Remote Worker Productivity Data with Team Assignment and Tenure Effects

Hideaki Ishikura's dataset from 2026 examines peer effects in a fully remote workforce. It uses panel data and quasi-random assignment of new hires to teams as a natural experiment. The data is stored in an XLS file and is 5.5 KB in size.

Use Cases

• Analyze the impact of team composition on individual productivity based on the described quasi-random assignment.
• Model the effect of teammate experience levels on worker output based on the reported 12.2% increase.
• Investigate tenure-specific productivity gains, particularly for new employees, based on the described 26.2% increase for the shortest-tenure workers.
• Study the relationship between communication volume and productivity effects as suggested by the description's findings.

Strengths

• Data is derived from a company that has operated with an entirely remote workforce since inception, providing a consistent context.
• Analysis leverages as-if random assignment of new hires to teams, a methodological strength for causal inference.
• Specific effect sizes are reported, such as a 12.2% productivity increase with experienced teammates and a 26.2% increase for short-tenure employees.

Limitations

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

Provenance

Source

Hideaki Ishikura via figshare.

Collection Method

Panel data from a single, fully remote company, using quasi-random team assignment as a natural experiment.

Time Range

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Freshness

Last updated 2026-03-17 17:25:37.

Geography

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