A new approach on measuring the knowledge complexity in the view of the bipartite network

Shenshen Zhang and Paul Zhang

Applied Economics Letters, 2024, vol. 31, issue 2, 146-151

Abstract: Developed countries are guaranteed to have the ownership of higher knowledge complexity, which is naturally accepted and universally acknowledged. However, the reality may tell a different story. In this article, a brand-new approach is utilized to quantify the knowledge complexity. Within the bipartite network model, based on the Fitness and Complexity algorithm and the matrix-estimation exercise, a couple of indicators are constructed to measure generalized knowledge complexities of countries and technologies. Results illuminate that admittedly knowledge complexities of countries and those of technologies are interrelated, and those established and developed countries, compared with developing ones, do not necessarily own higher knowledge complexity. To be more specific, an increasing number of facts demonstrate that some less-developed countries with less-advanced technologies are progressing in leaps and bounds, for they attach great importance to investing in high-knowledge-complexity technologies.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2022.2128290 (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:apeclt:v:31:y:2024:i:2:p:146-151

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20

DOI: 10.1080/13504851.2022.2128290

Access Statistics for this article

Applied Economics Letters is currently edited by Anita Phillips

More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().