Heckman Two-Stage Regression Data on Digital Transformation and Enterprise Productivity

Peng Song's dataset on figshare contains empirical data for a Heckman two-stage regression analysis of digital transformation's impact on enterprise total factor productivity. The data, last updated on 2026-04-28, is stored in a 5.5 KB XLS file. It supports research on the heterogeneous effects of AI, Big Data, Cloud Computing, Blockchain, and Digital Technology Application on firm productivity.

Use Cases

• Applying Heckman two-stage regression to correct for selection bias in enterprise digital transformation studies.
• Analyzing the differential impact of specific digital technology directions (AI, Big Data, etc.) on firm productivity.
• Investigating the mediating role of dynamic capabilities (coordination, change, learning) in the technology-productivity relationship.
• Conducting heterogeneity analysis on the effects across labor-intensive, technology-intensive, and manufacturing industries.

Strengths

• Data is structured for a specific, advanced econometric method (Heckman two-stage regression).
• The underlying research categorizes digital transformation into five distinct technological directions.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (5.5 KB), indicating limited scope or a highly aggregated summary.

Provenance

Source

figshare, author Peng Song.

Collection Method

Data likely derived from semantic analysis (LDA topic model) of annual reports from A-share listed companies.

Time Range

null

Freshness

Last updated 2026-04-28 17:48:34; freshness should be verified.

Geography

Focus on A-share listed companies, suggesting a Chinese context.