GitHub Copilot and Dev Productivity: An Observational Dose-Response Analysis

A study of 16,223 Microsoft engineers over 43 weeks found that GitHub Copilot increases developer productivity by 40.5% more pull requests completed in high-usage weeks compared to zero-usage weeks, holding development effort constant. The effect is monotonic with diminishing returns at high intensity, and seven robustness tests support the causal interpretation.

Computer Science Software Engineering Submitted on 30 May 2026 Title:GitHub Copilot and Developer Productivity: An Observational Dose-Response Analysis View PDF /pdf/2606.00438 HTML experimental https://arxiv.org/html/2606.00438v1 Abstract:Does GitHub Copilot GHCP make engineers more productive, or do the engineers who use it more differ from those who use it less? And even within a single engineer, are GHCP-heavy weeks just busy weeks in which more of everything gets done? We study these questions using 43 weeks of data from 16,223 software engineers across Microsoft's Cloud+AI organization. Engineer fixed effects address the first concern by comparing each engineer against themselves rather than against other engineers, eliminating time-invariant differences in skill, role, and team. Active coding time and browser time then enter a Poisson Pseudo-Maximum Likelihood model with two-way fixed effects to address the harder, within-engineer confound: that GHCP-heavy weeks coincide with high-effort weeks. This defines our estimand as an efficiency effect: more pull requests completed at equivalent levels of coding time. Engineers are estimated to complete 40.5% more PRs in their highest GHCP usage weeks relative to their zero-usage weeks, holding measured development effort constant. The gradient is monotonic with diminishing returns at high intensity. Seven robustness and falsification tests target the remaining plausible alternative explanations non-coding AI engagement, team-level shocks, within-week task reallocation, cross-week contamination, PR slicing into smaller units, shifts toward easier task types, and sensitivity to how the treatment is operationalized . Under an explicitly stated conditional-independence assumption, the within-engineer design estimates a tool-specific efficiency effect that is consistent with all seven robustness tests. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .