and time period of intervention, to features of the randomized research design (such as stratification or clustering) to contents of the data (such as how take-up/treatment compliance was measured). These options do not currently exist in commonly used data repositories in the social sciences (e.g. Harvard Dataverse, openICPSR, or the World Bank Microdata Catalog). We formulated a set of principles to guide decision making in selecting the final set of fields. These aim to balance the effort to contribute metadata, the usefulness of the metadata for different research purposes, and the complexity of the information collected. For example, we wanted to make the entry of new metadata straightforward for the majority of RCT datasets, while providing enough flexibility to describe unusual research designs or unique data properties.<sup>3</sup> In addition to our proposal, we also formulated a set of recommendations for implementing a catalog or database based on this metadata schema.

With this schema, we hope to provide a public good to the research community that facilitates the creation of inter-operable catalogs, with the goal of speeding up scientific discovery, reducing duplication of effort, and creating a systematic overview of existing RCT research. Research facilitated by RCT data catalogs on external validity, impact heterogeneity, and generalizability of policy impacts beyond the immediate study populations has the potential to help policymakers, funders, or impact investors make decisions about policy programs. Reuse of RCT data can in turn bolster investments in primary data collection and spur methods improvements that will make future experiments faster and more robust. Better access to RCT data can also offer new research opportunities for scholars who do not have the resources to undertake costly primary data collection themselves. Finally, original data is a citable contribution to science independent of an associated paper or report. Enhancing the visibility of the data and the data citation helps ensure that those responsible for creating the data receive credit (some of whom may not be co-authors of the academic study).

The next section gives a brief overview of areas of research that make use of secondary RCT data. Section 3 describes the process undertaken to create the metadata schema. Section 4 walks through the schema in detail, and section 5 describes considerations for creating a metadata catalog based on the schema.

# **2 Uses of Secondary Experimental Data in Research**

RCT data have many properties that make them useful for testing hypotheses and garnering insights that were not necessarily the focus of the original study. Randomization provides a credible exogenous source of variation in the data. Many RCTs collect representative data on large populations, often of groups that are underrepresented (e.g. because they are not part of the formal economy) or of particular interest for policy research (e.g. eligible for certain benefits). RCT datasets often contain indicators and variables of high policy relevance and may use innovative measurement methods such as lab-in-the-field preference measures.

> 3We also aimed to make the schema usable to describe laboratory experiments, although this is not the main focus.

3