This paper questions the conventional wisdom that publication bias must result from the biased preferences of researchers. When readers only compare the number of positive and negative results of papers to make their decisions, even unbiased researchers will omit noisy null results and inflate some marginally insignificant estimates. Nonetheless, the equilibrium with such publication bias is socially optimal. The model predicts that published non-positive results are either precise null results or noisy but extreme negative results. This paper shows this prediction holds with some data, and proposes a new stem-based bias correction method that is robust to this and other publication selection processes.

Over half a billion children lack adequate lighting and use dim, smoky and dangerous kerosenebased lighting for their evening studies. This article examines the conventional wisdom that the brighter, clean, safe and zero-marginal-cost light of solar lamps enhances children's learning outcomes. In a randomised experiment, unexpectedly, solar lamps lowered test scores by five points out of 100 (0.25 standard deviation), but increased reported study time by approximately 30 minutes per day. This may be due to flickering from lack of full charge, lowering their productivity. The nationwide learning assessment suggests that solar lamps likely have an insignificant effect on educational attainment.

The article titled “Fiscal Responses to the COVID-19 Crisis in Japan: The First Six Months,” published in The National Tax Journal, Vol. 73, No. 3, had an error in Figure 2, page 905. That error has since been corrected in online publishings of the article and the corrected version is below. The authors apologize for the error and any confusion that it may have caused.

While they are rare, superspreading events (SSEs), wherein a few primary cases infect an extraordinarily large number of secondary cases, are recognized as a prominent determinant of aggregate infection rates (R0). Existing stochastic SIR models incorporate SSEs by fitting distributions with thin tails, or finite variance, and therefore predicting almost deterministic epidemiological outcomes in large populations. This paper documents evidence from recent coronavirus outbreaks, including SARS, MERS, and COVID-19, that SSEs follow a power law distribution with fat tails, or infinite variance. We then extend an otherwise standard SIR model with estimated power law distributions, and show that idiosyncratic uncertainties in SSEs will lead to large aggregate uncertainties in infection dynamics, even with large populations. That is, the timing and magnitude of outbreaks will be unpredictable. While such uncertainties have social costs, we also find that they on average decrease the herd immunity thresholds and the cumulative infections because per-period infection rates have decreasing marginal effects. Our findings have implications for social distancing interventions: targeting SSEs reduce not only the average rate of infection (R0) but also its uncertainty. To understand this effect, and to improve inference of the average reproduction numbers under fat tails, estimating the tail distribution of SSEs is vital.