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

Esabella Fung

MPRA Paper from University Library of Munich, Germany

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.

Keywords: business; boosting; commerce and trade; digital divide; economics; ensemble learning; globalization; machine learning; random forest; social factors; statistical learning; sharing economy (search for similar items in EconPapers)
JEL-codes: C60 F14 F16 J11 J22 M2 (search for similar items in EconPapers)
Date: 2023-10-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-int and nep-lma
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/118844/1/MPRA_paper_118844.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/118981/1/MPRA_paper_118981.pdf revised version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:118844

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().