Research

We examine the relationship between country-by-country disclosures and the internal information environments of multinational entities (MNEs). We argue that the introduction of U.S. country-by-country reporting (CbCR) increases affected firms’ need for effective internal information flows, as country-specific information is not readily available for disclosure. However, the disclosure mandate simultaneously adds complexity to firms’ reporting environments. Using a difference-in-differences design, we find that firms affected by U.S. CbCR make more efficient investment decisions, utilize existing resources more efficiently, and report fewer financial restatements than firms not affected by the mandate. These effects are concentrated in the first year following the introduction of U.S. CbCR. Our findings are robust across various placebo tests and alternative research designs. Supplemental regression discontinuity analyses reinforce our main results. Moreover, we find that improvements in internal information quality are concentrated in accounting items directly tied to U.S. CbCR, suggesting that firms improve information flows to meet the mandate’s specific disclosure requirements. Finally, we show that our findings depend on disclosure practices and organizational complexity prior to the mandate. Our results suggest that U.S. CbCR prompts MNEs to alter their internal information processing structures, improving financial reporting and operational efficiency.

We examine how individual accounting employee movements across firms impact the transfer of corporate disclosure practices. We employ a large dataset of employee movements across firms through employee disclosures sourced from a professional networking platform and apply GPT-4 to identify employees in accounting roles. We use narrative disclosures in 10-K filings to measure corporate disclosure practices, which offer rich and nuanced discretion within a disclosure. We first identify an increase in similarity between the qualitative disclosures of firms experiencing an employee movement relative to other firm pairs. This increase is observable for both rank-and-file and executive-level employees and varies in magnitude by employee tenure and the similarity of the old and new roles. Moreover, the increase in similarity is more substantial in firm pairs that are industry peers. In subsequent tests, we use a difference-in-differences design at the firm-year level to show that firms hiring employees with previous exposure to highly informative disclosures increase their disclosure informativeness. Collectively, we report evidence that individual accounting employees at all levels transfer disclosure practices across firms.

We use large language models to examine the informational value of context in financial statements' narrative text for explaining the mapping from income to taxes. Conceptually, tax expense is a function of taxable income, where the function reflects the applicable statutory tax rate. In practice, however, this function is distorted by accounting rules and heterogenous tax codes. We aim to understand to which degree corporate narrative disclosures explain these distortions. To do so, we quantify textual information disclosed in annual reports and train deep neural networks that use this information as contextual input to explain deviations betwen book income and tax outcomes. We shot that context from the MD&A has significant explanatory power, improving the mapping betwen book income and tax outcomes by 17.6% to 23.9%. Context from income tax footnotes, however, often has the opposite effect and reduces explanatory power, suggesting that these disclosures are uninformative. We also provide insights into the relation between context informativeness and the narratives' underlying disclosure topics, as well as into the sensitivity of our results to firm characteristics. Collectively, our findings demonstrate the value of contextual information in understanding distortions between book and tax numbers.