Making Time Count : A Machine Learning Approach to Predict Time Use in Low-Income Countries from Physical Activity Tracking Data (English)

Understanding men’s and women’s time use is a key factor in addressing issues and formulating policies related to division of labor, domestic work, and related gender disparities. However, obtaining data on individuals’ time use can be difficult and costly in the context of household surveys. Leveraging unique survey data collected in rural Malawi, this study investigates the possibility of predicting men’s and women’s time allocation to an extensive...
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DETAILS

  • Mulder,Joris;
    Hocuk,Seyit;
    Talip Kilic;
    Zezza,Alberto;
    Kumar,Pradeep;

  • 2024/06/28

  • Policy Research Working Paper

  • WPS10835

  • 1

  • World,

  • Other,

  • 2024/06/28

  • Disclosed

  • Making Time Count : A Machine Learning Approach to Predict Time Use in Low-Income Countries from Physical Activity Tracking Data

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Citation

Mulder,Joris; Hocuk,Seyit; Talip Kilic; Zezza,Alberto; Kumar,Pradeep.

Making Time Count : A Machine Learning Approach to Predict Time Use in Low-Income Countries from Physical Activity Tracking Data (English). Policy Research working paper;PEOPLE;LSMS Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/099833406282428656

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