[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-205813-105":59,"doc-detail-205813-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","who-stays-who-goes-a-new-look-at-teacher-attrition-in-using-administrative-data","Who stays, who goes? - A new look at teacher attrition in using administrative data","","Existing estimates of teacher attrition differ widely, and survey-based explanations for why teachers leave often rely on low-response, selected samples. The work assesses where teachers go after exit and their longer-run outcomes, using linked administrative tax and education datasets. It compares common data sources (registration, workforce employment, and surveys) and argues that administrative records can overcome gaps, enabling clearer measurement of attrition patterns and post-leaving trajectories.",{"@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/who-stays-who-goes-a-new-look-at-teacher-attrition-in-using-administrative-data/205813/",{"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/who-stays-who-goes-a-new-look-at-teacher-attrition-in-using-administrative-data/205813.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Mercer","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-09-05",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why do existing estimates of teacher attrition vary so widely?","Question",{"text":113,"@type":114},"Early-career attrition estimates range from very low to very high depending on measurement approach, and much research relies on surveys with low response rates and selected samples.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What limitations exist in using teacher registration data?",{"text":118,"@type":114},"Registration counts qualified teachers, but many teachers who leave keep their registration, making it hard to compare attrition with other occupations and impossible to identify destinations or long-run outcomes.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the approach use administrative tax data to study attrition?",{"text":122,"@type":114},"The study links longitudinal employee-employer payroll information with business, personal income tax, higher education, and census data to track occupations, wages, and locations over time, helping estimate where teachers go and what outcomes follow.","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},205813,1788584095,{"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":41,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},962084925502,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Who stays, who goes?  \nA new look at teacher attrition in using administrative data  \nJack Buckley  \nJoint work with Silvia Griselda  \n7 July 2025  \nOverview  \n▪ Existing estimates of teacher attrition vary widely. For early career teachers, estimates for the share of teachers that leave within their first 5 years range from as low as 5% to as high as 50% .  \n▪ Research on the why teachers leave the profession is largely based on surveys of former teachers, which often have low response rates and potentially selected samples.  \n▪ Relatively little is known about where teachers go when they leave the profession and their long run outcomes  \n▪ We show that administrative tax data can be used to help answer these questions.  \nFindings preview  \n▪ Teacher attrition rates are relatively low and falling, although attrition remains elevated in certain settings  \n▪ Not all attrition is harmful with many teachers leaving to take up other education related roles or take time out of the workforce  \n▪ Financial factors appear to play a limited role in explaining teacher attrition with former teachers earning less in the long term than those who remain  \n▪ Teacher supply is constrained further up the pipeline  \nAgenda  \n01 Motivation  \n02 Data  \n03 Methodology  \n04 Results  \n05 Conclusion  \nMotivation  \nExisting estimates using surveysand teacher registration data have limitations  \nTeacher registration data  \n▪ Clear measure of the number of qualified teachers in a state  \n▪ But many teachers who leave teaching keep their registration (QCT, 2013; AITSL 2023)  \n▪ And there is no simple way to compare attrition rates with other occupations  \n▪ No way to identify where teachers are going, why they are leaving or what their long run outcomes are  \nWorkforce data  \n▪ Good measure of the number of teachers employed in the public system  \n▪ But misses the 15-20% of the teaching workforce employed in temporary positions (Weldon 2018)  \n▪ Does not capture teachers moving to other states or the private sector  \n▪ No way to identify where teachers are going, why they are leaving or what their long run outcomes are  \nSurvey data  \n▪ Ability to understand reasons for leaving teaching  \n▪ But surveys of teachers generally have very low response rates and likely suffer from selection  \n▪ May be difficult to compare attrition with other occupations  \n▪ Hard to identify long run outcomes for those that leave teaching  \nData  \nLongitudinal linked employee-employer dataset (L-LEED)  \nPayroll data (PAYG)  \nWages paid by employer to employee  \nBusiness data  \n(BLADE)  \nSector of employer and typeof employer  \nPersonal Income Tax returns  \nOccupation, salary income  \nHigher education data (HIED)  \nLocations and demographic data  \nCensus data  \n▪ Field and level of study  \n▪ Degrees completed (and year completed)  \n▪ ATAR  \n▪ Number of units completed in each field  \n▪ Demographics (CALD, ATSI)  \n▪ State of residence  \n▪ Location before and after university  \n▪ Demographics (Gender, ATSI)  \n▪ Place of work (SA2; SEIFA and Remoteness area)  \n▪ Sector of employment  \n▪ Occupation  \nAll individuals who report earning salary income must report their occupation  \nThe number of teachers in the tax data tracks public data sources  \nMethodology  \nMeasuring teacher attrition  \nUniversity  \nChanged  occupation  \nNEET  \n(not in employment, education, or training)","cbCaigThi6ZVOIRM","https://ap.wps.com/l/cbCaigThi6ZVOIRM","pdf",18768475,"English","# Overview\n## Findings preview\n# Agenda\n## Motivation\n## Data\n## Methodology\n## Results\n## Conclusion","[{\"question\":\"Why do existing estimates of teacher attrition vary so widely?\",\"answer\":\"Early-career attrition estimates range from very low to very high depending on measurement approach, and much research relies on surveys with low response rates and selected samples.\"},{\"question\":\"What limitations exist in using teacher registration data?\",\"answer\":\"Registration counts qualified teachers, but many teachers who leave keep their registration, making it hard to compare attrition with other occupations and impossible to identify destinations or long-run outcomes.\"},{\"question\":\"How does the approach use administrative tax data to study attrition?\",\"answer\":\"The study links longitudinal employee-employer payroll information with business, personal income tax, higher education, and census data to track occupations, wages, and locations over time, helping estimate where teachers go and what outcomes follow.\"}]","Who stays, who goes? - A new look at teacher attrition in using administrative data | PDF",76]