Charles Angelucci (MIT), "Beliefs About Political News in the Run-up to an Election" (w/Michel Gutmann and Andrea Prat)
Abstract
We use a large-scale news knowledge survey conducted just before the 2020 US presidential election, alongside monthly survey data, to explore how partisan differences in political news beliefs evolve. We exploit questions repeated in multiple surveys to identify changes in beliefs about the same news stories as the election approaches. Our findings indicate that partisan bias intensifies two to threefold during election periods. Within a framework of motivated beliefs, this change in partisan bias is predominantly driven by an amplification of the partisan identity effect, rather than differences in partisan recall. We also present findings from a counterfactual analysis that assesses the impact of a hypothetical targeted misinformation campaign during and outside of elections. ... Read more about Charles Angelucci (Alesina Seminar)
Anton Strezhnev (UChicago), "A Guide to Dynamic Difference-in-Differences Regressions for Political Scientists"
Abstract
Difference-in-differences (DiD) designs for estimating causal effects have grown in popularity throughout political science. Many DiD papers present their central results through an "event study" plot - a visualization that combines estimated dynamic average treatment effects for multiple post-treatment time periods alongside placebo tests of the main identifying assumption: parallel trends. Despite their...
Andrew O’Donohue, "The Court of Public Opinion: How Competing Rhetoric about Trump's Prosecution Affects Political Attitudes"
Abstract
Prosecutions of political leaders may have double-edged effects on public opinion. While legal interventions may turn public opinion against law-breaking politicians, prosecutions may also increase support for the accused leader and encourage his supporters to seek retaliation. Crucially, political elites seek to persuade citizens with competing framings of political prosecutions. Whereas legal officials...
Melissa Dell (Department of Economics), "Deep Learning for Political Economy"
Abstract
Deep learning provides a robust method for learning a mapping between unstructured data (e.g., text, images, audio) and computable representations that can power downstream analyses. These methods, which have already transformed a variety of disciplines, allow us to process traditional data sources at an unprecedented scale and to bring completely new types of data into political economy analyses. Yet taking existing methods off-the-shelf often has significant limitations - particularly for historical applications or those in non-Western societies – given the domain shift from the pre-training corpora that power much of deep learning. This talk will provide an overview of work developing novel datasets and methods for using deep learning to examine social science questions. These include a series of user-friendly open-source packages for deep learning-powered document layout analysis, OCR, record linkage, and other data wrangling tasks, designed to be highly extensible to a diversity of societies. I will also introduce massive-scale open-source text datasets that we curated by applying deep learning to historical newspapers. These are useful both for large-scale pre-training and for social science research. Finally, I will discuss deep learning methods designed to examine the influence of historical media.... Read more about Melissa Dell (Alesina Seminar)
Amanda Coston (Microsoft Research), "Addressing confounding in decision-making algorithms"
Abstract
Machine learning algorithms are used for decision-making in societally high-stakes settings from child welfare and criminal justice to healthcare and consumer lending. These algorithms are often intended to predict outcomes under a proposed decision. It is challenging to evaluate how well these algorithms perform because we only observe the relevant outcome under a biased sample of the population. In this talk, we explore how to use...
Kiara Hernandez, "Firm-level Ethnoracial Diversity and Support for Unionization"
Abstract
In the United States today, mass preferences for fiscal and social spending policies appear minimally responsive to rising earnings inequality and rapidly deteriorating job protections. Theories of political behavior and political economy maintain that because individuals’ preferences for redistribution depend on whether they perceive racial outgroups to be policy beneficiaries, racial animus may explain the mismatch between contemporary inequality and...