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Overview
In today's rapidly changing world, timely development data is crucial for effective policymaking. The World Bank and its partners are working to invest in frontier approaches to monitor poverty and welfare in real time. By combining traditional data sources with cutting-edge technologies and methodologies, we aim to develop cutting-edge tools to inform decision making and strengthen the adaptive capacity of policies.
Why Real-Time Monitoring Matters
Traditional poverty measures, while crucial, often reflect conditions from months or years past. In a world of increasing uncertainty - from pandemics to climate shocks - policymakers need timely information to design effective interventions. This work is particularly vital in data-deprived contexts, where household surveys may be infrequent or challenging to conduct.
Our Approach
The World Bank is developing and investing in new approaches to bridge gaps in welfare and poverty data. Many of these approaches leverage new machine learning methods and diverse data sources - from traditional household surveys to satellite imagery and mobile phone data – to create more frequent estimates of poverty. These efforts complement and build on traditional data collection efforts, enhancing the value of existing surveys and supporting the modernization of statistical systems.
What We Do
RESOURCES
Measuring welfare when it matters most: A typology of approaches for real-time monitoring
Event: Progress on Real Time Welfare Monitoring
The Survey Well-Being via Instant and Frequent Tracking (SWIFT)
Putting Mobile Phone Data to Work for Policy
BLOGS
Working together to launch the world’s largest cohort in mobile phone data for policy
Assessing poverty nowcasting amidst the COVID-19 crisis
Quantifying the poverty impact of the 2022 floods in Pakistan
How do we measure poverty in Paraguay for more agile responses?
Using big data and machine learning to locate the poor in Nigeria
Introducing SWIFT: Real-time poverty monitoring using machine learning