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Abstract
Jo Y. and Lee C.-Y. Technological capability, agglomeration economies and firm location choice, Regional Studies. This paper argues that a firm's ability to produce and absorb technological knowledge, or technological capability, influences its choice of location among regions characterized by different types of agglomeration. This paper found that geographically bounded knowledge externalities, one of the forces that attract firms into a particular location, have a differential effect on firm location choice across firms depending on the level of their technological capability: for firms with low technological capability knowledge externalities from co-located competitors, or competitive specialization, have a stronger positive effect on their location choice, while for firms with high technological capability knowledge externalities from co-located firms from related and complementary industries, or complementary specialization, more strongly influence their location choice. Furthermore, the differential effect of agglomeration economies between low- and high-capability firms is more pronounced in industries with strong non-legal appropriability, implying that firms can use their location choice as a strategic tool for dealing with the spillovers of tacit knowledge.
Jo Y. and Lee C.-Y. 技术能力、聚集经济与厂商区位选择，区域研究。本文主张，一个厂商生产与吸收技术知识的能力，或称为技术能力，将影响其在不同聚集型态区域之间的区位选择。本研究发现，在地理上受限的知识外部性，是吸引厂商进驻特定区域的趋力之一，而此外部性则随着厂商技术能力层级的差异，对不同厂商的区位选择产生殊异的影响：对于低技术能力的厂商而言，来自同一区位竞争者的知识外部性，或称为竞争专殊化，将对其区位选择产生较大的正向影响；对于高技术能力的厂商而言，同一区位相关或互补产业的知识外部性，或称为互补专殊化，则对其区位选择有着较为显著的影响。此外，聚集经济对高技术与低技术厂商的不同影响，对具有强烈非法定专属性的产业而言更为显著，表示厂商得以运用区位选择做为对付隐性知识外溢的策略性工具。
Jo Y. et Lee C.-Y. La capacité technologique, les économies d'agglomération et le choix d'emplacement des entreprises, Regional Studies. Cet article affirme que l'aptitude d'une entreprise à produire et à assimiler la connaissance technologique, ou la capacité technologique, influe sur son choix d'emplacement parmi les régions qui se caractérisent par une typologie différente d'agglomérations. Il s'est avéré que l'externalité de connaissance bien délimitée géographiqement, l'une des forces maîtresses qui attirent les entreprises vers un emplacement particulier, a un effet différentiel sur le choix d'emplacement des entreprises en fonction du niveau de leur capacité technologique: pour les entreprises dont la capacité technologique est faible, l'externalité de connaissance des concurrents co-localisés, ou la spécialisation compétitive, a un effet plus positif sur leur choix d'emplacement, tandis que, pour les entreprises dont la capacité technologique est forte, l'externalité de connaissance des entreprises co-localisées des industries annexes et complémentaires, ou la spécialisation complémentaire, influe plus fortement sur leur choix d'emplacement. Qui plus est, l'effet différentiel des économies d'agglomération entre les entreprises de faible et de forte capacité est plus marqué dans les industries dont la faculté d'appropriation non-juridique est forte, ce qui laisse supposer que les entreprises peuvent se servir de leur choix d'emplacement comme un outil stratégique pour traiter les retombées de la connaissance tacite.
Jo Y. und Lee C.-Y. Technologische Kapazität, Agglomerationsökonomien und Standortwahl von Firmen, Regional Studies. In diesem Beitrag argumentieren wir, dass sich die Fähigkeit einer Firma zur Erzeugung und Absorbierung von technologischem Wissen – d. h. ihre technologische Kapazität – auf ihre Standortwahl innerhalb von Regionen auswirkt, die sich durch verschiedene Arten von Agglomeration auszeichnen. Wir stellen fest, dass geografisch begrenzte Wissensexternalitäten – eine der Kräfte, die Firmen zu einem bestimmten Standort anziehen – je nach dem Umfang ihrer technologischen Kapazität eine differentielle Wirkung auf die Standortwahl verschiedener Firmen ausüben: Für Firmen mit geringer technologischer Kapazität haben Wissensexternalitäten von Konkurrenten am selben Standort bzw. eine konkurrenzbedingte Spezialisierung eine stärkere positive Auswirkung auf ihre Standortwahl, während sich für Firmen mit höherer technologischer Kapazität Wissensexternalitäten von Firmen am selben Standort aus verwandten und komplementären Branchen bzw. eine komplementäre Spezialisierung stärker auf ihre Standortwahl auswirken. Darüber hinaus fällt die differentielle Auswirkung von Agglomerationsökonomien für Firmen mit geringer und höherer Kapazität in Branchen mit starker nicht-rechtlicher Verwertbarkeit ausgeprägter aus, was darauf schließen lässt, dass Firmen ihre Standortwahl als strategisches Mittel zum Umgang mit den Spillovers von stillschweigendem Wissen nutzen können.
Jo Y. y Lee C.-Y. Capacidad tecnológica, economías de aglomeración y la elección del emplazamiento de las empresas, Regional Studies. En este artículo argumentamos que la habilidad de una empresa para producir y absorber conocimiento tecnológico, es decir, su capacidad tecnológica, influye en el emplazamiento que elige entre las regiones caracterizadas por diferentes tipos de aglomeración. Observamos que los efectos externos de conocimiento delimitados geográficamente, una de las fuerzas que atraen a las empresas hacia un lugar determinado, tienen un efecto diferencial en el lugar que eligen las empresas en función del nivel de su capacidad tecnológica: para empresas con baja capacidad tecnológica, los efectos externos de conocimiento de competidores situados en el mismo lugar, es decir, la especialización competitiva, tienen un efecto positivo más fuerte en el emplazamiento elegido, mientras que para las empresas con alta capacidad tecnológica, los efectos externos de conocimiento de empresas situadas en el mismo lugar que trabajan en industrias relacionadas y complementarias, es decir, la especialización complementaria, influyen de forma más pronunciada en el emplazamiento elegido. Además, el efecto diferencial de las economías de aglomeración entre las empresas con capacidades bajas y altas está más pronunciado en industrias con fuerte apropiabilidad no jurídica, lo que implica que las empresas pueden utilizar su elección del emplazamiento como una herramienta estratégica para tratar con los desbordamientos de conocimiento tácito.
Acknowledgement
The authors thank the Editors and the two anonymous referees for their invaluable comments to the original version of this paper. The authors are solely responsible for the remaining errors. An earlier version of this paper was presented at the Regional Studies Association Annual International Conference 2008 in Prague, Czech Republic. The authors acknowledge helpful comments by conference participants.
Notes
1. The notion of technological capability is similar to Hobday and Rush's (Citation2007) ‘technological capability’, Lee's (Citation2003) ‘technological competence’, and Ceccagnoli's (2005) ‘innovativeness’. Those studies have found that firms differ significantly in their technological capabilities.
2. The sources of agglomeration economies have been extensively explored. For example, Marshall Citation(1890) argued that they arose out of the production of specialized intermediate goods and services, a shared labour market and knowledge spillovers. This study focuses on knowledge spillovers.
3. Glaeser et al. Citation(1992) further mentioned another type of dynamic agglomeration externalities based on Porter's work on competition among firms. The phenomenon is ignored in this study mostly because such externalities are similar to Marshallian externalities in the sense that they are based on knowledge spillovers within regions with specialized industries. This paper also does not consider urbanization externalities because, even though they influence firm performance, they are often considered to be static externalities rather than dynamic externalities created by knowledge spillovers.
4. Many studies have emphasized that knowledge externalities are bounded both geographically and technologically (for example, Jaffe, Citation1986; Griliches, Citation1992; Adams and Jaffe, Citation1996; Orlando, Citation2004) and that information is weakly transmitted between technologically unrelated fields (for example, Adams and Jaffe, Citation1996; Autant-Bernard, Citation2001).
5. Nooteboom et al. Citation(2007) analysed the relationship between absorptive capacity and learning from cognitively distant business partners, and they share some similar logic with the present paper's . For example, they argued that cognitive distance between business partners yields opportunities for novel combinations of complementary resources, which provides an explanation for why highly innovative firms in can obtain significant benefits from technologically distant firms. However, they only dealt with learning from incoming spillovers and focused on the advantages of cognitively distant partners, not on the conditioning effect of firm-specific technological capability.
6. Studies have found that the concern over outgoing knowledge spillovers is an important factor influencing firm location choice. For example, Chung and Song Citation(2004) studied Japanese investment in the United States electronics sector from 1980 to 1998 and found that more experienced firms are less likely to co-locate with competitors. Looking at firm locations in Scotland and California, Oakey and Cooper Citation(1989) also pointed out that, although most firms are clustered, a significant number of small high-technology firms are located in peripheral areas. Cantwell and Santangelo Citation(2002), analysing US patent data granted to large European-owned corporations, concluded that industry competition encourages the geographical separation of competing firms' research activities.
7. It should be noted that is an explanatory illustration of the logic underlying the hypotheses. is made to visualize the diverse impacts of firm capability on agglomeration externalities, not to indicate, for example, that there is a linear relationship between technological distance and spillovers. Therefore, one should be cautious when using the figure for further implications.
8. Some recent studies have distinguished between types of agglomeration in terms of technological distance. Frenken et al. Citation(2007), for example, showed that the externalities from ‘related variety’ among technologically related but not-too-close firms enhance the employment growth of a region. Neffke et al. Citation(2012) also argued that ‘localization externalities can be expected to derive not only from plants in the own industry, but also from plants engaged in technologically related, yet different activities’ (p. 487).
9. The Korean Innovation Survey is based on the Oslo manual of the Organisation for Economic Co-operation and Development (OECD), which provides definitions and survey methods for the Community Innovation Survey (CIS).
10. The KIS dataset of 2002 only covers the manufacturing sectors in Korea. This study focuses on manufacturing firms primarily because service firms tend to be more interested in regional urbanization than specialization externalities stemming from knowledge spillovers.
11. The KIS survey does not cover the entire population of firms. The present data were compared with the KIS-VALUE database compiled by the Korea Information Service, which is one of the most comprehensive databases containing Korean firms' financial and accounting information. A strong correlation of the number of entrants during the period by location and by industry (two-digit KSIC level) between the two datasets was found. R2 was 0.98 for location pair and 0.93 for industry pair, respectively.
12. Technological capability is defined as a firm's ability to generate technological knowledge and to absorb external knowledge. This means that the notion of technological capability in this study encompasses both R&D productivity (R&D efficiency) and absorptive capacity. There are a variety of measures to operationalize empirically the concept of technological capability by using firm R&D inputs or performances including permanent R&D (Cassiman and Veugelers, Citation2000, 2002), the number of patents (Penner-Hahn and Shaver, Citation2005; Acs and Audretsch, Citation1989) and the number of products on the market (Zucker et al., Citation1998). In this study, given that the sample is composed of new entrants which mostly have not yet patented their inventions, the introduction of a technologically new product is used here as a proxy for their technological capability.
13. Given that Korean provinces differ significantly in their sizes as shown in , the density of firms is employed as a measure of agglomeration. The location quotients for competitive and complementary specialization are also constructed as an alternative measure and they do not qualitatively alter the estimation results reported in this paper.
14. The distance between two regions was measured as the distance between the capital cities of the regions.
15. The HHI index for location j is calculated as:
where N is the number of industries; sjm is the number of firms at the mth industry in location j; and Tj is the total number of manufacturing firms in location j.
16. McFadden's choice model, combined with firm-level data, is useful for investigating a firm's incentive to locate in a specific region in a dynamic perspective. Studies at the industry level fail to capture individual firms' location incentives due to aggregated information about entry and exit.
17. An earlier version of this paper employed non-log specialization variables; the results were largely consistent with the findings shown in . The specialization variables were log-transformed by adding the unity to their actual values.
18. If a variable is logarithmic, that is:
then the probability equation is simplified to:
with all other variables being constant in every alternative location. Then, the odds of locating in an alternative j is written by:
which yields the odds elasticity with respect to the variable as:
Log-transformation also helps reduce the skewness of the variable.
19. There might be some concern about interpreting interaction effects in non-linear regression models. As in Norton et al. Citation(2004), a non-linear function produces a complex form of marginal effect (that is, the partial derivative with the relevant variables). The real interaction effect may be different from the estimated coefficient and depends on the exact position of the non-linear curve. However, when focusing on average probability change, not marginal, the sign of the coefficient of an interaction term is largely consistent with its effect on the probability. This is because the odds of probability, P/(1 – P), is an increasing function of probability and can be presented as a linear combination of independent variables (for details of McFadden's choice model, see Agresti, Citation2002, p. 298). One way to interpret the interaction effect is to depict the estimated relationships between independent variables and dependent probability, as done in .
20. The two most common tests of the IIA are Hausman and McFadden's (Citation1984) test and Small and Hsiao's (Citation1985) test. Long and Freese Citation(2006) argued that the best solution in relation to the IIA is to use distinct alternatives that are not substitutes for one another. It is, however, not easy to validate empirically the distinctiveness between alternatives, so the Hausman and McFadden test was used to confirm the IIA property.
21. For the graphical illustration, semiconductor and other electronic parts industry (KSIC 321) was selected because it had the most entries in the sample (forty-one start-ups) and it was classified as Group II of industries with strong non-legal appropriability. The robustness of the results for several other industries was also checked.