Household Joblessness in U.S. Metropolitan Areas during the COVID-19 Pandemic: Polarization and the Role of Educational Profiles.

The authors use Current Population Survey 2016 to 2021 quarterly data to analyze changes in household joblessness across metropolitan areas in the United States during the coronavirus disease 2019 pandemic. The authors first use shift-share analysis to decompose the change in household joblessness into changes in individual joblessness, household compositions, and polarization. The focus is on polarization, which is the result of the unequal distribution of individual joblessness across households. The authors find that the rise in household joblessness during the pandemic varies strongly across U.S. metropolitan areas. The initial stark increase and subsequent recovery are due largely to changes in individual joblessness. Polarization contributes notably to household joblessness but to varying degree. Second, the authors use metropolitan area-level fixed-effects regressions to test whether the educational profile of the population is a helpful predictor of changes in household joblessness and polarization. They measure three distinct features: educational levels, educational heterogeneity, and educational homogamy. Although much of the variance remains unexplained, household joblessness increased less in areas with higher educational levels. The authors show that how polarization contributes to household joblessness is shaped by educational heterogeneity and educational homogamy.

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty.

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.