Modelling African Futures: A comparative technography of evidence-based welfare policy in Ghana, Senegal, Kenya and Botswana

Projektbeschreibung

Wohlfahrt in unsicheren Datenlandschaften gestalten

In vielen afrikanischen Ländern ist es durch unvollständige Daten zur Bevölkerung schwierig für Sozialsysteme, Ressourcen gezielt einzusetzen. Die nationalen statistischen Systeme sind oft unzusammenhängend, sodass es für die Behörden schwer ist, den Anspruch auf Sozialleistungen festzustellen. Die Ressourcenplanung und -zuweisung auf die Bedürfnisse der schutzbedürftigsten Personen auszurichten ist kompliziert. Daher wird im ERC-finanzierten Projekt ModelFutures ethnografische Forschung in Botsuana, Ghana, Kenia und Senegal durchgeführt. Das Team untersucht, wie trotz dieser Datenlücken Prognosen zu Sozialleistungen aufgestellt werden, um die Rolle statistischer Modelle für die Wohlfahrtspolitik zu bewerten. Außerdem wird geprüft, die Fachkräfte globale Modelle an lokale Kontexte anpassen, um neue Erkenntnisse zur evidenzbasiertes Wohlfahrtsplanung zu generieren.

Ziel

Welfare systems rely on extensive knowledge about the population to plan, finance, and expend limited resources in a targeted manner. In view of their often-fragmented national identification and statistical systems, Africa’s emerging welfare states are thus facing significant challenges in their capacity to establish categories of welfare entitlement and to target groups for intervention. Led by the PI, ModelFutures’ team will carry out ground-breaking, comparative ethnographic research at the statistics-welfare nexus of four African country cases – Ghana, Senegal, Kenya and Botswana – to understand: How do states generate truths about future welfare in contexts of uncertain knowledge about the population and its wellbeing? The project’s threefold objectives aim to (1) trace statistical modelling practices in the context of multi-facetted uncertainties about the population and its environs; (2) analyse the impact of adaptations and creative data practices on quantitative truth claims; and (3) connect statistical future-making and anticipatory welfare politics in sites of statistical innovation. ModelFutures’ ambition significantly extends beyond the state of the art of our understanding of statistical world-making by developing a novel concept of vernacular prediction that attunes to experts’ skilful adaptations of globally circulating computational models and standards, while attending to their modelling practices’ co-constitution with symbolic commitments and situated infrastructural arrangements. Combining expertise from anthropology, science and technology studies, and population statistics, ModelFutures generates novel theory on the production of evidence-based welfare policies in contexts of contested claims to the validity of data, methods, and the anticipatory politics that play out between short-term mitigation efforts and the pursuit of long-term dividends.