Key findings:
Targeted social protection interventions can play a valuable role in helping achieve and deliver Universal Social Protection. Targeted programs and universal programs together support broader social policy.
Targeting is an effective tool used in social protection to make the most of constrained fiscal space. For a given budget, prioritizing poorer households can produce more progress on reducing poverty and inequality, smoothing income, and other dimensions of welfare such as human capital.
There is no single targeting method that fits every situation. Context and policy objectives drive choices. Whether to use methods such as self-targeting, geographic targeting, demographic targeting, or household welfare-based targeting methods must be based on context and capacities.
Regardless of the targeting method, robust social protection delivery systems can help:
- reduce transactions costs or stigma for beneficiaries,
- minimize inclusion errors,
- facilitate crisis response,
- improve access to social assistance, especially for the poorest and most vulnerable populations such as indigenous, migrants, people living with disability and others.
Advances in technology—ICT, big data, artificial intelligence, and machine learning—offer the promise of significant improvements in targeting accuracy but are not a panacea. Better data may matter more than greater sophistication in data use.
Social protection targeting methods are changing as new data and technology as well as other innovations emerge.