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

Fung, Esabella (2023): A machine learning approach for assessing labor supply to the online labor market.

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 studies have been done to identify attributes associated to the global supply to the online labor market, few data modeling studies have been conducted to quantify the importance of these attributes to the online labor market. 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.

• Item Type: MPRA Paper
• Language: English
• Keywords: business, boosting, commerce and trade, digital divide, economics, ensemble learning, globalization, machine learning, random forest, social factors, statistical learning, sharing economy
• Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C60 - General; F - International Economics > F0 - General; F - International Economics > F1 - Trade > F16 - Trade and Labor Market Interactions; J - Labor and Demographic Economics > J0 - General; M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M0 - General

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