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Article
Publication date: 21 August 2023

Guanghao Wu, Xiuyi Shi and Jiajia Li

The purpose of this paper is to precisely measure the risk attitudes of Chinese agricultural laborers and then analyze the impact and mechanism of risk attitudes on the…

Abstract

Purpose

The purpose of this paper is to precisely measure the risk attitudes of Chinese agricultural laborers and then analyze the impact and mechanism of risk attitudes on the entrepreneurial choices of Chinese agricultural laborers.

Design/methodology/approach

This paper is based on the theory of expected utility function and utilizes the authoritative China Family Panel Studies (CFPS) to accurately measure the risk attitudes of 7,639 Chinese agricultural laborers through experimental methods. In the empirical analysis, this paper employed Probit, IV-Probit and mediation effect models to examine the research hypotheses.

Findings

First, agricultural laborers with the lowest risk appetite account for 54.8%, which is 8.69 times the number of agricultural laborers with the highest risk appetite. Second, agricultural laborers preferring risk are more likely to engage in entrepreneurship; this result has been validated through a series of robustness tests. Third, mechanism analysis shows that risk attitude drives the entrepreneurship of Chinese agricultural laborers through improving interpersonal trust, social interaction and formal credit behavior.

Originality/value

Existing research has mainly investigated the impact of risk attitudes on the entrepreneurial choices of the general population, with limited attention paid to agricultural laborers. The potential mechanisms in that process remain unclear, and the measurement results of risk attitude also require further precision. Based on experimental method, this paper not only helps clarify the relationship between risk attitudes and agricultural laborers entrepreneurship in China, but also provides policy recommendations to promote agricultural laborers entrepreneurship and drive rural development.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

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