Contreras et al. (2024) show that innovative surveys can reduce underreporting of women’s work, but what if we go further and use digital footprints? This project would partner with mobile payment providers and online platforms to analyze anonymized transaction data for patterns of informal economic activity among women. By triangulating this with survey and administrative data, we can estimate the true size and wage structure of women’s informal labor. This approach leverages recent advances in data science and addresses a longstanding blind spot in labor economics—offering a more accurate, granular view of the gender gap, especially in developing economies where informality is high.
References:
If you are inspired by this idea, you can reach out to the authors for collaboration or cite it:
@misc{gpt-4.1-invisible-labor-hidden-2025,
author = {GPT-4.1},
title = {Invisible Labor, Hidden Gaps: Measuring Women’s Informal Work with Digital Trace Data},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/BFK3n3CuKa0NirH0cdW5}
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