Climate change mitigation has been one of the world’s most salient issues for the past three decades. However, global policy attention has been partially diverted to address the COVID-19 pandemic for the past two years. Here, we explore the impact of the pandemic on the frequency and content of climate change discussions on Twitter for the period of 2019 – 2021. Consistent with the “finite pool of worry” hypothesis, both at the annual level and on a daily basis, a larger number of COVID-19 cases and deaths is associated with a smaller number of “climate change” tweets. Climate change discussion on Twitter decreased despite (a) a larger Twitter daily active usage in 2020 and 2021, (b) greater coverage of climate change in traditional media in 2021, (c) a larger number of North Atlantic ocean hurricanes, and (d) a larger wildland fires area in the United States in 2020 and 2021. Further evidence supporting the finite pool of worry is the significant relationship between daily COVID-19 cases/deaths on the one hand and the public sentiment and emotional content of “climate change” tweets on the other. In particular, increasing COVID-19 numbers decrease negative sentiment in climate change tweets and the emotions related to worry and anxiety, such as fear and anger.

We investigate three factors considered crucial in assessing the deservingness of a recipient of income redistribution: effort, luck, and need. Individuals rely on effort cues to assess deservingness, but need and effort push deservingness in opposite directions. More effort generally results in lower need. But unlucky individuals can be needy, despite exerting effort. Based on a model of conditional altruism, we design a real-effort experiment with two conditions: one where luck is observable to the redistributor, and one where luck is unobservable. Comparative static predictions derived from the model were pre-registered as hypotheses. When luck is observable, responses to both need and effort are consistent with the predictions of the model, and we observe clearly identifiable types: those who are insensitive to effort and respond only to need, and others who respond exclusively to effort. Without information about luck, however, the effort cue was obscured and the need effect predominated.

Why do we favor or disfavor redistribution? When making decisions about their preferences for redistribution, people rely on their experiences with income inequality. We propose that experiences with extreme income inequality will change attitudes and behaviors regarding redistribution. However, sometimes these experiences can be difficult to measure. Using an experimental economic game, we test for differences in self-reported attitudes and behavior among subjects who experienced extreme income inequality compared to those subjects who did not. We find that when subjects are exposed to extreme levels of income inequality, they redistribute more than subjects exposed to moderate levels of inequality. However, experiencing extreme inequality didn’t lead to changes in self-reported attitudes toward redistributive policies, like the minimum wage or top-income taxes. Additionally, when making decisions about redistribution, people may care about the fairness of how incomes are allocated. People may also care about the relative inequality between themselves and others. To disentangle between these two mechanisms, our experimental game also manipulates how incomes are generated: via luck or via merit. We found that there was slightly less redistribution in the extreme inequality condition when incomes were determined via merit as opposed to the luck condition. Additionally, subjects who experienced extreme inequality did not report that the game was any more or less fair than those who experienced moderate inequality. Those subjects who experienced extreme inequality were, however, more likely to report that the differences in incomes were too large.