Skip to content


The deep logo on a light grey background

Practical guide to Small Area Estimation of poverty using remotely sensed variables – Bangladesh example

A practical guide to statistical modelling of poverty at small administrative areas, i.e. Small Area Estimation. This book demonstrates how to leverage remotely sensed data in combination with household surveys to produce spatially disaggregated poverty statistics. It introduces theoretical concepts, and provides a step-by-step walkthough of the estimation procedure in R. You can follow the guide from the start (Chapter 1), or, if you are familiar with theory and want to dive straight into the estimation – skip to the practical part.

The deep logo on a light grey background

Poverty Dynamics in Bangladesh – Selective Review

This paper is a selective review of poverty dynamics in Bangladesh, looking at multiple data sources and considering COVID-19 and a high vulnerability to poverty across the country.

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

Social protection programmes 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. In this blog we will discuss 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 deep logo on a light grey background

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