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Using Big Data to transform the poverty conversation: a Small Area Estimation approach

Social protection programs and appropriate policy are impactful drivers of poverty reduction but they need up-to-date, comprehensive and accurate data in order to effectively tackle the causes of poverty. Here we talk about advanced statistical techniques using Big Data to generate granular poverty estimates at a much lower cost and more frequently than ever before, enabling more dynamic conversations to help end extreme poverty.

The peace predicament: How to make a meaningful impact on extreme poverty in a world of conflict

“There can be no sustainable development without peace and no peace without sustainable development”, the UN 2030 agenda for Sustainable development asserts.

How a new type of insurance helps to relieve poverty in Ethiopia

Livestock owners in Ethiopia are in a hugely vulnerable position. Their livelihood is highly dependent on weather patterns and a drought could lead to catastrophic herd loss, potentially pushing them into extreme poverty. Livestock owners are currently unable to mitigate these risks since traditional insurance markets are overpriced and underprovided due to issues of moral hazard and adverse selection. However, a new form of index based livestock insurance (IBLI) addresses these issues by issuing indemnity payments based on objectively verifiable climatic reasons. In IBLI’s case the insurance pay-out is ingeniously based on vegetation levels as seen by remote satellite imagery which can be used to estimate livestock mortality rates.

How Data could save the SDGs: Three approaches to Ending Extreme Poverty

Around half of the 140 SDG targets measured are described in the report as ‘weak or inefficient’ showing moderate or severe deviations from the desired trajectory. For more than 30 percent of the targets, including those concerning poverty, no progress is reported at all – and in some cases actual regression since 2015 is noted.