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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.

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Poverty dynamics and vulnerability during a growth episode: Evidence from Bangladesh: 2000 – 2016

Most of the world’s extreme poor live in middle income countries – but not for long

IIn the early 1990s, there was a near complete overlap between poor people and poor countries, with more than 9 out of 10 of the world’s extreme poor living in low-income countries at the time. This picture changed over the following two decades, in part driven by the movement in and out of the low-income group of populous countries like China and India where most of the extreme poor lived.

Pandemic, prices, and poverty

The world’s poorest population have faced two extraordinarily difficult years. The pandemic has caused unprecedented reversals in poverty reduction that are further exacerbated by rising inflation and the effects of the war in Ukraine.