Firm-Level Dataset on IFRS 16 Transition Method Choice, Financial Ratios, and Contextual Variables

This dataset contains firm-level observations for 300 companies operating in IFRS-reporting jurisdictions and belonging to lease-intensive industries: airlines, hotels, retail, and telecommunications. The data were manually compiled from published annual reports and notes to the financial statements around the initial application of IFRS 16. The main outcome variable identifies the transition method adopted by each firm, distinguishing among the Simplified Retrospective Approach (SRA), the Modified Retrospective Approach (MRA), and the Full Retrospective Approach (FRA).

The explanatory variables combine financial ratios, expected accounting-impact measures, firm size, lease-related indicators, and contextual characteristics. These include profitability, interest coverage, financial autonomy, liquidity, liability structure, resource coverage, total assets, changes in selected financial indicators under IFRS 16 relative to IAS 17, pre-IFRS 16 lease commitments, multiple rent, legal tradition, and industry classification.

The dataset is intended to support research on accounting choice under IFRS 16 using ordinal classification methods. It can be used to examine the determinants of transition-method selection and to explore the role of financial, institutional, and sector-specific factors in firms’ reporting decisions. The dataset may also be useful for research on lease accounting, financial reporting, explainable artificial intelligence, and machine learning applications in accounting and finance.