We study how author connections influence paper outcomes at the Journal of Human Resources. Authors who attended the same PhD program, worked with, affiliate with the same National Bureau of Economic Research program(s), or are closely linked via coauthorship networks as the handling editor are more likely to avoid a desk rejection. Reviewer recommendations are similarly influenced by PhD and employment matches. Matching on signals of ability—such as top five publishing, attending a high-ranked PhD program, or working in a high-ranked department—also impact peer review decisions. We find some evidence that published papers with greater connectivity subsequently receive fewer citations.

We use unique data from journal submissions to identify and unpack publication bias and p-hacking. We find initial submissions display significant bunching, suggesting the distribution among published statistics cannot be fully attributed to a publication bias in peer review. Desk-rejected manuscripts display greater heaping than those sent for review; i.e., marginally significant results are more likely to be desk rejected. Reviewer recommendations, in contrast, are positively associated with statistical significance. Overall, the peer review process has little effect on the distribution of test statistics. Lastly, we track rejected papers and present evidence that the prevalence of publication biases is perhaps not as prominent as feared. (JEL A11, A14, C13, L82)