韩国企业避税与治理数据集，助力财务研究与政策分析
KoTaP: A Panel Dataset for Corporate Tax Avoidance, Performance, and Governance in Korea
- 构建2011-2024年韩企避税面板数据，覆盖1754家非金融上市公司
- 包含12,653个观测值，涵盖避税、绩效、治理等多维度指标
- 数据结构平衡、变量标准化，适合跨国比较与智能模型验证
本文推出韩国企业避税面板数据集（KoTaP），涵盖2011至2024年间在KOSPI和KOSDAQ上市的非金融企业。剔除金融类企业、非12月结账企业、资本不足及税前利润为负的企业后，最终获得1,754家企业的12,653个企业-年度观测值。该数据集将企业避税作为预测变量，关联多个领域：盈余管理（应计与经营性）、盈利能力（ROA、ROE、CFO、LOSS）、稳定性（杠杆率、流动比率、规模、固定资产、年龄、投资回收期）、增长性（增长率、市净率、托宾Q）以及公司治理（是否四大所审计、外资持股、所有权集中度）。避税程度通过现金有效税率（CETR）、GAAP有效税率（GETR）及账税差异（TSTA、TSDA）衡量，并进行可解释性调整。KoTaP具有均衡面板结构、标准化变量，且与国际文献中核心指标分布和相关性一致；同时反映韩国企业特有的制度特征，如股权集中、外资持股高、流动性强。该数据集适用于计量经济与深度学习模型基准测试、外部有效性检验、可解释性AI分析，亦支持政策评估、审计规划与投资研究，是会计、金融及跨学科研究的重要开放资源。
原文摘要 · Abstract (English)
This study introduces the Korean Tax Avoidance Panel (KoTaP), a long-term panel dataset of non-financial firms listed on KOSPI and KOSDAQ between 2011 and 2024. After excluding financial firms, firms with non-December fiscal year ends, capital impairment, and negative pre-tax income, the final dataset consists of 12,653 firm-year observations from 1,754 firms. KoTaP is designed to treat corporate tax avoidance as a predictor variable and link it to multiple domains, including earnings management (accrual- and activity-based), profitability (ROA, ROE, CFO, LOSS), stability (LEV, CUR, SIZE, PPE, AGE, INVREC), growth (GRW, MB, TQ), and governance (BIG4, FORN, OWN). Tax avoidance itself is measured using complementary indicators cash effective tax rate (CETR), GAAP effective tax rate (GETR), and book-tax difference measures (TSTA, TSDA) with adjustments to ensure interpretability. A key strength of KoTaP is its balanced panel structure with standardized variables and its consistency with international literature on the distribution and correlation of core indicators. At the same time, it reflects distinctive institutional features of Korean firms, such as concentrated ownership, high foreign shareholding, and elevated liquidity ratios, providing both international comparability and contextual uniqueness. KoTaP enables applications in benchmarking econometric and deep learning models, external validity checks, and explainable AI analyses. It further supports policy evaluation, audit planning, and investment analysis, making it a critical open resource for accounting, finance, and interdisciplinary research.
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