Regression models with log-transformed dependent variables are widely used by social scientists to investigate nonlinear relationships between variables. Unfortunately, this transformation complicates the substantive interpretation of estimation results and often leads to incomplete and sometimes even misleading interpretations. We focus on one valuable but underused method, the presentation of quantities of interest such as expected values or first differences on the original scale of the dependent variable. The procedure to derive these quantities differs in seemingly minor but critical aspects from the well-known procedure based on standard linear models. To improve empirical practice, we explain the underlying problem and develop guidelines that help researchers to derive meaningful interpretations from regression results of models with log-transformed dependent variables.

A growing body of evidence indicates the public is less committed to democracy than conventional wisdom long held. One possibility is that many in the public have internalized the norm that democracy is “good” but that such support is not firmly held. An implication of this reasoning is that because there is an expectation to express support for democracy, responses will be influenced by the presence of an interviewer due to social desirability effects. In this note, we examine the 2012 and 2016 American National Election Studies—in each year, a portion of respondents were interviewed via the internet while others were interviewed face-to-face. We identify a politically relevant difference between the two survey modes: those interviewed face-to-face express greater satisfaction with democracy. Indeed, the difference we identify is similar in magnitude to the difference typically observed between electoral winners and electoral losers. Our result is robust to different measurement and estimation strategies. While levels of satisfaction are influenced by the presence of an interviewer, a followup analysis indicates that the relationship between satisfaction and winner–loser status is similar across modes.

Across a series of experiments, I show that racial threat from a stereotypically nonthreatening racial minority group, Asian Americans, has a direct impact on white Americans' views of discrimination toward the group. When white Americans learn the group is growing, they feel a distinct racial threat which decreases support for the idea that Asian Americans experience discrimination while simultaneously increasing support for policy which actively discriminates against Asian Americans. I show this concept to be portable over context, examining support for discriminatory policy toward Asians in education policy and COVID-19 policy. I conclude by discussing the implications for how racial threat can drive more racial conflict while simultaneously decreasing the perception that this discrimination is occurring.

While Amazon's Mechanical Turk (MTurk) has reduced the cost of collecting original data, in 2018, researchers noted the potential existence of a large number of bad actors on the platform. To evaluate data quality on MTurk, we fielded three surveys between 2018 and 2020. While we find no evidence of a “bot epidemic,” significant portions of the data—between 25 and 35 percent—are of dubious quality. While the number of IP addresses that completed the survey multiple times or circumvented location requirements fell almost 50 percent over time, suspicious IP addresses are more prevalent on MTurk than on other platforms. Furthermore, many respondents appear to respond humorously or insincerely, and this behavior increased over 200 percent from 2018 to 2020. Importantly, these low-quality responses attenuate observed treatment effects by magnitudes ranging from approximately 10 to 30 percent.

We estimate how asset prices respond to a range of political shocks, including changes in a country's economic stewardship, national elections, coup d'états, wars, and terrorist attacks. Using an event study approach and daily prices from the Buenos Aires exchange (Argentina) between 1967 and 2020, we find that stock-market volatility increases in the days immediately following major policy-shifting events. These results hold irrespective of whether returns are measured in nominal terms, in local consumption units, or in US dollars. The most significant increase in post-event risk is associated with irregular government turnovers. Volatility also increases in the days immediately following a defeat in an international war, national elections, and changes in the country's economic stewardship. No changes in stock-market volatility occur, however, after terrorist attacks or when the date of a new administration's inauguration is publicly known and determined sufficiently far in advance.

What impact do party leaflets and canvass visits have on voter turnout? Get Out The Vote (GOTV) experiments consistently find that campaigning needs to be personal in order to be effective. However, the imbalance between United States and European-based studies has led to recent calls for further European GOTV experiments. There are also comparatively few partisan experiments. I report the findings of a United Kingdom-based field experiment conducted with the Liberal Democrats in 2017. Results show that party leaflets boost turnout by 4.3 percentage points, while canvassing has a small additional effect (0.6 percentage points). The study also represents the first individual level experiment to compare GOTV effects between postal voters and in-person voters outside the United States.