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.

This paper uses a new empirical design to explore how labor market polarization affects individuals’ incentive to pursue education funded on the margin by student debt. We argue that the labor market polarization–where automation replaces mid-skill and mid-education-level job–changes the marginal benefits of education and training and sharpens incentives to incur student debt. We advance a new measure of labor market polarizations that allows to capture the heterogeneity of this phenomena across geographies and time. Using this measure, we find that U.S. CBSAs that experience deeper labor market polarization see an increase in student debt balances and in the number of people pursuing student debt. On average, the decline in middle-skill jobs and wages has little effect on individuals’ ability to pay down existing student debt. The effects are most pronounced in ZIP codes with lower average credit scores, lower incomes, and higher share of the minority population.

In statistics, samples are drawn from a population in a datagenerating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidencegenerating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.