UN (2019), and Biemer (2010)). We make sure to compare our framework with such other relevant frameworks.

Other collections of case studies of the impact of data on development exist as well, and we will draw examples from some of these other collections. Among these are the Data Impact Case Studies gathered by Open Data Watch (2021), the Value of Data Case Studies gathered by the Global Partnership for Sustainable Development Data (2021), Open Data Impact Map’s collection of use cases of open data (Center for Open Data Enterprise 2021), GovLab’s collection on the same topic (Verhulst and Young, 2016), a World Bank collection on the same topic (World Bank 2015), data2x’s collection of examples related to gender data (data2x 2018 and data2x 2019a), and the World Bank’s collection of examples related to digital identification in the health sector (World Bank 2018a), the public sector (World Bank 2018b), and the private sector (2018c).

We aim to contribute to the literature by directly tying the theoretical framework of data attributes to case studies, that is, to show how certain attributes of the data can help generate value for development. We make no attempt to estimate the net social value of government data, in part because many of the benefits occur in dimensions for which prices do not exist (e.g. improved health from drinking clean water), but this also reflects our view that there is no satisfactory way to assign a monetary value to an inexhaustible, or nonrival, good. The limitless scope for data to be used and reused to address new and unexpected problems, as well as the potential for data to be misused for public harm, suggests to us that any estimate of the expected net social value of government data is one that will be very imprecise. Rather by reviewing a series of examples where public data has been used to improve well-being, we provide evidence of the many ways in which data can be effectively used for the social good. Further by connecting the examples to our framework, we aim to bring structure to the series of examples which helps support our inferences on how best to scale up public data systems to realize greater value. Our hope is that this connection can arm data producers and users with evidence needed for decision makers to prioritize data.

# **2. Theory**

For data to generate value the data need to fulfill certain requirements related to their supply and demand. For the supply of data to generate value the data need to have adequate spatial and temporal coverage and be of high quality. With respect to coverage, the data need to cover all relevant units and time periods. Additionally, in the case of sample data, there needs to be proper sampling design, including adequate sample size, to support sufficiently precise inferences. We will argue that this means that the data need to be complete, frequent, and timely (these features, and the ones to follow will be defined more clearly in what follows). With respect to quality, the data need to accurately measure the concepts of interest to the users and be comparable and granular. To ensure a proper demand for data, the data should be easy to use and safe to use. Easy to use means that the data can be accessed, that they are understandable, and that they are 3