to market deterrent effects, especially for small new-firms. Thus, the effect of export markets on market entrants should not be clear. Minimum Effective Scale (MES) MES measures the ratio of the average size of largest 50% firms to average firm size in terms of number of employees. In certain industries, these scale economies are a barrier to entry since it is unlikely that multiple businesses can all attain the minimum efficient scale to be commercially viable. In most of the manufacturing sector, scale economies are unlikely to be as pronounced relative to market demand and thus, absent policy-induced constraints, such economic barriers to entry are likely to relatively modest, and some in cases, relatively low. This variable is designed to measure the entry barrier resulted from their production scales. MES, like capital intensity, included in the entry equation proxies for entry barriers. We presume that the coefficient of MES is negative in the entry equation. Market Growth (MAGR) and Market Room (MARR) MAGR is measured as the change in value of shipments over the period of 1992-95 (1994-97) divided by shipments in the initial year, 1992 (1994). As in Rosenbaum and Lamort (1992), an industry experiencing growth usually benefit for potential firms to make market entry. Moreover, MARR is defined as the ratio of MAGR/MES, that the increase in the number of minimum efficient scaled-sized plants, which could occur due to growth in the market. Such variable is measured as the value of market growth over minimum efficient scale at the initial years 1992 and 1994. Both variables can describe the inducement conditions of an industry. We expect that both variables MARR are positive on the market entry ratios. Industrial Scale (INDS) INDS refers to the scale of an industry in terms of number of employees. A larger scale industry may provide new entry firms with more market share for their survivals. In addition, this paper measures for an industry’s scale in number of employees. We may expect that an industry with comparatively high value of INDS can enjoy the advantage

in labor pooling and also benefit the new entry firms in saving the cost of looking for the suitable labor. Accordingly, we presume that the coefficient of INDS is positive in the entry equation. This variable takes natural logarithm. We summarize our hypothesis of the impact of each explanatory variable, and provide the corresponding summary statistics of these variables in Table 5. Table 5 Variable definitions

Definition Expected sign1992-95 Mean (Std. Dev.)

1994-97 Mean (Std. Dev.)

GH Herfindhal index for geographicalagglomeration + 0.062

(0.070) 0.695 (0.097)

KL Capital-labor ratio, measured by the fixedcapital stock over total labor compensation _ 4.978

(2.935) 4.831 (3.001)

CR4 Industrial concentration ? 0.304 (0.238)

0.305 (0.233)

IRD Industrial R&D intensity + 0.038 (0.029)

IEX Industrial export intensity ? 2.340 (1.752)

MES Minimum efficient scale _ 1.729 (0.111)

1.953 (0.250)

MARR Market room + 0.302 (0.734)

0.071 (0.138)

MAGR Market growth + 0.016 (0.078)

0.044 (0.084)

INDS Industrial scale in terms of employee (in terms of natural logarithm) + 9.243

(1.359) 9.352 (1.299)

Source: calculated by this study.

EMPIRICAL RESULTS Entry equation is estimated using structural variables, including inducements entry barriers and geographical agglomeration, as explanatory variables. Tobit estimates for Equation (1) and (2) are generated for the 1992-95 and 1992-97 samples, respectively. The empirical results are presented in Table 6, showing that in the case, LR χ2 reaches significance at the 5 per cent level and the explanatory variables of both models have significantly high explanatory power. There are 93 observations in the sample. The empirical results may demonstrate two kinds of perspectives related to market entry: namely entry barriers and inducement conditions. However, as is emphasized in Caves (1998), there are also chances that entry ‘barriers’ may become entry gateways for lucky entrants. − Entry Deterrent or facilitating factors

The hypothesis of entry barriers is examined in this study. Table 6 presents that the coefficient on GH (industrial geographical agglomeration) is statistically significant positive at the 5% level in the entry equation. That is, an industry characterized by spatial agglomeration has comparatively high market entry ratio. The evidence may confirm the presumption that industrial spatial agglomeration will reduce the entry barriers because the formation of industrial cluster can drive the formation of production

networking and then enhances the diffusion of local knowledge. In other word, the development of industrial clusters not only benefit local incumbent firms in production

specialization, knowledge sharing, and labor market pooling, which also benefit the potential firm entry in reducing entry barriers.

Table 6 Empirical Results- the Tobit Estimations 1992-95 1994-97

Model (1) Model (2) Model (1) Model (2) Constant 2.094**

(5.59) 2.107**

(5.63) 0.358**

(2.61) 0.371**

(2.65) GH 1.093**

(2.51) 1.100**

(2.53) 0.610**

(3.10) 0.589**

(3.00) KL -0.036**

(-3.48) -0.035**

(-3.49) -0.011*

(-1.78) -0.011*

(-1.77) CR4 0.388**

(2.77) 0.397**

(2.84) 0.165 (1.38)

0.167 (1.40)

IRD 3.260** (4.02)

3.233** (3.98)

1.250** (2.26)

1.205** (2.18)

IEX 0.006 (0.37)

0.006 (0.38)

0.003 (0.27)

0.002 (0.16)

MES -1.415** (-5.48)

-1.463** (-5.55)

-0.175* (-1.78)

-0.178* (-1.82)

MARR 0.079** (2.22) -- 0.205*

(1.83) --

MAGR -- 0.146** (2.16) -- 0.374**

(2.05) INDS 0.063**

(2.63) 0.064** (2.66)

0.018 (1.05)

0.017 (1.02)

σ 0.220 0.220 0.140 0.140

Observation 93 93 93 93

LR χ2 61.05 60.81 23.63 24.45

Note: *: significant levels at 10% and ** 5%.

Table 6 also shows that the capital intensity is included in the equation as a

proxy for sunk costs. The variable is statistically significant. The negative coefficient of KL indicates that industries with more capital intensity have the lower ratio of firm entry. And Table 6 also shows that the industrial concentration (CR4) has certain positive influence on the market entry. Similar to Jeong and Masson (1990) on the Korean case, and Masson and Shaanan (1986) and Rosenbaum and Lamort (1992) on the case of the U.S., the empirical result for Taiwan’s case supports the argument of signals of expected “cartel stability” or “accommodation,” but seems not support the argument of strategic entry deterrence of high concentration industries. More than that, this empirical evidence may also underline Taiwan’s policy reform in 1990s. Since 1990s, the policy reforms have been heading for internationalization and liberalization by reducing institutional barriers of entry to concentrated markets, which used to be monopolized by state-own enterprises. Some early studies, such as Bunch and Smiley (1992), regard R&D intensity as market deterrents. This is because the access to the industry with R&D intensity has comparatively high capital and technological requirements, and the incumbents in such industries have high likelihood in use the strategy of market deter to their potential

competitors. However, in the empirical result of this paper, the coefficient of IRD, measured by the ratio of R&D employee number to total employees is statistically positive upon market entry ratio. The empirical results is similar to some early studies, including Chen, Chung & Yang (2002), which stress that industries with higher R&D intensity comparatively have a broad room for driving new-firm entry. As in the case of other NIEs, Taiwan lacks a solid technological knowledge base, but we can expect that entrepreneurs in Taiwan will generally demonstrate an ability to adapt. The task for entrepreneurs in identifying market opportunities can be classified into ‘ordinary’ and ‘extraordinary’ discoveries. Ordinary discoveries involve making the discovery whilst keeping the system largely unchanged. This kind of entrepreneurial activity merely exploits market opportunities and can be referred to as ‘adaptive entrepreneurship’. In contrast to adaptive entrepreneurship, extraordinary discoveries involve the uncovering of hidden opportunities in the market by entrepreneurs, leading to a change in the system. Obviously, such ‘extraordinary entrepreneurship’ comes close to the Schumpeterian idea of entrepreneurship. Table 6 demonstrates that the coefficient of IEX in the entry equation is negative. This study lacks statistically significant evidence to explore whether an industry associated with high export orientation tends to yield market deterrent effect. This empirical result is also similar to Chen, et. al., (2002), in which the coefficient of industrial export propensity is negative at marginal significance. The coefficient of MES in the entry equation is statistically negative at 5%, indicating that MES is important in determining firm’s entry rate. The minimum efficient scale varies substantially across industries. Entrepreneurs need to start a new firm usually at a suboptimal scale (Audretsch, 1999). The starting scale is usually linkage to MES in order to avoid the scale disadvantage. − Inducement Conditions

Market growth (MARG) is defined as the change in value of shipments over the period divided by shipments in the initial year if a market experience positive growth, there should be incentives for the potential firms to entry. In Table 6, the coefficient on market growth is significantly positive at 1% in the entry equation, indicating that the empirical result is consistent with the presumption. Market room (MARR) is the percentage increase in the number of minimum efficient scale-sized firms that could take place due to growth in the market. The increase in market growth referring the future potential has to be tempered with the size of firm to fit in such markets. In Table 6, the coefficient on MARR is significantly positive in the entry equation, confirming the presumption. Finally, one of structural characteristics of industries, INDS, is measured by the industrial scale in terms of numbers of employees. As the presumption of this paper, the coefficient of INDS is positive on market entry rate. Taking this in the context of networking, we may argue that the influence of an industrial scale upon market entrants mainly depend their networking relationship rather than the absolute scale. In this paper, GH, the industrial spatial agglomerations, refers to the networking relationship of firms. As in Chell and Baines (2000), the advantage of firm owner-manager’s networking is to draw upon information, advice and assistance from a large, diverse pool. Accordingly, given the extent of networking relationship, an industry with more industrial scale leads to have higher market entry.

In summary, the empirical evidence from Taiwan’s manufacturing industrial sectors is generally in line with other early studies for identifying entry barriers and inducement conditions. The important contribution of this study is to highlight the significant role played by the industrial agglomeration in reducing entry barriers. An industrial cluster cannot be simply regarded as the spatial agglomeration of firms, but the formation of inter-firm business networking. Industrial clusters facilitate the information diffusion of markets and technologies and also benefit firms in the labor market pooling. These advantages from industrial clusters also help new entry firms in overcoming entry barriers. There are some important policy implications related to entrepreneurship behind this work, especially for new industrializing countries in the age of the knowledge-based economy.

CONCLUSIONS The purpose of this paper is twofold. First of all, drawing on Chell and Baines (2000) which stress on networking relationship on fundamental entrepreneurial behavior, and Lechner and Dowling (2000; 2003) which underlines the spatial proximity of industrial clusters as firms’ network relationship in essence, this study presumes that firms’ spatial proximity should be positive on new ventures. We use the Taiwan’s manufacturing industry database to examine the determinants of industrial market entry over two periods of 1992-95 and 1992-97. The determinants are grouped into sets, namely entry barriers and industrial inducement conditions. In this study, the entry barriers characterized by capital intensity, R&D intensity, export propensity, scale disadvantage, industrial concentration and spatial agglomeration, and the industrial inducement condition includes market growth, market room, and industrial scale. Generally, the empirical results are in line with the early studies. The more interesting result is that the role played by an industrial cluster can effectively reduce entry barrier, indicating that industrial clusters not only enhance the firms’ productivities, but also promote new ventures. More preciously, clusters may induce entry in two forms, which are new start-ups by entrepreneurs and diversification by existing firms. There is no telling which form is more important in driving the industry dynamics, but the factors that give rise to each form of entry may be different. Secondly, it is welled accepted that industrial clusters cannot simply be regarded as firms’ spatial agglomeration, but the inter-firm linkage relationship should be emphasized. A successful industrial cluster is able to facilitate the diffusion of technological and market information across clustering firms, effectively to reduce the transactional costs, and further to drive new ventures. From the empirical results, an industry R&D intensity have high market entry rate in Taiwan, demonstrating that even lacking a solid technological knowledge base, entrepreneurial activity in Taiwan may gradually shift from merely exploit of market opportunities, adaptive entrepreneurship, to extraordinary entrepreneurship referring to the discovery of hidden opportunities in the market. Innovation is an important means of competition leading to constructive destruction; thus, when innovation ceases, competition degenerates into a price war. The main feature of modern industrial clusters may therefore be a shift from

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