Search

Both in their quantity and their quality, informal political conversations can provide an important bellwether for democratic health. However, not everyone is willing to participate in political conversations in all settings, and systematic imbalances in who chooses not to share political attitudes can distort perceptions of public opinion. Using data from three original surveys, including both observational and experimental analysis, we examine people's decisions to initiate political discussions using a psychological framework of self-threat and self-affirmation. We find that political conversations pose a higher level of self-threat when disagreement is probable and the relationship with the potential discussion partner is weaker. High levels of self-threat, measured via self-reported anxiety, are associated with a lower willingness to initiate a political conversation. However, self-threat can be counteracted. While it does not reduce the anxiety associated with a threatening situation, self-affirmation increases people's willingness to initiate a political conversation in higher threat circumstances. This suggests that efforts to find common ground or boost confidence by reflecting on non-political values could increase the pool of people willing to bring up and share their political views. [ABSTRACT FROM AUTHOR]

We show that a common method of predicting individuals' race in administrative records, Bayesian Improved Surname Geocoding (BISG), produces misclassification errors that are strongly correlated with demographic and socioeconomic factors. In addition to the high error rates for some racial subgroups, the misclassification rates are correlated with the political and economic characteristics of a voter's neighborhood. Racial and ethnic minorities who live in wealthy, highly educated, and politically active areas are most likely to be misclassified as white by BISG. Inferences about the relationship between sociodemographic factors and political outcomes, like voting, are likely to be biased in models using BISG to infer race. We develop an improved method in which the BISG estimates are incorporated into a machine learning model that accounts for class imbalance and incorporates individual and neighborhood characteristics. Our model decreases the misclassification rates among non-white individuals, in some cases by as much as 50%. [ABSTRACT FROM AUTHOR]