Partisans hold unfavorable views of media they associate with the other party. They also avoid out-party news sources. We link these developments and argue that partisans assess out-party media based on negative and inaccurate stereotypes. This means cross-cutting exposure that challenges these misperceptions can improve assessments of out-party media. To support this argument, we use survey-linked web browsing data to show that the public has hostile views of out-party news sources they rarely encounter. We conduct three survey experiments that demonstrate cross-cutting exposure to nonpolitical or neutral political stories, forms of news widely available from online partisan sources, reduces oppositional media hostility. This explains how perceptions of rampant bias from out-party media coexist with more modest differences in the online content of major partisan news outlets. More broadly, we illustrate how negative misperceptions can sustain animus towards an out-group when people avoid encounters with them.

Objective. To demonstrate how a novel method enhances our understanding of determinants of inequality. Methods. We take advantage of recent advances in dynamic models of compositional dependent variables to simultaneously study tradeoffs across multiple slices of the composition of income in the United States between 1947 and 2014. Results. Our analyses demonstrate the utility of dynamic compositional models of income shares. Factors that increase the income share of the top income group often also increase the income share of the second income group and decrease the shares of lower groups at different rates. Polarization and marginal tax rates have large effects on relative income shares, while returns to labor, returns to capital, and partisan control of Congress have smaller, but statistically significant effects. Conclusion. Our suggested approach allows researchers to effectively explore interesting variation across multiple income groups in response to changes in determinants of inequality.

Partisans hold unfavorable views of media they associate with the other party. They also avoid out-party news sources. We link these developments and argue that, absent direct experience, partisans assess out-party media based off negative and inaccurate stereotypes. This means cross-cutting exposure that challenges these misperceptions can improve assessments of out-party media. To support this argument, we use survey-linked web browsing data to show the public has hostile views of out-party news sources they rarely encounter. We conduct three survey experiments that demonstrate cross-cutting exposure to non-political or neutral political coverage – forms of news widely available from partisan sources online – reduces oppositional media hostility. These findings explain how perceptions of rampant bias from out-party media coexist with modest differences in the online content major partisan news outlets provide. More broadly, we illustrate how negative misperceptions can sustain animus towards an out-group when individuals avoid direct encounters with them.

Commonly used unit-root tests in time-series analysis—such as the Dickey–Fuller and Phillips–Perron tests—use a null hypothesis that the series contains a unit root. Such tests have low power against the alternative—when a time series is near integrated or highly autoregressive—implying that they do poorly in distinguishing such a series from having a unit root. Kwiatkowski et al. (1992, Journal of Econometrics 54: 159–178) introduced the Kwiatkowski, Phillips, Schmidt, and Shin test, in which the null hypothesis is that the series is stationary, to deal with this problem. One shortcoming of the presently available Kwiatkowski, Phillips, Schmidt, and Shin test in Stata is that it uses asymptotic critical values regardless of the sample size. This poses a problem in that researchers—especially social scientists—are often presented with short time series. I introduce kpsstest, a command that extends the previous implementation by including an option for a zero-mean-stationary null hypothesis, generating sample and test-specific critical values, and reporting appropriate p-values.