There is growing evidence on the role of economic conditions in the recent successes of populist and extremist parties. However, little is known about the role of over-indebtedness, even though debtor distress has grown in Europe following the financial crisis. We study the unique case of the Czech Republic, where by 2017, nearly one in ten citizens had been served at least one debtor distress warrant even though the country consistently features low unemployment. Our municipality-level difference-in-differences analysis asks about the voting consequences of a rise in debtor distress following a 2001 deregulation of consumer-debt collection. We find that debtor distress has a positive effect on support for (new) extreme right and populist parties, but a negative effect on a (traditional) extreme-left party. The effects of debtor distress we uncover are robust to whether and how we control for economic hardship; the effects of debtor distress and economic hardship are of similar magnitude, but operate in opposing directions across the political spectrum.

We investigate public preferences for equity-enhancing policies in access to early child care, using a survey experiment with a representative sample of the German population (n ≈ 4, 800). We observe strong misperceptions about migrant-native inequalities in early child care that vary by respondents’ age and right-wing voting preferences. Randomly providing information about the actual extent of inequalities has a nuanced impact on the support for equity-enhancing policy reforms: it increases support for respondents who initially underestimated these inequalities, and tends to decrease support for those who initially overestimated them. This asymmetric effect leads to a more consensual policy view, substantially decreasing the polarization in policy support between under- and overestimators. Our results suggest that correcting misperceptions can align public policy preferences, potentially leading to less polarized debates about how to address inequalities and discrimination.

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

We investigate public preferences for equity-enhancing policies in access to early child care, using a survey experiment with a representative sample of the German population (n ≈ 4, 800). We observe strong misperceptions about migrant-native inequalities in early child care that vary by respondents’ age and right-wing voting preferences. Randomly providing information about the actual extent of inequalities has a nuanced impact on the support for equity-enhancing policy reforms: it increases support for respondents who initially underestimated these inequalities, and tends to decrease support for those who initially overestimated them. This asymmetric effect leads to a more consensual policy view, substantially decreasing the polarization in policy support between under- and overestimators. Our results suggest that correcting misperceptions can align public policy preferences, potentially leading to less polarized debates about how to address inequalities and discrimination.