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A machine learning approach for assessing labor supply to the online labor market

Author

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  • Fung, Esabella

Abstract

The online labor market, comprised of companies such as Upwork, Amazon Mechanical Turk, and their freelancer workforce, has expanded worldwide over the past 15 years and has changed the labor market landscape. Although qualitative studies have been done to identify factors related to the global supply to the online labor market, few data modeling studies have been conducted to quantify the importance of these factors in this area. This study applied tree-based supervised learning techniques, decision tree regression, random forest, and gradient boosting, to systematically evaluate the online labor supply with 70 features related to climate, population, economics, education, health, language, and technology adoption. To provide machine learning explainability, SHAP, based on the Shapley values, was introduced to identify features with high marginal contributions. The top 5 contributing features indicate the tight integration of technology adoption, language, and human migration patterns with the online labor market supply.

Suggested Citation

  • Fung, Esabella, 2023. "A machine learning approach for assessing labor supply to the online labor market," MPRA Paper 118844, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:118844
    as

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    File URL: https://mpra.ub.uni-muenchen.de/118981/1/MPRA_paper_118981.pdf
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    References listed on IDEAS

    as
    1. Fabian Stephany & Otto Kassi & Uma Rani & Vili Lehdonvirta, 2021. "Online Labour Index 2020: New ways to measure the world's remote freelancing market," Papers 2105.09148, arXiv.org, revised Jun 2021.
    2. Melitz, Jacques & Toubal, Farid, 2014. "Native language, spoken language, translation and trade," Journal of International Economics, Elsevier, vol. 93(2), pages 351-363.
    3. Khlystova, Olena & Kalyuzhnova, Yelena & Belitski, Maksim, 2022. "The impact of the COVID-19 pandemic on the creative industries: A literature review and future research agenda," Journal of Business Research, Elsevier, vol. 139(C), pages 1192-1210.
    4. Ludger Woessmann & Eric Bettinger, 2020. "New Directions in the Economics of Higher Education," Books, Edward Elgar Publishing, number 18408.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    business; boosting; commerce and trade; digital divide; economics; ensemble learning; globalization; machine learning; random forest; social factors; statistical learning; sharing economy;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F16 - International Economics - - Trade - - - Trade and Labor Market Interactions
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics

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