Regional variations in automation job risk and labour market thickness to agricultural employment. (April 2022)

• Record Type: Journal Article
• Authors: Rijnks, Richard Henry; Crowley, Frank; Doran, Justin
• Is Part Of: Journal of rural studies. Volume 91(2022)
• Page Start: 10
• Page End: 23
• Publication Date: 2022-04
• Subjects: Automation -- Agricultural employment -- Regional labour markets -- Knowledge bases -- Regional economic development; Sociology, Rural -- Periodicals; Country life -- Periodicals; Rural development -- Periodicals; Land use, Rural -- Planning -- Periodicals; Rural conditions -- Periodicals; Sociologie rurale -- Périodiques; Vie rurale -- Périodiques; Développement rural -- Périodiques; Sol, Utilisation agricole du -- Planification -- Périodiques; Conditions rurales -- Périodiques; Country life; Land use, Rural -- Planning; Rural conditions; Rural development; Sociology, Rural; Periodicals; 307.72
• DOI: 10.1016/j.jrurstud.2021.12.012
• Languages: English
• ISSNs: 0743-0167

Abstract

Abstract: Automation has the potential to transform entire agricultural value chains and the nature of agricultural business. Recent studies have emphasised barriers to adoption, as well as issues related to labour market and cultural outcomes of automation. However, thus far, very little attention has been afforded to the regional variations in the potential for automation adoption or threats to agricultural employment. Specifically, research to date does not take into account the local availability of similar occupations including those in different sectors to which displaced workers may transition. Threats to employment and lower numbers of similar jobs locally are particularly salient in rural contexts, given the thin and specialized local labour markets. The aims of this paper are to show the regional distribution of risk to automation for the agricultural sector specifically, and to link these patterns to indicators for occupation specific labour market thickness in Ireland. Using detailed occupational skills data, we construct indices for local labour market thickness conditioned on occupational skills and knowledge requirements. We show that there is substantial regional heterogeneity in the potential threat of automation to the employment prospects of workers currently active in the agricultural sector. This regional heterogeneity highlights the importance of the regional context for designing effective labour market policy in the face of job automation. Highlights: The K-means cluster analysis in this paper reveals four distinct types of agricultural employment separated on knowledge- and skills-bases. Agricultural employment at high risk of automation can transfer to low risk jobs in other sectors. The availability of low risk alternative jobs is heterogeneous across regions. We reveal regional clusters of automation risk and low numbers alternative jobs for farmers and forestry workers in the Borders region in Ireland.