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Entrepreneurial Ecosystems, New Business Formation, and Scale-up Activity: Evidence from 286 Chinese Cities

  • Yi Zhang EMAIL logo und Hein Roelfsema
Veröffentlicht/Copyright: 19. September 2022
Entrepreneurship Research Journal
Aus der Zeitschrift Entrepreneurship Research Journal Band 12 Heft 4

Abstract

This paper examines the effects of the quality of entrepreneurial ecosystems on new business formation and scale-up activity in China at the city-industry level. Accounting for only large and fast-growing firms, we focus on productive entrepreneurship which creates economic wealth. Based on a newly constructed panel dataset for 29 manufacturing industries and 286 prefecture-level cities of China during the period 1998–2009, we find that entrepreneurial ecosystem components, including access to finance, knowledge, marketization, local market demand, and entrepreneurial culture, are important determinants in explaining the differences in entrepreneurial activity across city-industry clusters and over time. Analysing a dynamic period in China’s industrialization with large regional variation in economic development, we show that the relative importance of the ecosystem components in shaping entrepreneurial activity changes over time when regions develop. In addition, we show that interaction between the ecosystem components – indicating system strength – has additional power in explaining new business formation and scale-up activity.

Keywords: entrepreneurial ecosystems; new firm formation; business scale-up
JEL Codes: L1; O14; O43
Corresponding author: Yi Zhang, Xian Jiaotong University Jinhe Center for Economic Research, Xian, 710049, China, E-mail:
Appendix I

Industry cluster Manufacturing industry
Machinery and equipment Processing of petroleum, coking, processing of nuclear fuel

Smelting and pressing of ferrous metals

Smelting and pressing of non-ferrous metals

Metal products

General purpose machinery

Special purpose machinery

Transport equipment

Electrical machinery and equipment
Chemicals and rubber Raw chemical materials and chemical products

Medicines

Chemical fibers

Rubber

Plastics

Non-metallic mineral products
Textile and leather goods Textile

Textile wearing apparel, footwear and caps

Leather, Fur, feather and related products

Artwork and other manufacturing
Electronics and computers Communication equipment, computers and other electronic equipment

Machinery for cultural activity and office work
Wood products Processing of timber, wood, bamboo and others

Furniture

Paper and paper products

Printing, reproduction of recording media

Articles for culture, education and sport activities
Food, tobacco, beverages Processing and food from agricultural products

Foods

Beverages

Tobacco

  1. Note: The industry clusters are identified using the approach proposed by Feser and Bergman (2000). Based on the Chinese input-output table in 2004 released by the National Bureau of Statistics of China, we first calculate four coefficients: x ij and x ji , intermediate good purchases by j (i) from i (j) as a proportion of j’s (i’s) total intermediate good purchases; y ij and y ji , intermediate good sales from i (j) to j (i) as a proportion of i’s (j’s) total intermediate good sales. This gives us one matrix of x’s on the intermediate input purchasing pattern and one matrix of y’s on the intermediate output sales pattern. We then run correlation analysis between pairs of industries based on these patterns and calculate four correlation coefficients for each pair of industries to capture the similarities in their input-output structure. Finally, we conduct a principal components factor analysis using these correlation coefficients for each sector as variables of the factor analysis. Industries for a given cluster (factor) are identified when their factor loadings on that factor are larger than 0.60.

Appendix II

Variable Definition
New_Business The number of newly established private firms, scaled by the total number of firms, in a specific industry cluster, city, and year, standardized
Gazelle The number of firms established less than five years and with growth rate of sales above 20 percent in each of the last three years, scaled by the total number of firms, in a specific industry cluster, city, and year, standardized
Finance The ratio of interest payments of private firms in total interest payments of all firms in a city, relative to the share of private firms in the local economy, standardized
Knowledge Based on two variables:

  1. The share of college students in total population in a city, standardized

  2. The number of approved patent applications per capita in a city, standardized

The index is created by taking the average of the two variables, standardized
Marketization Based on four variables:

  1. The number of total private firms per capita in a city, standardized

  2. Government spending as a share of GDP in a city, standardized

  3. The share of government subsidies to state-owned firms relative to the share of state-owned firms in the local economy, standardized

  4. The share of public sector employment in total population in a city, standardized

The index is created by taking the average of the first variable and the negative values of the last three variables, standardized
Demand Based on four variables:

  1. The number of incumbent firms within an industry cluster in a city, standardized

  2. Employment of incumbent firms within an industry cluster in a city, standardized

  3. Average wage of households in a city, standardized

  4. The deposit balance of households in a city at the year-end, standardized

The index is created by taking the average of the four variables, standardized
Culture Based on four variables:

  1. The trust measure from the China General Social Survey in 2003, standardized

  2. The ratio of self-employment in total population in a city, standardized

The index is created by taking the average of the two variables, standardized
Ecosystem Ecosystem_fac Finance*Knowledge*Marketization*Demand*Culture, standardized

An index constructed using the principal component analysis based on Finance, Knowledge, Marketization, Demand and Culture
Control variables
Agglomeration The total number of employees in all firms in a city, standardized
Manufacturing/GDP The share of manufacturing industry in GDP in a city, standardized
Service/GDP The share of service industry in GDP in a city, standardized
Year Year dummies

  1. Note: We use the simple average method instead of the factor analysis to create our ecosystem indices for two reasons. The main reason is that our ecosystem construct is derived from a large body of literature and do not follow from the underlying data. In addition, we consider the variables to be of equal importance in constructing the index. The results are not sensitive to weights used.

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Received: 2019-09-24
Accepted: 2020-07-10
Published Online: 2022-09-19

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/erj-2019-0265
Schlagwörter für diesen Artikel
entrepreneurial ecosystems; new firm formation; business scale-up

Artikel in diesem Heft

  1. Frontmatter
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Heruntergeladen am 28.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/erj-2019-0265/html
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