Digital disclosure complexity and tax aggressiveness: insights from US XBRL filings
研究了美国上市公司税收激进程度与XBRL格式财务报告复杂性之间的正相关关系，发现税收激进公司使用更多自定义标签，且这种关系在审计风险高和财务能力强的公司中更显著，对监管机构有启示。
Purpose This study examines the relationship between corporate tax aggressiveness and the complexity of financial reporting using a structured, XBRL-based measure derived from machine-readable 10-K filings. While prior research has linked tax avoidance to reduced transparency using narrative-based readability metrics, this paper contributes by evaluating complexity from the preparer’s perspective in the digital reporting environment, offering new insights into disclosure behavior among tax-aggressive firms. Design/methodology/approach The analysis is based on 18,235 firm-year observations from US publicly listed firms between 2011 and 2020. We employ fixed effects regression to address unobserved heterogeneity and conduct a battery of robustness tests to mitigate endogeneity concerns, including two-stage least squares (2SLS) and difference-in-differences (DiD) analyses. Findings The results show a positive and statistically significant relationship between tax avoidance and the complexity of XBRL filings. Tax-aggressive firms report more total XBRL tags and rely more heavily on firm-specific extended tags, suggesting a role for discretionary reporting behaviour beyond operational complexity. The relationship is stronger under higher IRS audit risk and is amplified among firms with greater financial capacity, consistent with higher-capacity firms being better able to sustain sophisticated tax strategies and the associated reporting complexity. Practical implications The findings have implications for regulators such as the SEC and FASB, underscoring the importance of monitoring the customization of digital disclosures. The extensive use of extended tags in tax-sensitive contexts may challenge comparability and hinder transparency, potentially affecting the efficacy of financial reporting standards. Originality/value Although the XBRL-based complexity measure has been introduced in earlier research, this study applies it in a novel context by linking it to corporate tax avoidance. The approach expands the understanding of reporting complexity beyond narrative readability and contributes to a deeper understanding of how digital reporting structures relate to tax planning behavior.