KoTaP: A Panel Dataset for Corporate Tax Avoidance, Performance, and Governance in Korea

2025-11-06

AI Summary

This paper addresses the scarcity of high-quality, localized data for corporate tax avoidance research in Korea. To this end, we construct KoTaP—the first long-term, standardized, balanced panel dataset for Korea (2011–2024)—covering non-financial firms listed on KOSPI/KOSDAQ, comprising 12,653 firm-year observations. KoTaP systematically integrates multidimensional variables: tax avoidance measures (cash- and GAAP-based effective tax rates, book-tax differences), earnings management, profitability (ROA/ROE), leverage (LEV), firm size (SIZE), audit quality (BIG4), and corporate governance, while explicitly incorporating Korean institutional features—including concentrated ownership and high foreign ownership. Designed to balance international comparability with local contextual fidelity, KoTaP supports rigorous econometric modeling, deep learning applications, and interpretable AI analysis. The dataset is publicly available, serving as critical infrastructure for empirical accounting and finance research, external validity testing, audit practice optimization, and evidence-based investment and policy decisions.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

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.

Problem

• Developing a panel dataset to study corporate tax avoidance in Korean firms
• Linking tax avoidance to performance, governance and financial indicators
• Creating standardized metrics for international comparability and local context

Innovation

• Panel dataset with standardized corporate tax avoidance indicators
• Balanced structure combining international comparability and local features
• Enables econometric benchmarking and explainable AI analyses