[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126698-en":3,"doc-seo-126698-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126698,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A machine learning approach for assessing labor supply to the online labor market - SHAP可解释性特征分析","The online labor market, spanning platforms such as Upwork and Amazon Mechanical Turk, has expanded globally over the past decade and reshaped labor supply dynamics. While prior work has qualitatively discussed drivers, limited data modeling has quantified their relative importance. This study trains supervised tree-based models—decision tree regression, random forest, and gradient boosting—using 70 features covering climate, population, economics, education, health, language, and technology adoption. SHAP explainability based on Shapley values identifies features with high marginal contributions, with the top factors linking technology adoption, language, and migration patterns to supply.","Munich Personal RePEc Archive  \nA machine learning approach for assessing labor supply to the online labor market  \nFung, Esabella  \n9 October 2023  \nOnline at [https://mpra. ub. uni-muenchen. de/118844/](https://mpra. ub. uni-muenchen. de/118844/)  \n[MPRA Paper No. 118844](MPRA Paper No. 118844) , [posted 14 Oct 2023 07:21 UTC](posted 14 Oct 2023 07:21 UTC)  \nA machine learning approach for assessing labor supply to the online labor market  \nEsabella Fung  \nAbstract  \nThe 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.  \nKeywords  \nbusiness, boosting, commerce and trade, digital divide, economics, ensemble learning, globalization, machine learning, random forest, social factors, statistical learning, sharing  \neconomy  \n1. Introduction  \nThe gig economy is a system where people provide short-term goods and services. An example of the gig economy is freelance work, which can be completed independently or under a more prominent company acting as an intermediary platform. The online labor market is part of this gig economy, and it facilitates exchanges of all virtual services: software development, multimedia content, translation, and marketing support.  \nFrom 2016 to 2021, the online labor market has grown by over 10% annually (Stephanyet al., 2021). This corroborates the idea that globalization increases the exchanges of products or services across countries (Friedman, 2005). The online labor market has gained greater visibility with the growth of internet accessibility and the COVID-19 pandemic from 2020, when there was a rise in the need for virtual services with limited face-to-face contact worldwide (Tan et al., 2021) .  \nTo better understand this growth, machine learning techniques were used to evaluate features associated with labor supply in the online labor market. Machine learning models were created by using data on the online labor market activities, climate, population, economics, education, health, language, and technology adoption over 5 years. With these data points, 6 models, multiple linear regression, Ridge, LASSO, decision tree regression, random forest, and gradient boosting, were trained, validated, and evaluated for factors related to the online labor market.  \n2. Data  \nMeasurement of online labor market activity is based on the Online Labour Index collected from the Online Labour Observatory created by the International Labour Organisation and the Oxford Internet Institute (Stephany et al., 2021) . This data on the online labor market supply were collected by examining the application programming interfaces from digital platforms or downloading the web user interface of 5 online labor platforms: Amazon Mechanical Turk, [Upwork.com](Upwork.com), [Freelancer.com](Freelancer.com), [Guru.com](Guru.com), [and Peopleperhour.com. These were](and Peopleperhour.com. These were) the top 5  \nplatforms representing at least 70% of the total online labor platform traffic, according to [Alexa.com](Alexa.com), when the index was created in 2016. As of","cbCaipNzr8mVPJVB","https://ap.wps.com/l/cbCaipNzr8mVPJVB","pdf",2415540,1,28,"English","en",105,"# Introduction\n# Data\n## Features\n# Modeling Approach\n# Results and Explainability\n# Conclusion","[{\"question\":\"What problem does the study address in the online labor market?\",\"answer\":\"It quantifies the importance of multiple factors influencing labor supply to the online labor market, rather than relying mainly on qualitative findings.\"},{\"question\":\"Which machine learning models are used to evaluate labor supply?\",\"answer\":\"The study uses decision tree regression, random forest, and gradient boosting, along with regression baselines such as multiple linear regression, Ridge, and LASSO.\"},{\"question\":\"How does the study explain which features matter most?\",\"answer\":\"It applies SHAP based on Shapley values to attribute high marginal contributions to the most influential features.\"}]","A machine learning approach for assessing labor supply to the online labor market - 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