[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-270941-105":59,"doc-detail-270941-en":135},{"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":128,"head_meta":130,"extra_data":132,"updated_unix":134},105,"en","wanna-get-away-regression-discontinuity-estimation-of-exam-school-effects-away-from-the-cutoff","Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff","","Regression discontinuity designs in admissions or award settings identify causal effects for applicants close to an assignment cutoff under smoothness assumptions. Effects on inframarginal applicants require stronger, regression-discontinuity-specific conditional independence conditions. This article develops identification strategies that use predictors of the dependent variable other than the running variable, treating the running variable as ignorable conditional on those predictors. The approach yields precise, robust estimates for Boston exam school effects away from cutoffs and extends to fuzzy designs for compliers.",{"@graph":69,"@context":127},[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/wanna-get-away-regression-discontinuity-estimation-of-exam-school-effects-away-from-the-cutoff/270941/",{"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/wanna-get-away-regression-discontinuity-estimation-of-exam-school-effects-away-from-the-cutoff/270941.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What distinguishes effects at the cutoff from effects away from the cutoff in RD studies?","Question",{"text":113,"@type":114},"RD identification at the cutoff relies on smooth conditional mean functions, making treatment changes near the cutoff interpretable as quasi-random. Effects away from the cutoff require additional assumptions, especially regression-discontinuity-specific conditional independence conditions.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the article identify causal effects away from the cutoff?",{"text":118,"@type":114},"It exploits predictors of the dependent variable other than the running variable. Conditional on these predictors, the running variable is assumed ignorable, enabling identification of causal effects away from admissions cutoffs.",{"name":120,"@type":111,"acceptedAnswer":121},"What does the Boston exam school application suggest for inframarginal applicants?",{"text":122,"@type":114},"The estimates indicate that causal effects of exam school attendance for 9th grade applicants far from cutoffs differ little from effects for applicants near the margin of acceptance.",{"name":124,"@type":111,"acceptedAnswer":125},"How is the method extended beyond sharp RD to fuzzy designs?",{"text":126,"@type":114},"The article shows how an extension to fuzzy designs can identify causal effects for compliers away from the cutoff.","https://schema.org",{"og:url":83,"og:type":129,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":131,"canonical":83},"index,follow",{"doc_id":133,"site_id":62},270941,1789458692,{"code":4,"msg":5,"data":136},{"doc_id":133,"user_id":137,"nickname":92,"user_avatar":138,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":139,"file_id":140,"file_url":141,"file_type":142,"file_size":143,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":144,"language":145,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":146,"faqs":147,"seo_title":148,"seo_description":67,"update_tm":134,"read_time":149,"preview_page_count":4},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Supplementary materials for this article are available online. Please go [to www.tandfonline.com/r/JASA](to www.tandfonline.com/r/JASA)  \nWanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff  \nQ1  \n5  \n10  \nQ2  \n15  \n20  \n25  \n30  \n35  \n40  \nJoshua D. ANGRIST and Miikka ROKKANEN  \nIn regression discontinuity (RD) studies of applicants who face an award or admissions cutoff, causal effects are nonparametrically identified for those near the cutoff. The effect of treatment on inframarginal applicants is also of interest, but identification of such effects requires stronger assumptions than those required for identification at the cutoff. This article discusses RD identification away from the cutoff. Our identification strategy exploits the availability of dependent variable predictors other than the running variable. Conditional on these predictors, the running variable is assumed to be ignorable. This identification strategy is used to study effects of Boston exam schools for inframarginal applicants. Identification based on RD-specific conditional independence assumptions produces reasonably precise and surprisingly robust estimates of the effects of exam school attendance on inframarginal applicants. These estimates suggest that the causal effects of exam school attendance for 9th grade applicants with running variable values well away from admissions cutoffs differ little from those for applicants with values that put them on the margin of acceptance. An extension to fuzzy designs is shown to identify causal effects for compliers away from the cutoff. Supplementary materials for this article are available online.  \nKEY WORDS:  \n1. INTRODUCTION  \nIn a regression discontinuity (RD) framework, treatment status changes discontinuously as a function of an underlying covariate, often called the running variable. Provided conditional mean functions for potential outcomes given the running variable are smooth, changes in outcome distributions at the assignment cutoff must be driven by discontinuities in the likelihood of treatment. RD identification comes from a kind of virtual random assignment, where small and presumably serendipitous variation in the running variable manipulates treatment. On the other hand, because running variables are usually related to outcomes, claims for unconditional “as-if random assignment” are most credible for samples near the point of discontinuity. RD methods need not identify causal effects for larger and perhaps more representative groups of subjects.  \nA recent study of causal effects at Boston’s selective public schools—known as “exam schools”—highlights the possibly local and potentially limiting nature of RD findings. Boston exam schools choose their students based on an index that combines admissions test scores with a student’s grade point average (GPA) . Abdulkadiro˘glu, Angrist, and Pathak (2014) used parametric and nonparametric RD estimators to capture the causal effects of exam school attendance for applicants with index val-  \nJoshua D. Angrist, Department of Economics, MIT, Cambridge, MA 02142- 1347 and NBER, Cambridge MA 02138-5398 (E-mail: [angrist@mit.edu](angrist@mit.edu)). Miikka Rokkanen, Department of Economics, Columbia University, New York, NY 10027 ([E-mail:](E-mail: mr3454@columbia.edu)[ mr3454@columbia.edu](E-mail: mr3454@columbia.edu)). The authors thank Parag Pathak for many helpful discussionsand comments, and to Alberto Abadie, Victor Chernozhukov, Yingying Dong, Ivan Fernandez-Val, Patrick Kline, Arthur Lewbel, and seminar participants at Berkeley, CREST, HECER, Northwestern, Stanford, the January 2013 Lech am Arlberg Labor Conference, the 2013 SOLE meetings, and the 2013 Human Capital, and Productivity conference at Warwick for helpful comments. Thanks also go to Peter Hull for expert research assistance. The authors gratefully acknowledge financial support from the Institute for Education Sciences (Under award R305A120269), the Nationa","cbCaiajEaYGXopxs","https://ap.wps.com/l/cbCaiajEaYGXopxs","pdf",5998376,14,"English","# Introduction\n## Regression Discontinuity Framework\n## Local vs Away-From-Cutoff Identification\n## Identification Strategy and Conditional Independence","[{\"question\":\"What distinguishes effects at the cutoff from effects away from the cutoff in RD studies?\",\"answer\":\"RD identification at the cutoff relies on smooth conditional mean functions, making treatment changes near the cutoff interpretable as quasi-random. Effects away from the cutoff require additional assumptions, especially regression-discontinuity-specific conditional independence conditions.\"},{\"question\":\"How does the article identify causal effects away from the cutoff?\",\"answer\":\"It exploits predictors of the dependent variable other than the running variable. Conditional on these predictors, the running variable is assumed ignorable, enabling identification of causal effects away from admissions cutoffs.\"},{\"question\":\"What does the Boston exam school application suggest for inframarginal applicants?\",\"answer\":\"The estimates indicate that causal effects of exam school attendance for 9th grade applicants far from cutoffs differ little from effects for applicants near the margin of acceptance.\"},{\"question\":\"How is the method extended beyond sharp RD to fuzzy designs?\",\"answer\":\"The article shows how an extension to fuzzy designs can identify causal effects for compliers away from the cutoff.\"}]","Wanna Get Away? Regression Discontinuity Estimation of Exam School Effects Away From the Cutoff | PDF",35]