The Samagra anti-poverty programme in Madhya Pradesh: integrating household data, overcoming silo-problems and leaving nobody behind.

Motivation: To “leave nobody behind” a state must maintain a comprehensive database of individuals and households within its jurisdiction. Such a database can also assist in overcoming silo-based fragmentation in government anti-poverty programmes and services. We explore the process of establishing such a database through a case study of the Samagra programme in the Indian state of Madhya Pradesh. Research questions: How was Samagra conceived and implemented, given the political, administrative and technical challenges confronting large data and programme consolidation in the public sector? What are the benefits and potential risks to rationalizing social protection programmes of this kind?. Approach: The article is an extended case study based on the main author’s experience and reflection as a leading participant in Samagra. It also draws on official evaluations of Samagra. Findings: Samagra demonstrates that it is feasible to rationalize siloed social protection programmes. It also demonstrates the scope for shifting welfare provision from being demand-led to entitlement-based. The successful establishment of Samagra arose from a shared narrative of improvement at state, national and global levels, strong political and administrative leadership at the state level, and the potential for relatively quick and substantial budgetary pay-offs. The efficiency gains achieved must be balanced against risks arising from the state’s centralization of data. Policy implications: The Samagra model is already being emulated in other states across India, and there is also scope for other countries to learn from it, particularly those with fragmented social protection systems. While holding out the promise of leaving fewer citizens behind in pursuit of the Sustainable Development Goals (SDGs) the approach also accentuates the need to strengthen the governance and public accountability of consolidated data, particularly when linked to public entitlements.