Network spatial patterns and determinants of China’s hometown chambers of commerce

Principal Investigator(s):  View help for Principal Investigator(s) Gou Jiesong, Southwestern University of Finance and Economics

Version:  View help for Version V1

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  dataset 06/28/2025 04:10:AM
readme.txt text/plain 15.1 KB 06/28/2025 12:46:AM

Project Citation: 

Jiesong, Gou. Network spatial patterns and determinants of China’s hometown chambers of commerce. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-06-28. https://doi.org/10.3886/E234621V1

Project Description

Summary:  View help for Summary
  • Based on the establishment data of provincial-provincial, city-city, provincial-city, city-provincial Hometown Chambers of Commerce (HCC) in China by the end of 2022, this paper combines social network analysis and exponential random graph model to explore the network spatial patterns and determinants of China’s HCC.
  • The primary data on HCC establishments as of the end of 2022 were obtained from the Tianyancha platform (https://www.tianyancha.com/), a widely used enterprise credit information database in China. Given the possibility of registration inconsistencies, missing information, or duplicate records, we conducted a multi-step validation process to ensure data reliability.
  • GDP, per capita GDP, and local general public budget expenditure data were all sourced from the China Statistical Yearbook and China Urban Statistical Yearbook. The dialect data were derived from the Atlas of Languages in China, including nine dialects: Xiang, Gan, Hui, Wu, Zhongyuan Mandarin, Jianghuai Mandarin, Southwest Mandarin, Hakka, and others. The urban agglomeration data were obtained from the 14th Five-Year Plan for National Economic and Social Development of the People's Republic of China, which mentions 19 urban agglomerations. The road distance data were calculated based on the shortest intercity highway distances from the 2022 Amap (Gaode Map) database.


Scope of Project

Subject Terms:  View help for Subject Terms nongovernmental organizations; network; china
Geographic Coverage:  View help for Geographic Coverage China
Time Period(s):  View help for Time Period(s) 1989 – 2022
Collection Date(s):  View help for Collection Date(s) 3/1/2024 – 6/28/2025
Universe:  View help for Universe The establishment data of provincial-provincial, city-city, provincial-city, city-provincial Hometown Chambers of Commerce (HCC) in China by the end of 2022
Data Type(s):  View help for Data Type(s) administrative records data; aggregate data; survey data
Collection Notes:  View help for Collection Notes
All HCC networks were converted into binary (0/1) matrices for analysis. The final matrix specifications were:
  1. Provincial-provincial HCC: 31×31 square matrix (675 records were coded as 1).
  2. City-city HCC: 286×286 square matrix (2071 records were coded as 1).
  3. Provincial-city HCC: 236×31 rectangular matrix (798 records were coded as 1).
  4. City-provincial HCC: 30×270 rectangular matrix (1762 records were coded as 1).
All continuous variables mentioned above were winsorized at the 1% level to mitigate the influence of extreme values. And all exogenous variables exhibit Variance Inflation Factors (VIF) below 3, indicating the absence of severe multicollinearity.

Methodology

Response Rate:  View help for Response Rate By the end of 2022, we had collected a total of 5,413 HCC records. Entries that had been revoked, cancelled, or were clearly misclassified (e.g., origin and destination locations being the same) were removed—resulting in the exclusion of 107 records.
Sampling:  View help for Sampling After cleaning and validation process, a total of 5,306 valid HCC records were retained for network construction.

Data Source:  View help for Data Source
  • https://www.tianyancha.com/
  • the China Statistical Yearbook and China Urban Statistical Yearbook, 2004-2023
  • Linguistic Atlas of China. 2nd ed., edited by Chinese Academy of Social Sciences, The Commercial Press, 2012.

Collection Mode(s):  View help for Collection Mode(s) web scraping; web-based survey
Weights:  View help for Weights Take ln for pergdp and road distance

Unit(s) of Observation:  View help for Unit(s) of Observation Province,city
Geographic Unit:  View help for Geographic Unit city

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V1 [2025-06-28]

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