[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-201997-105":59,"doc-detail-201997-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","extreme-quantile-treatment-effects-under-endogeneity-evaluating-policy-effects-for-the-most-vulnerable-individuals","Extreme Quantile Treatment Effects under Endogeneity - Evaluating Policy Effects for the Most Vulnerable Individuals","","Introduces a method to estimate and perform inference on extreme quantile treatment effects (QTEs) when treatment selection is endogenous. The approach covers diverse empirical designs, including instrumental variables and regression discontinuity, by using regular variation and subsampling to achieve robust performance in extreme tails where data can be sparse. Simulation evidence supports theoretical robustness. Applied to job training under the Job Training Partnership Act, the method finds significantly negative QTEs for the lowest quantiles, indicating adverse effects for the most disadvantaged individuals.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/extreme-quantile-treatment-effects-under-endogeneity-evaluating-policy-effects-for-the-most-vulnerable-individuals/201997/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/extreme-quantile-treatment-effects-under-endogeneity-evaluating-policy-effects-for-the-most-vulnerable-individuals/201997.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-04",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the paper address in quantile treatment effect estimation?","Question",{"text":113,"@type":114},"It addresses the lack of estimation and inference methods for extreme quantiles when endogeneity is present, focusing on very low or very high parts of the outcome distribution.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the proposed method estimate extreme QTEs?",{"text":118,"@type":114},"It first estimates tail indices of the treated and untreated potential outcome distributions, then uses Pareto approximation under regular variation to obtain extreme QTE estimates, and constructs subsampling-based inference.",{"name":120,"@type":111,"acceptedAnswer":121},"What does the empirical application to the JTPA program show?",{"text":122,"@type":114},"For the lowest quantiles, the estimated QTEs are significantly negative, contrasting with prior work reporting positive effects at intermediate quantiles and suggesting adverse outcomes for the most disadvantaged individuals.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},201997,1788540825,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","arXiv :2409 .03979v1 [ econ .EM] 6 Sep 2024  \nExtreme Quantile Treatment Effects under Endogeneity: Evaluating Policy Effects for the Most Vulnerable Individuals  \nYuya Sasaki∗ and Yulong Wang†  \nAbstract  \nWe introduce a novel method for estimating and conducting inference about extreme quantile treatment effects (QTEs) in the presence of endogeneity. Our approach is applicable to a broad range of empirical research designs, including instrumental variables design and regression discontinuity design, among others. By leveraging regular variation and subsampling, the method ensures robust performance even in extreme tails, where data may be sparse or entirely absent. Simulation studies confirm the theoretical robustness of our approach. Applying our method to assess the impact of job training provided by the Job Training Partnership Act (JTPA), we find significantly negative QTEs for the lowest quantiles (i.e., the most disadvantaged individuals), contrasting with previous literature that emphasizes positive QTEs for intermediate quantiles.  \nKeywords: endogeneity, extreme quantile, job training, quantile treatment effect, subsampling  \nJEL Code: C21  \n∗ Brian and Charlotte Grove Chair and Professor of Economics, Vanderbilt University. Email: [yuya.sasaki@vanderbilt.edu](yuya.sasaki@vanderbilt.edu).  \n†Associate Professor of Economics, Syracuse University. Email: [ywang402@syr.edu](ywang402@syr.edu).  \n1 Introduction  \nThe quantile treatment effect (QTE) is a widely adopted concept in empirical research for quantifying heterogeneous treatment effects. Various methods have been proposed to identify the QTE in the presence of endogeneity. Popular approaches include instrumental variables designs (Abadie, Angrist, and Imbens, 2002; Chernozhukov and Hansen, 2005), changes-inchanges designs (Athey and Imbens, 2006), and regression discontinuity designs (Frandsen, Fr¨olich, and Melly, 2012), among others. Their identification results are often accompanied by corresponding methods for estimation and inference.  \nHowever, existing methods of estimation and inference are predominantly developed for intermediate quantiles, leaving a gap in the literature regarding estimation and inference for extreme quantiles, such as those at the very low or very high ends of the distribution. This is a notable limitation from practical perspectives, as policymakers are often particularly interested in the effectiveness of policies for extreme subpopulations, such as individuals living in extreme poverty or those with severe health conditions.  \nTwo studies address this issue in specific contexts: Zhang (2018) focuses on the observed unconfoundedness design; and Sasaki and Wang (2024) focus on changes-in-changes design. However, extreme QTEs under other endogeneity designs remain unexamined. This paper aims to fill this gap by introducing a novel method for the estimation and inference of extreme QTEs across a broad class of endogeneity scenarios and research designs.  \nConsider situations where the distributions, F1 and F0 , of the potential outcomes under treatment and no treatment, respectively, are identifiable. In these cases, the QTE is quantified by F1 (q) − F0− 1 (q) for q ∈ (0 , 1), where Fj− 1 denotes the left inverse of Fj for each j ∈ {0 , 1} . Our proposed method is applicable in situations where F1 and F0 can be identified and estimated by ˆF1 and ˆF0 , respectively, with rn (ˆFj (·)−Fj (·)), j ∈ {0 , 1}, weakly converging to a Gaussian process at some rate rn. All the aforementioned examples, along with many others, satisfy this requirement, making our proposed method applicable to abroad class of designs.  \nInstead of directly using ˆF1 (q) − ˆF0− 1 (q) as the estimator, we first estimate the tail  \nindices of F1 and F0 , and then obtain estimates of the QTE through Pareto approximation, which is valid under the regular variation condition. This approach enables us to robustly estimate the QTE even in regions where there are few or eve","cbCaiiQ2J4uYlQnd","https://ap.wps.com/l/cbCaiiQ2J4uYlQnd","pdf",744743,32,"English","# Introduction\n## Extreme quantiles under endogeneity\n## Estimation strategy using regular variation and Pareto approximation\n## Subsampling inference\n## Empirical application: JTPA job training","[{\"question\":\"What problem does the paper address in quantile treatment effect estimation?\",\"answer\":\"It addresses the lack of estimation and inference methods for extreme quantiles when endogeneity is present, focusing on very low or very high parts of the outcome distribution.\"},{\"question\":\"How does the proposed method estimate extreme QTEs?\",\"answer\":\"It first estimates tail indices of the treated and untreated potential outcome distributions, then uses Pareto approximation under regular variation to obtain extreme QTE estimates, and constructs subsampling-based inference.\"},{\"question\":\"What does the empirical application to the JTPA program show?\",\"answer\":\"For the lowest quantiles, the estimated QTEs are significantly negative, contrasting with prior work reporting positive effects at intermediate quantiles and suggesting adverse outcomes for the most disadvantaged individuals.\"}]","Extreme Quantile Treatment Effects under Endogeneity - Evaluating Policy Effects for the Most Vulnerable Individuals | PDF",81]