Gender parity in science depends on a complex interplay of social, economic and educational variables. In this study, we compile a longitudinal dataset at the country level combining scientific bibliographic data from Dimensions, with the World Bank Open Data (WBOA), and the UNESCO Institute for Statistics (UIS). Our goal is to identify conditions and pathways that could lead to gender parity in different world regions, by applying time-series forecasting methods (ARIMA and Exponential Smoothing), along with correlation analysis and Bayesian networks. While results vary by region, one recurring recommendation emerging from our models is the need to increase the number of researchers and the percentage of women graduating in Engineering, Manufacturing, and Construction, as this appears to be a critical driver for reducing gender disparities in the scientific workforce.
González‐Salmón et al. (Thu,) studied this question.