JOURNAL ARTICLE

Between nature and nurture: The genetic overlap between psychological attributes and selection into public service employment.

  • Published In: Public Administration Review, 2023, v. 83, n. 4. P. 809 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Tao, Lei; Liang, Hailun; Wen, Bo; Huang, Tao 3 of 3

Abstract

Public administration scholars have had a long‐lasting interest in examining individual differences relevant to the attractiveness of public service employment. However, very few studies have explored the genetic underpinnings of these variations. This article builds upon recent behavioral genetics literature and explores whether there are genetic overlaps between psychological attributes and selection into public service employment. We construct the polygenic risk scores (PRSs) on two psychological attributes—neuroticism and positive affect—to model the genetic influence on public service employment in a nationwide UK dataset with 262,795 participants. The results suggest that the PRS of positive affect is a significant predictor of individuals' selection into public service employment, implying that individuals with high innate happiness are more likely to self‐select into service work. Taking the existing socialization literature and this result into consideration, our findings support that both nature and nurture factors shape individuals' selection into public service employment. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Public Administration Review. 2023/07, Vol. 83, Issue 4, p809
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:0033-3352
  • DOI:10.1111/puar.13582
  • Accession Number:164587755
  • Copyright Statement:Copyright of Public Administration Review 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.