At the same time, it has shown that socio economic benefit mitigates investment risk, as confirmed by case studies on the MeerKAT and Square Kilometre Array Telescope programmes.

Our research to date [Carla do you have any links to thesis or articles published?] established a multi-layered framework for understanding how large-scale space science and technology (SST) investments translate into sustained socio-economic benefit within developing economies. Rather than treating infrastructure, skills development, industry participation, and governance as isolated variables, the framework positions them as a dynamic system in which institutional quality, technological capability, and African value chains evolve. By embedding these elements within a phased investment and impact structure, the framework enables policy makers to distinguish between short-term construction effects, medium-term capability formation, and long-term productivity and innovation spillovers. In this way, the framework moves beyond project evaluation toward a systemic model of development, capable of explaining why some SST investments generate enduring economic transformation while others dissipate once initial funding cycles end.

A critical strength of this approach lies in its reliance on growing, longitudinal data sets rather than static or one-off indicators. As expanding datasets develop across employment, skills pipelines, supplier networks, research outputs, and firm-level productivity, these time-series allow the framework to test not only whether benefits exist, but how they emerge, compound, and interact over time. Importantly, this enables causal inference to improve as sample sizes grow and structural relationships stabilise, allowing early assumptions to be refined or rejected. The accumulation of data therefore converts what would otherwise be a theoretical framework into a progressively more precise and empirically grounded decision-support tool for government and funding agencies.

As these datasets deepen, the framework itself becomes a platform for adaptive learning and policy optimisation. Governments can identify which combinations of institutional support, procurement design, and skills investment most strongly amplify labour productivity, innovation, and industrial upgrading, and can recalibrate their SST strategies accordingly. This transforms SST programmes into living policy laboratories, where evidence-based adjustments continuously improve developmental outcomes. In this sense, the growing data infrastructure is not merely a by-product of the projects but a core strategic asset, enabling Africa’s SST investments to evolve from ambitious scientific endeavours into increasingly reliable engines of inclusive and sustainable economic growth.