A growing literature documents that complex financial statements negatively affect the information environment. In this paper, we examine whether managers use voluntary disclosure to mitigate these negative effects. Employing cross-sectional and within-firm designs, we find a robust positive relation between financial statement complexity and voluntary disclosure. This relation is stronger when liquidity decreases around the filing of the financial statements, is stronger when firms have more outside monitors, and is weaker when firms have poor performance and greater earnings management. We also examine the relation between financial statement complexity and voluntary disclosure using two quasi-natural experiments. Employing a generalized difference-in-differences design, we find firms affected by the adoption of complex accounting standards (e.g., SFAS 133 and SFAS 157) increase their voluntary disclosure to a greater extent than unaffected firms. Collectively, these findings suggest managers use voluntary disclosure to mitigate the negative effects of complex financial statements on the information environment.

This paper examines how the ex ante level of public scrutiny influences a manager's subsequent decision to misreport. The conventional wisdom is that high levels of public scrutiny facilitate monitoring, suggesting a negative relation between scrutiny and misreporting. However, public scrutiny also increases the weight that investors place on earnings in valuing the firm. This in turn increases the benefit of misreporting, suggesting a positive relation. We formalize these two countervailing forces–“monitoring” and “valuation”–in the context of a parsimonious model of misreporting. We show that the combination of these two forces leads to a unimodal relation. Specifically, as the level of public scrutiny increases, misreporting first increases, reaches a peak, and then decreases. We find evidence of such a relation across multiple empirical measures of misreporting, multiple measures of public scrutiny, and multiple research designs.

This paper reviews the empirical methods used in the accounting literature to draw causal inferences. Recent years have seen a burgeoning growth in the use of methods that seek to exploit as-if random variation in observational settings—i.e., “quasi-experiments.” We provide a synthesis of the major assumptions of these methods, discuss several practical considerations relevant to the application of these methods in the accounting literature, and provide a framework for thinking about whether and when quasi-experimental and non-experimental methods are well-suited for addressing causal questions of interest to accounting researchers. While there is growing interest in addressing causal questions within the literature, we caution against the idea that one should restrict attention to only those causal questions for which there are quasi-experiments. We offer a complementary approach for addressing causal questions that does not rely on the availability of a quasi-experiment, but rather relies on a combination of economic theory, developing and falsifying alternative explanations, triangulating results across multiple settings, measures, and research designs, and caveating results where appropriate.