When programmes expand, new complexities and indirect consequences must be studied.
An aid programme helped rural labourers in Bangladesh to get work as rickshaw drivers by migrating to dense cities such as Dhaka.Landless agricultural workers and their families often go hungry between planting and harvest, the ‘lean season’ when the labour demand falls. In northern Bangladesh, my colleagues and I tested a way to ease this hunger.
When programmes enter a ‘scaling stage’, the focus often immediately shifts to solving the practical issues of broader implementation of the programme . All that work, although essential, overlooks the crucial question of whether exciting pilot results still hold. Many — if not most — development programmes encounter uncertainties and complexities that emerge only at scale. These are rarely observed — and therefore cannot be analysed — during the initial pilots.
Second, pay attention to broader social changes beyond the outcome that the original programme targeted. In our example, the migrants’ spouses and children ate more reliably but might have faced new risks of divorce, domestic violence or communicable diseases brought back from the city. The migration programme collapsed after expanding: subsidies mostly reached those who would have migrated anyway.Fourth, scale up in reasonable increments. We chose to expand the migration programme in stages and over several years. At each stage, we developed new experimental designs to better understand the broader range of benefits and costs, and the new complexities and risks.
Often, researchers are tempted to declare victory after a successful pilot and to jump to an ‘implementation phase’, assuming that the pilot results will hold. But if we truly care about whether an expanded programme improves lives, we must continue to ask questions and analyse issues that arise only at scale.When I advocate evaluation of scaling, people often respond that this wastes time: any lifesaving programmes should be expanded as rapidly as possible.
Doing scaling research is tough. It requires multi-year initiatives and multi-site trials, plus a gathering in of diverse researchers using a variety of analytical tools to study problems, interventions and outcomes from multiple angles and to synthesize evidence to judge whether and how an intervention should grow.
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