The digital economy is transforming how data is collected, processed and used for evidence-based decisions to monitor and achieve the Sustainable Development Goals. Promising new methods that combine traditional household survey data with non-traditional data sources (such as mobile phone data, satellite data and text data) are creating opportunities to map poverty at a higher resolution and scale. Nonetheless, significant technical, practical and ethical challenges still hamper the operationalization of these methods.
Ending poverty is not only one of the twin goals of the World Bank, but also one of the Sustainable Development Goals. To design and optimize projects for poverty reduction, we need to measure their impact on poverty. This is quite difficult because changes in the poverty rate might take some time, and it is usually hard to attribute the impact to a particular project, especially without conducting a randomized controlled trial (RCT). But even if we manage to overcome these challenges, we need to measure poverty before the start of the project – as a baseline and to understand whether the project adequately targets the poor – and at the end of the project to assess its impact. And that is also not easy.