Co-authored with Tara Slough et al.
On what basis can we claim a scholarly community understands a phenomenon? Social scientists generally propagate many rival explanations for what they study. How best to discriminate between or aggregate them introduces myriad questions because we lack standard tools that synthesize discrete explanations. In this paper, we assemble and test a set of approaches to the selection and aggregation of predictive statistical models representing different social scientific explanations for a single outcome: original crowd-sourced predictive models of COVID-19 mortality. We evaluate social scientists' ability to select or discriminate between these models using an expert forecast elicitation exercise. We provide a framework for aggregating discrete explanations, including using an ensemble algorithm (model stacking). Although the best models outperform benchmark machine learning models, experts are generally unable to identify models' predictive accuracy. Findings support the use of algorithmic approaches for the aggregation of social scientific explanations over human judgement or ad-hoc processes.
Updated: Dec 20, 2022
The Personal Backgrounds of National Legislators in the World’s Democracies
This note describes the Global Legislators Database (GLD), a new crossnational dataset on characteristics — political party, gender, age, education, and occupational background — of the roughly 20,000 lawmakers in the world’s democracies. The database includes 97 democ- racies (of 103) with populations over 300,000, with information about the 99.9 percent of legislators who held office in each country’s lower chamber or unicameral legislature during one legislative session in 2016 or 2017. The GLD is the largest individual-level biographical database on national legislatures ever assembled, and it has a wide range of potential applica- tions. In this note, we show that the GLD’s estimates of characteristics such as female repre- sentation are strongly validated by alternative estimates; we preview one potential application by conducting tests of hypotheses about gender, education, and occupationally-based gaps in reelection rates; and we discuss other possible uses for this one-of-a-kind resource for studying representation in the world’s democracies
Download the note below.
Updated: Aug 18
We report results of a randomized control trial conducted in Pakistan that uses Interactive Voice Response (IVR) technology to augment existing face-to-face communication between politicians and voters. IVR allows politicians to script questions for voters and voters to respond on cell phones. The technology modifies the initiator, scope, content, scale, personalism, and frequency of communication. Both politicians and voters initially exhibit willingness to engage via IVR. However, IVR does not change downstream voter views about politicians or electoral behavior, nor do politicians leverage the opportunity politically. We interpret the null results as well as the reluctance of politicians to engage repeatedly with voters via IVR as functions of the underlying constraints faced by politicians, who lack the means to satisfy voter demands. Descriptive data also suggest that failures of responsiveness may occur for reasons other than clientelism or elite capture.