How Does Digital Government Governance Reshape the Accessibility of Basic Public Services?

Wang Qiannan's 2026 research uses a Double Machine Learning model to quantify the causal effects of digital government governance on basic public service accessibility. The dataset likely contains evidence from China's "Internet Plus Government Services" pilot policy. It was published on Harvard Dataverse on June 16, 2026.

Use Cases

• Estimating causal effects of digital governance policies based on the described Double Machine Learning model.
• Analyzing transmission pathways of policy impacts on service accessibility based on the research focus.
• Benchmarking public service accessibility metrics before and after policy implementation based on the pilot study context.

Strengths

• Dataset is associated with a specific causal inference methodology (Double Machine Learning model).
• Focuses on a defined policy intervention ("Internet Plus Government Services" pilot).
• Published on a recognized academic repository (Harvard Dataverse).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Harvard Dataverse

Collection Method

Likely contains quantitative research data from policy evaluation.

Time Range

null

Freshness

Last updated 2026-06-16 04:58:50; freshness should be verified.

Geography

Likely focuses on China, based on the "Internet Plus Government Services" policy context.