Modelling African Futures: A comparative technography of evidence-based welfare policy (ModelFutures)
ModelFutures carries out comparative ethnographic research at the statistics-welfare nexus of four African countries – Ghana, Senegal, Kenya and Botswana – to connect statistical innovation and anticipatory welfare politics in contexts of major demographic transitions.
Contemporary African welfare systems deploy a wide range of interventions covering health and work-related injuries, unemployment protection, pensions and disability insurance, family and childcare allowances, measures relating to housing, and education. ModelFutures’ undertakes multinational ethnographic research into the foundational knowledge practices and infrastructures informing the planning and implementation of evidence-based welfare policy in Ghana, Kenya, Senegal, and Botswana.
Drawing on a unique cross-disciplinary comparative approach to the study of statistical innovation in practice, ModelFutures attunes to experts’ skilful adaptations to the demands of globally circulating computational models and standards, while attending to their practices’ co-constitution with symbolic commitments and situated infrastructural arrangements. Taken together, this allows us to examine in a systematic manner how variously positioned knowledge practices, including computational vernaculars and skilful adaptions, participate in statistical world-making.
ModelFutures proceeds along three interconnected scientific objectives: (1) tracing the work of statistical modelling and innovation in the context of multi-facetted uncertainties about the population, (2) analysing the impact of adaptative data practices on quantitative truth claims, and (3) connecting statistical future-making and anticipatory welfare politics.
Objective 1 - Data practices and layered uncertainty
ModelFutures investigates the specific arrangements of actors, computational infrastructures and methods that inform investments in future welfare. The project specifically identifies contemporary challenges of modelling welfare futures and the work of statistical experts to navigate multiple and layered uncertainties about the population and its wellbeing. ModelFutures will trace the work of experts in key institutions (e.g., Ministries of Gender and Social Welfare, UNFPA, National Population Councils, National Statistical Organisations, but also NGOs and religious bodies participating in the provision of welfare and debates around social and reproductive justice) and identify instances of creativity, innovation and adaptations in the data collection and analysis. For example, statistical experts in the Global South are known to devise their own platforms in the interest of independent, complementary data collection (Villacis et al. 2022). Statisticians may further rely on personal networks to access data sources, while innovating in data science approaches generating new proxies and extrapolations.
Objective 2 - Statistical innovations and truth claims
ModelFutures’ thick comparative ethnography will reveal how innovations mobilized to address multi-facetted challenges in future modelling are trusted, questioned, and negotiated. Analysis of discursive repertoires in moments of political interventions (e.g. in fertility) or the intergenerational prioritization of welfare spending reveals variously positioned claims to validity, both of the model and as its counter-positions. ModelFutures further attunes to the professional adoption or rejection of certain adaptations within the larger “method assemblage” of official statistical production (Ruppert and Scheel 2019).
Objective 3 - Anticipatory welfare policy
In this objective, we study how claims to validity that accompany quantitative representations of future welfare are taken up in decision making arenas. To investigate how welfare modelling is invoked in policy design, implementation, and evaluation, ModelFutures’ team traces how policy makers mobilize or reject specific quantified futures to prioritize projected future dividends over pressing real-time concerns. Our ethnography will reveal how contestations around truth claims, political attention and scarce resources intersect in the making of African welfare futures.
ModelFutures combines ethnographies of data practices with statistical analyses and an STS-informed investigation into information infrastructures and the global circulation of statistical models. We connect these approaches in:
- the project’s grounded, technographic focus on technology use in practice
- the thick comparative analysis of our observations across the four country cases. The data collection will build on long-term ethnographic engagement with the respective field sites to foster a deep understanding of the complex social and infrastructural organisation of these settings.
- Professor Latif Dramani, University of Thiès, Senegal
- Professor Keith Breckenridge, University of the Witwatersrand Johannesburg, South Africa
- Professor Emmanuel Didier, Centre Maurice Halbwachs (ENS/EHESS), France
- Professor Morten Jerven, Norwegian University of Life Sciences, Norway
Internal researchers
| Name | Title | Phone | |
|---|---|---|---|
| Thiel, Alena | Associate Professor |
External researchers
| Navn | Titel |
|---|---|
| N.N. | Postdoc (Kenya) |
| N.N. | Postdoc (Senegal) |
| N.N. | Postdoc (Population statistics) |
| N.N. | PhD researcher (Ghana) |
