The impact of robots on informal employment: evidence from China

Jingying Linghu

Applied Economics Letters, 2025, vol. 32, issue 9, 1288-1294

Abstract: Informal employment, which has grown rapidly in recent years, is closely related to individual welfare, and is inevitably influenced by the rise of robots. Against this backdrop, this study aims to explore the impact of robots on informal employment. Applying a probit model and using data from the China Family Panel Studies in 2014, 2016, and 2018 and the International Federation of Robotics, this study reveals that robots do not have a significant impact on unemployment or labour force participation, but significantly increase the probability of informal employment. Further heterogeneity analysis indicates that robots have a greater positive impact on informal employment in the central and western regions of China. Moreover, individuals working in non-routine task-oriented industries, with higher general or specialized skills, exhibit a lower probability of being informally employed under the influence of robots. This study provides empirical evidence to maintain job market stability in the era of automation in China, which can also be used by policymakers in other countries.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2024.2302897 (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:32:y:2025:i:9:p:1288-1294

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

DOI: 10.1080/13504851.2024.2302897

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 ().