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

Area of Science:

• * Corporate Finance and Accounting
• * Econometrics and Financial Data Analysis

Background:

• * Existing research on corporate tax avoidance often lacks detailed, firm-specific panel data.
• * Understanding tax avoidance drivers and consequences requires robust datasets reflecting unique market structures.

Purpose of the Study:

• * To introduce the Korean Tax Avoidance Panel (KoTaP), a novel long-term dataset.
• * To facilitate research on corporate tax avoidance by providing standardized data on Korean firms.
• * To enable analysis linking tax avoidance to profitability, stability, growth, and governance.

Main Methods:

• * Construction of a panel dataset from KOSPI and KOSDAQ listed non-financial firms (2011-2024).
• * Exclusion criteria applied: financial firms, non-December fiscal year ends, capital impairment, negative pre-tax income.
• * Measurement of tax avoidance using cash effective tax rate, GAAP effective tax rate, and book-tax differences.

Main Results:

• * The final KoTaP dataset comprises 12,653 firm-year observations from 1,754 firms.
• * The dataset features standardized variables and panel structure for international comparability.
• * KoTaP incorporates unique Korean institutional features like concentrated ownership and high foreign shareholding.

Conclusions:

• * KoTaP provides a valuable resource for both econometric and machine-learning research on tax avoidance.
• * The dataset's design supports analysis of tax avoidance's impact on various firm performance metrics.
• * KoTaP enhances the study of corporate behavior within the specific context of the Korean market.