We develop an estimator that allows us to calculate an upper bound to the fraction of unrejected null hypotheses tested in economics journal articles that are in fact true. Our point estimate is that none of the unrejected nulls in our sample is true. We reject the hypothesis that more than one-third are true. We consider three explanations for this finding: that all null hypotheses are mere approximations, that data-mining biases reported standard errors downward, and that journals tend to publish papers that fail to reject their null hypotheses only when the null hypotheses are likely to be false. While all these explanations are important, the last seems best able to explain our findings.

We provide microfoundations for dynamic skill formation with a model of investment in multiple skills, when jobs place different weights on skills. We show that credit constraints may affect investment even when workers do not exhaust their credit. Firms may invest in their workers’ skills even when there are many similar competitors. Firm and worker incentives can lead to overinvestment. Optimal skill accumulation resembles—but is not—learning by doing. An example shows that shocks to skill productivity benefiting new workers but lowering one skill’s value may adversely affect even relatively young workers, and adjustment may be discontinuous in age.

We develop an estimator that allows us to calculate an upper bound to the fraction of unrejected null hypotheses tested in economics journal articles that are in fact true. Our point estimate is that none of the unrejected nulls in our sample is true. We reject the hypothesis that more than one-third are true. We consider three explanations for this finding: that all null hypotheses are mere approximations, that data-mining biases reported standard errors downward, and that journals tend to publish papers that fail to reject their null hypotheses only when the null hypotheses are likely to be false. While all these explanations are important, the last seems best able to explain our findings.

Happiness is reported in ordered intervals (e.g., very, pretty, not too happy). We review and apply standard statistical results to determine when such data permit identification of two groups’ relative average happiness. The necessary conditions for nonparametric identification are strong and unlikely to ever be satisfied. Standard parametric approaches cannot identify this ranking unless the variances are exactly equal. If not, ordered probit findings can be reversed by lognormal transformations. For nine prominent happiness research areas, conditions for nonparametric identification are rejected and standard parametric results are reversed using plausible transformations. Tests for a common reporting function consistently reject.