# **1. Introduction**

Around the world, the public sector is producing more and more data. Since 2005, the number of countries without a population and housing census conducted over the preceding ten years has fallen by nearly 80 percent (from 36 to 8), the number of countries without a labor force survey conducted over the same period has fallen by 50 percent (from 98 to 49), and the number of countries without nationally representative administrative data on learning assessments within a five-year range has fallen by 83 percent since 2008 (from 36 to 6) (World Bank 2021a).

As highlighted by the World Development Report (WDR) 2021: Data for Better Lives, the abundance of data produced by the public sector can have a transformational role in development and efforts to reduce poverty. Among many fruitful uses, the data can be used to increase access to government services, prepare for and respond to emergencies, target resources and foster the inclusion of marginalized groups, save money and resources in policy implementation and service delivery, monitor progress and track performance, increase accountability, and empower individuals (World Bank 2021b).

Yet are we really seeing the returns from the data that one could hope for given their abundance? This paper identifies the conditions under which data produced by the public sector are valuable for development, and expands on the assertions and examples highlighted by the WDR 2021. The clear identification of these conditions can help governments understand how to improve the supply of and demand for data. To this end, we first develop a theoretical framework detailing 12 conditions needed for data to generate value. Next, we use a collection of case studies to provide support for these conditions and showcase how they matter in practice. Our examples and analysis distill that for data to maximize value, the data should have adequate coverage (be complete, frequent, and timely), be of high quality (be accurate, comparable, and granular), be easy to use (be accessible, understandable, and interoperable), and be safe to use (be impartial, confidential, and appropriate). Too often, we find, the data produced by governments do not satisfy these conditions and thus are not conducive to transforming development outcomes. The data may be of poor quality, siloed in various administrative systems, not shared with the public, not readable by computers, and so forth.

We restrict our analysis to data collected by government agencies, such as surveys, censuses, and administrative data, with a focus on low and middle-income countries. This means that we will neglect private sector data, citizen generated data, and data from high-income countries. We believe that the case for improving the stock of high-quality data and the safe use of data is particularly pertinent for governments in low and middle-income countries. Our objective is to provide a series of examples that illustrate the conditions under which development data can generate value. The realized social value in these examples is large and typically occurs in nonmonetary dimensions, such as improved health and safety.

Several other frameworks exist that list features conducive for data to be valuable. Most of these frameworks have been developed by national statistical offices or international organizations to guide data producers (see for example Statistics Canada (2017), OECD (2011), 2