[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-297009-105":59,"doc-detail-297009-en":134},{"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":127,"head_meta":129,"extra_data":131,"updated_unix":133},105,"en","execution-and-evaluation-a-new-occupational-measure-and-long-run-employment-gradients","Execution and Evaluation - A New Occupational Measure and Long-Run Employment Gradients","","Artificial intelligence automates execution more readily than evaluation, since producing output is cheap while judging correctness is not. Exposure measures should therefore separate what AI can do from what human function remains valuable. This paper scores 19,265 O*NET task statements under fixed rubrics to build an occupation-level execution share and a separate AI-capability measure. Employment growth trends since 2012 are linked to execution-heavy occupations, with timing changes after 2022 showing chronology rather than causal AI effects.",{"@graph":69,"@context":126},[70,84,105],{"@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/execution-and-evaluation-a-new-occupational-measure-and-long-run-employment-gradients/297009/",{"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/execution-and-evaluation-a-new-occupational-measure-and-long-run-employment-gradients/297009.png","ImageObject",300,407,{"name":92,"@type":93},"Aria","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-18",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address about AI exposure measures?","Question",{"text":112,"@type":113},"It argues that many exposure measures capture AI capability but do not reveal which human function remains valuable when AI can generate first-pass output—especially the difference between execution and evaluation.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are the paper’s measures constructed?",{"text":117,"@type":113},"The author scores all 19,265 O*NET task statements under fixed rubrics to compute an occupation-level execution share, and scores the same statements separately for AI capability.",{"name":119,"@type":110,"acceptedAnswer":120},"What relationship does the execution share show with employment growth?",{"text":121,"@type":113},"In harmonized panels, employment growth is lower in execution-heavy white-collar occupations in every window since 2012, and the gradient appears to be a secular trend rather than an AI-era event.",{"name":123,"@type":110,"acceptedAnswer":124},"Does the evidence establish an AI-caused employment effect?",{"text":125,"@type":113},"No. The paper states that the evidence establishes a measure and chronology, and that changes after 2022 are dated but not causally attributable to AI.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},297009,1790387732,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149,"preview_page_count":4},2336464648322,"https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488","arXiv :2607 .20807v1 [ econ .GN] 23 Jul 2026  \nExecution and Evaluation: A New Occupational Measure and Long-Run Employment Gradients  \nLi Gan∗  \nJuly 24, 2026  \nAbstract  \nArtificial intelligence automates execution more readily than evaluation: producing output is cheap, judging whether it is correct is not. Exposure measures rank tasks by whether AI can perform them, not by which function the human supplies. I score all 19 ,265 O*NET task statements under fixed rubrics to build occupation-level execution and AI-capability shares. The execution share is reproducible across model coders and O*NET vintages and distinct from AI capability and routine-task intensity; it is a model-based measure, not human-validated ground truth, and adds only modest power beyond O*NET’s evaluation activities. In a harmonized panel, employment growth is lower in execution-heavy white-collar occupations in every window since 2012, and equality of slopes cannot be rejected: the gradient is a secular trend rather than an AI-era event, largely between occupational families. The vintage-valid capability gradient steepens after 2022, a change that is dated but not causally attributable. The evidence establishes a measure and a chronology, not an AI-caused effect.  \n1 Introduction  \nGenerative AI has renewed a longstanding question in labor economics: which dimensions of work determine whether a new technology substitutes for workers, complements them, or reorganizes the occupations in which they are employed? Most empirical exposure measures answer one part of that question. They rank tasks or occupations by whether a technology can perform the work, by the semantic overlap between tasks and patents, or by observed use of the technology (Eloundou et al., 2024; Webb, 2020; Handa et al. , 2025); Massenkoff and McCrory (2026) combine the last two, weighting theoretical capability by observed usage into a single “observed exposure” index. Those measures are indispensable for locating technological capability. They are less informative about the human function that remains valuable when the technology can generate a first-pass output.  \n∗ Department of Economics, Texas A&M University. Email: [ganli@tamu.edu](ganli@tamu.edu) ; [ligan98@gmail.com. I thank](ligan98@gmail.com. I thank) colleagues and students for discussions; all errors are mine. Data sources and measure construction are detailed in Section 3 .  \nThis distinction matters for interpreting recent evidence. Employment of younger workers has weakened relative to employment of more experienced workers in highly exposed occupations. Brynjolfsson et al. (2025) report a relative decline of about 16 percent for workers aged 22 to 25 in the most exposed occupations, concentrated where AI use appears to automate rather than augment work. Hosseini and Lichtinger (2025) find a parallel within-firm decline in junior relative to senior employment, driven mainly by hiring. The timing and interpretation remain contested. Iscenko and Curto Millet (2026) argue that part of the pattern predates plausible enterprise adoption, that the youngest age band is mechanically sensitive to a general hiring slowdown, and that exposed occupations are concentrated in interest-rate-sensitive sectors. Lambert and Schindler (2026) identify a further confound of the same kind: generative-AI exposure and post-pandemic work-from-home exposure fall on nearly the same white-collar, computer-intensive occupations, and when both are entered jointly in hiring and posting data covering 243 million new hires, the work-from-home effect persists while the AI coefficient attenuates sharply and is often indistinguishable from zero.  \nEven when an employment change is associated with AI, a capability ranking alone does not reveal why workers are affected. A task may involve execution: producing code, drafting a report, calculating an estimate, entering data, or carrying out a procedure. Another task may involve evaluation: diagnosing, auditing,","cbCaisNJEytbPTff","https://ap.wps.com/l/cbCaisNJEytbPTff","pdf",1342457,38,"English","# Introduction\n## Background: exposure measures in labor economics\n## Why capability rankings cannot explain effects\n## Execution vs evaluation as distinct human inputs\n# Measure construction and validation\n## Benchmark within O*NET work-activity ratings\n## Predictive employment-growth evidence","[{\"question\":\"What problem does the paper address about AI exposure measures?\",\"answer\":\"It argues that many exposure measures capture AI capability but do not reveal which human function remains valuable when AI can generate first-pass output—especially the difference between execution and evaluation.\"},{\"question\":\"How are the paper’s measures constructed?\",\"answer\":\"The author scores all 19,265 O*NET task statements under fixed rubrics to compute an occupation-level execution share, and scores the same statements separately for AI capability.\"},{\"question\":\"What relationship does the execution share show with employment growth?\",\"answer\":\"In harmonized panels, employment growth is lower in execution-heavy white-collar occupations in every window since 2012, and the gradient appears to be a secular trend rather than an AI-era event.\"},{\"question\":\"Does the evidence establish an AI-caused employment effect?\",\"answer\":\"No. The paper states that the evidence establishes a measure and chronology, and that changes after 2022 are dated but not causally attributable to AI.\"}]","Execution and Evaluation - A New Occupational Measure and Long-Run Employment Gradients | PDF",1789739559,96]