Consistent Good News and Inconsistent Bad News

Good news is more persuasive when it is more consistent, and bad news is less damaging when it is less consistent. We show when Bayesian updating supports this intuition so that a biased sender prefers more or less variance in the news depending on whether the mean of the news exceeds expectations, and we apply the result to selective news distortion by a manager of multiple projects. If news from the different projects is generally good, boosting relatively bad projects increases consistency across projects and provides a stronger signal that the manager is skilled. But if the news is generally bad, instead boosting relatively good projects reduces consistency and provides some hope that the manager is unlucky rather than incompetent. We test for evidence of such distortion by examining the consistency of reported segment earnings across different units in firms. As predicted by the model, we find that firms report more consistent earnings when overall earnings are above rather than below expectations. Firms appear to shift the allocation of overhead and other costs to help relatively weak units in good times and relatively strong units in bad times. The mean-variance news preferences that we identify apply in a range of situations beyond our career concerns application, and differ from standard mean-variance preferences in that more variable news sometimes helps and better news sometimes hurts. Harbaugh, Maxwell: Indiana University. Shue: University of Chicago and NBER. We thank conference and seminar participants at ESSET Gerzensee, Queen’s University Economics of Organization Conference, NBER Corporate Finance Meetings, Indiana University, the University of Chicago, the University of Toronto, and UNSW for helpful comments. We are also grateful to Mike Baye, Phil Berger, Bruce Carlin, Archishman Chakraborty, Alex Edmans, Alex Frankel, Emir Kamenica, and Anya Kleymenova for helpful comments. We thank Tarik Umar for excellent research assistance.