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Measuring Human Capital with Social Media Data and Machine Learning

Author

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  • Martina Jakob
  • Sebastian Heinrich

Abstract

In response to persistent gaps in the availability of survey data, a new strand of research leverages alternative data sources through machine learning to track global development. While previous applications have been successful at predicting outcomes such as wealth, poverty or population density, we show that educational outcomes can be accurately estimated using geo-coded Twitter data and machine learning. Based on various input features, including user and tweet characteristics, topics, spelling mistakes, and network indicators, we can account for ~70 percent of the variation in educational attainment in Mexican municipalities and US counties.

Suggested Citation

  • Martina Jakob & Sebastian Heinrich, 2023. "Measuring Human Capital with Social Media Data and Machine Learning," University of Bern Social Sciences Working Papers 46, University of Bern, Department of Social Sciences.
    • Handle: RePEc:bss:wpaper:46
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    File URL: https://boris.unibe.ch/182366/1/Jakob-Heinrich-2023-Measuring-Human-Capital-with-Social-Media-Data-and-Machine-Learning.pdf
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    References listed on IDEAS

    as
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    4. Emily Aiken & Suzanne Bellue & Dean Karlan & Chris Udry & Joshua E. Blumenstock, 2022. "Machine learning and phone data can improve targeting of humanitarian aid," Nature, Nature, vol. 603(7903), pages 864-870, March.
    5. Christopher Yeh & Anthony Perez & Anne Driscoll & George Azzari & Zhongyi Tang & David Lobell & Stefano Ermon & Marshall Burke, 2020. "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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    7. Barro, Robert J. & Lee, Jong Wha, 2013. "A new data set of educational attainment in the world, 1950–2010," Journal of Development Economics, Elsevier, vol. 104(C), pages 184-198.
    8. Serina Chang & Emma Pierson & Pang Wei Koh & Jaline Gerardin & Beth Redbird & David Grusky & Jure Leskovec, 2021. "Mobility network models of COVID-19 explain inequities and inform reopening," Nature, Nature, vol. 589(7840), pages 82-87, January.
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    Cited by:

    1. Martina Jakob, Konstantin Buechel, Daniel Steffen, Aymo Brunetti, 2023. "Participatory Teaching Improves Learning Outcomes: Evidence from a Field Experiment in Tanzania," Diskussionsschriften dp2310, Universitaet Bern, Departement Volkswirtschaft.

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    More about this item

    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I25 - Health, Education, and Welfare - - Education - - - Education and Economic Development

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