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Return migration, rural household investment decision, and poverty alleviation: Evidence from rural Guangdong, China.

  • Published In: Growth & Change, 2023, v. 54, n. 1. P. 304 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wu, Xinhui; Chen, Luan; Ma, Li; Cai, Liru; Li, Xun 3 of 3

Abstract

Return migration has been considered advantageous to the productivity of labor and the economic development of origin regions and countries. However, how and why return migrants make their investment decisions and how such processes contribute to poverty alleviation remains unclear. This study evaluated how migration experience influences rural families' choices for productive investments and the underlying mechanism of village poverty alleviation. The result indicates that, when all are given the same monetary budgets, return migrants are more inclined to invest in single agricultural‐related subjects rather than multiple subjects. A concentrated investment implies the investor's intention of expanding the production scale, which can further lead to a more organized, professional agricultural production that can be considered beneficial for community poverty alleviation. Moreover, different approaches of human capital accumulation led to varied capacity growth, among which migration experience effectively enhances the non‐cognitive ability of return migrants. Based on these findings, we suggest that more returnee‐preferential policies, supporting production and organization services should be established to promote agricultural entrepreneurship among the returning groups in poor rural areas. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Growth & Change. 2023/03, Vol. 54, Issue 1, p304
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:0017-4815
  • DOI:10.1111/grow.12656
  • Accession Number:162203982
  • Copyright Statement:Copyright of Growth & Change is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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