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Labor Investment

Question: What was the cost of an organization for its employees to create the counted contributions (e.g., commits, issues, and pull requests)?

Overview

Labor Investment tracks the monetary commitment organizations make in supporting open source projects through employee contributions. This metric assesses the labor costs incurred by organizations, providing transparency in the financial commitment tied to open source engagement.

Measuring labor investment informs organizational health and sustainability by highlighting the level of resources committed to open source projects. For Open Source Program Office (OSPO) managers, this metric offers insights into prioritizing resources, justifying budgets, and understanding the return on investment (ROI) across a portfolio of projects. For example, the Labor Investment metric can be used to prioritize investment or determine return on investment such as:

  • Labor Investment as a means of evaluating OSPO priorities and justifying budgets
  • Labor Investment as a way to explain product/program management priority
  • Labor Investment as an argument for the value of continued investing in OSPOs
  • Labor Investment to report and compare labor costs of contributed vs in-house work
  • Labor Investment to compare project effectiveness across a portfolio of projects

Want to Know More?

Click to read more about this metric.

Data Collection Strategies

  • Quantitative Data:

    • Count contributions by type (e.g., commits, issues, pull requests).
    • Break down contributions by contributor types (internal vs. external).
    • Track the average hours spent per contribution type and multiply by hourly labor rates to calculate total labor costs.
  • Calculation:
    Labor Investment = For each contribution type, sum (Number of contributions Average labor hours per contribution Average hourly rate).

Filters

  • Contributor Types: Filter by internal vs. external contributors to understand organizational versus community labor.
  • Contribution Types: Focus on specific contribution types like commits, issues, or pull requests.
  • Project Source: Differentiate between internal, open-source, or competitor open-source repositories.

Visualizations

  • Example Visualization: CSV export with parameterized metrics.
    CSV Export
    Figure 1: Labor investment visualization using CSV export (CHAOSS, 2023)


References

Contributors

  • Matt Germonprez
  • Sean Goggins
  • Dawn Foster
  • Vinod Ahuja
  • Elizabeth Barron
  • Georg Link
  • Yigakpoa L. Samuel

Additional Information

To edit this metric, please submit a Change Request here.
To reference this metric in software or publications, please use this stable URL: https://chaoss.community/?p=3559

The usage and dissemination of health metrics may lead to privacy violations. Organizations may be exposed to risks. These risks may flow from compliance with the GDPR in the EU, with state law in the US, or with other laws. There may also be contractual risks flowing from terms of service for data providers such as GitHub and GitLab. The usage of metrics must be examined for risk and potential data ethics problems. Please see CHAOSS Data Ethics document for additional guidance.

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