A new algorithm for matching Chinese NBS firm-level with customs data

Peter Egger, Susie Xi Rao and Sebastiano Papini

China Economic Journal, 2021, vol. 14, issue 3, 311-335

Abstract: Combining accounting-type firm data and transactions-type customs data has become increasingly important for research in international and industrial economics. The statistical authorities in several countries such as the United States or France provide such linked data without details on sources, and researchers have to assume that the matching is correct and the firm identifiers are unique and flawless in the source data. For some other countries such as Switzerland or China, firm and customs data contain information which permits such linking ex post using string matching based on firm names and their meta-information like addresses. Due to spelling and typos, such matching is prone to some errors. Obtaining the largest-possible number of high-quality matches helps avoid potential biases while keeping crucial details. We report on a new algorithm which improves considerably the hitherto available linking efforts of the National Bureau of Statistics firm-level and the Customs trade data for China.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/17538963.2021.1963046 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:rcejxx:v:14:y:2021:i:3:p:311-335

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rcej20

DOI: 10.1080/17538963.2021.1963046

Access Statistics for this article

China Economic Journal is currently edited by Tiechang Gao and Yiping Huang

More articles in China Economic Journal from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().