New #paper out: « The impact of the #COVID19 pandemic on women’s contribution to public code » (Empir. Softw. Eng. 30(1): 25 (2025)) where we establish, using #econometrics techniques and relying on the @swheritage archive, that the pandemic disproportionately impacted women's ability to contribute to the development of public code, relatively to men. #Openaccess preprint at: https://hal.science/hal-04716803/
With A. Casanueva, D.Rossi, and @Zimm_i48
hal.scienceThe Impact of the COVID-19 Pandemic on Women's Contribution to Public CodeDespite its promise of openness and inclusiveness, the development of free and open source software (FOSS) remains significantly unbalanced in terms of gender representation among contributors. To assist open source project maintainers and communities in addressing this imbalance, it is crucial to understand the causes of this inequality.
In this study, we aim to establish how the COVID-19 pandemic has influenced the ability of women to contribute to public code. To do so, we use the Software Heritage archive, which holds the largest dataset of commits to public code, and the difference in differences (DID) methodology from econometrics that enables the derivation of causality from historical data.
Our findings show that the COVID-19 pandemic has disproportionately impacted women's ability to contribute to the development of public code, relatively to men. Further, our observations of specific contributor subgroups indicate that COVID-19 particularly affected women hobbyists, identified using contribution patterns and email address domains.
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This package is the result of my research on long memory time series analysis, which is a fascinating topic in econometrics and statistics. Long memory models are useful for capturing the persistence and dependence of many real-world phenomena, such as inflation, interest rates, volatility, network traffic, and environmental data.
Why statistics tend not only to describe the world but to change it
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