This report presents an innovative predictive model based on big data and artificial intelligence, designed to bring about a conceptual shift ‒ from retrospective measurement of socioeconomic mobility that looks to the past, to prospective forecasting that looks ahead. The model was developed using administrative data made available through the research data center of the Israel Central Bureau of Statistics (CBS). It estimates young people’s future chances of success and bridges the inherent time lag characteristic of traditional mobility measurement.
This approach makes it possible to assess mobility potential at an earlier point in young people’s critical developmental stages, thereby rendering research insights actionable for the current generation. The model is intended to serve as the foundation for a decision-support tool that enables policymakers to act in real time and influence life trajectories as they unfold.
The analysis focuses on predicting mobility in income and education and on identifying risks of “sticky floors,” while examining the relative contribution of background and environmental variables. The findings reveal a complex picture: while demographic characteristics ‒ most notably gender and population group affiliation ‒ remain strong predictors that reflect structural inequalities, malleable environmental factors, such as teaching quality and classroom composition, exhibit substantial predictive power and may serve as levers for change.
The report demonstrates how adopting a proactive, early-intervention approach can enable policymakers to shift from reactive management to anticipatory governance ‒ identifying risks and opportunities in real time and targeting resources and interventions at the most critical junctures for promoting equality of opportunity.