Gains and losses reported as other comprehensive income (OCI) and as special items (SI) are often viewed as similar in nature: transitory items with little ability to predict future cash flows and minimal implications for company value. However, current accounting standards require SI gains and losses to be recognized in net income, while OCI gains and losses are deferred until realized. This study empirically compares OCI and SI gains and losses using a model that jointly estimates value relevance, predictive value, and persistence. Results show that both SI and OCI gains and losses are value-relevant, but SI gains and losses exhibit zero persistence (i.e., are transitory), while OCI gains and losses exhibit negative persistence (i.e., partially reverse over time). Further, we find that SI gains and losses have strong predictive value for forecasting both future net income and future cash flows, while OCI gains and losses have weaker predictive value. Data Availability: All data are publicly available from sources indicated in the text.

We examine how subjective performance evaluations are influenced by the level and controllability of an accompanying measure of a separate performance dimension. In our experiment, supervisors evaluate the office administration performance of a hypothetical subordinate. We find that supervisors' subjective evaluations are directionally influenced by an accompanying objective measure of sales performance, even after excluding participants who perceive informativeness across measures. Consistent with concerns for fairness and motivation, we also find an asymmetric uncontrollability effect—supervisors' evaluations are higher when an uncontrollable factor decreases the subordinate's sales (i.e., they compensate for bad luck), but are not lower when the uncontrollable factor increases the subordinate's sales (i.e., they do not punish for good luck). This evidence suggests that supervisors use discretion provided to evaluate performance on one task to adjust for perceived deficiencies in the evaluation of performance on other tasks. Our study integrates theories of cognitive bias and motivation, highlighting the need to consider the potentially interactive effects of different performance measures in multi-task settings.