In spite of the fact that labour market structures and mechanisms vary geographically between countries from the same region as the Gulf Cooperation Council (GCC) states, spatial variations of labour force participation can also be identified in every single country. Local variations in women’s employment are often influenced by several driving forces particularly modernization characteristics. In this research, advanced GIS algorithms were utilized to model female labour force participation (FLFP) in Oman. Several explanatory variables such as female education, urbanization, private sector jobs, divorce rates and female administrative jobs were used as predictors of FLFP rates. The global Ordinary Least Squares (OLS) and local Geographically Weighted Regression models were fitted to spatially investigate and predict the distribution patterns of FLFP rates over the Omani wilayats. Although the global model fitted the female employment data moderately well, the findings of the GWR model seems inherently more realistic since they allow the impacts of various parameters on FLFP to vary spatially over space. The results of this study revealed that education and urbanism both have significant positive impacts on predictions for the labour force participation of women. The synergy of local spatial modelling with GIS techniques provides insights into improving women’s employment shares in Oman. Quantifying spatial variations of FLFP rates and their associations helps further our understanding of the driving forces which are responsible for modernization while at the same time recognizing the factors that increase or decrease women’s participation in the local labour.
- female labour force
- spatial modelling
ASJC Scopus subject areas
- Computer Science Applications
- Earth and Planetary Sciences(all)