[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122786-en":3,"doc-seo-122786-105":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},122786,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","The gender pay gap in Spain - A machine learning approach - Master thesis","This master thesis investigates the gender pay gap in Spain by applying machine learning methods to uncover patterns, predict outcomes, and quantify disparities. A large labor-market dataset covering education, jobs, and industry is used to train models that both predict and explain the gender wage gap. The study targets key wage-gap drivers and potential discrimination areas, reporting an unadjusted gap estimated between 14% and 16%, while most variation remains unexplained.","The gender pay gap in Spain: A machine  \nlearning approach  \nMaster thesis  \nAuthor: Ander Sanchez Maudo  \nSupervisors: Alaitz Artabe Echevarria, Ainhoa Vega Bayo  \nMaster in Economics: Empirical Applications and Policies University of the Basque Country (UPV/EHU)  \nJuly 21st 2023  \nAbstract  \nThis thesis investigates the gender pay gap in Spain using Machine Learning (ML) techniques to provide insights and predictive models to understand and address this persistent problem. This study uses a large dataset that includes various labor market factors, such as education, jobs, and industry, to train ML models to predict and explain the gender wage gap. Using advanced methodology, the research aims to identify the key drivers of the wage gap and highlight potential areas where gender-based wage discrimination may exist. The results of this analysis indicate that the gender pay gap is between 14% and 16% . This indicates that according to the estimated models, men are paid between 14 and 16% more in Spain. On the other hand, it is not possible to establish the variables that can explain this gender gap, since most ofit is unexplained.  \nKeywords: unadjusted gender gap, adjusted gender pay gap, machine learning, decomposition.  \nTable of contents  \n1. Introduction ........................................................................................................................... 4  \n2. Data ....................................................................................................................................... 6  \n2.1 Descriptive statistics...................................................................................................... 7  \n2.1.1 Worker level variables .......................................................................................... 7  \n2.1.2 Firm level variables ............................................................................................... 9  \n2.2 Unadjusted gender pay gap ......................................................................................... 10  \n3. Methodology ....................................................................................................................... 12  \n3.1 Adjusted gender gap: Ordinary Least Squares ............................................................ 12  \n3.2 Adjusted gender gap: Regularization .......................................................................... 13  \n3.3 Blinder-Oaxaca Decomposition .................................................................................. 14  \n4. Results ................................................................................................................................. 15  \n4.1 Model estimations and comparison ............................................................................. 15  \n4.2 Decomposition ............................................................................................................ 17  \n5. Conclusions and discussion................................................................................................. 21  \n6. Bibliography........................................................................................................................ 23  \n7. Appendix ............................................................................................................................. 25  \nIndex of tables  \nTable 1. Number of observations in SES ....................................................................................... 7  \nTable 2. Different model estimation results ................................................................................ 16  \nIndex of figures  \nFigure 1. Worker Level variables ................................................................................................. 8  \nFigure 2. Company characteristics ............................................................................................... 9  \nFigure 3. Average gross hourly and monthly earnings ........","cbCair1g7R5Alv67","https://ap.wps.com/l/cbCair1g7R5Alv67","pdf",1484011,1,30,"English","en",105,"# Introduction\n# Data\n## Descriptive statistics\n## Unadjusted gender pay gap\n# Methodology\n## Adjusted gender gap: Ordinary Least Squares\n## Adjusted gender gap: Regularization\n## Blinder-Oaxaca Decomposition\n# Results\n## Model estimations and comparison\n## Decomposition\n# Conclusions and discussion\n# Bibliography\n# Appendix","[{\"question\":\"What is the main goal of the thesis on the gender pay gap in Spain?\",\"answer\":\"To use machine learning to provide insights and predictive models for understanding and addressing the persistent gender wage gap in Spain.\"},{\"question\":\"Which data factors are used to train the machine learning models?\",\"answer\":\"The study uses a large dataset including labor market factors such as education, jobs, and industry.\"},{\"question\":\"What do the results indicate about the magnitude of the gender pay gap?\",\"answer\":\"The analysis estimates the gender pay gap between 14% and 16%, meaning men are paid 14% to 16% more according to the models.\"}]","The gender pay gap in Spain - A machine learning approach - Master thesis | PDF",1785812882,76,{"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},"the-gender-pay-gap-in-spain-a-machine-learning-approach-master-thesis","",{"@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/the-gender-pay-gap-in-spain-a-machine-learning-approach-master-thesis/122786/",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","2026-08-04",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},"What is the main goal of the thesis on the gender pay gap in Spain?","Question",{"text":75,"@type":76},"To use machine learning to provide insights and predictive models for understanding and addressing the persistent gender wage gap in Spain.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data factors are used to train the machine learning models?",{"text":80,"@type":76},"The study uses a large dataset including labor market factors such as education, jobs, and industry.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results indicate about the magnitude of the gender pay gap?",{"text":84,"@type":76},"The analysis estimates the gender pay gap between 14% and 16%, meaning men are paid 14% to 16% more according to the models.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":21,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]