We study the effects of climate change and mitigation on U.S. politics. We combine 2000-2020 precinct-level voting information and congressional candidate positions on environmental policy with high-resolution temperature and precipitation data and census-block level measures of ``green" and ``brown" employment shares. Holding politician positions fixed within a district, we find that Democratic vote share increases with exogenous changes in local climate and green transition employment. We incorporate these estimates into a structural model of political competition, including both direct and demand-driven effects of shocks on candidate policy platform supply. Incorporating our model estimates into 2025-2050 projections of climate change and green employment transition, we find that voting for the Democrats increases, even as both parties move slightly to the right on climate policy. Under worst-case climate projections and current mitigation trajectories, the median 2050 Congressperson has roughly the same environmental ideology as the median 2010 Democrat –for instance supporting carbon pricing.

We present a methodology for decoupling taste-based versus statistical discrimination in political behavior. We combine a flexible empirical model of voting, featuring vertical and horizontal candidate differentiation in gender, ability, and policy positions, with a large-scale micro-targeted electoral experiment, aimed at increasing female candidate vote shares. Our structural econometric approach allows to separately identify preference parameters that drive taste-based discrimination and beliefs parameters that drive statistical discrimination via expectations about ability and policy positions of female politicians. Our application to Brazilian municipal elections uncovers substantial levels of taste-based and statistical discrimination. Counterfactual political campaigns show promise in reducing both.

This paper presents a theory and an empirical investigation of strategic communication in rulemaking, the process through which government agencies make regulations. It highlights the different empirical implications of models based on hard (verifiable), soft (cheap), and hybrid information in shaping agency policy. Using hundreds of thousands of rules and millions of pages of public comments directed at regulatory agencies and recorded in a large U.S. government repository, we show that theories based on purely hard or purely soft information fail to match key moments in the data. Hybrid theories, based on combinations of hard and soft information, show promise.