Timely, reliable data are key for governments to respond effectively to fast-changing crises such as climate shocks, conflict, and pandemics. / Credit: Shutterstock.
Out-of-date information can leave policymakers “flying blind” just when they need to adapt to fast-changing realities such as extreme weather, conflict, or pandemics. In today’s world, where governments need to respond to multiple, often overlapping crises, tracking household welfare in a timely manner has become not just useful but essential.
Toward real-time welfare monitoring: combining surveys, big data, and models
To address this challenge, there is a growing toolbox of approaches that combine traditional information on welfare (such as from a household budget survey) with more frequently available sources of data. These more frequent inputs can come from a range of sources including other household surveys, macroeconomic data, rapid phone or online surveys, administrative records, geospatial information, and even mobile phone metadata. By pairing these inputs with robust modeling techniques, researchers can generate more up-to-date estimates of household welfare.
A roadmap for practitioners
The World Bank’s Measuring Welfare When It Matters Most: A Typology of Approaches for Real-Time Monitoring can help practitioners navigate this growing menu of options. This volume systematizes the decisions faced by practitioners by mapping methods to different scenarios and clarifying their respective data requirements, assumptions, and trade-offs to help users identify the best approach for their context.
Ultimately, the goal is to ensure that governments have the timely information they need to design adaptive, evidence-based policies. With more effective monitoring systems, policymakers will be better equipped to protect people living in poverty and vulnerable communities when shocks strike and course-correct policies as conditions evolve. Investing in the capacity for real-time welfare monitoring is not a luxury but a necessity in today’s increasingly uncertain world.
For practical examples of how these approaches have been applied on the ground, see the companion volume Measuring Welfare When It Matters Most: Learning from Country Applications.
“Best fit” rather than best practice
When it comes to real-time monitoring, there is no one-size-fits-all solution. The first step is to clarify the policy question. Do you need to estimate the poverty rate? Do you need to understand how shocks affect households? Or do you need to understand how proxy or leading indicators such as employment, prices, or service delivery are changing? The motivating question should guide the choice of method.
Each approach comes with its own advantages and trade-offs. Covariate-based nowcasting, which uses patterns from past surveys and auxiliary indicators, can provide relatively quick updates but requires careful validation and may not work with large shocks. GDP-based models are simple and cost-effective, but they may miss important distributional changes. Microsimulation models allow for detailed analysis of how shocks or policies affect different groups, but they demand more data, more time to set up, and also require validation.
The same is true for data sources. Phone surveys can deliver rapid information but risk excluding hard-to-reach populations. Big data sources such as satellite imagery or mobile phone metadata offer wide coverage, but they may not help assess direct changes in monetary welfare.
In practice, the “best fit” approach will vary depending on the question, the availability and quality of data inputs, and other country-specific constraints. And whatever the choice, context matters — rigorous validation and triangulation are essential to ensure that results are credible and useful for decision-making.
Building on strong foundations, modernizing for the future
One lesson is clear in the move toward real-time welfare monitoring: new tools can’t replace strong baseline data. Household surveys remain the gold standard for assessing welfare, poverty, and living standards. Without them, models risk producing results that are inaccurate — or misleading. That’s why investments in national statistical systems are essential. Encouragingly, many countries are already modernizing their data systems, often with support from the World Bank.
Still, surveys as we know them need to evolve. They are expensive, complex, and often conducted only once every several years. In fragile and low-income countries, the gaps can stretch for a decade or more. For governments trying to respond to shocks or adjust safety nets, that’s simply too slow.
The solution is not to replace surveys but to make them smarter and more connected. With tight budgets, surveys must become more efficient and nimbler. Linking them with censuses, geospatial data, administrative records, and even satellite imagery or mobile phone metadata can boost cost-effectiveness and accuracy. These integrations allow policymakers to monitor welfare in more detail — both across regions and over time.
Practical examples already exist. Poverty maps, for instance, combine survey and census data with geospatial information to show where poor households are located. Today, georeferenced surveys can also be paired with satellite imagery and machine learning to generate high-resolution estimates of poverty, wealth, and agricultural outcomes. Linking surveys to hazard maps even makes it possible to identify which communities are most at risk from extreme weather.
Another step is standardizing core variables — such as age, gender, or access to services—across surveys. This makes it easier to connect datasets, compare results, and apply advanced tools like AI to spot patterns or generate timely predictions.
The bottom line: real-time systems work best when built on strong survey foundations. Household surveys aren’t going away—but they must be modernized and combined with new data sources to deliver the timely, reliable insights that adaptive policymaking requires.
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