Open Source Is a Black Box for Economists

Developers rarely need convincing that open source accelerates innovation—reusing battle-tested code instead of reinventing it lets engineers focus on problems nobody has solved yet. But outside the developer community, that value proposition is much harder to sell. Policymakers who fund public goods often overlook digital infrastructure, and badly designed rules can inadvertently stifle the collaboration that open source depends on. Helping governments understand what open source is actually worth is one of the core jobs of GitHub’s Policy Team, and recent research is finally giving that conversation some hard numbers.

A 2021 study estimated that open source contributed between €65 billion and €95 billion to European GDP in 2018 alone. The European Commission took notice quickly, citing the finding when it streamlined rules for open-sourcing its own software. That’s a promising start, but the research field is still young. Three broad questions should drive the next phase of inquiry—and they’re questions that need input from across the developer ecosystem.

Measuring Open Source at Macroeconomic Scale

Society-wide policy decisions need society-wide evidence. Existing studies have linked open source activity to higher GDP, increased labor productivity, and greater startup formation. But these findings raise at least as many questions as they answer:

  • Most leading work focuses on the European Union. Comparable analyses for the US, India, Africa, and other regions are missing—and distributional effects (which countries benefit most, and why) remain unexplored.
  • The causality problem is real. Open source might drive growth, or it might simply be a byproduct of it. Natural experiments—such as cases where developers lost access to open source communities or policy shifts altered activity—could disentangle cause from correlation.
  • Earlier studies made specific, testable predictions: a 10% increase in EU contributors would yield a 0.4–0.6% GDP gain; recreating Debian 3.1 through 2010 would have cost €100 billion; a 1% rise in commits in a country corresponds to 0.6–1.2% more startups. With hindsight, these projections can now be checked for accuracy.

What Is a Single Project Worth?

Aggregate macroeconomic impact is really the sum of thousands of individual projects. So far, project-level valuation has relied on three approaches: estimating what a project would cost to build from scratch, calculating how much paid software it replaces, and documenting specific innovations a project enabled. Some studies also tie firm revenues to open source contributions. Each method captures part of the picture, and none captures all of it.

Modern development practice makes this harder and more tractable at the same time. The npm is-odd package—a single trivial utility that resolves one line of code—appears in countless dependency graphs, whereas a massive library used pervasively but invisibly may register less clearly. That suggests value isn’t purely a function of how central a package is in the dependency network; the developer’s ability to recreate functionality matters too.

Several research directions would sharpen the picture:

  • Dependency graphs encode information about a project’s value and could be mined more aggressively. Security research already does this; economics is just beginning.
  • Counterfactual analysis is needed. If a developer could write a drop-in replacement for a heavily used package in minutes, network analysis will overstate its economic significance.
  • Hedonic pricing methods—used elsewhere in economics to decompose value into attributes—might help identify what specific qualities of a project are worth, and link them to firm investment and maintainer income.
  • Better value measures ultimately support better compensation models and smarter government investment in securing critical open source dependencies.

Rethinking Innovation Metrics

Patents have long been the default metric for innovation, even though surveys going back over a decade show that companies find open source a more productive source of new ideas. This is partly a measurement gap: forks and stars on GitHub carry innovation signal that isn’t captured by conventional economic indicators. GitHub is working to standardize such metrics to make them usable in research, but there is much more to study.

  • Open source is an input to scientific research as well as an output of it. Initial efforts to quantify this role need expansion—and it's worth asking whether targeted research funding for open source would increase the return.
  • As open source business models evolve—especially with the rise of cloud-hosted software-as-a-service—how do large providers’ contributions shift? The relative contribution of big cloud firms is an open question.
  • Developer tools themselves have value: GitHub Actions, GitHub Copilot, and similar tooling affect maintainer productivity and workload. Methods for valuing that contribution are still coarse.
  • New institutional arrangements such as Open Source Program Offices (OSPOs), now moving from industry into government and the social sector, carry measurable economic effects that are largely unexamined.

Unmeasured value is invisible value, and invisible value is hard to protect in policy. The economics lens is not the only way to assess open source—questions of transparency, trust, and inclusive design matter as well—but it carries particular weight with governments. The goal is to build a body of research showing policymakers that open source isn’t a hobby or an externality, but a strategic economic asset worth deliberate support.