In addition, as RCTs as a method mature, researchers have turned their attention to consolidating and systematizing the RCT-based evidence, as well as expanding and improving experimental methodology and econometric analysis methods. In short, many emerging areas of secondary research could benefit from improved access, systematic cataloging, and harmonized documentation of RCT data. Here we provide a brief overview of research that reuses RCT data and informed the development of the schema.

_Combining evidence._ Meta-analysis techniques such as Bayesian hierarchical models (BHM) can increase external validity and generalizability of experimental results. Recent examples include Bandiera et al. (2021), who combine 16 laboratory and field experiments to estimate the impact of performance pay on women, and Meager (2019), who estimates the impact of micro credit on income-generating activities and consumption. When available data is not catalogued, meta-studies run the risk of overlooking less prominent studies.<sup>4</sup> _Assessing estimation and prediction methods._ In seminal work, LaLonde (1986) used experiments as a benchmark to assess the bias in non-experimental estimation methods. The literature that followed (Fraker and Maynard, 1987; Dehejia and Wahba, 1999, 2002; Glazerman et al., 2003, and many more) has matured to the point of being able to draw conclusions about the full distribution of bias (e.g Chaplin et al., 2018). Researchers have also used experimental data for making out-of-sample predictions and evaluating external validity, e.g. by comparing different prediction methods or quantifying site selection bias (e.g. Hotz et al., 2005; Allcott, 2015; Gechter et al., 2019).

_Measurement._ Researchers use existing data to validate methods of measurement for important concept and indicators. Recent work has for example examined sources of measurement error in agricultural data (e.g. Beegle et al., 2012; Rosenzweig and Udry, 2019) and measurement methods for women’s agency (e.g. Donald et al., 2020; Jayachandran et al., 2021).

_Estimating structural models._ Structural models can exploit the experimental variation for identification and enrich experimental data, for example by evaluating the role of underlying preferences and behavioral factors for take-up decisions as a means to conduct welfare analysis or make predictions for the effects of new policies (e.g. Todd and Wolpin, 2006, 2010; Meghir et al., 2019; Guiteras et al., 2019).

_Statistical learning and machine learning._ A rapidly growing literature applies machine learning methods to RCT data. Examples include regularization methods to discipline covariate selection or the identification of treatment effect heterogeneity (e.g. Chernozhukov et al., 2018; Chernozhukov et al., 2018), and new sampling methods, e.g. multi-arm bandits and related adaptive experimental algorithms (e.g. Dimakopoulou et al. (2018); Caria et al. (2021); Kasy and Sautmann (2021)). Existing RCT datasets can be used to optimize algorithms, check large- or small-sample behavior, and simulate thousands of trials to improve speed and evaluate performance without incurring costs or burdening subjects.

> 4An issue potentially exacerbated by biases inherent to the publication process (Andrews and Kasy, 2019).

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