The impact of technological innovation on unemployment in Nigeria: an Autoregressive distributed lag and Frequency Domain Causality approach
Oluwatoyin Abidemi Somoye ()
Additional contact information
Oluwatoyin Abidemi Somoye: Near East University
SN Business & Economics, 2024, vol. 4, issue 5, 1-16
Abstract:
Abstract Although technology is known to enhance labor and capital productivity, there are also concerns that technological progress can lead to unemployment. Hence, using methods like Autoregressive Distributed Lag (ARDL), Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Square (DOLS), Canonical Cointegration Regression (CCR), and Frequency Domain Causality, this study explores the effect of technological innovation on unemployment in Nigeria from 1991 to 2021. The ARDL results showed that a rise in technological innovation increases unemployment. More specifically, a 1% increase in technological innovation increases unemployment by 0.15% and 0.02% in the long and short–run, respectively. In addition, the FMOLS, DOLS, and CCR outcomes confirmed the ARDL findings. Furthermore, the Frequency Domain Causality result showed that technological innovation granger causes unemployment in the short, medium, and long–term. Based on these results, policymakers in Nigeria should create favorable educational and training policies. These policies can foster human capital development and help guarantee that workers have the skills to prosper in the new economy.
Keywords: Technological innovation; Unemployment; ARDL; Frequency domain causality; Nigeria (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s43546-024-00657-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.
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:spr:snbeco:v:4:y:2024:i:5:d:10.1007_s43546-024-00657-y
Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43546
DOI: 10.1007/s43546-024-00657-y
Access Statistics for this article
SN Business & Economics is currently edited by Gino D'Oca
More articles in SN Business & Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().