Spatial Pattern of Unemployment in the ECOWAS Member States: A Panel Data Analysis Approach

Abstract

Background: The eighth Sustainable Development Goal aims to lower the unemployment rate. It is impossible to overstate the threat of unemployment. The majority of ECOWAS members are low-income nations that are severely impacted by this threat. The causes of unemployment are complicated and differ from nation to nation. However, understanding the drivers of unemployment in each nation can be accomplished by a spatiotemporal analysis of the factors affecting unemployment in ECOWAS states. The Panel data analysis was used to examine data on unemployment that covered a 30-year period from the World Bank database for Foreign Direct Investment, Gross Domestic Product, Gross Fixed Capital Investment, Inflation, Age Dependency Ratio, Population Growth, and Private Monetary Sector at 1%, 5% and 10% levels of significance. Several maptools and shapefiles were taken into consideration for the spatiotemporal analysis using the R-software, and the Rook rule was employed as a spatial weight. The Hausman tests showed that the random effect model is better. Fixed Direct Investment and Gross Domestic Product had a significant effect on unemployment. The spatial pattern of unemployment on the selected variables in the ECOWAS states was presented. To increase the competitiveness of the workforce in the labour market and draw more Foreign Direct Investment to generate more employment possibilities, governments and policymakers should concentrate on enhancing their workforce's skills and qualifications. Growing the Gross Domestic Product will result in a significant reduction in unemployment. Keywords: ECOWAS, Fixed Direct Investment, Gross Domestic Product, Panel Data, Rook rule, Spatial weight, Unemployment Journal Reference Format: Alaba, O.O. & Muse, B.O. (2023): Spatial Pattern of Unemployment in the ECOWAS Member States: A Panel Data Analysis Approach. Journal of Behavioural Informatics, Digital Humanities and Development Research. Vol. 9. No. 1, Pp 35-48. Available online at https://www.isteams.net/behavioralinformaticsjournal. dx.doi.org/10.22624/AIMS/BHI/V9N1P3