[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126699-en":3,"doc-seo-126699-105":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},126699,962084925636,"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 - Research and analysis","The online labor market, including platforms such as Upwork and Amazon Mechanical Turk, has expanded rapidly and reshaped how work is sourced globally. While prior research identified attributes linked to online labor supply, fewer studies quantify the relative importance of those attributes. This work uses tree-based supervised learning—decision tree regression, random forest, and gradient boosting—on 70 features spanning climate, population, economics, education, health, language, and technology adoption. SHAP explainability highlights the top marginal contributors, revealing strong links between technology adoption, language, and human migration patterns.","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/118981/](https://mpra. ub. uni-muenchen. de/118981/)  \n[MPRA Paper No. 118981](MPRA Paper No. 118981) , [posted 27 Oct 2023 04:39 UTC](posted 27 Oct 2023 04:39 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 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.  \nKeywords  \nbusiness, boosting, commerce and trade, digital divide, economics, ensemble learning, globalization, machine learning, random forest, social factors, statistical learning, sharing economy  \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 ofall virtual services: software development, multimedia content, translation, and marketing support.  \nFrom 2016 to 2021, the online labor market has grown by over 10% annually (Stephany et 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 to predict the supply and the importance of features were also evaluated.  \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  \n[Peopleperhour.com. These were](Peopleperhour.com. These were) the top 5 platforms representing at least 70% of the total online labor platform traffic, [according to Alexa.com](according to Alexa.com), when the index was creat","cbCaimh0Zb8Kfq4v","https://ap.wps.com/l/cbCaimh0Zb8Kfq4v","pdf",2485613,1,26,"English","en",105,"# Introduction\n# Data\n## Features","[{\"question\":\"Which machine learning models are used to assess labor supply in the online labor market?\",\"answer\":\"The study applies decision tree regression, random forest, and gradient boosting, along with earlier linear baselines (multiple linear regression, Ridge, and LASSO) for prediction and feature evaluation.\"},{\"question\":\"How does the study measure online labor market activity and where does the data come from?\",\"answer\":\"Activity is based on the Online Labour Index from the Online Labour Observatory (International Labour Organisation and Oxford Internet Institute), derived from platform application programming interfaces and web interfaces across major online labor platforms.\"},{\"question\":\"How is model explainability achieved and what do the results emphasize?\",\"answer\":\"SHAP (Shapley values) is used to identify features with high marginal contributions. The top contributing features point to tight integration among technology adoption, language, and migration patterns affecting online labor supply.\"}]","A machine learning approach for assessing labor supply to the online labor market - Research and analysis | PDF",1785934295,66,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-approach-for-assessing-labor-supply-to-the-online-labor-market-research-and-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-approach-for-assessing-labor-supply-to-the-online-labor-market-research-and-analysis/126699/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are used to assess labor supply in the online labor market?","Question",{"text":75,"@type":76},"The study applies decision tree regression, random forest, and gradient boosting, along with earlier linear baselines (multiple linear regression, Ridge, and LASSO) for prediction and feature evaluation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study measure online labor market activity and where does the data come from?",{"text":80,"@type":76},"Activity is based on the Online Labour Index from the Online Labour Observatory (International Labour Organisation and Oxford Internet Institute), derived from platform application programming interfaces and web interfaces across major online labor platforms.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model explainability achieved and what do the results emphasize?",{"text":84,"@type":76},"SHAP (Shapley values) is used to identify features with high marginal contributions. The top contributing features point to tight integration among technology adoption, language, and migration patterns affecting online labor supply.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]