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.
- golden247
Updated: Dec 20, 2022
The Personal Backgrounds of National Legislators in the World’s Democracies
Co-authored with Nicholas Carnes, Joshua Ferrer, Esme Lillywhite, Noam Lupu, and Eugenia Nazrullaeva
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
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- Co-authored with Saad Gulzar and Luke Sonnet
Updated: Apr 4
Co-authored with Saad Gulzar and Luke Sonnet
We report results of a randomized control trial conducted in Pakistan that uses Interactive Voice Response (IVR) 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. Politicians initially exhibit willingness to engage via IVR, commit- ting to recording multiple rounds. However, the intervention unexpectedly transformed from intensive to light-touch when politicians uniformly withdrew after a single round. Drawing on three dozen open-ended interviews, we find the about-face was triggered when voters misinterpreted communication as policy commitments by politicians. Politicians lack ade- quate resources to deliver services requested by voters. That the intervention backfired for politicians highlights objective constraints on service delivery in poor countries that hinder responsiveness by even well-intentioned political representatives.