Women remain underrepresented in the labour market. Although significant advancements are being made to increase female participation in the workforce, the gender gap is still far from being bridged. We contribute to the growing literature on gender inequalities in the labour market, evaluating the potential of the LinkedIn estimates to monitor the evolution of the gender gaps sustainably, complementing the official data sources. In particular, assessing the labour market patterns at a subnational level in Italy. Our findings show that the LinkedIn estimates accurately capture the gender disparities in Italy regarding sociodemographic attributes such as gender, age, geographic location, seniority, and industry category. At the same time, we assess data biases such as the digitalisation gap, which impacts the representativity of the workforce in an imbalanced manner, confirming that women are under-represented in Southern Italy. Additionally to confirming the gender disparities to the official census, LinkedIn estimates are a valuable tool to provide dynamic insights; we showed an immigration flow of highly skilled women, predominantly from the South. Digital surveillance of gender inequalities with detailed and timely data is particularly significant to enable policymakers to tailor impactful campaigns.
Highly skilled professionals' forced migration from Ukraine was triggered by the conflict in Ukraine in 2014 and amplified by the Russian invasion in 2022. Here, we utilize LinkedIn estimates and official refugee data from the World Bank and the United Nations Refugee Agency, to understand which are the main pull factors that drive the decision-making process of the host country. We identify an ongoing and escalating exodus of educated individuals, largely drawn to Poland and Germany, and underscore the crucial role of pre-existing networks in shaping these migration flows. Key findings include a strong correlation between LinkedIn's estimates of highly educated Ukrainian displaced people and official UN refugee statistics, pointing to the significance of prior relationships with Ukraine in determining migration destinations. We train a series of multilinear regression models and the SHAP method revealing that the existence of a support network is the most critical factor in choosing a destination country, while distance is less important. Our main findings show that the migration patterns of Ukraine's highly skilled workforce, and their impact on both the origin and host countries, are largely influenced by pre-existing networks and communities. This insight can inform strategies to tackle the economic challenges posed by this loss of talent and maximize the benefits of such migration for both Ukraine and the receiving nations.
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