What shapes voter perceptions of election outcomes? Recent disputes in Malawi and Kenya highlight the vulnerability of local vote counts to accusations of malfeasance, which often generate negative public perceptions of vote reliability. Election monitoring in these countries is thought to crucially affect both the quality of the election and voters’ perceptions of the same. To date, most research on this topic has focused on the effect of non-partisan electoral observers. However, in many countries, two other interest groups also monitor the vote-counting process: political party agents and government election officials. Does the presence of these actors also affect voter perceptions of election integrity? To answer this question, I conducted a conjoint experiment in Malawi and Kenya in which voters evaluate the reliability of vote counts from hypothetical polling stations where the presence of party agents, non-partisan observers, and election officials is varied. I find that the presence of each of these groups does indeed shape voter perceptions: voters are more likely to view vote counts as reliable when they are co-signed by a party agent, election official, or non-partisan observer. Further, these preferences persist regardless of the voters’ own party affiliation or trust in electoral institutions.

Presidential elections can be forecast using information from political and economic conditions, polls, and a statistical model of changes in public opinion over time. However, these “knowns” about how to make a good presidential election forecast come with many unknowns due to the challenges of evaluating forecast calibration and communication. We highlight how incentives may shape forecasts, and particularly forecast uncertainty, in light of calibration challenges. We illustrate these challenges in creating, communicating, and evaluating election predictions, using the Economist and Fivethirtyeight forecasts of the 2020 election as examples, and offer recommendations for forecasters and scholars.

This study analyzes the relative accuracy of experts, polls, and the so-called ‘fundamentals’ in predicting the popular vote in the four U.S. presidential elections from 2004 to 2016. Although the majority (62%) of 452 expert forecasts correctly predicted the directional error of polls, the typical expert’s vote share forecast was 7% (of the error) less accurate than a simple polling average from the same day. The results further suggest that experts follow the polls and do not sufficiently harness information incorporated in the fundamentals. Combining expert forecasts and polls with a fundamentals-based reference class forecast reduced the error of experts and polls by 24% and 19%, respectively. The findings demonstrate the benefits of combining forecasts and the effectiveness of taking the outside view for debiasing expert judgment.

This letter reports the results of a study that combined a unique natural experiment and a local randomization regression discontinuity approach to estimate the effect of polls on turnout intention. We found that the release of a poll increases turnout intention by 5%. This effect is robust to a number of falsification tests of predetermined covariates, placebo outcomes, and changes in the time window selected to estimate the effect. The letter discusses the advantages of the local randomization approach over the standard continuity-based design to study important cases in political science where the running variable is discrete; a method that may expand the range of empirical topics that can be analyzed using regression discontinuity methods.